



from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
     import PIL

# soft dependency
if is_pytesseract_available():
     import pytesseract

a_						:     str	     =		logging.get_logger(__name__)

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      return [
          int(1000 * (box[0] / width) ),
          int(1000 * (box[1] / height) ),
          int(1000 * (box[2] / width) ),
          int(1000 * (box[3] / height) ),
      ]

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		to_pil_image(UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase								=		pil_image.size
      lowerCamelCase								=		pytesseract.image_to_data(UpperCAmelCase__   ,   lang=UpperCAmelCase__   ,   output_type="dict"   ,   config=UpperCAmelCase__ )
      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		data["text"], data["left"], data["top"], data["width"], data["height"]

      # filter empty words and corresponding coordinates
      lowerCamelCase								=		[idx for idx, word in enumerate(UpperCAmelCase__ ) if not word.strip()]
      lowerCamelCase								=		[word for idx, word in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
      lowerCamelCase								=		[coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
      lowerCamelCase								=		[coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
      lowerCamelCase								=		[coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]
      lowerCamelCase								=		[coord for idx, coord in enumerate(UpperCAmelCase__ ) if idx not in irrelevant_indices]

      # turn coordinates into (left, top, left+width, top+height) format
      lowerCamelCase								=		[]
      for x, y, w, h in zip(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):
            lowerCamelCase								=		[x, y, x + w, y + h]
            actual_boxes.append(UpperCAmelCase__ )

      # finally, normalize the bounding boxes
      lowerCamelCase								=		[]
      for box in actual_boxes:
            normalized_boxes.append(normalize_box(UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ) )

      assert len(UpperCAmelCase__ ) == len(UpperCAmelCase__ ), "Not as many words as there are bounding boxes"

      return words, normalized_boxes






class    lowerCamelCase__  (						UpperCAmelCase_):
     """simple docstring"""



     _A       =			['pixel_values']







     def __init__(self				,			__a = True				,			__a = None				,			__a = PILImageResampling.BILINEAR				,			__a = True				,			__a = 1 / 2_55				,			__a = True				,			__a = None				,			__a = None				,			__a = True				,			__a = None				,			__a = ""				,			**__a				,			):






           '''simple docstring'''



           super().__init__(**__a    )
           lowerCamelCase								=		size if size is not None else {"height": 2_24, "width": 2_24}
           lowerCamelCase								=		get_size_dict(__a    )

           lowerCamelCase								=		do_resize
           lowerCamelCase								=		size
           lowerCamelCase								=		resample
           lowerCamelCase								=		do_rescale
           lowerCamelCase								=		rescale_value
           lowerCamelCase								=		do_normalize
           lowerCamelCase								=		image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowerCamelCase								=		image_std if image_std is not None else IMAGENET_STANDARD_STD
           lowerCamelCase								=		apply_ocr
           lowerCamelCase								=		ocr_lang
           lowerCamelCase								=		tesseract_config







     def 						_a      (self				,			__a				,			__a				,			__a = PILImageResampling.BILINEAR				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		get_size_dict(__a    )
           if "height" not in size or "width" not in size:
                 raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""    )
           lowerCamelCase								=		(size["height"], size["width"])
           return resize(__a				,			size=__a				,			resample=__a				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           return rescale(__a				,			scale=__a				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a				,			__a				,			__a = None				,			**__a				,			):






           '''simple docstring'''



           return normalize(__a				,			mean=__a				,			std=__a				,			data_format=__a				,			**__a    )







     def 						_a      (self				,			__a				,			__a = None				,			__a = None				,			__a=None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = None				,			__a = ChannelDimension.FIRST				,			**__a				,			):






           '''simple docstring'''



           lowerCamelCase								=		do_resize if do_resize is not None else self.do_resize
           lowerCamelCase								=		size if size is not None else self.size
           lowerCamelCase								=		get_size_dict(__a    )
           lowerCamelCase								=		resample if resample is not None else self.resample
           lowerCamelCase								=		do_rescale if do_rescale is not None else self.do_rescale
           lowerCamelCase								=		rescale_factor if rescale_factor is not None else self.rescale_factor
           lowerCamelCase								=		do_normalize if do_normalize is not None else self.do_normalize
           lowerCamelCase								=		image_mean if image_mean is not None else self.image_mean
           lowerCamelCase								=		image_std if image_std is not None else self.image_std
           lowerCamelCase								=		apply_ocr if apply_ocr is not None else self.apply_ocr
           lowerCamelCase								=		ocr_lang if ocr_lang is not None else self.ocr_lang
           lowerCamelCase								=		tesseract_config if tesseract_config is not None else self.tesseract_config

           lowerCamelCase								=		make_list_of_images(__a    )

           if not valid_images(__a    ):
                 raise ValueError(
                     "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                     "torch.Tensor, tf.Tensor or jax.ndarray."    )

           if do_resize and size is None:
                 raise ValueError("Size must be specified if do_resize is True."    )

           if do_rescale and rescale_factor is None:
                 raise ValueError("Rescale factor must be specified if do_rescale is True."    )

           if do_normalize and (image_mean is None or image_std is None):
                 raise ValueError("If do_normalize is True, image_mean and image_std must be specified."    )

           # All transformations expect numpy arrays.
           lowerCamelCase								=		[to_numpy_array(__a    ) for image in images]

           # Tesseract OCR to get words + normalized bounding boxes
           if apply_ocr:
                 requires_backends(self				,			"pytesseract"    )
                 lowerCamelCase								=		[]
                 lowerCamelCase								=		[]
                 for image in images:
                       lowerCamelCase			,							lowerCamelCase								=		apply_tesseract(__a				,			__a				,			__a    )
                       words_batch.append(__a    )
                       boxes_batch.append(__a    )

           if do_resize:
                 lowerCamelCase								=		[self.resize(image=__a				,			size=__a				,			resample=__a    ) for image in images]

           if do_rescale:
                 lowerCamelCase								=		[self.rescale(image=__a				,			scale=__a    ) for image in images]

           if do_normalize:
                 lowerCamelCase								=		[self.normalize(image=__a				,			mean=__a				,			std=__a    ) for image in images]

           lowerCamelCase								=		[to_channel_dimension_format(__a				,			__a    ) for image in images]

           lowerCamelCase								=		BatchFeature(data={"pixel_values": images}				,			tensor_type=__a    )

           if apply_ocr:
                 lowerCamelCase								=		words_batch
                 lowerCamelCase								=		boxes_batch
           return data




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_						:     Any	     =		'docs/source/en/tasks'

def     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__   ,   UpperCAmelCase__ ):


      """simple docstring"""



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

      lowerCamelCase								=		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_						:     Union[str, 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_						:     Union[str, Any]	     =		{
    'summarization.md': ('nllb',),
    'translation.md': ('nllb',),
}

def     __lowercase(	UpperCAmelCase__ ):


      """simple docstring"""



      lowerCamelCase								=		TASK_GUIDE_TO_MODELS[task_guide]
      lowerCamelCase								=		SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(UpperCAmelCase__   ,   set() )
      lowerCamelCase								=		{
          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     __lowercase(	UpperCAmelCase__   ,   UpperCAmelCase__=False ):


      """simple docstring"""



      lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase			,							lowerCamelCase								=		_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-->"   ,   )

      lowerCamelCase								=		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_						:     str	     =		argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     a_						:     str	     =		parser.parse_args()

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