






"""simple docstring"""




import enum
import inspect
from typing import Iterable, List, Optional, Tuple, Union





class __lowerCamelCase			(				enum.Enum):





	"""simple docstring"""






	UpperCamelCase__								=				'timm'
	UpperCamelCase__								=				'transformers'



def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase     )->  List[str]:





						"""simple docstring"""



						if stage_names is None:
												raise ValueError('Stage_names must be set for transformers backbones'     )

						if out_features is not None:
												if not isinstance(SCREAMING_SNAKE_CASE__			,		(list,)     ):
																		raise ValueError(F"""out_features must be a list {type(SCREAMING_SNAKE_CASE__     )}"""     )
												if any(feat not in stage_names for feat in out_features     ):
																		raise ValueError(F"""out_features must be a subset of stage_names: {stage_names} got {out_features}"""     )

						if out_indices is not None:
												if not isinstance(SCREAMING_SNAKE_CASE__			,		(list, tuple)     ):
																		raise ValueError(F"""out_indices must be a list or tuple, got {type(SCREAMING_SNAKE_CASE__     )}"""     )
												if any(idx >= len(SCREAMING_SNAKE_CASE__     ) for idx in out_indices     ):
																		raise ValueError('out_indices must be valid indices for stage_names {stage_names}, got {out_indices}'     )

						if out_features is not None and out_indices is not None:
												if len(SCREAMING_SNAKE_CASE__     ) != len(SCREAMING_SNAKE_CASE__     ):
																		raise ValueError('out_features and out_indices should have the same length if both are set'     )
												if out_features != [stage_names[idx] for idx in out_indices]:
																		raise ValueError('out_features and out_indices should correspond to the same stages if both are set'     )


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)->  str:





						"""simple docstring"""



						if out_indices is None and out_features is None:
												_UpperCAmelCase     =      [len(SCREAMING_SNAKE_CASE__     ) - 1]
												_UpperCAmelCase     =      [stage_names[-1]]
						elif out_indices is None and out_features is not None:
												_UpperCAmelCase     =      [stage_names.index(SCREAMING_SNAKE_CASE__     ) for layer in out_features]
						elif out_features is None and out_indices is not None:
												_UpperCAmelCase     =      [stage_names[idx] for idx in out_indices]
						return out_features, out_indices


def __A   (				__lowerCAmelCase			,		__lowerCAmelCase			,		__lowerCAmelCase			,		)->  Dict:





						"""simple docstring"""



						verify_out_features_out_indices(out_features=SCREAMING_SNAKE_CASE__			,		out_indices=SCREAMING_SNAKE_CASE__			,		stage_names=SCREAMING_SNAKE_CASE__     )
						_UpperCAmelCase     =      _align_output_features_output_indices(
						    out_features=SCREAMING_SNAKE_CASE__			,		out_indices=SCREAMING_SNAKE_CASE__			,		stage_names=SCREAMING_SNAKE_CASE__     )
						# Verify that the aligned out_features and out_indices are valid
						verify_out_features_out_indices(out_features=SCREAMING_SNAKE_CASE__			,		out_indices=SCREAMING_SNAKE_CASE__			,		stage_names=SCREAMING_SNAKE_CASE__     )
						return output_features, output_indices





class __lowerCamelCase			:





	"""simple docstring"""






	UpperCamelCase__								=				None

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							if getattr(self	,   '_backbone'	,   lowercase_   ) is None:
													raise ValueError('self._backbone must be set before calling _init_timm_backbone'   )

							# These will diagree with the defaults for the transformers models e.g. for resnet50
							# the transformer model has out_features = ['stem', 'stage1', 'stage2', 'stage3', 'stage4']
							# the timm model has out_features = ['act', 'layer1', 'layer2', 'layer3', 'layer4']
							_UpperCAmelCase     =      [stage['module'] for stage in self._backbone.feature_info.info]
							_UpperCAmelCase     =      [stage['num_chs'] for stage in self._backbone.feature_info.info]
							_UpperCAmelCase     =      self._backbone.feature_info.out_indices
							_UpperCAmelCase     =      self._backbone.feature_info.module_name()

							# We verify the out indices and out features are valid
							verify_out_features_out_indices(
							    out_features=lowercase_	,   out_indices=lowercase_	,   stage_names=self.stage_names   )
							_UpperCAmelCase     =      out_features, out_indices

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      getattr(lowercase_	,   'stage_names'   )
							_UpperCAmelCase     =      getattr(lowercase_	,   'out_features'	,   lowercase_   )
							_UpperCAmelCase     =      getattr(lowercase_	,   'out_indices'	,   lowercase_   )

							_UpperCAmelCase     =      stage_names
							_UpperCAmelCase     =      get_aligned_output_features_output_indices(
							    out_features=lowercase_	,   out_indices=lowercase_	,   stage_names=lowercase_   )
							# Number of channels for each stage. This is set in the transformer backbone model init
							_UpperCAmelCase     =      None

	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      config

							_UpperCAmelCase     =      getattr(lowercase_	,   'use_timm_backbone'	,   lowercase_   )
							_UpperCAmelCase     =      BackboneType.TIMM if self.use_timm_backbone else BackboneType.TRANSFORMERS

							if self.backbone_type == BackboneType.TIMM:
													self._init_timm_backbone(lowercase_   )
							elif self.backbone_type == BackboneType.TRANSFORMERS:
													self._init_transformers_backbone(lowercase_   )
							else:
													raise ValueError(F"""backbone_type {self.backbone_type} not supported."""   )

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return self._out_features

	@out_features.setter
	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_aligned_output_features_output_indices(
							    out_features=lowercase_	,   out_indices=lowercase_	,   stage_names=self.stage_names   )

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return self._out_indices

	@out_indices.setter
	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_aligned_output_features_output_indices(
							    out_features=lowercase_	,   out_indices=lowercase_	,   stage_names=self.stage_names   )

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return {stage: self.num_features[i] for i, stage in enumerate(self.stage_names   )}

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return [self.out_feature_channels[name] for name in self.out_features]

	def 	UpperCamelCase   (							self	,   *UpperCAmelCase	,   **UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      dict(inspect.signature(self.forward   ).parameters   )
							_UpperCAmelCase     =      {k: v for k, v in kwargs.items() if k in signature}
							return self(*lowercase_	,   **lowercase_   )

	def 	UpperCamelCase   (							self	,   UpperCAmelCase	,   UpperCAmelCase = None	,   UpperCAmelCase = None	,   UpperCAmelCase = None	,   ):
							"""simple docstring"""







							raise NotImplementedError('This method should be implemented by the derived class.'   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      super().to_dict()
							_UpperCAmelCase     =      output.pop('_out_features'   )
							_UpperCAmelCase     =      output.pop('_out_indices'   )
							return output





class __lowerCamelCase			:





	"""simple docstring"""






	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return self._out_features

	@out_features.setter
	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_aligned_output_features_output_indices(
							    out_features=lowercase_	,   out_indices=lowercase_	,   stage_names=self.stage_names   )

	@property
	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							return self._out_indices

	@out_indices.setter
	def 	UpperCamelCase   (							self	,   UpperCAmelCase   ):
							"""simple docstring"""







							_UpperCAmelCase     =      get_aligned_output_features_output_indices(
							    out_features=lowercase_	,   out_indices=lowercase_	,   stage_names=self.stage_names   )

	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      super().to_dict()
							_UpperCAmelCase     =      output.pop('_out_features'   )
							_UpperCAmelCase     =      output.pop('_out_indices'   )
							return output








import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference





@slow
@require_torch_gpu
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'runwayml/stable-diffusion-v1-5'
							_UpperCAmelCase     =      TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase	,   torch_dtype=torch.floataa   ).to('cuda'   )
							_UpperCAmelCase     =      DDIMScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase     =      torch.Generator(device='cuda'   ).manual_seed(0   )

							_UpperCAmelCase     =      'A bear is playing a guitar on Times Square'
							_UpperCAmelCase     =      pipe(prompt=UpperCAmelCase	,   generator=UpperCAmelCase   ).images

							_UpperCAmelCase     =      load_pt(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'   )

							assert_mean_pixel_difference(UpperCAmelCase	,   UpperCAmelCase   )
