



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class       __lowerCAmelCase   (      __a						):



			def __init__(						self       :  Optional[int]			,		A       :  Union[str, Any]			,		A       :  int)       ->   int:



						"""simple docstring"""






						super().__init__()

						# make sure scheduler can always be converted to DDIM
						_UpperCAmelCase			=       DDIMScheduler.from_config(scheduler.config)

						self.register_modules(unet=a__			,		scheduler=a__)



			@torch.no_grad()
			def __call__(						self       :  List[Any]			,		A       :  int = 1			,		A       :  Optional[Union[torch.Generator, List[torch.Generator]]] = None			,		A       :  float = 0.0			,		A       :  int = 50			,		A       :  Optional[bool] = None			,		A       :  Optional[str] = "pil"			,		A       :  bool = True			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						if isinstance(self.unet.config.sample_size			,		a__):
									_UpperCAmelCase			=       (
									    batch_size,
									    self.unet.config.in_channels,
									    self.unet.config.sample_size,
									    self.unet.config.sample_size,
									)
						else:
									_UpperCAmelCase			=       (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

						if isinstance(a__			,		a__) and len(a__) != batch_size:
									raise ValueError(
									    F"You have passed a list of generators of length {len(a__)}, but requested an effective batch"
									    F" size of {batch_size}. Make sure the batch size matches the length of the generators.")

						_UpperCAmelCase			=       randn_tensor(a__			,		generator=a__			,		device=self.device			,		dtype=self.unet.dtype)

						# set step values
						self.scheduler.set_timesteps(a__)

						for t in self.progress_bar(self.scheduler.timesteps):
									# 1. predict noise model_output
									_UpperCAmelCase			=       self.unet(a__			,		a__).sample

									# 2. predict previous mean of image x_t-1 and add variance depending on eta
									# eta corresponds to η in paper and should be between [0, 1]
									# do x_t -> x_t-1
									_UpperCAmelCase			=       self.scheduler.step(
									    a__			,		a__			,		a__			,		eta=a__			,		use_clipped_model_output=a__			,		generator=a__).prev_sample

						_UpperCAmelCase			=       (image / 2 + 0.5).clamp(0			,		1)
						_UpperCAmelCase			=       image.cpu().permute(0			,		2			,		3			,		1).numpy()
						if output_type == "pil":
									_UpperCAmelCase			=       self.numpy_to_pil(a__)

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=a__)





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__       =	"src/transformers"
UpperCAmelCase__       =	"docs/source/en/tasks"





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Any   )    ->			int:







			'''simple docstring'''
			with open(_UpperCAmelCase							,       'r'							,       encoding='utf-8'							,       newline='\n'   ) as f:
						_UpperCAmelCase			=       f.readlines()
			# Find the start prompt.
			_UpperCAmelCase			=       0
			while not lines[start_index].startswith(_UpperCAmelCase   ):
						start_index += 1
			start_index += 1

			_UpperCAmelCase			=       start_index
			while not lines[end_index].startswith(_UpperCAmelCase   ):
						end_index += 1
			end_index -= 1

			while len(lines[start_index]   ) <= 1:
						start_index += 1
			while len(lines[end_index]   ) <= 1:
						end_index -= 1
			end_index += 1
			return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase__       =	direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__       =	{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
UpperCAmelCase__       =	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,       set()   )
			_UpperCAmelCase			=       {
			    code: name
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if (code in model_maping_names or code in special_model_types)
			}
			return ", ".join([F"[{name}](../model_doc/{code})" for code, name in model_names.items()]   ) + "\n"





def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict=False   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       _find_text_in_file(
			    filename=os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,       end_prompt='<!--End of the generated tip-->'							,       )

			_UpperCAmelCase			=       get_model_list_for_task(_UpperCAmelCase   )

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       'w'							,       encoding='utf-8'							,       newline='\n'   ) as f:
												f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
						else:
									raise ValueError(
									    F"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
									    ' to fix this.'   )


if __name__ == "__main__":
							UpperCAmelCase__       =	argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCAmelCase__       =	parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)
