





"""simple docstring"""




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		:				Optional[int]  			=    '''src/transformers'''
a		:				List[str]  			=    '''docs/source/en/tasks'''






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[Any]					, _lowercase		:					int					, _lowercase		:					int      )				->List[Any]:






 '''simple docstring'''






 with open(_lowercase					, "r"					, encoding="utf-8"					, newline="\n"      ) as f:
  a   :	int			=       f.readlines()
 # Find the start prompt.
 a   :	Dict			=       0
 while not lines[start_index].startswith(_lowercase      ):
  start_index += 1
 start_index += 1

 a   :	int			=       start_index
 while not lines[end_index].startswith(_lowercase      ):
  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		:				Any  			=    direct_transformers_import(TRANSFORMERS_PATH)

a		:				List[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`).
a		:				Optional[Any]  			=    {
    '''summarization.md''': ('''nllb''',),
    '''translation.md''': ('''nllb''',),
}






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Tuple      )				->Optional[Any]:






 '''simple docstring'''






 a   :	str			=       TASK_GUIDE_TO_MODELS[task_guide]
 a   :	int			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_lowercase					, 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    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Optional[int]					, _lowercase		:					int=False      )				->Union[str, Any]:






 '''simple docstring'''






 a   :	str			=       _find_text_in_file(
     filename=os.path.join(_lowercase					, _lowercase      )					, start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->"					, end_prompt="<!--End of the generated tip-->"					, )

 a   :	Union[str, Any]			=       get_model_list_for_task(_lowercase      )

 if current_list != new_list:
  if overwrite:
   with open(os.path.join(_lowercase					, _lowercase      )					, "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		:				Optional[Any]  			=    argparse.ArgumentParser()
  parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
  a		:				Dict  			=    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"""




from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class  __UpperCamelCase       (		a__		):
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      torch.FloatTensor
							lowerCamelCase     :      Optional[torch.FloatTensor]         =None


class  __UpperCamelCase       (		a__	, a__		):
							lowerCamelCase     :      Tuple         =2






							@register_to_config
							def __init__(     self   ,      lowerCAmelCase__ = 0.02   ,      lowerCAmelCase__ = 100   ,      lowerCAmelCase__ = 1.007   ,      lowerCAmelCase__ = 80   ,      lowerCAmelCase__ = 0.05   ,      lowerCAmelCase__ = 50   ,      )			->				Union[str, Any]:
								# standard deviation of the initial noise distribution
								a   :	Tuple			=       sigma_max

								# setable values
								a   :	int			=       None
								a   :	np.IntTensor			=       None
								a   :	torch.FloatTensor			=       None  # sigma(t_i)






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				torch.FloatTensor:
								return sample






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				List[str]:
								a   :	List[Any]			=       num_inference_steps
								a   :	List[str]			=       np.arange(0   ,      self.num_inference_steps   )[::-1].copy()
								a   :	int			=       torch.from_numpy(lowerCAmelCase__   ).to(lowerCAmelCase__   )
								a   :	List[str]			=       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								a   :	Any			=       torch.tensor(lowerCAmelCase__   ,      dtype=torch.floataa   ,      device=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = None   )			->				Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
									a   :	str			=       min(self.config.s_churn / self.num_inference_steps   ,      2**0.5 - 1   )
								else:
									a   :	Dict			=       0

								# sample eps ~ N(0, S_noise^2 * I)
								a   :	Union[str, Any]			=       self.config.s_noise * randn_tensor(sample.shape   ,      generator=lowerCAmelCase__   ).to(sample.device   )
								a   :	Any			=       sigma + gamma * sigma
								a   :	Tuple			=       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Union[str, Any]			=       sample_hat + sigma_hat * model_output
								a   :	Tuple			=       (sample_hat - pred_original_sample) / sigma_hat
								a   :	List[Any]			=       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__ = True   ,      )			->				Union[KarrasVeOutput, Tuple]:
								a   :	Optional[int]			=       sample_prev + sigma_prev * model_output
								a   :	str			=       (sample_prev - pred_original_sample) / sigma_prev
								a   :	Dict			=       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
									return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=lowerCAmelCase__   ,      derivative=lowerCAmelCase__   ,      pred_original_sample=lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				str:
								raise NotImplementedError()


