
import unittest

from transformers import PegasusTokenizer, PegasusTokenizerFast
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


UpperCamelCase_   	=	get_tests_dir("fixtures/test_sentencepiece_no_bos.model")

@require_sentencepiece
@require_tokenizers
class       a		(   __UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						Optional[Any]   							=  PegasusTokenizer
							lowercase_		:						str   							=  PegasusTokenizerFast
							lowercase_		:						Optional[Any]   							=  True
							lowercase_		:						Optional[int]   							=  True







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								super().setUp()

								# We have a SentencePiece fixture for testing
								__lowerCAmelCase         =							PegasusTokenizer(snake_case__ )
								tokenizer.save_pretrained(self.tmpdirname )







							@cached_property
							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								return PegasusTokenizer.from_pretrained("google/pegasus-large" )







							def       UpperCAmelCase__					(						self  :  Dict      ,				**snake_case__  :  Tuple ):
								"""simple docstring"""







								return PegasusTokenizer.from_pretrained(self.tmpdirname      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				snake_case__  :  Tuple ):
								"""simple docstring"""







								return ("This is a test", "This is a test")







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							"</s>"
								__lowerCAmelCase         =							1

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







							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							list(self.get_tokenizer().get_vocab().keys() )

								self.assertEqual(vocab_keys[0]      ,				"<pad>" )
								self.assertEqual(vocab_keys[1]      ,				"</s>" )
								self.assertEqual(vocab_keys[-1]      ,				"v" )
								self.assertEqual(len(snake_case__ )      ,				1_103 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								self.assertEqual(self.get_tokenizer().vocab_size      ,				1_103 )







							def       UpperCAmelCase__					(						self  :  Union[str, Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
								__lowerCAmelCase         =							self.tokenizer_class.from_pretrained(self.tmpdirname )
								__lowerCAmelCase         =							(
								    "Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
								    " </s> <pad> <pad> <pad>"
								)
								__lowerCAmelCase         =							rust_tokenizer([raw_input_str]      ,				return_tensors=snake_case__      ,				add_special_tokens=snake_case__ ).input_ids[0]
								__lowerCAmelCase         =							py_tokenizer([raw_input_str]      ,				return_tensors=snake_case__      ,				add_special_tokens=snake_case__ ).input_ids[0]
								self.assertListEqual(snake_case__      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							self._large_tokenizer
								# <mask_1> masks whole sentence while <mask_2> masks single word
								__lowerCAmelCase         =							"<mask_1> To ensure a <mask_2> flow of bank resolutions."
								__lowerCAmelCase         =							[2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
								__lowerCAmelCase         =							tokenizer([raw_input_str]      ,				return_tensors=snake_case__ ).input_ids[0]
								self.assertListEqual(snake_case__      ,				snake_case__ )







							def       UpperCAmelCase__					(						self  :  Tuple ):
								"""simple docstring"""







								__lowerCAmelCase         =							self._large_tokenizer
								# The tracebacks for the following asserts are **better** without messages or self.assertEqual
								assert tokenizer.vocab_size == 96_103
								assert tokenizer.pad_token_id == 0
								assert tokenizer.eos_token_id == 1
								assert tokenizer.offset == 103
								assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
								assert tokenizer.unk_token == "<unk>"
								assert tokenizer.model_max_length == 1_024
								__lowerCAmelCase         =							"To ensure a smooth flow of bank resolutions."
								__lowerCAmelCase         =							[413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
								__lowerCAmelCase         =							tokenizer([raw_input_str]      ,				return_tensors=snake_case__ ).input_ids[0]
								self.assertListEqual(snake_case__      ,				snake_case__ )
								assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]







							@require_torch
							def       UpperCAmelCase__					(						self  :  Any ):
								"""simple docstring"""







								__lowerCAmelCase         =							["This is going to be way too long." * 150, "short example"]
								__lowerCAmelCase         =							["not super long but more than 5 tokens", "tiny"]
								__lowerCAmelCase         =							self._large_tokenizer(snake_case__      ,				padding=snake_case__      ,				truncation=snake_case__      ,				return_tensors="pt" )
								__lowerCAmelCase         =							self._large_tokenizer(
								    text_target=snake_case__      ,				max_length=5      ,				padding=snake_case__      ,				truncation=snake_case__      ,				return_tensors="pt" )

								assert batch.input_ids.shape == (2, 1_024)
								assert batch.attention_mask.shape == (2, 1_024)
								assert targets["input_ids"].shape == (2, 5)
								assert len(snake_case__ ) == 2  # input_ids, attention_mask.







							@slow
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								__lowerCAmelCase         =							{"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
								# fmt: on

								self.tokenizer_integration_test_util(
								    expected_encoding=snake_case__      ,				model_name="google/bigbird-pegasus-large-arxiv"      ,				revision="ba85d0851d708441f91440d509690f1ab6353415"      ,				)

@require_sentencepiece
@require_tokenizers
class       a		(   __UpperCAmelCase      ,	unittest.TestCase					):
							lowercase_		:						List[str]   							=  PegasusTokenizer
							lowercase_		:						List[Any]   							=  PegasusTokenizerFast
							lowercase_		:						Any   							=  True
							lowercase_		:						int   							=  True







							def       UpperCAmelCase__					(						self  :  List[Any] ):
								"""simple docstring"""







								super().setUp()

								# We have a SentencePiece fixture for testing
								__lowerCAmelCase         =							PegasusTokenizer(snake_case__      ,				offset=0      ,				mask_token_sent=snake_case__      ,				mask_token="[MASK]" )
								tokenizer.save_pretrained(self.tmpdirname )







							@cached_property
							def       UpperCAmelCase__					(						self  :  Optional[Any] ):
								"""simple docstring"""







								return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )







							def       UpperCAmelCase__					(						self  :  Optional[int]      ,				**snake_case__  :  Optional[int] ):
								"""simple docstring"""







								return PegasusTokenizer.from_pretrained(self.tmpdirname      ,				**snake_case__ )







							def       UpperCAmelCase__					(						self  :  Union[str, Any]      ,				snake_case__  :  Any ):
								"""simple docstring"""







								return ("This is a test", "This is a test")







							def       UpperCAmelCase__					(						self  :  List[str] ):
								"""simple docstring"""







								__lowerCAmelCase         =							self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
								__lowerCAmelCase         =							self.tokenizer_class.from_pretrained(self.tmpdirname )
								__lowerCAmelCase         =							(
								    "Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
								    " <pad> <pad> <pad>"
								)
								__lowerCAmelCase         =							rust_tokenizer([raw_input_str]      ,				return_tensors=snake_case__      ,				add_special_tokens=snake_case__ ).input_ids[0]
								__lowerCAmelCase         =							py_tokenizer([raw_input_str]      ,				return_tensors=snake_case__      ,				add_special_tokens=snake_case__ ).input_ids[0]
								self.assertListEqual(snake_case__      ,				snake_case__ )







							@require_torch
							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							["This is going to be way too long." * 1_000, "short example"]
								__lowerCAmelCase         =							["not super long but more than 5 tokens", "tiny"]
								__lowerCAmelCase         =							self._large_tokenizer(snake_case__      ,				padding=snake_case__      ,				truncation=snake_case__      ,				return_tensors="pt" )
								__lowerCAmelCase         =							self._large_tokenizer(
								    text_target=snake_case__      ,				max_length=5      ,				padding=snake_case__      ,				truncation=snake_case__      ,				return_tensors="pt" )

								assert batch.input_ids.shape == (2, 4_096)
								assert batch.attention_mask.shape == (2, 4_096)
								assert targets["input_ids"].shape == (2, 5)
								assert len(snake_case__ ) == 2  # input_ids, attention_mask.







							def       UpperCAmelCase__					(						self  :  Optional[int] ):
								"""simple docstring"""







								__lowerCAmelCase         =							(
								    "This is an example string that is used to test the original TF implementation against the HF"
								    " implementation"
								)

								__lowerCAmelCase         =							self._large_tokenizer(snake_case__ ).input_ids

								self.assertListEqual(
								    snake_case__      ,				[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1]      ,				)








from ....configuration_utils import PretrainedConfig
from ....utils import logging


UpperCamelCase_   	=	logging.get_logger(__name__)

UpperCamelCase_   	=	{
    "speechbrain/m-ctc-t-large": "https://huggingface.co/speechbrain/m-ctc-t-large/resolve/main/config.json",
    # See all M-CTC-T models at https://huggingface.co/models?filter=mctct
}

class       a		(   __UpperCAmelCase					):
							lowercase_		:						Union[str, Any]   							=  'mctct'







							def __init__(						self  :  Dict      ,				snake_case__  :  Any=8_065      ,				snake_case__  :  Tuple=1_536      ,				snake_case__  :  Union[str, Any]=36      ,				snake_case__  :  List[Any]=6_144      ,				snake_case__  :  Tuple=4      ,				snake_case__  :  List[str]=384      ,				snake_case__  :  List[Any]=920      ,				snake_case__  :  Any=1E-5      ,				snake_case__  :  List[Any]=0.3      ,				snake_case__  :  List[str]="relu"      ,				snake_case__  :  List[Any]=0.0_2      ,				snake_case__  :  Optional[Any]=0.3      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  List[Any]=1      ,				snake_case__  :  Optional[int]=0      ,				snake_case__  :  Any=2      ,				snake_case__  :  Any=1      ,				snake_case__  :  List[str]=0.3      ,				snake_case__  :  Tuple=1      ,				snake_case__  :  Union[str, Any]=(7,)      ,				snake_case__  :  List[Any]=(3,)      ,				snake_case__  :  List[Any]=80      ,				snake_case__  :  Optional[int]=1      ,				snake_case__  :  List[str]=None      ,				snake_case__  :  int="sum"      ,				snake_case__  :  Dict=False      ,				**snake_case__  :  List[str]      ,				):
								"""simple docstring"""







								super().__init__(**snake_case__      ,				pad_token_id=snake_case__      ,				bos_token_id=snake_case__      ,				eos_token_id=snake_case__ )
								__lowerCAmelCase         =							vocab_size
								__lowerCAmelCase         =							hidden_size
								__lowerCAmelCase         =							num_hidden_layers
								__lowerCAmelCase         =							intermediate_size
								__lowerCAmelCase         =							num_attention_heads
								__lowerCAmelCase         =							attention_head_dim
								__lowerCAmelCase         =							max_position_embeddings
								__lowerCAmelCase         =							layer_norm_eps
								__lowerCAmelCase         =							layerdrop
								__lowerCAmelCase         =							hidden_act
								__lowerCAmelCase         =							initializer_range
								__lowerCAmelCase         =							hidden_dropout_prob
								__lowerCAmelCase         =							attention_probs_dropout_prob
								__lowerCAmelCase         =							pad_token_id
								__lowerCAmelCase         =							bos_token_id
								__lowerCAmelCase         =							eos_token_id
								__lowerCAmelCase         =							conv_glu_dim
								__lowerCAmelCase         =							conv_dropout
								__lowerCAmelCase         =							num_conv_layers
								__lowerCAmelCase         =							input_feat_per_channel
								__lowerCAmelCase         =							input_channels
								__lowerCAmelCase         =							conv_channels
								__lowerCAmelCase         =							ctc_loss_reduction
								__lowerCAmelCase         =							ctc_zero_infinity

								# prevents config testing fail with exporting to json
								__lowerCAmelCase         =							list(snake_case__ )
								__lowerCAmelCase         =							list(snake_case__ )

								if len(self.conv_kernel ) != self.num_conv_layers:
									raise ValueError(
									    "Configuration for convolutional module is incorrect. "
									    "It is required that `len(config.conv_kernel)` == `config.num_conv_layers` "
									    F"but is `len(config.conv_kernel) = {len(self.conv_kernel )}`, "
									    F"`config.num_conv_layers = {self.num_conv_layers}`." )






