from __future__ import annotations

import math

A   :     List[Any]       =				"2020.9.26"
A   :     Optional[Any]       =				"xcodz-dot, cclaus, dhruvmanila"


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      if not all(isinstance(__UpperCamelCase   ,					(float, int) ) for val in locals().values() ):
            SCREAMING_SNAKE_CASE_      =     F'''Input values must either be float or int: {list(locals().values() )}'''
            raise TypeError(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     ((x * distance) / (z + distance)) * scale
      SCREAMING_SNAKE_CASE_      =     ((y * distance) / (z + distance)) * scale
      return projected_x, projected_y


def    a__       (						__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase   ,					__UpperCamelCase ):
      if not isinstance(__UpperCamelCase   ,					__UpperCamelCase ):
            raise TypeError("Axis must be a str" )
      SCREAMING_SNAKE_CASE_      =     locals()
      del input_variables["axis"]
      if not all(isinstance(__UpperCamelCase   ,					(float, int) ) for val in input_variables.values() ):
            SCREAMING_SNAKE_CASE_      =     (
                "Input values except axis must either be float or int: "
                F'''{list(input_variables.values() )}'''
            )
            raise TypeError(__UpperCamelCase )
      SCREAMING_SNAKE_CASE_      =     (angle % 3_6_0) / 4_5_0 * 1_8_0 / math.pi
      if axis == "z":
            SCREAMING_SNAKE_CASE_      =     x * math.cos(__UpperCamelCase ) - y * math.sin(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     y * math.cos(__UpperCamelCase ) + x * math.sin(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     z
      elif axis == "x":
            SCREAMING_SNAKE_CASE_      =     y * math.cos(__UpperCamelCase ) - z * math.sin(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     z * math.cos(__UpperCamelCase ) + y * math.sin(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     x
      elif axis == "y":
            SCREAMING_SNAKE_CASE_      =     x * math.cos(__UpperCamelCase ) - z * math.sin(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     z * math.cos(__UpperCamelCase ) + x * math.sin(__UpperCamelCase )
            SCREAMING_SNAKE_CASE_      =     y
      else:
            raise ValueError("not a valid axis, choose one of 'x', 'y', 'z'" )

      return new_x, new_y, new_z


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(f"{convert_to_ad(1.0, 2.0, 3.0, 10.0, 10.0) = }")
      print(f"{rotate(1.0, 2.0, 3.0, 'y', 90.0) = }")



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   :     Tuple       =				"src/transformers"
A   :     Optional[Any]       =				"docs/source/en/tasks"


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

      SCREAMING_SNAKE_CASE_      =     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.
A   :     List[str]       =				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   :     Any       =				{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}


def    a__       (						__UpperCamelCase ):
      SCREAMING_SNAKE_CASE_      =     TASK_GUIDE_TO_MODELS[task_guide]
      SCREAMING_SNAKE_CASE_      =     SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__UpperCamelCase   ,					set() )
      SCREAMING_SNAKE_CASE_      =     {
          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   ,					__UpperCamelCase=False ):
      SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     _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-->"   ,					)

      SCREAMING_SNAKE_CASE_      =     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__":
      A   :     Tuple       =				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)


