
import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors







@require_tokenizers
class        A_   (			__lowerCamelCase      ,	unittest.TestCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     str							 =       MvpTokenizer
	_UpperCamelCase     :     Tuple							 =       MvpTokenizerFast
	_UpperCamelCase     :     List[Any]							 =       True
	_UpperCamelCase     :     Any							 =       filter_roberta_detectors





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		super().setUp()
		lowercase       =    [
		    'l',
		    'o',
		    'w',
		    'e',
		    'r',
		    's',
		    't',
		    'i',
		    'd',
		    'n',
		    '\u0120',
		    '\u0120l',
		    '\u0120n',
		    '\u0120lo',
		    '\u0120low',
		    'er',
		    '\u0120lowest',
		    '\u0120newer',
		    '\u0120wider',
		    '<unk>',
		]
		lowercase       =    dict(zip(snake_case		,    range(len(snake_case       )       )       )       )
		lowercase       =    ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
		lowercase       =    {'unk_token': '<unk>'}

		lowercase       =    os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['vocab_file']       )
		lowercase       =    os.path.join(self.tmpdirname		,    VOCAB_FILES_NAMES['merges_file']       )
		with open(self.vocab_file		,    'w'		,    encoding='utf-8'       ) as fp:
			fp.write(json.dumps(snake_case       ) + '\n'       )
		with open(self.merges_file		,    'w'		,    encoding='utf-8'       ) as fp:
			fp.write('\n'.join(snake_case       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		kwargs.update(self.special_tokens_map       )
		return self.tokenizer_class.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		kwargs.update(self.special_tokens_map       )
		return self.rust_tokenizer_class.from_pretrained(self.tmpdirname		,    **snake_case       )





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case       ):
		return "lower newer", "lower newer"





	@cached_property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return MvpTokenizer.from_pretrained('RUCAIBox/mvp'       )





	@cached_property
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'       )





	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
		lowercase       =    [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowercase       =    tokenizer(snake_case		,    max_length=len(snake_case       )		,    padding=snake_case		,    return_tensors='pt'       )
			self.assertIsInstance(snake_case		,    snake_case       )

			self.assertEqual((2, 9)		,    batch.input_ids.shape       )
			self.assertEqual((2, 9)		,    batch.attention_mask.shape       )
			lowercase       =    batch.input_ids.tolist()[0]
			self.assertListEqual(snake_case		,    snake_case       )
			# Test that special tokens are reset





	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['A long paragraph for summarization.', 'Another paragraph for summarization.']
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowercase       =    tokenizer(snake_case		,    padding=snake_case		,    return_tensors='pt'       )
			# check if input_ids are returned and no labels
			self.assertIn('input_ids'		,    snake_case       )
			self.assertIn('attention_mask'		,    snake_case       )
			self.assertNotIn('labels'		,    snake_case       )
			self.assertNotIn('decoder_attention_mask'		,    snake_case       )





	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    [
		    'Summary of the text.',
		    'Another summary.',
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowercase       =    tokenizer(text_target=snake_case		,    max_length=32		,    padding='max_length'		,    return_tensors='pt'       )
			self.assertEqual(32		,    targets['input_ids'].shape[1]       )





	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowercase       =    tokenizer(
			    ['I am a small frog' * 1024, 'I am a small frog']		,    padding=snake_case		,    truncation=snake_case		,    return_tensors='pt'       )
			self.assertIsInstance(snake_case		,    snake_case       )
			self.assertEqual(batch.input_ids.shape		,    (2, 1024)       )





	@require_torch
	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    ['A long paragraph for summarization.']
		lowercase       =    [
		    'Summary of the text.',
		]
		for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
			lowercase       =    tokenizer(snake_case		,    text_target=snake_case		,    return_tensors='pt'       )
			lowercase       =    inputs['input_ids']
			lowercase       =    inputs['labels']
			self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()       )
			self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()       )
			self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()       )
			self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		pass





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''       ):
				lowercase       =    self.rust_tokenizer_class.from_pretrained(snake_case		,    **snake_case       )
				lowercase       =    self.tokenizer_class.from_pretrained(snake_case		,    **snake_case       )
				lowercase       =    'A, <mask> AllenNLP sentence.'
				lowercase       =    tokenizer_r.encode_plus(snake_case		,    add_special_tokens=snake_case		,    return_token_type_ids=snake_case       )
				lowercase       =    tokenizer_p.encode_plus(snake_case		,    add_special_tokens=snake_case		,    return_token_type_ids=snake_case       )

				# token_type_ids should put 0 everywhere
				self.assertEqual(sum(tokens_r['token_type_ids']       )		,    sum(tokens_p['token_type_ids']       )       )

				# attention_mask should put 1 everywhere, so sum over length should be 1
				self.assertEqual(
				    sum(tokens_r['attention_mask']       ) / len(tokens_r['attention_mask']       )		,    sum(tokens_p['attention_mask']       ) / len(tokens_p['attention_mask']       )		,    )

				lowercase       =    tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']       )
				lowercase       =    tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']       )

				# Rust correctly handles the space before the mask while python doesnt
				self.assertSequenceEqual(tokens_p['input_ids']		,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )
				self.assertSequenceEqual(tokens_r['input_ids']		,    [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]       )

				self.assertSequenceEqual(
				    snake_case		,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )
				self.assertSequenceEqual(
				    snake_case		,    ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']       )



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

UpperCAmelCase	      = logging.get_logger(__name__)






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






def 							UpperCAmelCase_		(							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE ,							__SCREAMING_SNAKE_CASE							):
	lowercase       =    to_pil_image(__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase       =    pil_image.size
	lowercase       =    pytesseract.image_to_data(__SCREAMING_SNAKE_CASE ,							lang=__SCREAMING_SNAKE_CASE ,							output_type='dict' ,							config=__SCREAMING_SNAKE_CASE							)
	lowercase  ,					lowercase  ,					lowercase  ,					lowercase  ,					lowercase       =    data['text'], data['left'], data['top'], data['width'], data['height']

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

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

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

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

	return words, normalized_boxes







class        A_   (			__lowerCamelCase   ):




	'''simple docstring'''


	_UpperCamelCase     :     Dict							 =       ["""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		,    ):
		super().__init__(**snake_case       )
		lowercase       =    size if size is not None else {'height': 224, 'width': 224}
		lowercase       =    get_size_dict(snake_case       )

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





	def 	SCREAMING_SNAKE_CASE__						(       self		,    snake_case		,    snake_case		,    snake_case = PILImageResampling.BILINEAR		,    snake_case = None		,    **snake_case		,    ):
		lowercase       =    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       =    (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		,    ):
		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		,    ):
		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		,    ):
		lowercase       =    do_resize if do_resize is not None else self.do_resize
		lowercase       =    size if size is not None else self.size
		lowercase       =    get_size_dict(snake_case       )
		lowercase       =    resample if resample is not None else self.resample
		lowercase       =    do_rescale if do_rescale is not None else self.do_rescale
		lowercase       =    rescale_factor if rescale_factor is not None else self.rescale_factor
		lowercase       =    do_normalize if do_normalize is not None else self.do_normalize
		lowercase       =    image_mean if image_mean is not None else self.image_mean
		lowercase       =    image_std if image_std is not None else self.image_std
		lowercase       =    apply_ocr if apply_ocr is not None else self.apply_ocr
		lowercase       =    ocr_lang if ocr_lang is not None else self.ocr_lang
		lowercase       =    tesseract_config if tesseract_config is not None else self.tesseract_config

		lowercase       =    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       =    [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       =    []
			lowercase       =    []
			for image in images:
				lowercase  ,					lowercase       =    apply_tesseract(snake_case		,    snake_case		,    snake_case       )
				words_batch.append(snake_case       )
				boxes_batch.append(snake_case       )

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

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

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

		lowercase       =    [to_channel_dimension_format(snake_case		,    snake_case       ) for image in images]

		lowercase       =    BatchFeature(data={'pixel_values': images}		,    tensor_type=snake_case       )

		if apply_ocr:
			lowercase       =    words_batch
			lowercase       =    boxes_batch
		return data

