


'''simple docstring'''



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	:						Optional[Any]       =	logging.get_logger(__name__)






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE      :   Any						,      _SCREAMING_SNAKE_CASE      :   str						,      _SCREAMING_SNAKE_CASE      :   Optional[int]   ):
					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							(_SCREAMING_SNAKE_CASE      :   List[str]						,      _SCREAMING_SNAKE_CASE      :   Optional[int]						,      _SCREAMING_SNAKE_CASE      :   int   ):
					__a			:       List[Any]					    =  to_pil_image(SCREAMING_SNAKE_CASE_   )
					__a			:       Dict					    =  pil_image.size
					__a			:       Optional[int]					    =  pytesseract.image_to_data(SCREAMING_SNAKE_CASE_						,      lang=SCREAMING_SNAKE_CASE_						,      output_type='dict'						,      config=SCREAMING_SNAKE_CASE_   )
					__a			:       int					    =  data['text'], data['left'], data['top'], data['width'], data['height']

					# filter empty words and corresponding coordinates
					__a			:       Any					    =  [idx for idx, word in enumerate(SCREAMING_SNAKE_CASE_   ) if not word.strip()]
					__a			:       Any					    =  [word for idx, word in enumerate(SCREAMING_SNAKE_CASE_   ) if idx not in irrelevant_indices]
					__a			:       Union[str, Any]					    =  [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_   ) if idx not in irrelevant_indices]
					__a			:       Dict					    =  [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_   ) if idx not in irrelevant_indices]
					__a			:       str					    =  [coord for idx, coord in enumerate(SCREAMING_SNAKE_CASE_   ) if idx not in irrelevant_indices]
					__a			:       List[str]					    =  [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
					__a			:       List[str]					    =  []
					for x, y, w, h in zip(SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_						,      SCREAMING_SNAKE_CASE_   ):
										__a			:       List[str]					    =  [x, y, x + w, y + h]
										actual_boxes.append(SCREAMING_SNAKE_CASE_   )

					# finally, normalize the bounding boxes
					__a			:       Optional[int]					    =  []
					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 							__UpperCamelCase				(		lowerCAmelCase_					):
	A_				=    ["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						,				):

						'''simple docstring'''





						super().__init__(**__a							)
						__a			:       Dict					    =  size if size is not None else {'height': 224, 'width': 224}
						__a			:       Union[str, Any]					    =  get_size_dict(__a							)

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




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

						'''simple docstring'''





						__a			:       str					    =  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()}"""							)
						__a			:       Union[str, Any]					    =  (size['height'], size['width'])
						return resize(__a						,				size=__a						,				resample=__a						,				data_format=__a						,				**__a							)




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

						'''simple docstring'''





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




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

						'''simple docstring'''





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





	def       __UpperCAmelCase							(   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'''





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

						__a			:       int					    =  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.
						__a			:       Optional[int]					    =  [to_numpy_array(__a							) for image in images]

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

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

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

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

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

						__a			:       Union[str, Any]					    =  BatchFeature(data={'pixel_values': images}						,				tensor_type=__a							)

						if apply_ocr:
											__a			:       int					    =  words_batch
											__a			:       List[str]					    =  boxes_batch
						return data





import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
					from transformers.models.rag.configuration_rag import RagConfig
					from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class      A__							(     __magic_name__	):






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		tempfile.mkdtemp()
							lowerCAmelCase__      :      Dict         =		8

							# DPR tok
							lowerCAmelCase__      :      str         =		[
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							lowerCAmelCase__      :      List[str]         =		os.path.join(self.tmpdirname    ,     'dpr_tokenizer'   )
							os.makedirs(a    ,     exist_ok=a   )
							lowerCAmelCase__      :      Union[str, Any]         =		os.path.join(a    ,     DPR_VOCAB_FILES_NAMES['vocab_file']   )
							with open(self.vocab_file    ,     'w'    ,     encoding='utf-8'   ) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

							# BART tok
							lowerCAmelCase__      :      Optional[Any]         =		[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							lowerCAmelCase__      :      str         =		dict(zip(a    ,     range(len(a   )   )   )   )
							lowerCAmelCase__      :      int         =		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							lowerCAmelCase__      :      Dict         =		{'unk_token': '<unk>'}

							lowerCAmelCase__      :      Dict         =		os.path.join(self.tmpdirname    ,     'bart_tokenizer'   )
							os.makedirs(a    ,     exist_ok=a   )
							lowerCAmelCase__      :      Optional[Any]         =		os.path.join(a    ,     BART_VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Any         =		os.path.join(a    ,     BART_VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write(json.dumps(a   ) + '\n'   )
							with open(self.merges_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write('\n'.join(a   )   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,     'dpr_tokenizer'   )   )






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,     'bart_tokenizer'   )   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							shutil.rmtree(self.tmpdirname   )






				@require_tokenizers
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		os.path.join(self.tmpdirname    ,     'rag_tokenizer'   )
							lowerCAmelCase__      :      Any         =		RagConfig(question_encoder=DPRConfig().to_dict()    ,     generator=BartConfig().to_dict()   )
							lowerCAmelCase__      :      Any         =		RagTokenizer(question_encoder=self.get_dpr_tokenizer()    ,     generator=self.get_bart_tokenizer()   )
							rag_config.save_pretrained(a   )
							rag_tokenizer.save_pretrained(a   )
							lowerCAmelCase__      :      List[str]         =		RagTokenizer.from_pretrained(a    ,     config=a   )
							self.assertIsInstance(new_rag_tokenizer.question_encoder    ,     a   )
							self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()    ,     rag_tokenizer.question_encoder.get_vocab()   )
							self.assertIsInstance(new_rag_tokenizer.generator    ,     a   )
							self.assertEqual(new_rag_tokenizer.generator.get_vocab()    ,     rag_tokenizer.generator.get_vocab()   )






				@slow
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		RagTokenizer.from_pretrained('facebook/rag-token-nq'   )
							lowerCAmelCase__      :      Any         =		[
							    'who got the first nobel prize in physics',
							    'when is the next deadpool movie being released',
							    'which mode is used for short wave broadcast service',
							    'who is the owner of reading football club',
							    'when is the next scandal episode coming out',
							    'when is the last time the philadelphia won the superbowl',
							    'what is the most current adobe flash player version',
							    'how many episodes are there in dragon ball z',
							    'what is the first step in the evolution of the eye',
							    'where is gall bladder situated in human body',
							    'what is the main mineral in lithium batteries',
							    'who is the president of usa right now',
							    'where do the greasers live in the outsiders',
							    'panda is a national animal of which country',
							    'what is the name of manchester united stadium',
							]
							lowerCAmelCase__      :      List[str]         =		tokenizer(a   )
							self.assertIsNotNone(a   )






				@slow
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		RagTokenizer.from_pretrained('facebook/rag-sequence-nq'   )
							lowerCAmelCase__      :      List[str]         =		[
							    'who got the first nobel prize in physics',
							    'when is the next deadpool movie being released',
							    'which mode is used for short wave broadcast service',
							    'who is the owner of reading football club',
							    'when is the next scandal episode coming out',
							    'when is the last time the philadelphia won the superbowl',
							    'what is the most current adobe flash player version',
							    'how many episodes are there in dragon ball z',
							    'what is the first step in the evolution of the eye',
							    'where is gall bladder situated in human body',
							    'what is the main mineral in lithium batteries',
							    'who is the president of usa right now',
							    'where do the greasers live in the outsiders',
							    'panda is a national animal of which country',
							    'what is the name of manchester united stadium',
							]
							lowerCAmelCase__      :      int         =		tokenizer(a   )
							self.assertIsNotNone(a   )