


import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


__UpperCamelCase				:  Union[str, Any]      					=							get_tests_dir('fixtures/test_sentencepiece_with_bytefallback.model')





@require_sentencepiece
@require_tokenizers
class  lowerCAmelCase__(    snake_case__    ,    unittest.TestCase						):






    '''simple docstring'''





    A_  :     Tuple			= GPTSwaTokenizer
    A_  :     Dict			= False
    A_  :     str			= True
    A_  :     Optional[Any]			= False





    def 					_lowerCamelCase (	self				:   Optional[Any]	):





       '''simple docstring'''
       super().setUp()

       # We have a SentencePiece fixture for testing
       UpperCAmelCase_							:      int						=   GPTSwaTokenizer(__snake_case       ,   eos_token='''<unk>'''       ,   bos_token='''<unk>'''       ,   pad_token='''<unk>'''	)

       tokenizer.save_pretrained(self.tmpdirname	)





    def 					_lowerCamelCase (	self				:   str       ,   __snake_case				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   '''This is a test'''
       UpperCAmelCase_							:      Optional[int]						=   '''This is a test'''
       return input_text, output_text





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Dict						=   '''<s>'''
       UpperCAmelCase_							:      int						=   1

       self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case	)       ,   __snake_case	)
       self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case	)       ,   __snake_case	)





    def 					_lowerCamelCase (	self				:   int	):





       '''simple docstring'''
       UpperCAmelCase_							:      Union[str, Any]						=   list(self.get_tokenizer().get_vocab().keys()	)

       self.assertEqual(vocab_keys[0]       ,   '''<unk>'''	)
       self.assertEqual(vocab_keys[1]       ,   '''<s>'''	)
       self.assertEqual(vocab_keys[-1]       ,   '''j'''	)
       self.assertEqual(len(__snake_case	)       ,   2_000	)





    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       self.assertEqual(self.get_tokenizer().vocab_size       ,   2_000	)





    def 					_lowerCamelCase (	self				:   List[Any]	):





       '''simple docstring'''
       UpperCAmelCase_							:      Optional[int]						=   GPTSwaTokenizer(__snake_case	)

       UpperCAmelCase_							:      Dict						=   tokenizer.tokenize('''This is a test'''	)
       self.assertListEqual(__snake_case       ,   ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']	)

       self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case	)       ,   [465, 287, 265, 631, 842]	)

       UpperCAmelCase_							:      Dict						=   tokenizer.tokenize('''I was born in 92000, and this is falsé.'''	)
       # fmt: off
       self.assertListEqual(
           __snake_case       ,   ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']       ,   )
       # fmt: on

       UpperCAmelCase_							:      List[str]						=   tokenizer.convert_tokens_to_ids(__snake_case	)
       self.assertListEqual(
           __snake_case       ,   [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]       ,   )

       UpperCAmelCase_							:      Optional[Any]						=   tokenizer.convert_ids_to_tokens(__snake_case	)
       # fmt: off
       self.assertListEqual(
           __snake_case       ,   ['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']	)
       # fmt: on





    def 					_lowerCamelCase (	self				:   List[str]	):





       '''simple docstring'''
       UpperCAmelCase_							:      List[str]						=   GPTSwaTokenizer(__snake_case	)
       UpperCAmelCase_							:      str						=   ['''This is a test''', '''I was born in 92000, and this is falsé.''']
       UpperCAmelCase_							:      List[str]						=   [
           [465, 287, 265, 631, 842],
           [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
       ]

       # Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
       for text, expected_ids in zip(__snake_case       ,   __snake_case	):
          self.assertListEqual(tokenizer.encode_fast(__snake_case	)       ,   __snake_case	)

       # Test that decode_fast returns the input text
       for text, token_ids in zip(__snake_case       ,   __snake_case	):
          self.assertEqual(tokenizer.decode_fast(__snake_case	)       ,   __snake_case	)





    @slow
    def 					_lowerCamelCase (	self				:   Optional[int]	):





       '''simple docstring'''
       UpperCAmelCase_							:      str						=   [
           '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
           '''Hey there, how are you doing this fine day?''',
           '''This is a text with a trailing spaces followed by a dot     .''',
           '''Häj sväjs lillebrör! =)''',
           '''Det är inget fel på Mr. Cool''',
       ]

       # fmt: off
       UpperCAmelCase_							:      Any						=   {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
       # fmt: on
       self.tokenizer_integration_test_util(
           expected_encoding=__snake_case       ,   model_name='''AI-Sweden/gpt-sw3-126m'''       ,   sequences=__snake_case       ,   )



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__(    snake_case__						):






    '''simple docstring'''





    A_  :     str			= ['image_processor', 'tokenizer']
    A_  :     int			= 'LayoutLMv2ImageProcessor'
    A_  :     str			= ('LayoutXLMTokenizer', 'LayoutXLMTokenizerFast')





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





       '''simple docstring'''
       if "feature_extractor" in kwargs:
          warnings.warn(
              '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
              ''' instead.'''       ,   __snake_case       ,   )
          UpperCAmelCase_							:      List[Any]						=   kwargs.pop('''feature_extractor'''	)

       UpperCAmelCase_							:      Optional[int]						=   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__(__snake_case       ,   __snake_case	)





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





       '''simple docstring'''
       # verify input
       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
       UpperCAmelCase_							:      Tuple						=   self.image_processor(images=__snake_case       ,   return_tensors=__snake_case	)

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

       UpperCAmelCase_							:      str						=   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=__snake_case       ,   add_special_tokens=__snake_case       ,   padding=__snake_case       ,   truncation=__snake_case       ,   max_length=__snake_case       ,   stride=__snake_case       ,   pad_to_multiple_of=__snake_case       ,   return_token_type_ids=__snake_case       ,   return_attention_mask=__snake_case       ,   return_overflowing_tokens=__snake_case       ,   return_special_tokens_mask=__snake_case       ,   return_offsets_mapping=__snake_case       ,   return_length=__snake_case       ,   verbose=__snake_case       ,   return_tensors=__snake_case       ,   **__snake_case       ,   )

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

       return encoded_inputs





    def 					_lowerCamelCase (	self				:   Union[str, Any]       ,   __snake_case				:   str       ,   __snake_case				:   List[Any]	):





       '''simple docstring'''
       # in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
       UpperCAmelCase_							:      List[str]						=   []
       for sample_idx in overflow_to_sample_mapping:
          images_with_overflow.append(images[sample_idx]	)

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

       return images_with_overflow





    def 					_lowerCamelCase (	self				:   List[Any]       ,   *__snake_case				:   Optional[int]       ,   **__snake_case				:   List[Any]	):





       '''simple docstring'''
       return self.tokenizer.batch_decode(*__snake_case       ,   **__snake_case	)





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





       '''simple docstring'''
       return self.tokenizer.decode(*__snake_case       ,   **__snake_case	)





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





       '''simple docstring'''
       return ["input_ids", "bbox", "attention_mask", "image"]





    @property
    def 					_lowerCamelCase (	self				:   List[str]	):





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





    @property
    def 					_lowerCamelCase (	self				:   Dict	):





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