
import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest
@require_torchsde
class 					lowercase__(							UpperCAmelCase				):



	"""simple docstring"""




	a     :Optional[Any]       	=      (DPMSolverSDEScheduler,)
	a     :Dict       	=      10
	def 		_lowercase    (			self    :     Dict			,				**SCREAMING_SNAKE_CASE_    :     Union[str, Any]			)			->	Dict:
					lowercase_	    =   {
					    '''num_train_timesteps''': 1_1_0_0,
					    '''beta_start''': 0.00_01,
					    '''beta_end''': 0.02,
					    '''beta_schedule''': '''linear''',
					    '''noise_sampler_seed''': 0,
					}

					config.update(**SCREAMING_SNAKE_CASE_			)
					return config
	def 		_lowercase    (			self    :     int			)			->	List[Any]:
					for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
									self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     str			)			->	str:
					for beta_start, beta_end in zip([0.0_00_01, 0.00_01, 0.0_01]			,				[0.00_02, 0.0_02, 0.02]			):
									self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_			,				beta_end=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Any			)			->	Optional[Any]:
					for schedule in ["linear", "scaled_linear"]:
									self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     List[str]			)			->	str:
					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
	def 		_lowercase    (			self    :     Tuple			)			->	Optional[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter * scheduler.init_noise_sigma
					lowercase_	    =   sample.to(SCREAMING_SNAKE_CASE_			)

					for i, t in enumerate(scheduler.timesteps			):
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_67.47_82_10_44_92_18_75			) < 1e-2
									assert abs(result_mean.item() - 0.21_78_70_59_64_56_52_77			) < 1e-3
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_71.59_35_21_11_81_64_06			) < 1e-2
									assert abs(result_mean.item() - 0.2_23_42_90_68_92_29_96_52			) < 1e-3
					else:
									assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62			) < 1e-2
									assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26			) < 1e-3
	def 		_lowercase    (			self    :     Optional[Any]			)			->	Tuple:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config(prediction_type='''v_prediction'''			)
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter * scheduler.init_noise_sigma
					lowercase_	    =   sample.to(SCREAMING_SNAKE_CASE_			)

					for i, t in enumerate(scheduler.timesteps			):
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_24.77_14_92_00_43_94_53			) < 1e-2
									assert abs(result_mean.item() - 0.1_62_26_28_90_14_81_62_84			) < 1e-3
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_28.1_66_33_60_59_57_03			) < 1e-2
									assert abs(result_mean.item() - 0.1_66_88_32_60_01_16_72_97			) < 1e-3
					else:
									assert abs(result_sum.item() - 1_19.8_48_75_48_82_81_25			) < 1e-2
									assert abs(result_mean.item() - 0.15_60_53_06_62_53_66_21			) < 1e-3
	def 		_lowercase    (			self    :     str			)			->	Optional[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			,				device=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma

					for t in scheduler.timesteps:
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_67.46_95_73_97_46_09_38			) < 1e-2
									assert abs(result_mean.item() - 0.2_18_05_93_46_07_98_26_35			) < 1e-3
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_71.59_35_36_37_69_53_12			) < 1e-2
									assert abs(result_mean.item() - 0.2_23_42_90_83_82_41_57_71			) < 1e-3
					else:
									assert abs(result_sum.item() - 1_62.52_38_34_22_85_15_62			) < 1e-2
									assert abs(result_mean.item() - 0.2_11_61_95_70_85_13_26			) < 1e-3




	def 		_lowercase    (			self    :     List[Any]			)			->	List[Any]:
					lowercase_	    =   self.scheduler_classes[0]
					lowercase_	    =   self.get_scheduler_config()
					lowercase_	    =   scheduler_class(**SCREAMING_SNAKE_CASE_			,				use_karras_sigmas=SCREAMING_SNAKE_CASE_			)

					scheduler.set_timesteps(self.num_inference_steps			,				device=SCREAMING_SNAKE_CASE_			)

					lowercase_	    =   self.dummy_model()
					lowercase_	    =   self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE_			) * scheduler.init_noise_sigma
					lowercase_	    =   sample.to(SCREAMING_SNAKE_CASE_			)

					for t in scheduler.timesteps:
									lowercase_	    =   scheduler.scale_model_input(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   model(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)

									lowercase_	    =   scheduler.step(SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			,				SCREAMING_SNAKE_CASE_			)
									lowercase_	    =   output.prev_sample

					lowercase_	    =   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
					lowercase_	    =   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

					if torch_device in ["mps"]:
									assert abs(result_sum.item() - 1_76.66_97_41_35_74_21_88			) < 1e-2
									assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11			) < 1e-2
					elif torch_device in ["cuda"]:
									assert abs(result_sum.item() - 1_77.63_65_35_64_45_31_25			) < 1e-2
									assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11			) < 1e-2
					else:
									assert abs(result_sum.item() - 1_70.3_13_52_23_38_86_72			) < 1e-2
									assert abs(result_mean.item() - 0.2_30_03_87_27_30_98_18_11			) < 1e-2






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
__a         =   'src/transformers'
__a         =   'docs/source/en/tasks'




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Union[str, Any]		,  snake_case__:   Any   ):


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

				lowercase_	    =   start_index
				while not lines[end_index].startswith(snake_case__   ):
								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.
__a         =   direct_transformers_import(TRANSFORMERS_PATH)

__a         =   {
    '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`).
__a         =   {
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}




def 							a		(				snake_case__:   Tuple   ):


				'''simple docstring'''
				lowercase_	    =   TASK_GUIDE_TO_MODELS[task_guide]
				lowercase_	    =   SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__		,  set()   )
				lowercase_	    =   {
				    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		(				snake_case__:   List[Any]		,  snake_case__:   Optional[int]=False   ):


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

				lowercase_	    =   get_model_list_for_task(snake_case__   )

				if current_list != new_list:
								if overwrite:
												with open(os.path.join(snake_case__		,  snake_case__   )		,  '''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__":
						__a         =   argparse.ArgumentParser()
						parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
						__a         =   parser.parse_args()

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




