





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


UpperCAmelCase						:   str			  =     logging.get_logger(__name__)

UpperCAmelCase						:   List[Any]			  =     {
    '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}





class 						_A(					snake_case__   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[Any]					    =	'''blenderbot-small'''
				UpperCamelCase    :  Any					    =	['''past_key_values''']
				UpperCamelCase    :  Optional[int]					    =	{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}



				def __init__(			self     ,					_A=50265     ,					_A=512     ,					_A=8     ,					_A=2048     ,					_A=16     ,					_A=8     ,					_A=2048     ,					_A=16     ,					_A=0.0     ,					_A=0.0     ,					_A=True     ,					_A=True     ,					_A="gelu"     ,					_A=512     ,					_A=0.1     ,					_A=0.0     ,					_A=0.0     ,					_A=0.0_2     ,					_A=1     ,					_A=False     ,					_A=0     ,					_A=1     ,					_A=2     ,					_A=2     ,					**_A     ,					):
									__A	:						List[str]       =				vocab_size
									__A	:						Tuple       =				max_position_embeddings
									__A	:						Dict       =				d_model
									__A	:						Any       =				encoder_ffn_dim
									__A	:						str       =				encoder_layers
									__A	:						Union[str, Any]       =				encoder_attention_heads
									__A	:						Tuple       =				decoder_ffn_dim
									__A	:						Optional[int]       =				decoder_layers
									__A	:						str       =				decoder_attention_heads
									__A	:						Union[str, Any]       =				dropout
									__A	:						Any       =				attention_dropout
									__A	:						Union[str, Any]       =				activation_dropout
									__A	:						Optional[int]       =				activation_function
									__A	:						Any       =				init_std
									__A	:						Union[str, Any]       =				encoder_layerdrop
									__A	:						str       =				decoder_layerdrop
									__A	:						List[Any]       =				use_cache
									__A	:						Optional[int]       =				encoder_layers
									__A	:						str       =				scale_embedding  # scale factor will be sqrt(d_model) if True

									super().__init__(
									    pad_token_id=_A     ,					bos_token_id=_A     ,					eos_token_id=_A     ,					is_encoder_decoder=_A     ,					decoder_start_token_id=_A     ,					forced_eos_token_id=_A     ,					**_A     ,					)





class 						_A(					snake_case__   ):


				"""simple docstring"""


				@property
				def 					UpperCAmelCase_		(			self     ):
									if self.task in ["default", "seq2seq-lm"]:
														__A	:						str       =				OrderedDict(
														    [
														        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
														        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
														    ]     )

														if self.use_past:
																			__A	:						Any       =				{0: 'batch'}
																			__A	:						List[str]       =				{0: 'batch', 1: 'past_decoder_sequence + sequence'}
														else:
																			__A	:						Tuple       =				{0: 'batch', 1: 'decoder_sequence'}
																			__A	:						Optional[Any]       =				{0: 'batch', 1: 'decoder_sequence'}

														if self.use_past:
																			self.fill_with_past_key_values_(_A     ,					direction='inputs'     )
									elif self.task == "causal-lm":
														# TODO: figure this case out.
														__A	:						Optional[Any]       =				OrderedDict(
														    [
														        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
														        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
														    ]     )
														if self.use_past:
																			__A    ,				__A	:						Union[str, Any]       =				self.num_layers
																			for i in range(_A     ):
																								__A	:						Union[str, Any]       =				{0: 'batch', 2: 'past_sequence + sequence'}
																								__A	:						Union[str, Any]       =				{0: 'batch', 2: 'past_sequence + sequence'}
									else:
														__A	:						Any       =				OrderedDict(
														    [
														        ('input_ids', {0: 'batch', 1: 'encoder_sequence'}),
														        ('attention_mask', {0: 'batch', 1: 'encoder_sequence'}),
														        ('decoder_input_ids', {0: 'batch', 1: 'decoder_sequence'}),
														        ('decoder_attention_mask', {0: 'batch', 1: 'decoder_sequence'}),
														    ]     )

									return common_inputs



				@property
				def 					UpperCAmelCase_		(			self     ):
									if self.task in ["default", "seq2seq-lm"]:
														__A	:						Tuple       =				super().outputs
									else:
														__A	:						List[Any]       =				super(_A     ,					self     ).outputs
														if self.use_past:
																			__A    ,				__A	:						List[str]       =				self.num_layers
																			for i in range(_A     ):
																								__A	:						List[str]       =				{0: 'batch', 2: 'past_sequence + sequence'}
																								__A	:						Union[str, Any]       =				{0: 'batch', 2: 'past_sequence + sequence'}
									return common_outputs



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = -1     ,					_A = -1     ,					_A = False     ,					_A = None     ,					):
									__A	:						List[Any]       =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _A     ,					_A     ,					_A     ,					_A     ,					_A     )

									# Generate decoder inputs
									__A	:						Optional[int]       =				seq_length if not self.use_past else 1
									__A	:						Any       =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _A     ,					_A     ,					_A     ,					_A     ,					_A     )
									__A	:						Dict       =				{F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
									__A	:						Optional[int]       =				dict(**_A     ,					**_A     )

									if self.use_past:
														if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
														else:
																			import torch
														__A    ,				__A	:						Union[str, Any]       =				common_inputs['input_ids'].shape
														__A	:						Optional[int]       =				common_inputs['decoder_input_ids'].shape[1]
														__A    ,				__A	:						Tuple       =				self.num_attention_heads
														__A	:						str       =				(
														    batch,
														    num_encoder_attention_heads,
														    encoder_seq_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)
														__A	:						Optional[int]       =				decoder_seq_length + 3
														__A	:						List[Any]       =				(
														    batch,
														    num_decoder_attention_heads,
														    decoder_past_length,
														    self._config.hidden_size // num_decoder_attention_heads,
														)

														__A	:						str       =				torch.cat(
														    [common_inputs['decoder_attention_mask'], torch.ones(_A     ,					_A     )]     ,					dim=1     )

														__A	:						str       =				[]
														# If the number of encoder and decoder layers are present in the model configuration, both are considered
														__A    ,				__A	:						str       =				self.num_layers
														__A	:						int       =				min(_A     ,					_A     )
														__A	:						Tuple       =				max(_A     ,					_A     ) - min_num_layers
														__A	:						List[str]       =				'encoder' if num_encoder_layers > num_decoder_layers else 'decoder'

														for _ in range(_A     ):
																			common_inputs["past_key_values"].append(
																			    (
																			        torch.zeros(_A     ),
																			        torch.zeros(_A     ),
																			        torch.zeros(_A     ),
																			        torch.zeros(_A     ),
																			    )     )
														# TODO: test this.
														__A	:						List[str]       =				encoder_shape if remaining_side_name == 'encoder' else decoder_shape
														for _ in range(_A     ,					_A     ):
																			common_inputs["past_key_values"].append((torch.zeros(_A     ), torch.zeros(_A     ))     )
									return common_inputs



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = -1     ,					_A = -1     ,					_A = False     ,					_A = None     ,					):
									__A	:						Optional[int]       =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    _A     ,					_A     ,					_A     ,					_A     ,					_A     )

									if self.use_past:
														if not is_torch_available():
																			raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
														else:
																			import torch
														__A    ,				__A	:						str       =				common_inputs['input_ids'].shape
														# Not using the same length for past_key_values
														__A	:						str       =				seqlen + 2
														__A    ,				__A	:						Any       =				self.num_layers
														__A    ,				__A	:						Any       =				self.num_attention_heads
														__A	:						str       =				(
														    batch,
														    num_encoder_attention_heads,
														    past_key_values_length,
														    self._config.hidden_size // num_encoder_attention_heads,
														)

														__A	:						int       =				common_inputs['attention_mask'].dtype
														__A	:						Dict       =				torch.cat(
														    [common_inputs['attention_mask'], torch.ones(_A     ,					_A     ,					dtype=_A     )]     ,					dim=1     )
														__A	:						Dict       =				[
														    (torch.zeros(_A     ), torch.zeros(_A     )) for _ in range(_A     )
														]
									return common_inputs



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = -1     ,					_A = -1     ,					_A = False     ,					_A = None     ,					):
									# Copied from OnnxConfig.generate_dummy_inputs
									# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
									# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
									__A	:						List[Any]       =				compute_effective_axis_dimension(
									    _A     ,					fixed_dimension=OnnxConfig.default_fixed_batch     ,					num_token_to_add=0     )

									# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
									__A	:						List[Any]       =				tokenizer.num_special_tokens_to_add(_A     )
									__A	:						Optional[Any]       =				compute_effective_axis_dimension(
									    _A     ,					fixed_dimension=OnnxConfig.default_fixed_sequence     ,					num_token_to_add=_A     )

									# Generate dummy inputs according to compute batch and sequence
									__A	:						Optional[int]       =				[' '.join([tokenizer.unk_token]     ) * seq_length] * batch_size
									__A	:						Optional[int]       =				dict(tokenizer(_A     ,					return_tensors=_A     )     )
									return common_inputs



				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A = -1     ,					_A = -1     ,					_A = False     ,					_A = None     ,					):
									if self.task in ["default", "seq2seq-lm"]:
														__A	:						Tuple       =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
														    _A     ,					batch_size=_A     ,					seq_length=_A     ,					is_pair=_A     ,					framework=_A     )

									elif self.task == "causal-lm":
														__A	:						Dict       =				self._generate_dummy_inputs_for_causal_lm(
														    _A     ,					batch_size=_A     ,					seq_length=_A     ,					is_pair=_A     ,					framework=_A     )
									else:
														__A	:						Dict       =				self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
														    _A     ,					batch_size=_A     ,					seq_length=_A     ,					is_pair=_A     ,					framework=_A     )

									return common_inputs





				def 					UpperCAmelCase_		(			self     ,					_A     ,					_A     ,					_A     ,					_A     ):
									if self.task in ["default", "seq2seq-lm"]:
														__A	:						Optional[int]       =				super()._flatten_past_key_values_(_A     ,					_A     ,					_A     ,					_A     )
									else:
														__A	:						Optional[Any]       =				super(_A     ,					self     )._flatten_past_key_values_(
														    _A     ,					_A     ,					_A     ,					_A     )











import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase						:   List[str]			  =     '''▁'''

UpperCAmelCase						:   Optional[Any]			  =     get_tests_dir('''fixtures/test_sentencepiece.model''')





@require_sentencepiece
class 						_A(					snake_case__				, unittest.TestCase   ):


				"""simple docstring"""


				UpperCamelCase    :  Optional[int]					    =	BertGenerationTokenizer
				UpperCamelCase    :  str					    =	False
				UpperCamelCase    :  Tuple					    =	True



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

									__A	:						Tuple       =				BertGenerationTokenizer(_A     ,					keep_accents=_A     )
									tokenizer.save_pretrained(self.tmpdirname     )



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				'<s>'
									__A	:						str       =				1

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



				def 					UpperCAmelCase_		(			self     ):
									__A	:						int       =				list(self.get_tokenizer().get_vocab().keys()     )

									self.assertEqual(vocab_keys[0]     ,					'<unk>'     )
									self.assertEqual(vocab_keys[1]     ,					'<s>'     )
									self.assertEqual(vocab_keys[-1]     ,					'<pad>'     )
									self.assertEqual(len(_A     )     ,					1002     )



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



				def 					UpperCAmelCase_		(			self     ):
									__A	:						str       =				BertGenerationTokenizer(_A     ,					keep_accents=_A     )

									__A	:						Dict       =				tokenizer.tokenize('This is a test'     )
									self.assertListEqual(_A     ,					['▁This', '▁is', '▁a', '▁t', 'est']     )

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

									__A	:						int       =				tokenizer.tokenize('I was born in 92000, and this is falsé.'     )
									self.assertListEqual(
									    _A     ,					[
									        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(_A     )
									self.assertListEqual(
									    _A     ,					[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,					)

									__A	:						Optional[int]       =				tokenizer.convert_ids_to_tokens(_A     )
									self.assertListEqual(
									    _A     ,					[
									        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 					UpperCAmelCase_		(			self     ):
									return BertGenerationTokenizer.from_pretrained('google/bert_for_seq_generation_L-24_bbc_encoder'     )



				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						List[Any]       =				'Hello World!'
									__A	:						Optional[Any]       =				[18536, 2260, 101]

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



				@slow
				def 					UpperCAmelCase_		(			self     ):
									__A	:						Dict       =				(
									    '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       =				[
									    871,
									    419,
									    358,
									    946,
									    991,
									    2521,
									    452,
									    358,
									    1357,
									    387,
									    7751,
									    3536,
									    112,
									    985,
									    456,
									    126,
									    865,
									    938,
									    5400,
									    5734,
									    458,
									    1368,
									    467,
									    786,
									    2462,
									    5246,
									    1159,
									    633,
									    865,
									    4519,
									    457,
									    582,
									    852,
									    2557,
									    427,
									    916,
									    508,
									    405,
									    34324,
									    497,
									    391,
									    408,
									    11342,
									    1244,
									    385,
									    100,
									    938,
									    985,
									    456,
									    574,
									    362,
									    12597,
									    3200,
									    3129,
									    1172,
									]

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



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

									from transformers import BertGenerationConfig, BertGenerationEncoder

									# Build sequence
									__A	:						Tuple       =				list(self.big_tokenizer.get_vocab().keys()     )[:10]
									__A	:						List[Any]       =				' '.join(_A     )
									__A	:						Union[str, Any]       =				self.big_tokenizer.encode_plus(_A     ,					return_tensors='pt'     ,					return_token_type_ids=_A     )
									__A	:						Optional[Any]       =				self.big_tokenizer.batch_encode_plus(
									    [sequence + ' ' + sequence]     ,					return_tensors='pt'     ,					return_token_type_ids=_A     )

									__A	:						int       =				BertGenerationConfig()
									__A	:						List[str]       =				BertGenerationEncoder(_A     )

									assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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





				@slow
				def 					UpperCAmelCase_		(			self     ):
									# fmt: off
									__A	:						str       =				{'input_ids': [[39286, 458, 36335, 2001, 456, 13073, 13266, 455, 113, 7746, 1741, 11157, 391, 13073, 13266, 455, 113, 3967, 35412, 113, 4936, 109, 3870, 2377, 113, 30084, 45720, 458, 134, 17496, 112, 503, 11672, 113, 118, 112, 5665, 13347, 38687, 112, 1496, 31389, 112, 3268, 47264, 134, 962, 112, 16377, 8035, 23130, 430, 12169, 15518, 28592, 458, 146, 41697, 109, 391, 12169, 15518, 16689, 458, 146, 41358, 109, 452, 726, 4034, 111, 763, 35412, 5082, 388, 1903, 111, 9051, 391, 2870, 48918, 1900, 1123, 550, 998, 112, 9586, 15985, 455, 391, 410, 22955, 37636, 114], [448, 17496, 419, 3663, 385, 763, 113, 27533, 2870, 3283, 13043, 1639, 24713, 523, 656, 24013, 18550, 2521, 517, 27014, 21244, 420, 1212, 1465, 391, 927, 4833, 388, 578, 11786, 114, 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], [484, 2169, 7687, 21932, 18146, 726, 363, 17032, 3391, 114, 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, 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, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0], [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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
									# fmt: on

									self.tokenizer_integration_test_util(
									    expected_encoding=_A     ,					model_name='google/bert_for_seq_generation_L-24_bbc_encoder'     ,					revision='c817d1fd1be2ffa69431227a1fe320544943d4db'     ,					)




