
"""simple docstring"""


import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class 					lowerCAmelCase_					(   _lowercase		,       unittest.TestCase     ):



						'''simple docstring'''


						_lowerCamelCase:     str   =     RobertaTokenizer
						_lowerCamelCase:     int   =     RobertaTokenizerFast
						_lowerCamelCase:     Tuple   =     True
						_lowerCamelCase:     Optional[int]   =     {'''cls_token''': '''<s>'''}


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Optional[int]:
							super().setUp()

							# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
							A     = [
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							A     = dict(zip(A_	,range(len(A_   )   )   )   )
							A     = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							A     = {'unk_token': '<unk>'}

							A     = os.path.join(self.tmpdirname	,VOCAB_FILES_NAMES['vocab_file']   )
							A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]	,**A_  :   Dict   )  -> List[str]:
							kwargs.update(self.special_tokens_map   )
							return self.tokenizer_class.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,**A_  :   Optional[int]   )  -> Any:
							kwargs.update(self.special_tokens_map   )
							return RobertaTokenizerFast.from_pretrained(self.tmpdirname	,**A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int	,A_  :   Optional[int]   )  -> str:
							A     = 'lower newer'
							A     = 'lower newer'
							return input_text, output_text


						def     _SCREAMING_SNAKE_CASE   (			self  :   Any   )  -> List[Any]:
							A     = self.tokenizer_class(self.vocab_file	,self.merges_file	,**self.special_tokens_map   )
							A     = 'lower newer'
							A     = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
							A     = tokenizer.tokenize(A_   )  # , add_prefix_space=True)
							self.assertListEqual(A_	,A_   )

							A     = tokens + [tokenizer.unk_token]
							A     = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
							self.assertListEqual(tokenizer.convert_tokens_to_ids(A_   )	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> List[str]:
							A     = self.get_tokenizer()

							self.assertListEqual(tokenizer.encode('Hello world!'	,add_special_tokens=A_   )	,[0, 3_1414, 232, 328, 2]   )
							self.assertListEqual(
							    tokenizer.encode('Hello world! cécé herlolip 418'	,add_special_tokens=A_   )	,[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]	,)


						@slow
						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict   )  -> Union[str, Any]:
							A     = self.tokenizer_class.from_pretrained('roberta-base'   )

							A     = tokenizer.encode('sequence builders'	,add_special_tokens=A_   )
							A     = tokenizer.encode('multi-sequence build'	,add_special_tokens=A_   )

							A     = tokenizer.encode(
							    'sequence builders'	,add_special_tokens=A_	,add_prefix_space=A_   )
							A     = tokenizer.encode(
							    'sequence builders'	,'multi-sequence build'	,add_special_tokens=A_	,add_prefix_space=A_   )

							A     = tokenizer.build_inputs_with_special_tokens(A_   )
							A     = tokenizer.build_inputs_with_special_tokens(A_	,A_   )

							assert encoded_sentence == encoded_text_from_decode
							assert encoded_pair == encoded_pair_from_decode


						def     _SCREAMING_SNAKE_CASE   (			self  :   str   )  -> Any:
							A     = self.get_tokenizer()

							A     = 'Encode this sequence.'
							A     = tokenizer.byte_encoder[' '.encode('utf-8'   )[0]]

							# Testing encoder arguments
							A     = tokenizer.encode(A_	,add_special_tokens=A_	,add_prefix_space=A_   )
							A     = tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertNotEqual(A_	,A_   )

							A     = tokenizer.encode(A_	,add_special_tokens=A_	,add_prefix_space=A_   )
							A     = tokenizer.convert_ids_to_tokens(encoded[0]   )[0]
							self.assertEqual(A_	,A_   )

							tokenizer.add_special_tokens({'bos_token': '<s>'}   )
							A     = tokenizer.encode(A_	,add_special_tokens=A_   )
							A     = tokenizer.convert_ids_to_tokens(encoded[1]   )[0]
							self.assertNotEqual(A_	,A_   )

							# Testing spaces after special tokens
							A     = '<mask>'
							tokenizer.add_special_tokens(
							    {'mask_token': AddedToken(A_	,lstrip=A_	,rstrip=A_   )}   )  # mask token has a left space
							A     = tokenizer.convert_tokens_to_ids(A_   )

							A     = 'Encode <mask> sequence'
							A     = 'Encode <mask>sequence'

							A     = tokenizer.encode(A_   )
							A     = encoded.index(A_   )
							A     = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertEqual(A_	,A_   )

							A     = tokenizer.encode(A_   )
							A     = encoded.index(A_   )
							A     = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]   )[0]
							self.assertNotEqual(A_	,A_   )


						def     _SCREAMING_SNAKE_CASE   (			self  :   int   )  -> Optional[int]:
							pass


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

									A     = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']   )
									A     = 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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )
									self.assertSequenceEqual(tokens_r['input_ids']	,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 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     _SCREAMING_SNAKE_CASE   (			self  :   List[str]   )  -> str:
							for trim_offsets, add_prefix_space in itertools.product([True, False]	,repeat=2   ):
								A     = self.rust_tokenizer_class.from_pretrained(
								    self.tmpdirname	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )

								A     = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()   )
								A     = 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     _SCREAMING_SNAKE_CASE   (			self  :   Tuple   )  -> List[str]:
							# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
							# `trim_offsets`
							for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
								with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'   ):
									A     = 'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
									A     = F'{text_of_1_token} {text_of_1_token}'

									A     = self.rust_tokenizer_class.from_pretrained(
									    A_	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )
									A     = 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_   ))	,)

									A     = self.rust_tokenizer_class.from_pretrained(
									    A_	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )
									A     = 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_   ))	,)

									A     = self.rust_tokenizer_class.from_pretrained(
									    A_	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )
									A     = 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_   ))	,)

									A     = self.rust_tokenizer_class.from_pretrained(
									    A_	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )
									A     = 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_   ))	,)

									A     = 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)),
									# )

									A     = self.rust_tokenizer_class.from_pretrained(
									    A_	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )
									A     = 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_   ))	,)

									A     = self.rust_tokenizer_class.from_pretrained(
									    A_	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )
									A     = 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_   ))	,)

									A     = self.rust_tokenizer_class.from_pretrained(
									    A_	,use_fast=A_	,add_prefix_space=A_	,trim_offsets=A_   )
									A     = 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"""


from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase				 =					{
    '''configuration_mctct''': ['''MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MCTCTConfig'''],
    '''feature_extraction_mctct''': ['''MCTCTFeatureExtractor'''],
    '''processing_mctct''': ['''MCTCTProcessor'''],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase				 =					[
		    '''MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''MCTCTForCTC''',
		    '''MCTCTModel''',
		    '''MCTCTPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
		from .feature_extraction_mctct import MCTCTFeatureExtractor
		from .processing_mctct import MCTCTProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
		import sys

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