



import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					(DEISMultistepScheduler,)
			UpperCamelCase        =					(('''num_inference_steps''', 2_5),)



			def    _lowerCamelCase	(						self       :  List[Any]			,		**A       :  List[Any])       ->   int:



						"""simple docstring"""






						_UpperCAmelCase			=       {
						    'num_train_timesteps': 10_00,
						    'beta_start': 0.0_0_0_1,
						    'beta_end': 0.0_2,
						    'beta_schedule': 'linear',
						    'solver_order': 2,
						}

						config.update(**A)
						return config



			def    _lowerCamelCase	(						self       :  Optional[Any]			,		A       :  List[str]=0			,		**A       :  int)       ->   List[Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       dict(self.forward_default_kwargs)
						_UpperCAmelCase			=       kwargs.pop('num_inference_steps'			,		A)
						_UpperCAmelCase			=       self.dummy_sample
						_UpperCAmelCase			=       0.1 * sample
						_UpperCAmelCase			=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
									_UpperCAmelCase			=       self.get_scheduler_config(**A)
									_UpperCAmelCase			=       scheduler_class(**A)
									scheduler.set_timesteps(A)
									# copy over dummy past residuals
									_UpperCAmelCase			=       dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(A)
												_UpperCAmelCase			=       scheduler_class.from_pretrained(A)
												new_scheduler.set_timesteps(A)
												# copy over dummy past residuals
												_UpperCAmelCase			=       dummy_past_residuals[: new_scheduler.config.solver_order]

									_UpperCAmelCase	,					_UpperCAmelCase			=       sample, sample
									for t in range(A			,		time_step + scheduler.config.solver_order + 1):
												_UpperCAmelCase			=       scheduler.step(A			,		A			,		A			,		**A).prev_sample
												_UpperCAmelCase			=       new_scheduler.step(A			,		A			,		A			,		**A).prev_sample

												assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



			def    _lowerCamelCase	(						self       :  List[str])       ->   Optional[Any]:



						"""simple docstring"""






						pass



			def    _lowerCamelCase	(						self       :  Any			,		A       :  Dict=0			,		**A       :  Any)       ->   Tuple:



						"""simple docstring"""






						_UpperCAmelCase			=       dict(self.forward_default_kwargs)
						_UpperCAmelCase			=       kwargs.pop('num_inference_steps'			,		A)
						_UpperCAmelCase			=       self.dummy_sample
						_UpperCAmelCase			=       0.1 * sample
						_UpperCAmelCase			=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

						for scheduler_class in self.scheduler_classes:
									_UpperCAmelCase			=       self.get_scheduler_config()
									_UpperCAmelCase			=       scheduler_class(**A)
									scheduler.set_timesteps(A)

									# copy over dummy past residuals (must be after setting timesteps)
									_UpperCAmelCase			=       dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
												scheduler.save_config(A)
												_UpperCAmelCase			=       scheduler_class.from_pretrained(A)
												# copy over dummy past residuals
												new_scheduler.set_timesteps(A)

												# copy over dummy past residual (must be after setting timesteps)
												_UpperCAmelCase			=       dummy_past_residuals[: new_scheduler.config.solver_order]

									_UpperCAmelCase			=       scheduler.step(A			,		A			,		A			,		**A).prev_sample
									_UpperCAmelCase			=       new_scheduler.step(A			,		A			,		A			,		**A).prev_sample

									assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"



			def    _lowerCamelCase	(						self       :  List[str]			,		A       :  List[str]=None			,		**A       :  int)       ->   Any:



						"""simple docstring"""






						if scheduler is None:
									_UpperCAmelCase			=       self.scheduler_classes[0]
									_UpperCAmelCase			=       self.get_scheduler_config(**A)
									_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config(**A)
						_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       10
						_UpperCAmelCase			=       self.dummy_model()
						_UpperCAmelCase			=       self.dummy_sample_deter
						scheduler.set_timesteps(A)

						for i, t in enumerate(scheduler.timesteps):
									_UpperCAmelCase			=       model(A			,		A)
									_UpperCAmelCase			=       scheduler.step(A			,		A			,		A).prev_sample

						return sample



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



						"""simple docstring"""






						_UpperCAmelCase			=       dict(self.forward_default_kwargs)

						_UpperCAmelCase			=       kwargs.pop('num_inference_steps'			,		A)

						for scheduler_class in self.scheduler_classes:
									_UpperCAmelCase			=       self.get_scheduler_config()
									_UpperCAmelCase			=       scheduler_class(**A)

									_UpperCAmelCase			=       self.dummy_sample
									_UpperCAmelCase			=       0.1 * sample

									if num_inference_steps is not None and hasattr(A			,		'set_timesteps'):
												scheduler.set_timesteps(A)
									elif num_inference_steps is not None and not hasattr(A			,		'set_timesteps'):
												_UpperCAmelCase			=       num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									_UpperCAmelCase			=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									_UpperCAmelCase			=       dummy_past_residuals[: scheduler.config.solver_order]

									_UpperCAmelCase			=       scheduler.timesteps[5]
									_UpperCAmelCase			=       scheduler.timesteps[6]

									_UpperCAmelCase			=       scheduler.step(A			,		A			,		A			,		**A).prev_sample
									_UpperCAmelCase			=       scheduler.step(A			,		A			,		A			,		**A).prev_sample

									self.assertEqual(output_a.shape			,		sample.shape)
									self.assertEqual(output_a.shape			,		output_a.shape)



			def    _lowerCamelCase	(						self       :  List[str])       ->   Any:



						"""simple docstring"""






						_UpperCAmelCase			=       DEISMultistepScheduler(**self.get_scheduler_config())
						_UpperCAmelCase			=       self.full_loop(scheduler=A)
						_UpperCAmelCase			=       torch.mean(torch.abs(A))

						assert abs(result_mean.item() - 0.2_3_9_1_6) < 1E-3

						_UpperCAmelCase			=       DPMSolverSinglestepScheduler.from_config(scheduler.config)
						_UpperCAmelCase			=       DPMSolverMultistepScheduler.from_config(scheduler.config)
						_UpperCAmelCase			=       UniPCMultistepScheduler.from_config(scheduler.config)
						_UpperCAmelCase			=       DEISMultistepScheduler.from_config(scheduler.config)

						_UpperCAmelCase			=       self.full_loop(scheduler=A)
						_UpperCAmelCase			=       torch.mean(torch.abs(A))

						assert abs(result_mean.item() - 0.2_3_9_1_6) < 1E-3



			def    _lowerCamelCase	(						self       :  Union[str, Any])       ->   int:



						"""simple docstring"""






						for timesteps in [25, 50, 1_00, 9_99, 10_00]:
									self.check_over_configs(num_train_timesteps=A)



			def    _lowerCamelCase	(						self       :  int)       ->   Union[str, Any]:



						"""simple docstring"""






						self.check_over_configs(thresholding=A)
						for order in [1, 2, 3]:
									for solver_type in ["logrho"]:
												for threshold in [0.5, 1.0, 2.0]:
															for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(
																		    thresholding=A			,		prediction_type=A			,		sample_max_value=A			,		algorithm_type='deis'			,		solver_order=A			,		solver_type=A			,		)



			def    _lowerCamelCase	(						self       :  Optional[Any])       ->   Tuple:



						"""simple docstring"""






						for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=A)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Any:



						"""simple docstring"""






						for algorithm_type in ["deis"]:
									for solver_type in ["logrho"]:
												for order in [1, 2, 3]:
															for prediction_type in ["epsilon", "sample"]:
																		self.check_over_configs(
																		    solver_order=A			,		solver_type=A			,		prediction_type=A			,		algorithm_type=A			,		)
																		_UpperCAmelCase			=       self.full_loop(
																		    solver_order=A			,		solver_type=A			,		prediction_type=A			,		algorithm_type=A			,		)
																		assert not torch.isnan(A).any(), "Samples have nan numbers"



			def    _lowerCamelCase	(						self       :  List[str])       ->   str:



						"""simple docstring"""






						self.check_over_configs(lower_order_final=A)
						self.check_over_configs(lower_order_final=A)



			def    _lowerCamelCase	(						self       :  int)       ->   str:



						"""simple docstring"""






						for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
									self.check_over_forward(num_inference_steps=A			,		time_step=0)



			def    _lowerCamelCase	(						self       :  Tuple)       ->   List[str]:



						"""simple docstring"""






						_UpperCAmelCase			=       self.full_loop()
						_UpperCAmelCase			=       torch.mean(torch.abs(A))

						assert abs(result_mean.item() - 0.2_3_9_1_6) < 1E-3



			def    _lowerCamelCase	(						self       :  Tuple)       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.full_loop(prediction_type='v_prediction')
						_UpperCAmelCase			=       torch.mean(torch.abs(A))

						assert abs(result_mean.item() - 0.0_9_1) < 1E-3



			def    _lowerCamelCase	(						self       :  List[Any])       ->   Dict:



						"""simple docstring"""






						_UpperCAmelCase			=       self.scheduler_classes[0]
						_UpperCAmelCase			=       self.get_scheduler_config(thresholding=A			,		dynamic_thresholding_ratio=0)
						_UpperCAmelCase			=       scheduler_class(**A)

						_UpperCAmelCase			=       10
						_UpperCAmelCase			=       self.dummy_model()
						_UpperCAmelCase			=       self.dummy_sample_deter.half()
						scheduler.set_timesteps(A)

						for i, t in enumerate(scheduler.timesteps):
									_UpperCAmelCase			=       model(A			,		A)
									_UpperCAmelCase			=       scheduler.step(A			,		A			,		A).prev_sample

						assert sample.dtype == torch.floataa





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)
