"""simple docstring"""





import itertools
import json
import os
import unittest

from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin




@require_tokenizers
class  _snake_case		(    __snake_case   ,       unittest.TestCase ):







				"""simple docstring"""






				a			=						LongformerTokenizer
				a			=						True
				a			=						LongformerTokenizerFast
				a			=						True





				def 		_lowerCAmelCase		(      self							:							Optional[Any]):



											"""simple docstring"""







											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											_SCREAMING_SNAKE_CASE		:						int			  =    [
											    """l""",
											    """o""",
											    """w""",
											    """e""",
											    """r""",
											    """s""",
											    """t""",
											    """i""",
											    """d""",
											    """n""",
											    """\u0120""",
											    """\u0120l""",
											    """\u0120n""",
											    """\u0120lo""",
											    """\u0120low""",
											    """er""",
											    """\u0120lowest""",
											    """\u0120newer""",
											    """\u0120wider""",
											    """<unk>""",
											]
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    dict(zip(_A      ,    range(len(_A))))
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    {"""unk_token""": """<unk>"""}

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""vocab_file"""])
											_SCREAMING_SNAKE_CASE		:						Any			  =    os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES["""merges_file"""])
											with open(self.vocab_file      ,    """w"""      ,    encoding="""utf-8""") as fp:
																		fp.write(json.dumps(_A) + """\n""")
											with open(self.merges_file      ,    """w"""      ,    encoding="""utf-8""") as fp:
																		fp.write("""\n""".join(_A))





				def 		_lowerCAmelCase		(      self							:							Optional[Any]      ,    **_A							:							List[Any]):



											"""simple docstring"""







											kwargs.update(self.special_tokens_map)
											return self.tokenizer_class.from_pretrained(self.tmpdirname      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Any      ,    **_A							:							Any):



											"""simple docstring"""







											kwargs.update(self.special_tokens_map)
											return self.rust_tokenizer_class.from_pretrained(self.tmpdirname      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							Any      ,    _A							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    """lower newer"""
											_SCREAMING_SNAKE_CASE		:						Dict			  =    """lower newer"""
											return input_text, output_text





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    self.tokenizer_class(self.vocab_file      ,    self.merges_file      ,    **self.special_tokens_map)
											_SCREAMING_SNAKE_CASE		:						str			  =    """lower newer"""
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    ["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.tokenize(_A)  # , add_prefix_space=True)
											self.assertListEqual(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						int			  =    tokens + [tokenizer.unk_token]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [0, 1, 2, 1_5, 1_0, 9, 3, 2, 1_5, 1_9]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(_A)      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.get_tokenizer()

											self.assertListEqual(tokenizer.encode("""Hello world!"""      ,    add_special_tokens=_A)      ,    [0, 3_1_4_1_4, 2_3_2, 3_2_8, 2])
											self.assertListEqual(
											    tokenizer.encode("""Hello world! cécé herlolip 418"""      ,    add_special_tokens=_A)      ,    [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]      ,    )





				@slow
				def 		_lowerCAmelCase		(      self							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.tokenizer_class.from_pretrained("""allenai/longformer-base-4096""")

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tokenizer.encode("""sequence builders"""      ,    add_special_tokens=_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    tokenizer.encode("""multi-sequence build"""      ,    add_special_tokens=_A)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer.encode(
											    """sequence builders"""      ,    add_special_tokens=_A      ,    add_prefix_space=_A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer.encode(
											    """sequence builders"""      ,    """multi-sequence build"""      ,    add_special_tokens=_A      ,    add_prefix_space=_A)

											_SCREAMING_SNAKE_CASE		:						str			  =    tokenizer.build_inputs_with_special_tokens(_A)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tokenizer.build_inputs_with_special_tokens(_A      ,    _A)

											assert encoded_sentence == encoded_text_from_decode
											assert encoded_pair == encoded_pair_from_decode





				def 		_lowerCAmelCase		(      self							:							Optional[int]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.get_tokenizer()

											_SCREAMING_SNAKE_CASE		:						Any			  =    """Encode this sequence."""
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    tokenizer.byte_encoder[""" """.encode("""utf-8""")[0]]

											# Testing encoder arguments
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tokenizer.encode(_A      ,    add_special_tokens=_A      ,    add_prefix_space=_A)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    tokenizer.convert_ids_to_tokens(encoded[0])[0]
											self.assertNotEqual(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tokenizer.encode(_A      ,    add_special_tokens=_A      ,    add_prefix_space=_A)
											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.convert_ids_to_tokens(encoded[0])[0]
											self.assertEqual(_A      ,    _A)

											tokenizer.add_special_tokens({"""bos_token""": """<s>"""})
											_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer.encode(_A      ,    add_special_tokens=_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    tokenizer.convert_ids_to_tokens(encoded[1])[0]
											self.assertNotEqual(_A      ,    _A)

											# Testing spaces after special tokens
											_SCREAMING_SNAKE_CASE		:						Dict			  =    """<mask>"""
											tokenizer.add_special_tokens(
											    {"""mask_token""": AddedToken(_A      ,    lstrip=_A      ,    rstrip=_A)})  # mask token has a left space
											_SCREAMING_SNAKE_CASE		:						str			  =    tokenizer.convert_tokens_to_ids(_A)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """Encode <mask> sequence"""
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    """Encode <mask>sequence"""

											_SCREAMING_SNAKE_CASE		:						int			  =    tokenizer.encode(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    encoded.index(_A)
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
											self.assertEqual(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						str			  =    tokenizer.encode(_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    encoded.index(_A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1])[0]
											self.assertNotEqual(_A      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											pass





				def 		_lowerCAmelCase		(      self							:							Any):



											"""simple docstring"""







											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
																									_SCREAMING_SNAKE_CASE		:						int			  =    self.rust_tokenizer_class.from_pretrained(_A      ,    **_A)
																									_SCREAMING_SNAKE_CASE		:						int			  =    self.tokenizer_class.from_pretrained(_A      ,    **_A)
																									_SCREAMING_SNAKE_CASE		:						str			  =    """A, <mask> AllenNLP sentence."""
																									_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    tokenizer_r.encode_plus(_A      ,    add_special_tokens=_A      ,    return_token_type_ids=_A)
																									_SCREAMING_SNAKE_CASE		:						Tuple			  =    tokenizer_p.encode_plus(_A      ,    add_special_tokens=_A      ,    return_token_type_ids=_A)

																									# token_type_ids should put 0 everywhere
																									self.assertEqual(sum(tokens_r["""token_type_ids"""])      ,    sum(tokens_p["""token_type_ids"""]))

																									# attention_mask should put 1 everywhere, so sum over length should be 1
																									self.assertEqual(
																									    sum(tokens_r["""attention_mask"""]) / len(tokens_r["""attention_mask"""])      ,    sum(tokens_p["""attention_mask"""]) / len(tokens_p["""attention_mask"""])      ,    )

																									_SCREAMING_SNAKE_CASE		:						Any			  =    tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""])
																									_SCREAMING_SNAKE_CASE		:						str			  =    tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""])

																									# Rust correctly handles the space before the mask while python doesnt
																									self.assertSequenceEqual(tokens_p["""input_ids"""]      ,    [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])
																									self.assertSequenceEqual(tokens_r["""input_ids"""]      ,    [0, 2_5_0, 6, 5_0_2_6_4, 3_8_2_3, 4_8_7, 2_1_9_9_2, 3_6_4_5, 4, 2])

																									self.assertSequenceEqual(
																									    _A      ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])
																									self.assertSequenceEqual(
																									    _A      ,    ["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""])





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											for trim_offsets, add_prefix_space in itertools.product([True, False]      ,    repeat=2):
																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.rust_tokenizer_class.from_pretrained(
																		    self.tmpdirname      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)

																		_SCREAMING_SNAKE_CASE		:						int			  =    json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__())
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__())

																		self.assertEqual(pre_tokenizer_state["""add_prefix_space"""]      ,    _A)

																		self.assertEqual(post_processor_state["""add_prefix_space"""]      ,    _A)
																		self.assertEqual(post_processor_state["""trim_offsets"""]      ,    _A)





				def 		_lowerCAmelCase		(      self							:							Dict):



											"""simple docstring"""







											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																		with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""):
																									_SCREAMING_SNAKE_CASE		:						int			  =    """hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																									_SCREAMING_SNAKE_CASE		:						int			  =    f"""{text_of_1_token} {text_of_1_token}"""

																									_SCREAMING_SNAKE_CASE		:						int			  =    self.rust_tokenizer_class.from_pretrained(
																									    _A      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tokenizer_r(_A      ,    return_offsets_mapping=_A      ,    add_special_tokens=_A)
																									self.assertEqual(encoding.offset_mapping[0]      ,    (0, len(_A)))
																									self.assertEqual(
																									    encoding.offset_mapping[1]      ,    (len(_A) + 1, len(_A) + 1 + len(_A))      ,    )

																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.rust_tokenizer_class.from_pretrained(
																									    _A      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)
																									_SCREAMING_SNAKE_CASE		:						Dict			  =    tokenizer_r(_A      ,    return_offsets_mapping=_A      ,    add_special_tokens=_A)
																									self.assertEqual(encoding.offset_mapping[0]      ,    (0, len(_A)))
																									self.assertEqual(
																									    encoding.offset_mapping[1]      ,    (len(_A) + 1, len(_A) + 1 + len(_A))      ,    )

																									_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.rust_tokenizer_class.from_pretrained(
																									    _A      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)
																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    tokenizer_r(_A      ,    return_offsets_mapping=_A      ,    add_special_tokens=_A)
																									self.assertEqual(encoding.offset_mapping[0]      ,    (0, len(_A)))
																									self.assertEqual(
																									    encoding.offset_mapping[1]      ,    (len(_A), len(_A) + 1 + len(_A))      ,    )

																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    self.rust_tokenizer_class.from_pretrained(
																									    _A      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)
																									_SCREAMING_SNAKE_CASE		:						str			  =    tokenizer_r(_A      ,    return_offsets_mapping=_A      ,    add_special_tokens=_A)
																									self.assertEqual(encoding.offset_mapping[0]      ,    (0, len(_A)))
																									self.assertEqual(
																									    encoding.offset_mapping[1]      ,    (len(_A), len(_A) + 1 + len(_A))      ,    )

																									_SCREAMING_SNAKE_CASE		:						Any			  =    f""" {text}"""

																									# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																									#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																									# )
																									# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																									# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																									# self.assertEqual(
																									#     encoding.offset_mapping[1],
																									#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																									# )

																									_SCREAMING_SNAKE_CASE		:						Dict			  =    self.rust_tokenizer_class.from_pretrained(
																									    _A      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tokenizer_r(_A      ,    return_offsets_mapping=_A      ,    add_special_tokens=_A)
																									self.assertEqual(encoding.offset_mapping[0]      ,    (1, 1 + len(_A)))
																									self.assertEqual(
																									    encoding.offset_mapping[1]      ,    (1 + len(_A) + 1, 1 + len(_A) + 1 + len(_A))      ,    )

																									_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.rust_tokenizer_class.from_pretrained(
																									    _A      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)
																									_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    tokenizer_r(_A      ,    return_offsets_mapping=_A      ,    add_special_tokens=_A)
																									self.assertEqual(encoding.offset_mapping[0]      ,    (0, 1 + len(_A)))
																									self.assertEqual(
																									    encoding.offset_mapping[1]      ,    (1 + len(_A), 1 + len(_A) + 1 + len(_A))      ,    )

																									_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.rust_tokenizer_class.from_pretrained(
																									    _A      ,    use_fast=_A      ,    add_prefix_space=_A      ,    trim_offsets=_A)
																									_SCREAMING_SNAKE_CASE		:						str			  =    tokenizer_r(_A      ,    return_offsets_mapping=_A      ,    add_special_tokens=_A)
																									self.assertEqual(encoding.offset_mapping[0]      ,    (0, 1 + len(_A)))
																									self.assertEqual(
																									    encoding.offset_mapping[1]      ,    (1 + len(_A), 1 + len(_A) + 1 + len(_A))      ,    )



"""simple docstring"""





import warnings

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




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						["image_processor", "tokenizer"]
				a			=						"ChineseCLIPImageProcessor"
				a			=						("BertTokenizer", "BertTokenizerFast")





				def __init__(      self							:							Dict      ,    _A							:							Tuple=None      ,    _A							:							List[Any]=None      ,    **_A							:							int):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    None
											if "feature_extractor" in kwargs:
																		warnings.warn(
																		    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
																		    """ instead."""      ,    _A      ,    )
																		_SCREAMING_SNAKE_CASE		:						str			  =    kwargs.pop("""feature_extractor""")

											_SCREAMING_SNAKE_CASE		:						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__(_A      ,    _A)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.image_processor





				def __call__(      self							:							Optional[int]      ,    _A							:							Optional[Any]=None      ,    _A							:							Any=None      ,    _A							:							Tuple=None      ,    **_A							:							int):



											"""simple docstring"""







											if text is None and images is None:
																		raise ValueError("""You have to specify either text or images. Both cannot be none.""")

											if text is not None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer(_A      ,    return_tensors=_A      ,    **_A)

											if images is not None:
																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    self.image_processor(_A      ,    return_tensors=_A      ,    **_A)

											if text is not None and images is not None:
																		_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    image_features.pixel_values
																		return encoding
											elif text is not None:
																		return encoding
											else:
																		return BatchEncoding(data=dict(**_A)      ,    tensor_type=_A)





				def 		_lowerCAmelCase		(      self							:							str      ,    *_A							:							Any      ,    **_A							:							Any):



											"""simple docstring"""







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





				def 		_lowerCAmelCase		(      self							:							Union[str, Any]      ,    *_A							:							List[Any]      ,    **_A							:							Any):



											"""simple docstring"""







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





				@property
				def 		_lowerCAmelCase		(      self							:							str):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    self.tokenizer.model_input_names
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    self.image_processor.model_input_names
											return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





				@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."""      ,    _A      ,    )
											return self.image_processor_class


