




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

lowercase  : Dict						   =							logging.get_logger(__name__)







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   List[str]:
       lowercase							:					str	     =				to_pil_image(_lowerCAmelCase    )
       lowercase							:					Optional[int]	     =				pil_image.size
       lowercase							:					int	     =				pytesseract.image_to_data(_lowerCAmelCase ,     lang=_lowerCAmelCase ,     output_type="""dict""" ,     config=_lowerCAmelCase    )
       lowercase							:					List[Any]	     =				data["""text"""], data["""left"""], data["""top"""], data["""width"""], data["""height"""]

       # filter empty words and corresponding coordinates
       lowercase							:					Optional[Any]	     =				[idx for idx, word in enumerate(_lowerCAmelCase    ) if not word.strip()]
       lowercase							:					Dict	     =				[word for idx, word in enumerate(_lowerCAmelCase    ) if idx not in irrelevant_indices]
       lowercase							:					List[str]	     =				[coord for idx, coord in enumerate(_lowerCAmelCase    ) if idx not in irrelevant_indices]
       lowercase							:					Union[str, Any]	     =				[coord for idx, coord in enumerate(_lowerCAmelCase    ) if idx not in irrelevant_indices]
       lowercase							:					int	     =				[coord for idx, coord in enumerate(_lowerCAmelCase    ) if idx not in irrelevant_indices]
       lowercase							:					List[Any]	     =				[coord for idx, coord in enumerate(_lowerCAmelCase    ) if idx not in irrelevant_indices]

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

       # finally, normalize the bounding boxes
       lowercase							:					List[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 __snake_case		(				_a					):
    _a		:						Any=    ["pixel_values"]
    def __init__(  self		,snake_case = True		,snake_case = None		,snake_case = PILImageResampling.BILINEAR		,snake_case = True		,snake_case = 1 / 255		,snake_case = True		,snake_case = None		,snake_case = None		,snake_case = True		,snake_case = None		,snake_case = ""		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:					Optional[Any]	     =				do_resize
           lowercase							:					Tuple	     =				size
           lowercase							:					Tuple	     =				resample
           lowercase							:					Any	     =				do_rescale
           lowercase							:					str	     =				rescale_value
           lowercase							:					Union[str, Any]	     =				do_normalize
           lowercase							:					Any	     =				image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
           lowercase							:					Tuple	     =				image_std if image_std is not None else IMAGENET_STANDARD_STD
           lowercase							:					str	     =				apply_ocr
           lowercase							:					Tuple	     =				ocr_lang
           lowercase							:					Tuple	     =				tesseract_config
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case = PILImageResampling.BILINEAR		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           lowercase							:					Optional[Any]	     =				get_size_dict(snake_case_				)
           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()}"				)
           lowercase							:					str	     =				(size["""height"""], size["""width"""])
           return resize(snake_case_		,size=snake_case_		,resample=snake_case_		,data_format=snake_case_		,**snake_case_				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return rescale(snake_case_		,scale=snake_case_		,data_format=snake_case_		,**snake_case_				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case		,snake_case		,snake_case = None		,**snake_case		,):



           '''simple docstring'''







           return normalize(snake_case_		,mean=snake_case_		,std=snake_case_		,data_format=snake_case_		,**snake_case_				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case		,snake_case = None		,snake_case = None		,snake_case=None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = None		,snake_case = ChannelDimension.FIRST		,**snake_case		,):



           '''simple docstring'''







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

           lowercase							:					Dict	     =				make_list_of_images(snake_case_				)

           if not valid_images(snake_case_				):
                  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.
           lowercase							:					List[Any]	     =				[to_numpy_array(snake_case_				) for image in images]

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

           if do_resize:
                  lowercase							:					Tuple	     =				[self.resize(image=snake_case_		,size=snake_case_		,resample=snake_case_				) for image in images]

           if do_rescale:
                  lowercase							:					str	     =				[self.rescale(image=snake_case_		,scale=snake_case_				) for image in images]

           if do_normalize:
                  lowercase							:					Optional[int]	     =				[self.normalize(image=snake_case_		,mean=snake_case_		,std=snake_case_				) for image in images]

           lowercase							:					List[str]	     =				[to_channel_dimension_format(snake_case_		,snake_case_				) for image in images]

           lowercase							:					Optional[int]	     =				BatchFeature(data={"""pixel_values""": images}		,tensor_type=snake_case_				)

           if apply_ocr:
                  lowercase							:					Tuple	     =				words_batch
                  lowercase							:					Union[str, Any]	     =				boxes_batch
           return data


import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class __snake_case		(				lowerCAmelCase ,     unittest.TestCase					):
    _a		:						Optional[int]=    None
    _a		:						Optional[Any]=    BloomTokenizerFast
    _a		:						Tuple=    BloomTokenizerFast
    _a		:						str=    True
    _a		:						Optional[int]=    False
    _a		:						List[Any]=    "tokenizer_file"
    _a		:						List[Any]=    {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "pad_token": "<pad>"}
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           super().setUp()
           lowercase							:					Optional[Any]	     =				BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""				)
           tokenizer.save_pretrained(self.tmpdirname				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,**snake_case				):



           '''simple docstring'''







           kwargs.update(self.special_tokens_map				)
           return BloomTokenizerFast.from_pretrained(self.tmpdirname		,**snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					Tuple	     =				self.get_rust_tokenizer()

           lowercase							:					List[str]	     =				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
           lowercase							:					Optional[int]	     =				[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

           lowercase							:					Any	     =				tokenizer.batch_encode_plus(snake_case				)["""input_ids"""]
           self.assertListEqual(snake_case		,snake_case				)

           lowercase							:					Optional[int]	     =				tokenizer.batch_decode(snake_case				)
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self		,snake_case=6				):



           '''simple docstring'''







           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                  with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"				):
                         lowercase							:					Dict	     =				self.rust_tokenizer_class.from_pretrained(snake_case		,**snake_case				)
                         # tokenizer_r.pad_token = None # Hotfixing padding = None
                         # Simple input
                         lowercase							:					Dict	     =				"""This is a simple input"""
                         lowercase							:					Tuple	     =				["""This is a simple input 1""", """This is a simple input 2"""]
                         lowercase							:					Dict	     =				("""This is a simple input""", """This is a pair""")
                         lowercase							:					Optional[Any]	     =				[
                             ("""This is a simple input 1""", """This is a simple input 2"""),
                             ("""This is a simple pair 1""", """This is a simple pair 2"""),
                         ]

                         # Simple input tests
                         try:
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode_plus(snake_case		,max_length=snake_case				)

                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                                tokenizer_r.encode(snake_case		,max_length=snake_case				)
                                tokenizer_r.batch_encode_plus(snake_case		,max_length=snake_case				)
                         except ValueError:
                                self.fail("""Bloom Tokenizer should be able to deal with padding"""				)

                         lowercase							:					Optional[int]	     =				None  # Hotfixing padding = None
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Simple input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(snake_case		,tokenizer_r.encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""				)

                         # Pair input
                         self.assertRaises(
                             snake_case		,tokenizer_r.batch_encode_plus		,snake_case		,max_length=snake_case		,padding="""max_length"""		,)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					List[Any]	     =				self.get_rust_tokenizer()
           lowercase							:					List[str]	     =				load_dataset("""xnli"""		,"""all_languages"""		,split="""test"""		,streaming=snake_case				)

           lowercase							:					Optional[Any]	     =				next(iter(snake_case				)				)["""premise"""]  # pick up one data
           lowercase							:					str	     =				list(sample_data.values()				)

           lowercase							:					Optional[int]	     =				list(map(tokenizer.encode		,snake_case				)				)
           lowercase							:					Dict	     =				[tokenizer.decode(snake_case		,clean_up_tokenization_spaces=snake_case				) for x in output_tokens]
           self.assertListEqual(snake_case		,snake_case				)
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,1				)
           self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,1				)
