import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
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_				(		SCREAMING_SNAKE_CASE ,  unittest.TestCase		):






 '''simple docstring'''






 UpperCAmelCase_							=      BartTokenizer
 UpperCAmelCase_							=      BartTokenizerFast
 UpperCAmelCase_							=      True
 UpperCAmelCase_							=      filter_roberta_detectors




 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



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

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




 def 	__snake_case		(    self      :						Union[str, Any]    ,					**lowercase__      :						Any):







        '''simple docstring'''



        kwargs.update(self.special_tokens_map)
        return self.tokenizer_class.from_pretrained(self.tmpdirname    ,					**lowercase__)




 def 	__snake_case		(    self      :						Union[str, Any]    ,					**lowercase__      :						str):







        '''simple docstring'''



        kwargs.update(self.special_tokens_map)
        return self.rust_tokenizer_class.from_pretrained(self.tmpdirname    ,					**lowercase__)




 def 	__snake_case		(    self      :						List[Any]    ,					lowercase__      :						Any):







        '''simple docstring'''



        return "lower newer", "lower newer"




 @cached_property
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        return BartTokenizer.from_pretrained('facebook/bart-large')




 @cached_property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        return BartTokenizerFast.from_pretrained('facebook/bart-large')




 @require_torch
 def 	__snake_case		(    self      :						str):







        '''simple docstring'''



        lowerCAmelCase__  			=      ['A long paragraph for summarization.', 'Another paragraph for summarization.']
        lowerCAmelCase__  			=      [0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

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

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




 @require_torch
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



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




 @require_torch
 def 	__snake_case		(    self      :						Any):







        '''simple docstring'''



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




 @require_torch
 def 	__snake_case		(    self      :						Union[str, Any]):







        '''simple docstring'''



        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowerCAmelCase__  			=      tokenizer(
                   ['I am a small frog' * 1_024, 'I am a small frog']    ,					padding=lowercase__    ,					truncation=lowercase__    ,					return_tensors='pt')
               self.assertIsInstance(lowercase__    ,					lowercase__)
               self.assertEqual(batch.input_ids.shape    ,					(2, 1_024))




 @require_torch
 def 	__snake_case		(    self      :						List[str]):







        '''simple docstring'''



        lowerCAmelCase__  			=      ['A long paragraph for summarization.']
        lowerCAmelCase__  			=      [
            'Summary of the text.',
        ]
        for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
               lowerCAmelCase__  			=      tokenizer(lowercase__    ,					return_tensors='pt')
               lowerCAmelCase__  			=      tokenizer(text_target=lowercase__    ,					return_tensors='pt')
               lowerCAmelCase__  			=      inputs['input_ids']
               lowerCAmelCase__  			=      targets['input_ids']
               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 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        pass




 def 	__snake_case		(    self      :						Optional[int]):







        '''simple docstring'''



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

                      # 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'])    ,					)

                      lowerCAmelCase__  			=      tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'])
                      lowerCAmelCase__  			=      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, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])
                      self.assertSequenceEqual(tokens_r['input_ids']    ,					[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2])

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

import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class        a_				(		SCREAMING_SNAKE_CASE		):






 '''simple docstring'''






 UpperCAmelCase_							=      ['image_processor', 'tokenizer']
 UpperCAmelCase_							=      'LayoutLMv2ImageProcessor'
 UpperCAmelCase_							=      ('LayoutLMv2Tokenizer', 'LayoutLMv2TokenizerFast')




 def __init__(    self      :						List[Any]    ,					lowercase__      :						List[Any]=None    ,					lowercase__      :						int=None    ,					**lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      None
        if "feature_extractor" in kwargs:
               warnings.warn(
                   'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                   ' instead.'    ,					lowercase__    ,					)
               lowerCAmelCase__  			=      kwargs.pop('feature_extractor')

        lowerCAmelCase__  			=      image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
               raise ValueError('You need to specify an `image_processor`.')
        if tokenizer is None:
               raise ValueError('You need to specify a `tokenizer`.')

        super().__init__(lowercase__    ,					lowercase__)




 def __call__(    self      :						Any    ,					lowercase__      :						Union[str, Any]    ,					lowercase__      :						Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None    ,					lowercase__      :						Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None    ,					lowercase__      :						Union[List[List[int]], List[List[List[int]]]] = None    ,					lowercase__      :						Optional[Union[List[int], List[List[int]]]] = None    ,					lowercase__      :						bool = True    ,					lowercase__      :						Union[bool, str, PaddingStrategy] = False    ,					lowercase__      :						Union[bool, str, TruncationStrategy] = False    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						int = 0    ,					lowercase__      :						Optional[int] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						Optional[bool] = None    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = False    ,					lowercase__      :						bool = True    ,					lowercase__      :						Optional[Union[str, TensorType]] = None    ,					**lowercase__      :						List[str]    ,					):







        '''simple docstring'''



        if self.image_processor.apply_ocr and (boxes is not None):
               raise ValueError(
                   'You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True.')

        if self.image_processor.apply_ocr and (word_labels is not None):
               raise ValueError(
                   'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.')

        if return_overflowing_tokens is True and return_offsets_mapping is False:
               raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.')

        # first, apply the image processor
        lowerCAmelCase__  			=      self.image_processor(images=lowercase__    ,					return_tensors=lowercase__)

        # second, apply the tokenizer
        if text is not None and self.image_processor.apply_ocr and text_pair is None:
               if isinstance(lowercase__    ,					lowercase__):
                      lowerCAmelCase__  			=      [text]  # add batch dimension (as the image processor always adds a batch dimension)
               lowerCAmelCase__  			=      features['words']

        lowerCAmelCase__  			=      self.tokenizer(
            text=text if text is not None else features['words']    ,					text_pair=text_pair if text_pair is not None else None    ,					boxes=boxes if boxes is not None else features['boxes']    ,					word_labels=lowercase__    ,					add_special_tokens=lowercase__    ,					padding=lowercase__    ,					truncation=lowercase__    ,					max_length=lowercase__    ,					stride=lowercase__    ,					pad_to_multiple_of=lowercase__    ,					return_token_type_ids=lowercase__    ,					return_attention_mask=lowercase__    ,					return_overflowing_tokens=lowercase__    ,					return_special_tokens_mask=lowercase__    ,					return_offsets_mapping=lowercase__    ,					return_length=lowercase__    ,					verbose=lowercase__    ,					return_tensors=lowercase__    ,					**lowercase__    ,					)

        # add pixel values
        lowerCAmelCase__  			=      features.pop('pixel_values')
        if return_overflowing_tokens is True:
               lowerCAmelCase__  			=      self.get_overflowing_images(lowercase__    ,					encoded_inputs['overflow_to_sample_mapping'])
        lowerCAmelCase__  			=      images

        return encoded_inputs




 def 	__snake_case		(    self      :						Optional[int]    ,					lowercase__      :						Optional[Any]    ,					lowercase__      :						Optional[Any]):







        '''simple docstring'''



        lowerCAmelCase__  			=      []
        for sample_idx in overflow_to_sample_mapping:
               images_with_overflow.append(images[sample_idx])

        if len(lowercase__) != len(lowercase__):
               raise ValueError(
                   'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
                   F""" {len(lowercase__)} and {len(lowercase__)}""")

        return images_with_overflow




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						Tuple    ,					**lowercase__      :						int):







        '''simple docstring'''



        return self.tokenizer.batch_decode(*lowercase__    ,					**lowercase__)




 def 	__snake_case		(    self      :						List[Any]    ,					*lowercase__      :						str    ,					**lowercase__      :						List[Any]):







        '''simple docstring'''



        return self.tokenizer.decode(*lowercase__    ,					**lowercase__)




 @property
 def 	__snake_case		(    self      :						Tuple):







        '''simple docstring'''



        return ["input_ids", "bbox", "token_type_ids", "attention_mask", "image"]




 @property
 def 	__snake_case		(    self      :						Optional[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,					lowercase__    ,					)
        return self.image_processor_class




 @property
 def 	__snake_case		(    self      :						List[Any]):







        '''simple docstring'''



        warnings.warn(
            '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,					lowercase__    ,					)
        return self.image_processor
