import os
import tempfile
import unittest
from typing import List

from transformers.models.esm.tokenization_esm import VOCAB_FILES_NAMES, EsmTokenizer
from transformers.testing_utils import require_tokenizers
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import PreTrainedTokenizerBase


@require_tokenizers
class 						a__							(   unittest.TestCase		):
			A__				:   Any								=			EsmTokenizer



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    str:
								super().setUp()
								__a							=   tempfile.mkdtemp()
								# fmt: off
								__a							=   ["<cls>", "<pad>", "<eos>", "<unk>", "L", "A", "G", "V", "S", "E", "R", "T", "I", "D", "P", "K", "Q", "N", "F", "Y", "M", "H", "W", "C", "X", "B", "U", "Z", "O", ".", "-", "<null_1>", "<mask>"]  # noqa: E501
								# fmt: on
								__a							=   os.path.join(self.tmpdirname      ,						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]    )    )



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    str:
								return [self.get_tokenizer(**UpperCAmelCase    )]



			def __SCREAMING_SNAKE_CASE							(						self      ,						**UpperCAmelCase    ) ->    int:
								return self.tokenizer_class.from_pretrained(self.tmpdirname      ,						**UpperCAmelCase    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    int:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   tokenizer.tokenize('LAGVS'    )
								self.assertListEqual(UpperCAmelCase      ,						['L', 'A', 'G', 'V', 'S']    )
								self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCAmelCase    )      ,						[4, 5, 6, 7, 8]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Optional[int]:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   'LAGVS'
								self.assertListEqual(tokenizer.encode(UpperCAmelCase    )      ,						[0, 4, 5, 6, 7, 8, 2]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    List[Any]:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   ['LAGVS', 'WCB']
								__a							=   tokenizer(UpperCAmelCase      ,						padding=UpperCAmelCase    )['input_ids']

								self.assertListEqual(UpperCAmelCase      ,						[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2]]    )



			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Tuple:
								__a							=   self.tokenizer_class(self.vocab_file    )

								__a							=   ['LAGVS', 'WCB']
								__a							=   tokenizer(UpperCAmelCase      ,						padding=UpperCAmelCase    )['input_ids']

								self.assertListEqual(UpperCAmelCase      ,						[[0, 4, 5, 6, 7, 8, 2], [0, 2_2, 2_3, 2_5, 2, 1, 1]]    )







			def __SCREAMING_SNAKE_CASE							(						self    ) ->    Dict:
								__a							=   self.get_tokenizers(fast=UpperCAmelCase    )
								for tokenizer in tokenizers:
													with self.subTest(f'''{tokenizer.__class__.__name__}'''    ):
																		__a							=   '<unk>'
																		__a							=   '<mask>'

																		__a							=   tokenizer.tokenize(UpperCAmelCase    )
																		__a							=   tokenizer.tokenize(UpperCAmelCase    )

																		self.assertEqual(len(UpperCAmelCase    )      ,						1    )
																		self.assertEqual(len(UpperCAmelCase    )      ,						1    )
																		self.assertEqual(token_a[0]      ,						UpperCAmelCase    )
																		self.assertEqual(token_a[0]      ,						UpperCAmelCase    )











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 				_lowerCAmelCase			(	UpperCAmelCase_							):


  '''simple docstring'''
  a_							:				List[str]					    =["""image_processor""", """tokenizer"""]
  a_							:				Optional[Any]					    ="""LayoutLMv2ImageProcessor"""
  a_							:				Any					    =("""LayoutLMv2Tokenizer""", """LayoutLMv2TokenizerFast""")







  def __init__(					self					:    Union[str, Any]					, UpperCamelCase					:    str=None					, UpperCamelCase					:    List[Any]=None					, **UpperCamelCase					:    List[str]							):


    '''simple docstring'''





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

    _snake_case      : Optional[Any]														=		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__(UpperCamelCase					, UpperCamelCase							)







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


    '''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
    _snake_case      : Optional[Any]														=		self.image_processor(images=UpperCamelCase					, return_tensors=UpperCamelCase							)

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

    _snake_case      : Optional[int]														=		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=UpperCamelCase					, add_special_tokens=UpperCamelCase					, padding=UpperCamelCase					, truncation=UpperCamelCase					, max_length=UpperCamelCase					, stride=UpperCamelCase					, pad_to_multiple_of=UpperCamelCase					, return_token_type_ids=UpperCamelCase					, return_attention_mask=UpperCamelCase					, return_overflowing_tokens=UpperCamelCase					, return_special_tokens_mask=UpperCamelCase					, return_offsets_mapping=UpperCamelCase					, return_length=UpperCamelCase					, verbose=UpperCamelCase					, return_tensors=UpperCamelCase					, **UpperCamelCase					, )

    # add pixel values
    _snake_case      : Optional[int]														=		features.pop('pixel_values'							)
    if return_overflowing_tokens is True:
      _snake_case      : Any														=		self.get_overflowing_images(UpperCamelCase					, encoded_inputs['overflow_to_sample_mapping']							)
    _snake_case      : int														=		images

    return encoded_inputs







  def UpperCamelCase_       (					self					:    List[Any]					, UpperCamelCase					:    List[str]					, UpperCamelCase					:    List[str]							):


    '''simple docstring'''





    _snake_case      : Optional[Any]														=		[]
    for sample_idx in overflow_to_sample_mapping:
      images_with_overflow.append(images[sample_idx]							)

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

    return images_with_overflow







  def UpperCamelCase_       (					self					:    List[Any]					, *UpperCamelCase					:    Any					, **UpperCamelCase					:    int							):


    '''simple docstring'''





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







  def UpperCamelCase_       (					self					:    Any					, *UpperCamelCase					:    Optional[int]					, **UpperCamelCase					:    Any							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_       (					self					:    str							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_       (					self					:    Dict							):


    '''simple docstring'''





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







  @property
  def UpperCamelCase_       (					self					:    Tuple							):


    '''simple docstring'''





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




