




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    lowerCamelCase    (		__a				,unittest.TestCase						):






			'''simple docstring'''





			UpperCamelCase__								=LongformerTokenizer
			UpperCamelCase__								=True
			UpperCamelCase__								=LongformerTokenizerFast
			UpperCamelCase__								=True

			def 							UpperCAmelCase__       ( self			:   Any  )						->   int:
								super().setUp()

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

								__magic_name__							:	int    							=							os.path.join(self.tmpdirname       ,						VOCAB_FILES_NAMES['''vocab_file''']  )
								__magic_name__							:	Tuple    							=							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 							UpperCAmelCase__       ( self			:   Union[str, Any]       ,						**lowerCamelCase_			:   List[Any]  )						->   Union[str, Any]:
								kwargs.update(self.special_tokens_map  )
								return self.tokenizer_class.from_pretrained(self.tmpdirname       ,						**a_  )

			def 							UpperCAmelCase__       ( self			:   str       ,						**lowerCamelCase_			:   str  )						->   Tuple:
								kwargs.update(self.special_tokens_map  )
								return self.rust_tokenizer_class.from_pretrained(self.tmpdirname       ,						**a_  )

			def 							UpperCAmelCase__       ( self			:   Tuple       ,						lowerCamelCase_			:   Tuple  )						->   str:
								__magic_name__							:	int    							=							"""lower newer"""
								__magic_name__							:	Optional[int]    							=							"""lower newer"""
								return input_text, output_text

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   List[Any]:
								__magic_name__							:	Any    							=							self.tokenizer_class(self.vocab_file       ,						self.merges_file       ,						**self.special_tokens_map  )
								__magic_name__							:	int    							=							"""lower newer"""
								__magic_name__							:	Union[str, Any]    							=							["""l""", """o""", """w""", """er""", """\u0120""", """n""", """e""", """w""", """er"""]
								__magic_name__							:	int    							=							tokenizer.tokenize(a_  )  # , add_prefix_space=True)
								self.assertListEqual(a_       ,						a_  )

								__magic_name__							:	Any    							=							tokens + [tokenizer.unk_token]
								__magic_name__							:	str    							=							[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(a_  )       ,						a_  )

			def 							UpperCAmelCase__       ( self			:   List[Any]  )						->   List[Any]:
								__magic_name__							:	int    							=							self.get_tokenizer()

								self.assertListEqual(tokenizer.encode('''Hello world!'''       ,						add_special_tokens=a_  )       ,						[0, 31414, 232, 328, 2]  )
								self.assertListEqual(
								    tokenizer.encode('''Hello world! cécé herlolip 418'''       ,						add_special_tokens=a_  )       ,						[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]       ,						)

			@slow
			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   int:
								__magic_name__							:	Optional[int]    							=							self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096'''  )

								__magic_name__							:	Union[str, Any]    							=							tokenizer.encode('''sequence builders'''       ,						add_special_tokens=a_  )
								__magic_name__							:	Tuple    							=							tokenizer.encode('''multi-sequence build'''       ,						add_special_tokens=a_  )

								__magic_name__							:	Tuple    							=							tokenizer.encode(
								    '''sequence builders'''       ,						add_special_tokens=a_       ,						add_prefix_space=a_  )
								__magic_name__							:	Optional[Any]    							=							tokenizer.encode(
								    '''sequence builders'''       ,						'''multi-sequence build'''       ,						add_special_tokens=a_       ,						add_prefix_space=a_  )

								__magic_name__							:	List[str]    							=							tokenizer.build_inputs_with_special_tokens(a_  )
								__magic_name__							:	Union[str, Any]    							=							tokenizer.build_inputs_with_special_tokens(a_       ,						a_  )

								assert encoded_sentence == encoded_text_from_decode
								assert encoded_pair == encoded_pair_from_decode

			def 							UpperCAmelCase__       ( self			:   str  )						->   Any:
								__magic_name__							:	Optional[Any]    							=							self.get_tokenizer()

								__magic_name__							:	Dict    							=							"""Encode this sequence."""
								__magic_name__							:	Optional[Any]    							=							tokenizer.byte_encoder[""" """.encode('''utf-8'''  )[0]]

								# Testing encoder arguments
								__magic_name__							:	Union[str, Any]    							=							tokenizer.encode(a_       ,						add_special_tokens=a_       ,						add_prefix_space=a_  )
								__magic_name__							:	Optional[int]    							=							tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
								self.assertNotEqual(a_       ,						a_  )

								__magic_name__							:	Optional[int]    							=							tokenizer.encode(a_       ,						add_special_tokens=a_       ,						add_prefix_space=a_  )
								__magic_name__							:	Optional[int]    							=							tokenizer.convert_ids_to_tokens(encoded[0]  )[0]
								self.assertEqual(a_       ,						a_  )

								tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}  )
								__magic_name__							:	Union[str, Any]    							=							tokenizer.encode(a_       ,						add_special_tokens=a_  )
								__magic_name__							:	str    							=							tokenizer.convert_ids_to_tokens(encoded[1]  )[0]
								self.assertNotEqual(a_       ,						a_  )

								# Testing spaces after special tokens
								__magic_name__							:	List[str]    							=							"""<mask>"""
								tokenizer.add_special_tokens(
								    {'''mask_token''': AddedToken(a_       ,						lstrip=a_       ,						rstrip=a_  )}  )  # mask token has a left space
								__magic_name__							:	str    							=							tokenizer.convert_tokens_to_ids(a_  )

								__magic_name__							:	int    							=							"""Encode <mask> sequence"""
								__magic_name__							:	Tuple    							=							"""Encode <mask>sequence"""

								__magic_name__							:	List[str]    							=							tokenizer.encode(a_  )
								__magic_name__							:	Optional[int]    							=							encoded.index(a_  )
								__magic_name__							:	Tuple    							=							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
								self.assertEqual(a_       ,						a_  )

								__magic_name__							:	Union[str, Any]    							=							tokenizer.encode(a_  )
								__magic_name__							:	Optional[int]    							=							encoded.index(a_  )
								__magic_name__							:	Any    							=							tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]  )[0]
								self.assertNotEqual(a_       ,						a_  )

			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   str:
								pass

			def 							UpperCAmelCase__       ( self			:   List[str]  )						->   Union[str, Any]:



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																		__magic_name__							:	str    							=							self.rust_tokenizer_class.from_pretrained(a_       ,						**a_  )
																		__magic_name__							:	Dict    							=							self.tokenizer_class.from_pretrained(a_       ,						**a_  )
																		__magic_name__							:	List[str]    							=							"""A, <mask> AllenNLP sentence."""
																		__magic_name__							:	Tuple    							=							tokenizer_r.encode_plus(a_       ,						add_special_tokens=a_       ,						return_token_type_ids=a_  )
																		__magic_name__							:	Optional[int]    							=							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''']  )       ,						)

																		__magic_name__							:	int    							=							tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']  )
																		__magic_name__							:	int    							=							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, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]  )
																		self.assertSequenceEqual(tokens_r['''input_ids''']       ,						[0, 250, 6, 50264, 3823, 487, 21992, 3645, 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 							UpperCAmelCase__       ( self			:   List[str]  )						->   Union[str, Any]:



								for trim_offsets, add_prefix_space in itertools.product([True, False]       ,						repeat=2  ):
													__magic_name__							:	Any    							=							self.rust_tokenizer_class.from_pretrained(
													    self.tmpdirname       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )

													__magic_name__							:	Optional[Any]    							=							json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()  )
													__magic_name__							:	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 							UpperCAmelCase__       ( self			:   List[str]  )						->   Optional[int]:



								for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
																		__magic_name__							:	Optional[int]    							=							"""hello"""  # `hello` is a token in the vocabulary of `pretrained_name`
																		__magic_name__							:	int    							=							F'''{text_of_1_token} {text_of_1_token}'''

																		__magic_name__							:	Optional[int]    							=							self.rust_tokenizer_class.from_pretrained(
																		    a_       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )
																		__magic_name__							:	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_  ))       ,						)

																		__magic_name__							:	Union[str, Any]    							=							self.rust_tokenizer_class.from_pretrained(
																		    a_       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )
																		__magic_name__							:	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_  ) + 1, len(a_  ) + 1 + len(a_  ))       ,						)

																		__magic_name__							:	Union[str, Any]    							=							self.rust_tokenizer_class.from_pretrained(
																		    a_       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )
																		__magic_name__							:	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_  ), len(a_  ) + 1 + len(a_  ))       ,						)

																		__magic_name__							:	Tuple    							=							self.rust_tokenizer_class.from_pretrained(
																		    a_       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )
																		__magic_name__							:	Union[str, 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_  ))       ,						)

																		__magic_name__							:	Tuple    							=							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)),
																		# )

																		__magic_name__							:	Optional[Any]    							=							self.rust_tokenizer_class.from_pretrained(
																		    a_       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )
																		__magic_name__							:	Union[str, Any]    							=							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_  ))       ,						)

																		__magic_name__							:	Tuple    							=							self.rust_tokenizer_class.from_pretrained(
																		    a_       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )
																		__magic_name__							:	Any    							=							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_  ))       ,						)

																		__magic_name__							:	List[str]    							=							self.rust_tokenizer_class.from_pretrained(
																		    a_       ,						use_fast=a_       ,						add_prefix_space=a_       ,						trim_offsets=a_  )
																		__magic_name__							:	Optional[Any]    							=							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_  ))       ,						)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[Any]				=			{
    '''configuration_speech_to_text''': ['''SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''Speech2TextConfig'''],
    '''processing_speech_to_text''': ['''Speech2TextProcessor'''],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			['''Speech2TextTokenizer''']

try:
	if not is_speech_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			['''Speech2TextFeatureExtractor''']

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFSpeech2TextForConditionalGeneration''',
	    '''TFSpeech2TextModel''',
	    '''TFSpeech2TextPreTrainedModel''',
	]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Any				=			[
	    '''SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''Speech2TextForConditionalGeneration''',
	    '''Speech2TextModel''',
	    '''Speech2TextPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_speech_to_text import SPEECH_TO_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, SpeechaTextConfig
	from .processing_speech_to_text import SpeechaTextProcessor

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_speech_to_text import SpeechaTextTokenizer

	try:
		if not is_speech_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_speech_to_text import SpeechaTextFeatureExtractor

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_speech_to_text import (
		    TF_SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFSpeechaTextForConditionalGeneration,
		    TFSpeechaTextModel,
		    TFSpeechaTextPreTrainedModel,
		)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_speech_to_text import (
		    SPEECH_TO_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    SpeechaTextForConditionalGeneration,
		    SpeechaTextModel,
		    SpeechaTextPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						List[str]				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



