






"""simple docstring"""
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


_lowerCAmelCase         =							get_tests_dir("fixtures/test_sentencepiece.model")



@require_sentencepiece
@require_tokenizers
class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE       ,       unittest.TestCase						):





	"""simple docstring"""




	_A       :				Dict    =				ReformerTokenizer
	_A       :				Optional[int]    =				ReformerTokenizerFast
	_A       :				Union[str, Any]    =				True
	_A       :				Union[str, Any]    =				False
	_A       :				List[Any]    =				True





	def    lowerCamelCase(self							):
		super().setUp()

		A_		:						str	    =   ReformerTokenizer(lowerCAmelCase_     ,     keep_accents=lowerCAmelCase_							)
		tokenizer.save_pretrained(self.tmpdirname							)





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   """<s>"""
		A_		:						Any	    =   1

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





	def    lowerCamelCase(self							):
		A_		:						Tuple	    =   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(lowerCAmelCase_							)     ,     1000							)





	def    lowerCamelCase(self							):
		self.assertEqual(self.get_tokenizer().vocab_size     ,     1000							)





	def    lowerCamelCase(self							):
		if not self.test_rust_tokenizer:
			return

		A_		:						Optional[Any]	    =   self.get_tokenizer()
		A_		:						Any	    =   self.get_rust_tokenizer()

		A_		:						Optional[Any]	    =   """I was born in 92000, and this is falsé."""

		A_		:						int	    =   tokenizer.tokenize(lowerCAmelCase_							)
		A_		:						List[Any]	    =   rust_tokenizer.tokenize(lowerCAmelCase_							)
		self.assertListEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)

		A_		:						List[str]	    =   tokenizer.encode(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_							)
		A_		:						Union[str, Any]	    =   rust_tokenizer.encode(lowerCAmelCase_     ,     add_special_tokens=lowerCAmelCase_							)
		self.assertListEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)

		A_		:						List[Any]	    =   self.get_rust_tokenizer()
		A_		:						List[Any]	    =   tokenizer.encode(lowerCAmelCase_							)
		A_		:						Dict	    =   rust_tokenizer.encode(lowerCAmelCase_							)
		self.assertListEqual(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     lowerCAmelCase_=15							):
		for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
			with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
				A_		:						List[str]	    =   self.rust_tokenizer_class.from_pretrained(lowerCAmelCase_     ,     **lowerCAmelCase_							)

				# Simple input
				A_		:						Tuple	    =   """This is a simple input"""
				A_		:						Any	    =   ["""This is a simple input 1""", """This is a simple input 2"""]
				A_		:						Optional[int]	    =   ("""This is a simple input""", """This is a pair""")
				A_		:						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
				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    lowerCamelCase(self							):
		pass





	def    lowerCamelCase(self							):
		A_		:						str	    =   ReformerTokenizer(lowerCAmelCase_     ,     keep_accents=lowerCAmelCase_							)

		A_		:						Tuple	    =   tokenizer.tokenize("""This is a test"""							)
		self.assertListEqual(lowerCAmelCase_     ,     ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]							)

		self.assertListEqual(
		    tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)     ,     [285, 46, 10, 170, 382]     ,     )

		A_		:						int	    =   tokenizer.tokenize("""I was born in 92000, and this is falsé."""							)
		self.assertListEqual(
		    lowerCAmelCase_     ,     [
		        SPIECE_UNDERLINE + """I""",
		        SPIECE_UNDERLINE + """was""",
		        SPIECE_UNDERLINE + """b""",
		        """or""",
		        """n""",
		        SPIECE_UNDERLINE + """in""",
		        SPIECE_UNDERLINE + """""",
		        """9""",
		        """2""",
		        """0""",
		        """0""",
		        """0""",
		        """,""",
		        SPIECE_UNDERLINE + """and""",
		        SPIECE_UNDERLINE + """this""",
		        SPIECE_UNDERLINE + """is""",
		        SPIECE_UNDERLINE + """f""",
		        """al""",
		        """s""",
		        """é""",
		        """.""",
		    ]     ,     )
		A_		:						Dict	    =   tokenizer.convert_tokens_to_ids(lowerCAmelCase_							)
		self.assertListEqual(
		    lowerCAmelCase_     ,     [8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,     )

		A_		:						Tuple	    =   tokenizer.convert_ids_to_tokens(lowerCAmelCase_							)
		self.assertListEqual(
		    lowerCAmelCase_     ,     [
		        SPIECE_UNDERLINE + """I""",
		        SPIECE_UNDERLINE + """was""",
		        SPIECE_UNDERLINE + """b""",
		        """or""",
		        """n""",
		        SPIECE_UNDERLINE + """in""",
		        SPIECE_UNDERLINE + """""",
		        """<unk>""",
		        """2""",
		        """0""",
		        """0""",
		        """0""",
		        """,""",
		        SPIECE_UNDERLINE + """and""",
		        SPIECE_UNDERLINE + """this""",
		        SPIECE_UNDERLINE + """is""",
		        SPIECE_UNDERLINE + """f""",
		        """al""",
		        """s""",
		        """<unk>""",
		        """.""",
		    ]     ,     )





	@cached_property
	def    lowerCamelCase(self							):
		return ReformerTokenizer.from_pretrained("""google/reformer-crime-and-punishment"""							)





	@slow
	def    lowerCamelCase(self							):
		A_		:						Optional[int]	    =   """Hello World!"""
		A_		:						List[Any]	    =   [126, 32, 262, 152, 38, 72, 287]

		self.assertListEqual(lowerCAmelCase_     ,     self.big_tokenizer.encode(lowerCAmelCase_							)							)





	@slow
	def    lowerCamelCase(self							):
		A_		:						Optional[Any]	    =   (
		    """This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
		    """ add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
		)
		A_		:						int	    =   [
		    108,
		    265,
		    24,
		    111,
		    4,
		    258,
		    156,
		    35,
		    28,
		    275,
		    3,
		    259,
		    297,
		    260,
		    84,
		    4,
		    35,
		    110,
		    44,
		    8,
		    259,
		    91,
		    268,
		    21,
		    11,
		    209,
		    274,
		    109,
		    266,
		    277,
		    117,
		    86,
		    93,
		    315,
		    258,
		    278,
		    258,
		    277,
		    258,
		    0,
		    258,
		    288,
		    258,
		    319,
		    258,
		    0,
		    258,
		    0,
		    258,
		    0,
		    258,
		    0,
		    258,
		    287,
		    258,
		    315,
		    258,
		    289,
		    258,
		    278,
		    99,
		    269,
		    266,
		    262,
		    8,
		    259,
		    241,
		    4,
		    217,
		    230,
		    268,
		    266,
		    55,
		    168,
		    106,
		    75,
		    193,
		    266,
		    223,
		    27,
		    49,
		    26,
		    282,
		    25,
		    264,
		    299,
		    19,
		    26,
		    0,
		    258,
		    277,
		    117,
		    86,
		    93,
		    176,
		    183,
		    270,
		    11,
		    262,
		    42,
		    61,
		    265,
		]

		self.assertListEqual(lowerCAmelCase_     ,     self.big_tokenizer.encode(lowerCAmelCase_							)							)





	@require_torch
	@slow
	def    lowerCamelCase(self							):
		import torch

		from transformers import ReformerConfig, ReformerModel

		# Build sequence
		A_		:						Dict	    =   list(self.big_tokenizer.get_vocab().keys()							)[:10]
		A_		:						Optional[int]	    =   """ """.join(lowerCAmelCase_							)
		A_		:						Dict	    =   self.big_tokenizer.encode_plus(lowerCAmelCase_     ,     return_tensors="""pt"""							)
		A_		:						Tuple	    =   self.big_tokenizer.batch_encode_plus([sequence, sequence]     ,     return_tensors="""pt"""							)

		A_		:						Dict	    =   ReformerConfig()
		# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
		A_		:						Any	    =   encoded_sequence["""input_ids"""].shape
		A_		:						Optional[Any]	    =   ReformerModel(lowerCAmelCase_							)

		# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
		assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

		with torch.no_grad():
			model(**lowerCAmelCase_							)
			model(**lowerCAmelCase_							)





	@slow
	def    lowerCamelCase(self							):
		# fmt: off
		A_		:						Optional[Any]	    =   {"""input_ids""": [[108, 265, 24, 111, 4, 258, 156, 7, 51, 279, 58, 7, 76, 25, 69, 278], [140, 243, 264, 134, 17, 267, 77, 263, 22, 262, 297, 258, 304, 177, 279, 266, 14, 89, 13, 35, 261, 299, 272, 137, 275, 278]], """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, 1, 1, 1, 1, 1]]}  # noqa: E501
		# fmt: on

		# This tokenizer does not know some characters like ")".
		# That is the reason why we use very simple texts here.
		# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
		A_		:						str	    =   [
		    """This is a very simple sentence.""",
		    """The quick brown fox jumps over the lazy dog.""",
		]

		self.tokenizer_integration_test_util(
		    expected_encoding=lowerCAmelCase_     ,     model_name="""google/reformer-crime-and-punishment"""     ,     revision="""0e6c3decb8211d49bf881013425dc8b0448b3f5a"""     ,     padding=lowerCAmelCase_     ,     sequences=lowerCAmelCase_     ,     )









"""simple docstring"""
import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class      SCREAMING_SNAKE_CASE  (				_SCREAMING_SNAKE_CASE						):





	"""simple docstring"""




	_A       :				Optional[int]    =				["""image_processor""", """tokenizer"""]
	_A       :				List[Any]    =				"""ViTImageProcessor"""
	_A       :				Optional[Any]    =				("""CLIPTokenizer""", """CLIPTokenizerFast""")





	def __init__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		A_		:						Union[str, Any]	    =   None
		if "feature_extractor" in kwargs:
			warnings.warn(
			    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
			    """ instead."""     ,     lowerCAmelCase_     ,     )
			A_		:						Union[str, Any]	    =   kwargs.pop("""feature_extractor"""							)

		A_		:						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__(lowerCAmelCase_     ,     lowerCAmelCase_							)





	def __call__(self     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     lowerCAmelCase_=None     ,     **lowerCAmelCase_							):
		if text is None and visual_prompt is None and images is None:
			raise ValueError("""You have to specify either text, visual prompt or images."""							)

		if text is not None and visual_prompt is not None:
			raise ValueError("""You have to specify exactly one type of prompt. Either text or visual prompt."""							)

		if text is not None:
			A_		:						Tuple	    =   self.tokenizer(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None:
			A_		:						Union[str, Any]	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if images is not None:
			A_		:						Any	    =   self.image_processor(lowerCAmelCase_     ,     return_tensors=lowerCAmelCase_     ,     **lowerCAmelCase_							)

		if visual_prompt is not None and images is not None:
			A_		:						List[str]	    =   {
			    """pixel_values""": image_features.pixel_values,
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		elif text is not None and images is not None:
			A_		:						str	    =   image_features.pixel_values
			return encoding
		elif text is not None:
			return encoding
		elif visual_prompt is not None:
			A_		:						Any	    =   {
			    """conditional_pixel_values""": prompt_features.pixel_values,
			}
			return encoding
		else:
			return BatchEncoding(data=dict(**lowerCAmelCase_							)     ,     tensor_type=lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.batch_decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	def    lowerCamelCase(self     ,     *lowerCAmelCase_     ,     **lowerCAmelCase_							):
		return self.tokenizer.decode(*lowerCAmelCase_     ,     **lowerCAmelCase_							)





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor_class





	@property
	def    lowerCamelCase(self							):
		warnings.warn(
		    """`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."""     ,     lowerCAmelCase_     ,     )
		return self.image_processor

