






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_  (		__lowerCamelCase							,      unittest.TestCase		):
     __lowerCamelCase      				=  None
     __lowerCamelCase      				=  BloomTokenizerFast
     __lowerCamelCase      				=  BloomTokenizerFast
     __lowerCamelCase      				=  True
     __lowerCamelCase      				=  False
     __lowerCamelCase      				=  'tokenizer_file'
     __lowerCamelCase      				=  {'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'pad_token': '<pad>'}




     def   __UpperCAmelCase  (				self		):
          super().setUp()
          UpperCAmelCase__      :  Optional[int]				      =			BloomTokenizerFast.from_pretrained("""bigscience/tokenizer"""		)
          tokenizer.save_pretrained(self.tmpdirname		)




     def   __UpperCAmelCase  (				self	,						**_lowerCAmelCase		):
          kwargs.update(self.special_tokens_map		)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname	,						**_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			self.get_rust_tokenizer()

          UpperCAmelCase__      :  str				      =			["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
          UpperCAmelCase__      :  int				      =			[[2175, 23714, 73173, 144252, 2], [77, 132619, 3478, 368, 109586, 35433, 2]]

          UpperCAmelCase__      :  Optional[Any]				      =			tokenizer.batch_encode_plus(_lowerCAmelCase		)["""input_ids"""]
          self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)

          UpperCAmelCase__      :  str				      =			tokenizer.batch_decode(_lowerCAmelCase		)
          self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=6		):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})"		):
                    UpperCAmelCase__      :  List[str]				      =			self.rust_tokenizer_class.from_pretrained(_lowerCAmelCase	,						**_lowerCAmelCase		)
                    # tokenizer_r.pad_token = None # Hotfixing padding = None
                    # Simple input
                    UpperCAmelCase__      :  Tuple				      =			"""This is a simple input"""
                    UpperCAmelCase__      :  Tuple				      =			["""This is a simple input 1""", """This is a simple input 2"""]
                    UpperCAmelCase__      :  List[str]				      =			("""This is a simple input""", """This is a pair""")
                    UpperCAmelCase__      :  str				      =			[
                        ("""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(_lowerCAmelCase	,						max_length=_lowerCAmelCase		)
                         tokenizer_r.encode_plus(_lowerCAmelCase	,						max_length=_lowerCAmelCase		)

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

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

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

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

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

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

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




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  Optional[int]				      =			self.get_rust_tokenizer()
          UpperCAmelCase__      :  Optional[int]				      =			load_dataset("""xnli"""	,						"""all_languages"""	,						split="""test"""	,						streaming=_lowerCAmelCase		)

          UpperCAmelCase__      :  int				      =			next(iter(_lowerCAmelCase		)		)["""premise"""]  # pick up one data
          UpperCAmelCase__      :  Dict				      =			list(sample_data.values()		)

          UpperCAmelCase__      :  List[Any]				      =			list(map(tokenizer.encode	,						_lowerCAmelCase		)		)
          UpperCAmelCase__      :  Any				      =			[tokenizer.decode(_lowerCAmelCase	,						clean_up_tokenization_spaces=_lowerCAmelCase		) for x in output_tokens]
          self.assertListEqual(_lowerCAmelCase	,						_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self		):
          # 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		)














'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


__lowerCAmelCase									=   {'configuration_deit': ['DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DeiTConfig', 'DeiTOnnxConfig']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   ['DeiTFeatureExtractor']
		__lowerCAmelCase									=   ['DeiTImageProcessor']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'DeiTForImageClassification',
		    'DeiTForImageClassificationWithTeacher',
		    'DeiTForMaskedImageModeling',
		    'DeiTModel',
		    'DeiTPreTrainedModel',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCAmelCase									=   [
		    'TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFDeiTForImageClassification',
		    'TFDeiTForImageClassificationWithTeacher',
		    'TFDeiTForMaskedImageModeling',
		    'TFDeiTModel',
		    'TFDeiTPreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_deit import DeiTFeatureExtractor
				from .image_processing_deit import DeiTImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_deit import (
				    DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    DeiTForImageClassification,
				    DeiTForImageClassificationWithTeacher,
				    DeiTForMaskedImageModeling,
				    DeiTModel,
				    DeiTPreTrainedModel,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_deit import (
				    TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFDeiTForImageClassification,
				    TFDeiTForImageClassificationWithTeacher,
				    TFDeiTForMaskedImageModeling,
				    TFDeiTModel,
				    TFDeiTPreTrainedModel,
				)


else:
		import sys

		__lowerCAmelCase									=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




