






import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class 		UpperCAmelCase_						(	lowercase__					,  unittest.TestCase					):


      '''simple docstring'''




      A     :   List[str]						=					None
      A     :   Dict						=					BloomTokenizerFast
      A     :   List[Any]						=					BloomTokenizerFast
      A     :   str						=					True
      A     :   Optional[Any]						=					False
      A     :   List[str]						=					'tokenizer_file'
      A     :   str						=					{'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}






      def       _lowerCAmelCase       (	self				) ->				List[str]:
        super().setUp()
        snake_case_				:							Union[str, Any]						 =       BloomTokenizerFast.from_pretrained("bigscience/tokenizer"				)
        tokenizer.save_pretrained(self.tmpdirname				)






      def       _lowerCAmelCase       (	self		,				**_SCREAMING_SNAKE_CASE				) ->				int:
        kwargs.update(self.special_tokens_map				)
        return BloomTokenizerFast.from_pretrained(self.tmpdirname		,				**_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self				) ->				Union[str, Any]:
        snake_case_				:							int						 =       self.get_rust_tokenizer()

        snake_case_				:							List[str]						 =       ["The quick brown fox</s>", "jumps over the lazy dog</s>"]
        snake_case_				:							Optional[int]						 =       [[2175, 2_3714, 7_3173, 14_4252, 2], [77, 13_2619, 3478, 368, 10_9586, 3_5433, 2]]

        snake_case_				:							Optional[int]						 =       tokenizer.batch_encode_plus(_SCREAMING_SNAKE_CASE				)["input_ids"]
        self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)

        snake_case_				:							List[Any]						 =       tokenizer.batch_decode(_SCREAMING_SNAKE_CASE				)
        self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self		,				_SCREAMING_SNAKE_CASE=6				) ->				str:
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
          with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''				):
            snake_case_				:							List[Any]						 =       self.rust_tokenizer_class.from_pretrained(_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)
            # tokenizer_r.pad_token = None # Hotfixing padding = None
            # Simple input
            snake_case_				:							Dict						 =       "This is a simple input"
            snake_case_				:							Optional[Any]						 =       ["This is a simple input 1", "This is a simple input 2"]
            snake_case_				:							List[str]						 =       ("This is a simple input", "This is a pair")
            snake_case_				:							Any						 =       [
                ("This is a simple input 1", "This is a simple input 2"),
                ("This is a simple pair 1", "This is a simple pair 2"),
            ]

            # Simple input tests
            try:
              tokenizer_r.encode(_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE				)
              tokenizer_r.encode_plus(_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE				)

              tokenizer_r.batch_encode_plus(_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE				)
              tokenizer_r.encode(_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE				)
              tokenizer_r.batch_encode_plus(_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE				)
            except ValueError:
              self.fail("Bloom Tokenizer should be able to deal with padding"				)

            snake_case_				:							str						 =       None  # Hotfixing padding = None
            self.assertRaises(_SCREAMING_SNAKE_CASE		,				tokenizer_r.encode		,				_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding="max_length"				)

            # Simple input
            self.assertRaises(_SCREAMING_SNAKE_CASE		,				tokenizer_r.encode_plus		,				_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding="max_length"				)

            # Simple input
            self.assertRaises(
                _SCREAMING_SNAKE_CASE		,				tokenizer_r.batch_encode_plus		,				_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding="max_length"		,				)

            # Pair input
            self.assertRaises(_SCREAMING_SNAKE_CASE		,				tokenizer_r.encode		,				_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding="max_length"				)

            # Pair input
            self.assertRaises(_SCREAMING_SNAKE_CASE		,				tokenizer_r.encode_plus		,				_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding="max_length"				)

            # Pair input
            self.assertRaises(
                _SCREAMING_SNAKE_CASE		,				tokenizer_r.batch_encode_plus		,				_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				padding="max_length"		,				)






      def       _lowerCAmelCase       (	self				) ->				List[str]:
        snake_case_				:							Optional[Any]						 =       self.get_rust_tokenizer()
        snake_case_				:							List[str]						 =       load_dataset("xnli"		,				"all_languages"		,				split="test"		,				streaming=_SCREAMING_SNAKE_CASE				)

        snake_case_				:							Optional[Any]						 =       next(iter(_SCREAMING_SNAKE_CASE				)				)["premise"]  # pick up one data
        snake_case_				:							Any						 =       list(sample_data.values()				)

        snake_case_				:							List[str]						 =       list(map(tokenizer.encode		,				_SCREAMING_SNAKE_CASE				)				)
        snake_case_				:							Optional[Any]						 =       [tokenizer.decode(_SCREAMING_SNAKE_CASE		,				clean_up_tokenization_spaces=_SCREAMING_SNAKE_CASE				) for x in output_tokens]
        self.assertListEqual(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)






      def       _lowerCAmelCase       (	self				) ->				str:
        # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
        # any sequence length constraints. This test of the parent class will fail since it relies on the
        # maximum sequence length of the positoonal embeddings.
        self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map				)		,				1				)
        self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()				)[0]				)		,				1				)








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 		UpperCAmelCase_						(	SCREAMING_SNAKE_CASE__					):


						'''simple docstring'''




						A     :   Tuple						=					['image_processor', 'tokenizer']
						A     :   List[Any]						=					'ViltImageProcessor'
						A     :   Optional[Any]						=					('BertTokenizer', 'BertTokenizerFast')






						def __init__(	self		,				_SCREAMING_SNAKE_CASE=None		,				_SCREAMING_SNAKE_CASE=None		,				**_SCREAMING_SNAKE_CASE				) ->				int:
								snake_case_				:							str						 =       None
								if "feature_extractor" in kwargs:
										warnings.warn(
										    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
										    " instead."		,				_SCREAMING_SNAKE_CASE		,				)
										snake_case_				:							str						 =       kwargs.pop("feature_extractor"				)

								snake_case_				:							Tuple						 =       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__(_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE				)
								snake_case_				:							str						 =       self.image_processor






						def __call__(	self		,				_SCREAMING_SNAKE_CASE		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = 0		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = None		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = False		,				_SCREAMING_SNAKE_CASE = True		,				_SCREAMING_SNAKE_CASE = None		,				**_SCREAMING_SNAKE_CASE		,				) ->				BatchEncoding:
								snake_case_				:							List[Any]						 =       self.tokenizer(
								    text=_SCREAMING_SNAKE_CASE		,				add_special_tokens=_SCREAMING_SNAKE_CASE		,				padding=_SCREAMING_SNAKE_CASE		,				truncation=_SCREAMING_SNAKE_CASE		,				max_length=_SCREAMING_SNAKE_CASE		,				stride=_SCREAMING_SNAKE_CASE		,				pad_to_multiple_of=_SCREAMING_SNAKE_CASE		,				return_token_type_ids=_SCREAMING_SNAKE_CASE		,				return_attention_mask=_SCREAMING_SNAKE_CASE		,				return_overflowing_tokens=_SCREAMING_SNAKE_CASE		,				return_special_tokens_mask=_SCREAMING_SNAKE_CASE		,				return_offsets_mapping=_SCREAMING_SNAKE_CASE		,				return_length=_SCREAMING_SNAKE_CASE		,				verbose=_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE		,				)
								# add pixel_values + pixel_mask
								snake_case_				:							str						 =       self.image_processor(_SCREAMING_SNAKE_CASE		,				return_tensors=_SCREAMING_SNAKE_CASE				)
								encoding.update(_SCREAMING_SNAKE_CASE				)

								return encoding






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				List[str]:
								return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						def       _lowerCAmelCase       (	self		,				*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				) ->				Dict:
								return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE		,				**_SCREAMING_SNAKE_CASE				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Tuple:
								snake_case_				:							List[Any]						 =       self.tokenizer.model_input_names
								snake_case_				:							Tuple						 =       self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[Any]:
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor_class






						@property
						def       _lowerCAmelCase       (	self				) ->				Optional[int]:
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."		,				_SCREAMING_SNAKE_CASE		,				)
								return self.image_processor
