






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

SCREAMING_SNAKE_CASE 							=						logging.get_logger(__name__)

def      _lowerCamelCase	(						__A    :							Optional[Any]		,		__A    :							int		,		__A    :							Optional[int] )      ->					List[Any]:
    return [
        int(1_000 * (box[0] / width) ),
        int(1_000 * (box[1] / height) ),
        int(1_000 * (box[2] / width) ),
        int(1_000 * (box[3] / height) ),
    ]

def      _lowerCamelCase	(						__A    :							str		,		__A    :							int		,		__A    :							int )      ->					Any:
    _UpperCAmelCase				:      Tuple			  =				to_pil_image(lowerCamelCase_ )
    _UpperCAmelCase				:      List[str]			  =				pil_image.size
    _UpperCAmelCase				:      List[str]			  =				pytesseract.image_to_data(lowerCamelCase_		,		lang=lowerCamelCase_		,		output_type='''dict'''		,		config=lowerCamelCase_ )
    _UpperCAmelCase				:      int			  =				data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

    # filter empty words and corresponding coordinates
    _UpperCAmelCase				:      int			  =				[idx for idx, word in enumerate(lowerCamelCase_ ) if not word.strip()]
    _UpperCAmelCase				:      int			  =				[word for idx, word in enumerate(lowerCamelCase_ ) if idx not in irrelevant_indices]
    _UpperCAmelCase				:      str			  =				[coord for idx, coord in enumerate(lowerCamelCase_ ) if idx not in irrelevant_indices]
    _UpperCAmelCase				:      int			  =				[coord for idx, coord in enumerate(lowerCamelCase_ ) if idx not in irrelevant_indices]
    _UpperCAmelCase				:      str			  =				[coord for idx, coord in enumerate(lowerCamelCase_ ) if idx not in irrelevant_indices]
    _UpperCAmelCase				:      int			  =				[coord for idx, coord in enumerate(lowerCamelCase_ ) if idx not in irrelevant_indices]

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

    # finally, normalize the bounding boxes
    _UpperCAmelCase				:      Optional[Any]			  =				[]
    for box in actual_boxes:
        normalized_boxes.append(normalize_box(lowerCamelCase_		,		lowerCamelCase_		,		lowerCamelCase_ ) )

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

    return words, normalized_boxes


class 							A_    (						lowercase_			):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:			Any =					["pixel_values"]


 def __init__(	self							,  _A = True							,  _A = None							,  _A = PILImageResampling.BILINEAR							,  _A = True							,  _A = 1 / 255							,  _A = True							,  _A = None							,  _A = None							,  _A = True							,  _A = None							,  _A = ""							,  **_A							,  ) ->		Dict:

     """simple docstring"""



     super().__init__(**lowerCamelCase_)
     _UpperCAmelCase				:      Union[str, Any]			  =				size if size is not None else {"""height""": 224, """width""": 224}
     _UpperCAmelCase				:      List[Any]			  =				get_size_dict(lowerCamelCase_)

     _UpperCAmelCase				:      Dict			  =				do_resize
     _UpperCAmelCase				:      str			  =				size
     _UpperCAmelCase				:      Any			  =				resample
     _UpperCAmelCase				:      Union[str, Any]			  =				do_rescale
     _UpperCAmelCase				:      int			  =				rescale_value
     _UpperCAmelCase				:      List[str]			  =				do_normalize
     _UpperCAmelCase				:      Dict			  =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
     _UpperCAmelCase				:      List[str]			  =				image_std if image_std is not None else IMAGENET_STANDARD_STD
     _UpperCAmelCase				:      List[str]			  =				apply_ocr
     _UpperCAmelCase				:      List[str]			  =				ocr_lang
     _UpperCAmelCase				:      List[str]			  =				tesseract_config


 def  snake_case__				(	self							,  _A							,  _A							,  _A = PILImageResampling.BILINEAR							,  _A = None							,  **_A							,  ) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      List[Any]			  =				get_size_dict(lowerCamelCase_)
     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()}''')
     _UpperCAmelCase				:      List[str]			  =				(size["""height"""], size["""width"""])
     return resize(lowerCamelCase_							,  size=lowerCamelCase_							,  resample=lowerCamelCase_							,  data_format=lowerCamelCase_							,  **lowerCamelCase_)


 def  snake_case__				(	self							,  _A							,  _A							,  _A = None							,  **_A							,  ) ->		str:

     """simple docstring"""



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


 def  snake_case__				(	self							,  _A							,  _A							,  _A							,  _A = None							,  **_A							,  ) ->		List[Any]:

     """simple docstring"""



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


 def  snake_case__				(	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							,  ) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				do_resize if do_resize is not None else self.do_resize
     _UpperCAmelCase				:      str			  =				size if size is not None else self.size
     _UpperCAmelCase				:      Any			  =				get_size_dict(lowerCamelCase_)
     _UpperCAmelCase				:      List[Any]			  =				resample if resample is not None else self.resample
     _UpperCAmelCase				:      int			  =				do_rescale if do_rescale is not None else self.do_rescale
     _UpperCAmelCase				:      Optional[Any]			  =				rescale_factor if rescale_factor is not None else self.rescale_factor
     _UpperCAmelCase				:      Dict			  =				do_normalize if do_normalize is not None else self.do_normalize
     _UpperCAmelCase				:      Any			  =				image_mean if image_mean is not None else self.image_mean
     _UpperCAmelCase				:      List[str]			  =				image_std if image_std is not None else self.image_std
     _UpperCAmelCase				:      List[Any]			  =				apply_ocr if apply_ocr is not None else self.apply_ocr
     _UpperCAmelCase				:      Union[str, Any]			  =				ocr_lang if ocr_lang is not None else self.ocr_lang
     _UpperCAmelCase				:      Optional[int]			  =				tesseract_config if tesseract_config is not None else self.tesseract_config

     _UpperCAmelCase				:      List[Any]			  =				make_list_of_images(lowerCamelCase_)

     if not valid_images(lowerCamelCase_):
         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.
     _UpperCAmelCase				:      Any			  =				[to_numpy_array(lowerCamelCase_) for image in images]

     # Tesseract OCR to get words + normalized bounding boxes
     if apply_ocr:
         requires_backends(self							,  '''pytesseract''')
         _UpperCAmelCase				:      List[str]			  =				[]
         _UpperCAmelCase				:      Optional[Any]			  =				[]
         for image in images:
             _UpperCAmelCase				:      Dict			  =				apply_tesseract(lowerCamelCase_							,  lowerCamelCase_							,  lowerCamelCase_)
             words_batch.append(lowerCamelCase_)
             boxes_batch.append(lowerCamelCase_)

     if do_resize:
         _UpperCAmelCase				:      Union[str, Any]			  =				[self.resize(image=lowerCamelCase_							,  size=lowerCamelCase_							,  resample=lowerCamelCase_) for image in images]

     if do_rescale:
         _UpperCAmelCase				:      Any			  =				[self.rescale(image=lowerCamelCase_							,  scale=lowerCamelCase_) for image in images]

     if do_normalize:
         _UpperCAmelCase				:      List[Any]			  =				[self.normalize(image=lowerCamelCase_							,  mean=lowerCamelCase_							,  std=lowerCamelCase_) for image in images]

     _UpperCAmelCase				:      Optional[Any]			  =				[to_channel_dimension_format(lowerCamelCase_							,  lowerCamelCase_) for image in images]

     _UpperCAmelCase				:      List[Any]			  =				BatchFeature(data={'''pixel_values''': images}							,  tensor_type=lowerCamelCase_)

     if apply_ocr:
         _UpperCAmelCase				:      str			  =				words_batch
         _UpperCAmelCase				:      Any			  =				boxes_batch
     return data












import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


SCREAMING_SNAKE_CASE 							=						'\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n'
class 							A_    (						unittest.TestCase ,   __lowercase			):






 '''simple docstring'''




 def  snake_case__				(	self) ->		int:

     """simple docstring"""



     _UpperCAmelCase				:      int			  =				load_tool('''text-question-answering''')
     self.tool.setup()
     _UpperCAmelCase				:      Any			  =				load_tool('''text-question-answering'''							,  remote=_A)


 def  snake_case__				(	self) ->		Union[str, Any]:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[Any]			  =				self.tool(_A							,  '''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		str:

     """simple docstring"""



     _UpperCAmelCase				:      str			  =				self.remote_tool(_A							,  '''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		Optional[int]:

     """simple docstring"""



     _UpperCAmelCase				:      Dict			  =				self.tool(text=_A							,  question='''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')


 def  snake_case__				(	self) ->		Dict:

     """simple docstring"""



     _UpperCAmelCase				:      Optional[int]			  =				self.remote_tool(text=_A							,  question='''What did Hugging Face do in April 2021?''')
     self.assertEqual(_A							,  '''launched the BigScience Research Workshop''')




