





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
lowercase      :       List[str]									=  """src/transformers"""
lowercase      :       Optional[int]									=  """docs/source/en/tasks"""



def A_	(		A__		,      A__		,      A__				)  ->		Tuple:
       with open(A__		,      'r'		,      encoding='utf-8'		,      newline='\n'				) as f:
              a__						:	Any =				f.readlines()
       # Find the start prompt.
       a__						:	str =				0
       while not lines[start_index].startswith(A__				):
              start_index += 1
       start_index += 1

       a__						:	int =				start_index
       while not lines[end_index].startswith(A__				):
              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.
lowercase      :       Tuple									=  direct_transformers_import(TRANSFORMERS_PATH)

lowercase      :       Optional[Any]									=  {
    """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`).
lowercase      :       Optional[Any]									=  {
    """summarization.md""": ("""nllb""",),
    """translation.md""": ("""nllb""",),
}



def A_	(		A__				)  ->		Optional[int]:
       a__						:	Optional[Any] =				TASK_GUIDE_TO_MODELS[task_guide]
       a__						:	int =				SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(A__		,      set()				)
       a__						:	Optional[Any] =				{
           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_	(		A__		,      A__=False				)  ->		Optional[int]:
       a__ ,							a__ ,							a__ ,							a__						:	Dict =				_find_text_in_file(
           filename=os.path.join(A__		,      A__				)		,      start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'		,      end_prompt='<!--End of the generated tip-->'		,      )

       a__						:	List[Any] =				get_model_list_for_task(A__				)

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

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









'''simple docstring'''

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    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				(DPMSolverSDEScheduler,)
 __lowercase   		=				10
 def      UpperCAmelCase_							(				self      :List[Any]						,       **lowercase_      :Optional[int]							)->							str:
     A__				=			{
         "num_train_timesteps": 11_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
         "noise_sampler_seed": 0,
     }

     config.update(**lowercase_							)
     return config
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,       [0.0_0_0_2, 0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,       beta_end=lowercase_							)
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Union[str, Any]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

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

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

         A__				=			model(lowercase_						,       lowercase_							)

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

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

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config(prediction_type="v_prediction"							)
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

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

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

         A__				=			model(lowercase_						,       lowercase_							)

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

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

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							List[str]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

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

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

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

         A__				=			model(lowercase_						,       lowercase_							)

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

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

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3



 def      UpperCAmelCase_							(				self      :Tuple							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_						,       use_karras_sigmas=lowercase_							)

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

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

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

         A__				=			model(lowercase_						,       lowercase_							)

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

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

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2



