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https://github.com/ynput/ayon-core.git
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♻️ use product name templates for render instances
remove hardcoded instance names in favor of product name templates
This commit is contained in:
Ondřej Samohel
parent
cfe8f0881d
commit
8fa1317486
1 changed files with 88 additions and 63 deletions
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@ -12,6 +12,8 @@ from ayon_core.pipeline import (
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get_current_project_name,
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get_representation_path,
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)
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from ayon_core.pipeline.create import get_product_name
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from ayon_core.lib import Logger
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from ayon_core.pipeline.publish import KnownPublishError
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from ayon_core.pipeline.farm.patterning import match_aov_pattern
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@ -463,7 +465,9 @@ def create_instances_for_aov(instance, skeleton, aov_filter,
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Args:
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instance (pyblish.api.Instance): Original instance.
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skeleton (dict): Skeleton instance data.
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aov_filter (dict): AOV filter.
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skip_integration_repre_list (list): skip
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do_not_add_review (bool): explicitly disable review
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Returns:
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list of pyblish.api.Instance: Instances created from
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@ -525,10 +529,10 @@ def _create_instances_for_aov(instance, skeleton, aov_filter, additional_data,
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instance (pyblish.api.Instance): Original instance.
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skeleton (dict): Skeleton data for instance (those needed) later
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by collector.
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additional_data (dict): ..
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additional_data (dict): ...
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skip_integration_repre_list (list): list of extensions that shouldn't
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be published
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do_not_addbe _review (bool): explicitly disable review
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do_not_add_review (bool): explicitly disable review
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Returns:
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@ -538,68 +542,60 @@ def _create_instances_for_aov(instance, skeleton, aov_filter, additional_data,
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ValueError:
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"""
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# TODO: this needs to be taking the task from context or instance
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task = os.environ["AYON_TASK_NAME"]
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task_name = instance.data['taskEntity']['name']
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anatomy = instance.context.data["anatomy"]
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s_product_name = skeleton["productName"]
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src_product_name = skeleton["productName"]
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cameras = instance.data.get("cameras", [])
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exp_files = instance.data["expectedFiles"]
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expected_files = instance.data["expectedFiles"]
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log = Logger.get_logger("farm_publishing")
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instances = []
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# go through AOVs in expected files
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for aov, files in exp_files[0].items():
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cols, rem = clique.assemble(files)
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# we shouldn't have any reminders. And if we do, it should
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# be just one item for single frame renders.
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if not cols and rem:
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if len(rem) != 1:
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raise ValueError("Found multiple non related files "
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"to render, don't know what to do "
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"with them.")
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col = rem[0]
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ext = os.path.splitext(col)[1].lstrip(".")
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else:
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# but we really expect only one collection.
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# Nothing else make sense.
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if len(cols) != 1:
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raise ValueError("Only one image sequence type is expected.") # noqa: E501
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ext = cols[0].tail.lstrip(".")
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col = list(cols[0])
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for aov, files in expected_files[0].items():
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collected_files = _collect_expected_files_for_aov(files)
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# create product name `<product type><Task><Product name>`
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# TODO refactor/remove me
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product_type = skeleton["productType"]
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if not s_product_name.startswith(product_type):
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# get file path (use first from the list or single frame)
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expected_filepath = collected_files[0] if isinstance(collected_files, (list, tuple)) else collected_files
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dynamic_data = {
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"aov": aov,
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"renderlayer": instance.data.get("renderlayer"),
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}
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# find if camera is used in the file path
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camera = [cam for cam in cameras if cam in expected_filepath]
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# Is there just one camera matching?
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# TODO: this is not true, we can have multiple cameras in the scene
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# and we should be able to detect them all. Currently, we are
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# keeping the old behavior, taking the first one found.
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if camera:
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dynamic_data["camera"] = camera[0]
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product_name = get_product_name(
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project_name=instance.context.data["projectName"],
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task_name=task_name,
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task_type=instance.data['taskEntity']['taskType'],
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host_name=instance.context.data["hostName"],
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product_type=skeleton['productType'],
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dynamic_data=dynamic_data,
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variant=instance.data.get('variant', ''),
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project_settings=instance.context.data.get("project_settings"),
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)
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# Group name isn't based on a product name template as it is
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# difficult to differentiate in the custom defined template
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# what part is the least common denominator.
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if not src_product_name.startswith(skeleton["productType"]):
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group_name = '{}{}{}{}{}'.format(
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product_type,
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task[0].upper(), task[1:],
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s_product_name[0].upper(), s_product_name[1:])
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skeleton["productType"],
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task_name[0].upper(), task_name[1:],
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src_product_name[0].upper(), src_product_name[1:])
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else:
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group_name = s_product_name
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group_name = src_product_name
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# if there are multiple cameras, we need to add camera name
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expected_filepath = col[0] if isinstance(col, (list, tuple)) else col
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cams = [cam for cam in cameras if cam in expected_filepath]
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if cams:
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for cam in cams:
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if not aov:
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product_name = '{}_{}'.format(group_name, cam)
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elif not aov.startswith(cam):
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product_name = '{}_{}_{}'.format(group_name, cam, aov)
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else:
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product_name = "{}_{}".format(group_name, aov)
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else:
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if aov:
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product_name = '{}_{}'.format(group_name, aov)
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else:
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product_name = '{}'.format(group_name)
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if isinstance(col, (list, tuple)):
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staging = os.path.dirname(col[0])
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else:
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staging = os.path.dirname(col)
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staging = os.path.dirname(expected_filepath)
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try:
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staging = remap_source(staging, anatomy)
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@ -610,20 +606,18 @@ def _create_instances_for_aov(instance, skeleton, aov_filter, additional_data,
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app = os.environ.get("AYON_HOST_NAME", "")
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if isinstance(col, list):
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render_file_name = os.path.basename(col[0])
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else:
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render_file_name = os.path.basename(col)
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aov_patterns = aov_filter
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render_file_name = os.path.basename(expected_filepath)
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aov_patterns = aov_filter
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preview = match_aov_pattern(app, aov_patterns, render_file_name)
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new_instance = deepcopy(skeleton)
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new_instance["productName"] = product_name
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new_instance["productGroup"] = group_name
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new_instance["aov"] = aov
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# toggle preview on if multipart is on
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# Because we cant query the multipartExr data member of each AOV we'll
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# Because we can't query the multipartExr data member of each AOV we'll
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# need to have hardcoded rule of excluding any renders with
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# "cryptomatte" in the file name from being a multipart EXR. This issue
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# happens with Redshift that forces Cryptomatte renders to be separate
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@ -649,10 +643,9 @@ def _create_instances_for_aov(instance, skeleton, aov_filter, additional_data,
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new_instance["review"] = True
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# create representation
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if isinstance(col, (list, tuple)):
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files = [os.path.basename(f) for f in col]
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else:
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files = os.path.basename(col)
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ext = os.path.splitext(
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os.path.basename(expected_filepath)
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)[1].lstrip(".")
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# Copy render product "colorspace" data to representation.
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colorspace = ""
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@ -707,6 +700,36 @@ def _create_instances_for_aov(instance, skeleton, aov_filter, additional_data,
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return instances
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def _collect_expected_files_for_aov(files):
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"""Collect expected files.
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Args:
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files (list): List of files.
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Returns:
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list or str: Collection of files or single file.
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Raises:
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ValueError: If there are multiple collections.
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"""
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cols, rem = clique.assemble(files)
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# we shouldn't have any reminders. And if we do, it should
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# be just one item for single frame renders.
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if not cols and rem:
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if len(rem) != 1:
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raise ValueError("Found multiple non related files "
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"to render, don't know what to do "
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"with them.")
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return rem[0]
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else:
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# but we really expect only one collection.
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# Nothing else make sense.
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if len(cols) != 1:
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raise ValueError("Only one image sequence type is expected.") # noqa: E501
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return list(cols[0])
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def get_resources(project_name, version_entity, extension=None):
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"""Get the files from the specific version.
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@ -836,6 +859,8 @@ def create_skeleton_instance_cache(instance):
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# map inputVersions `ObjectId` -> `str` so json supports it
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"inputVersions": list(map(str, data.get("inputVersions", []))),
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}
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if instance.data.get("renderlayer"):
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instance_skeleton_data["renderlayer"] = instance.data["renderlayer"]
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# skip locking version if we are creating v01
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instance_version = data.get("version") # take this if exists
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