

"""simple docstring"""






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

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


snake_case_   :   Optional[Any]        =       logging.get_logger(__name__)

snake_case_   :   int        =       {
    """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json""",
    # See all Marian models at https://huggingface.co/models?filter=marian
}







class       snake_case__  ( lowerCAmelCase_     ):
			SCREAMING_SNAKE_CASE__     				=						'''marian'''
			SCREAMING_SNAKE_CASE__     				=						['''past_key_values''']
			SCREAMING_SNAKE_CASE__     				=						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}







			def __init__(			self			: Optional[Any]			,  lowercase			: Any=5_81_01			,  lowercase			: Any=None			,  lowercase			: str=10_24			,  lowercase			: Tuple=12			,  lowercase			: Dict=40_96			,  lowercase			: str=16			,  lowercase			: List[Any]=12			,  lowercase			: List[str]=40_96			,  lowercase			: int=16			,  lowercase			: Any=0.0			,  lowercase			: Union[str, Any]=0.0			,  lowercase			: int=True			,  lowercase			: Optional[Any]=True			,  lowercase			: List[str]="gelu"			,  lowercase			: List[Any]=10_24			,  lowercase			: str=0.1			,  lowercase			: Dict=0.0			,  lowercase			: List[str]=0.0			,  lowercase			: Tuple=0.0_2			,  lowercase			: Optional[Any]=5_81_00			,  lowercase			: Dict=False			,  lowercase			: Optional[Any]=5_81_00			,  lowercase			: Union[str, Any]=0			,  lowercase			: List[Any]=0			,  lowercase			: Optional[Any]=True			,  **lowercase			: Dict			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				vocab_size
								UpperCAmelCase			:   List[Any]      =				decoder_vocab_size or vocab_size
								UpperCAmelCase			:   str      =				max_position_embeddings
								UpperCAmelCase			:   List[Any]      =				d_model
								UpperCAmelCase			:   Tuple      =				encoder_ffn_dim
								UpperCAmelCase			:   Tuple      =				encoder_layers
								UpperCAmelCase			:   Union[str, Any]      =				encoder_attention_heads
								UpperCAmelCase			:   str      =				decoder_ffn_dim
								UpperCAmelCase			:   List[Any]      =				decoder_layers
								UpperCAmelCase			:   Dict      =				decoder_attention_heads
								UpperCAmelCase			:   int      =				dropout
								UpperCAmelCase			:   Any      =				attention_dropout
								UpperCAmelCase			:   str      =				activation_dropout
								UpperCAmelCase			:   Optional[int]      =				activation_function
								UpperCAmelCase			:   List[str]      =				init_std
								UpperCAmelCase			:   int      =				encoder_layerdrop
								UpperCAmelCase			:   Any      =				decoder_layerdrop
								UpperCAmelCase			:   Any      =				use_cache
								UpperCAmelCase			:   List[str]      =				encoder_layers
								UpperCAmelCase			:   List[str]      =				scale_embedding  # scale factor will be sqrt(d_model) if True
								UpperCAmelCase			:   Union[str, Any]      =				share_encoder_decoder_embeddings
								super().__init__(
								    pad_token_id=lowercase			,  eos_token_id=lowercase			,  is_encoder_decoder=lowercase			,  decoder_start_token_id=lowercase			,  forced_eos_token_id=lowercase			,  **lowercase			,  )







class       snake_case__  ( lowerCAmelCase_     ):







			@property
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase			:   Union[str, Any]      =				OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]  )

													if self.use_past:
																		UpperCAmelCase			:   Union[str, Any]      =				{0: "batch"}
																		UpperCAmelCase			:   List[Any]      =				{0: "batch", 1: "past_decoder_sequence + sequence"}
													else:
																		UpperCAmelCase			:   List[Any]      =				{0: "batch", 1: "decoder_sequence"}
																		UpperCAmelCase			:   List[Any]      =				{0: "batch", 1: "decoder_sequence"}

													if self.use_past:
																		self.fill_with_past_key_values_(lowercase			,  direction="inputs"  )
								elif self.task == "causal-lm":
													# TODO: figure this case out.
													UpperCAmelCase			:   str      =				OrderedDict(
													    [
													        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
													        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
													    ]  )
													if self.use_past:
																		UpperCAmelCase      ,							UpperCAmelCase			:   Dict      =				self.num_layers
																		for i in range(lowercase  ):
																							UpperCAmelCase			:   List[Any]      =				{0: "batch", 2: "past_sequence + sequence"}
																							UpperCAmelCase			:   List[Any]      =				{0: "batch", 2: "past_sequence + sequence"}
								else:
													UpperCAmelCase			:   Tuple      =				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
			# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase			:   Optional[int]      =				super().outputs
								else:
													UpperCAmelCase			:   str      =				super(lowercase			,  self  ).outputs
													if self.use_past:
																		UpperCAmelCase      ,							UpperCAmelCase			:   int      =				self.num_layers
																		for i in range(lowercase  ):
																							UpperCAmelCase			:   List[str]      =				{0: "batch", 2: "past_sequence + sequence"}
																							UpperCAmelCase			:   Dict      =				{0: "batch", 2: "past_sequence + sequence"}
								return common_outputs







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: PreTrainedTokenizer			,  lowercase			: int = -1			,  lowercase			: int = -1			,  lowercase			: bool = False			,  lowercase			: Optional[TensorType] = None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Optional[Any]      =				self._generate_dummy_inputs_for_encoder_and_decoder(
								    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase  )

								# Generate decoder inputs
								UpperCAmelCase			:   Dict      =				seq_length if not self.use_past else 1
								UpperCAmelCase			:   Any      =				self._generate_dummy_inputs_for_encoder_and_decoder(
								    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase  )
								UpperCAmelCase			:   List[str]      =				{f"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
								UpperCAmelCase			:   Optional[Any]      =				dict(**lowercase			,  **lowercase  )

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

													UpperCAmelCase			:   Tuple      =				torch.cat(
													    [common_inputs["decoder_attention_mask"], torch.ones(lowercase			,  lowercase  )]			,  dim=1  )

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

													for _ in range(lowercase  ):
																		common_inputs["past_key_values"].append(
																		    (
																		        torch.zeros(lowercase  ),
																		        torch.zeros(lowercase  ),
																		        torch.zeros(lowercase  ),
																		        torch.zeros(lowercase  ),
																		    )  )
													# TODO: test this.
													UpperCAmelCase			:   Optional[int]      =				encoder_shape if remaining_side_name == "encoder" else decoder_shape
													for _ in range(lowercase			,  lowercase  ):
																		common_inputs["past_key_values"].append((torch.zeros(lowercase  ), torch.zeros(lowercase  ))  )
								return common_inputs







			def 			__lowerCAmelCase				(			self			: List[str]			,  lowercase			: PreTrainedTokenizer			,  lowercase			: int = -1			,  lowercase			: int = -1			,  lowercase			: bool = False			,  lowercase			: Optional[TensorType] = None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   int      =				self._generate_dummy_inputs_for_encoder_and_decoder(
								    lowercase			,  lowercase			,  lowercase			,  lowercase			,  lowercase  )

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

													UpperCAmelCase			:   List[Any]      =				common_inputs["attention_mask"].dtype
													UpperCAmelCase			:   Optional[int]      =				torch.cat(
													    [common_inputs["attention_mask"], torch.ones(lowercase			,  lowercase			,  dtype=lowercase  )]			,  dim=1  )
													UpperCAmelCase			:   List[str]      =				[
													    (torch.zeros(lowercase  ), torch.zeros(lowercase  )) for _ in range(lowercase  )
													]
								return common_inputs







			def 			__lowerCAmelCase				(			self			: List[Any]			,  lowercase			: PreTrainedTokenizer			,  lowercase			: int = -1			,  lowercase			: int = -1			,  lowercase			: bool = False			,  lowercase			: Optional[TensorType] = None			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				compute_effective_axis_dimension(
								    lowercase			,  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
								UpperCAmelCase			:   Tuple      =				tokenizer.num_special_tokens_to_add(lowercase  )
								UpperCAmelCase			:   Dict      =				compute_effective_axis_dimension(
								    lowercase			,  fixed_dimension=OnnxConfig.default_fixed_sequence			,  num_token_to_add=lowercase  )

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







			def 			__lowerCAmelCase				(			self			: Any			,  lowercase			: PreTrainedTokenizer			,  lowercase			: int = -1			,  lowercase			: int = -1			,  lowercase			: bool = False			,  lowercase			: Optional[TensorType] = None			,  ):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase			:   Optional[int]      =				self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase  )

								else:
													UpperCAmelCase			:   str      =				self._generate_dummy_inputs_for_causal_lm(
													    lowercase			,  batch_size=lowercase			,  seq_length=lowercase			,  is_pair=lowercase			,  framework=lowercase  )

								return common_inputs







			def 			__lowerCAmelCase				(			self			: Tuple			,  lowercase			: int			,  lowercase			: str			,  lowercase			: Union[str, Any]			,  lowercase			: str  ):





								'''simple docstring'''



								if self.task in ["default", "seq2seq-lm"]:
													UpperCAmelCase			:   Union[str, Any]      =				super()._flatten_past_key_values_(lowercase			,  lowercase			,  lowercase			,  lowercase  )
								else:
													UpperCAmelCase			:   Optional[int]      =				super(lowercase			,  self  )._flatten_past_key_values_(
													    lowercase			,  lowercase			,  lowercase			,  lowercase  )







			@property
			def 			__lowerCAmelCase				(			self			: Dict  ):





								'''simple docstring'''



								return 1E-4





"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotSmallConfig, BlenderbotSmallTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel







@require_tf
class       snake_case__  :
			SCREAMING_SNAKE_CASE__     				=						BlenderbotSmallConfig
			SCREAMING_SNAKE_CASE__     				=						{}
			SCREAMING_SNAKE_CASE__     				=						'''gelu'''







			def __init__(			self			: Tuple			,  lowercase			: Any			,  lowercase			: Optional[Any]=13			,  lowercase			: Any=7			,  lowercase			: List[Any]=True			,  lowercase			: List[str]=False			,  lowercase			: Optional[Any]=99			,  lowercase			: Union[str, Any]=32			,  lowercase			: List[Any]=2			,  lowercase			: Tuple=4			,  lowercase			: Union[str, Any]=37			,  lowercase			: str=0.1			,  lowercase			: Optional[Any]=0.1			,  lowercase			: Optional[Any]=20			,  lowercase			: str=2			,  lowercase			: int=1			,  lowercase			: Optional[int]=0			,  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				parent
								UpperCAmelCase			:   Union[str, Any]      =				batch_size
								UpperCAmelCase			:   Tuple      =				seq_length
								UpperCAmelCase			:   Dict      =				is_training
								UpperCAmelCase			:   str      =				use_labels
								UpperCAmelCase			:   Union[str, Any]      =				vocab_size
								UpperCAmelCase			:   Tuple      =				hidden_size
								UpperCAmelCase			:   List[Any]      =				num_hidden_layers
								UpperCAmelCase			:   Union[str, Any]      =				num_attention_heads
								UpperCAmelCase			:   Dict      =				intermediate_size

								UpperCAmelCase			:   Optional[int]      =				hidden_dropout_prob
								UpperCAmelCase			:   Optional[Any]      =				attention_probs_dropout_prob
								UpperCAmelCase			:   Dict      =				max_position_embeddings
								UpperCAmelCase			:   List[Any]      =				eos_token_id
								UpperCAmelCase			:   List[Any]      =				pad_token_id
								UpperCAmelCase			:   Union[str, Any]      =				bos_token_id







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





								'''simple docstring'''



								UpperCAmelCase			:   List[str]      =				ids_tensor([self.batch_size, self.seq_length - 1]			,  self.vocab_size  )
								UpperCAmelCase			:   Optional[int]      =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )			,  1  )
								UpperCAmelCase			:   Optional[int]      =				tf.concat([input_ids, eos_tensor]			,  axis=1  )

								UpperCAmelCase			:   str      =				ids_tensor([self.batch_size, self.seq_length]			,  self.vocab_size  )

								UpperCAmelCase			:   int      =				self.config_cls(
								    vocab_size=self.vocab_size			,  d_model=self.hidden_size			,  encoder_layers=self.num_hidden_layers			,  decoder_layers=self.num_hidden_layers			,  encoder_attention_heads=self.num_attention_heads			,  decoder_attention_heads=self.num_attention_heads			,  encoder_ffn_dim=self.intermediate_size			,  decoder_ffn_dim=self.intermediate_size			,  dropout=self.hidden_dropout_prob			,  attention_dropout=self.attention_probs_dropout_prob			,  max_position_embeddings=self.max_position_embeddings			,  eos_token_ids=[2]			,  bos_token_id=self.bos_token_id			,  pad_token_id=self.pad_token_id			,  decoder_start_token_id=self.pad_token_id			,  **self.config_updates			,  )
								UpperCAmelCase			:   Optional[Any]      =				prepare_blenderbot_small_inputs_dict(lowercase			,  lowercase			,  lowercase  )
								return config, inputs_dict







			def 			__lowerCAmelCase				(			self			: int			,  lowercase			: List[Any]			,  lowercase			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				TFBlenderbotSmallModel(config=lowercase  ).get_decoder()
								UpperCAmelCase			:   List[Any]      =				inputs_dict["input_ids"]

								UpperCAmelCase			:   int      =				input_ids[:1, :]
								UpperCAmelCase			:   Union[str, Any]      =				inputs_dict["attention_mask"][:1, :]
								UpperCAmelCase			:   Union[str, Any]      =				inputs_dict["head_mask"]
								UpperCAmelCase			:   List[Any]      =				1

								# first forward pass
								UpperCAmelCase			:   Optional[Any]      =				model(lowercase			,  attention_mask=lowercase			,  head_mask=lowercase			,  use_cache=lowercase  )

								UpperCAmelCase      ,							UpperCAmelCase			:   List[str]      =				outputs.to_tuple()

								# create hypothetical next token and extent to next_input_ids
								UpperCAmelCase			:   Any      =				ids_tensor((self.batch_size, 3)			,  config.vocab_size  )
								UpperCAmelCase			:   Optional[int]      =				tf.cast(ids_tensor((self.batch_size, 3)			,  2  )			,  tf.inta  )

								# append to next input_ids and
								UpperCAmelCase			:   Union[str, Any]      =				tf.concat([input_ids, next_tokens]			,  axis=-1  )
								UpperCAmelCase			:   str      =				tf.concat([attention_mask, next_attn_mask]			,  axis=-1  )

								UpperCAmelCase			:   Optional[Any]      =				model(lowercase			,  attention_mask=lowercase  )[0]
								UpperCAmelCase			:   List[str]      =				model(lowercase			,  attention_mask=lowercase			,  past_key_values=lowercase  )[0]

								self.parent.assertEqual(next_tokens.shape[1]			,  output_from_past.shape[1]  )

								# select random slice
								UpperCAmelCase			:   List[str]      =				int(ids_tensor((1,)			,  output_from_past.shape[-1]  )  )
								UpperCAmelCase			:   Optional[int]      =				output_from_no_past[:, -3:, random_slice_idx]
								UpperCAmelCase			:   Optional[Any]      =				output_from_past[:, :, random_slice_idx]

								# test that outputs are equal for slice
								tf.debugging.assert_near(lowercase			,  lowercase			,  rtol=1E-3  )



def   lowercase_	(    _lowercase       :		Optional[Any]							,      _lowercase       :		Optional[int]							,      _lowercase       :		Tuple							,      _lowercase       :		Tuple=None							,      _lowercase       :		Any=None							,      _lowercase       :		str=None							,      _lowercase       :		Optional[Any]=None							,      _lowercase       :		Dict=None							,      ):




					'''simple docstring'''






					if attention_mask is None:
										UpperCAmelCase			:   List[str]      =				tf.cast(tf.math.not_equal(_lowercase							,      config.pad_token_id	)							,      tf.inta	)
					if decoder_attention_mask is None:
										UpperCAmelCase			:   str      =				tf.concat(
										    [
										        tf.ones(decoder_input_ids[:, :1].shape							,      dtype=tf.inta	),
										        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,      config.pad_token_id	)							,      tf.inta	),
										    ]							,      axis=-1							,      )
					if head_mask is None:
										UpperCAmelCase			:   Dict      =				tf.ones((config.encoder_layers, config.encoder_attention_heads)	)
					if decoder_head_mask is None:
										UpperCAmelCase			:   Optional[Any]      =				tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
					if cross_attn_head_mask is None:
										UpperCAmelCase			:   str      =				tf.ones((config.decoder_layers, config.decoder_attention_heads)	)
					return {
					    "input_ids": input_ids,
					    "decoder_input_ids": decoder_input_ids,
					    "attention_mask": attention_mask,
					    "decoder_attention_mask": decoder_attention_mask,
					    "head_mask": head_mask,
					    "decoder_head_mask": decoder_head_mask,
					    "cross_attn_head_mask": cross_attn_head_mask,
					}







@require_tf
class       snake_case__  ( lowerCAmelCase_					,  lowerCAmelCase_					,  unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						(
			    (TFBlenderbotSmallForConditionalGeneration, TFBlenderbotSmallModel) if is_tf_available() else ()
			)
			SCREAMING_SNAKE_CASE__     				=						(TFBlenderbotSmallForConditionalGeneration,) if is_tf_available() else ()
			SCREAMING_SNAKE_CASE__     				=						(
			    {
			        '''conversational''': TFBlenderbotSmallForConditionalGeneration,
			        '''feature-extraction''': TFBlenderbotSmallModel,
			        '''summarization''': TFBlenderbotSmallForConditionalGeneration,
			        '''text2text-generation''': TFBlenderbotSmallForConditionalGeneration,
			        '''translation''': TFBlenderbotSmallForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			SCREAMING_SNAKE_CASE__     				=						True
			SCREAMING_SNAKE_CASE__     				=						False
			SCREAMING_SNAKE_CASE__     				=						False







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				TFBlenderbotSmallModelTester(self  )
								UpperCAmelCase			:   Dict      =				ConfigTester(self			,  config_class=lowercase  )







			def 			__lowerCAmelCase				(			self			: Optional[Any]  ):





								'''simple docstring'''



								self.config_tester.run_common_tests()







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





								'''simple docstring'''



								UpperCAmelCase			:   Dict      =				self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowercase  )







@require_tokenizers
@require_tf
class       snake_case__  ( unittest.TestCase     ):
			SCREAMING_SNAKE_CASE__     				=						[
			    '''Social anxiety\nWow, I am never shy. Do you have anxiety?\nYes. I end up sweating and blushing and feel like '''
			    '''  i\'m going to throw up.\nand why is that?'''
			]
			SCREAMING_SNAKE_CASE__     				=						'''facebook/blenderbot_small-90M'''







			@cached_property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								return BlenderbotSmallTokenizer.from_pretrained("facebook/blenderbot-90M"  )







			@cached_property
			def 			__lowerCAmelCase				(			self			: Any  ):





								'''simple docstring'''



								UpperCAmelCase			:   Union[str, Any]      =				TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
								return model







			@slow
			def 			__lowerCAmelCase				(			self			: Optional[int]  ):





								'''simple docstring'''



								UpperCAmelCase			:   List[Any]      =				self.tokenizer(self.src_text			,  return_tensors="tf"  )
								UpperCAmelCase			:   List[Any]      =				self.model.generate(
								    model_inputs.input_ids			,  attention_mask=model_inputs.attention_mask			,  num_beams=2			,  use_cache=lowercase			,  )
								UpperCAmelCase			:   Dict      =				self.tokenizer.batch_decode(generated_ids.numpy()			,  skip_special_tokens=lowercase  )[0]
								assert generated_words in (
								    "i don't know. i just feel like i'm going to throw up. it's not fun.",
								    "i'm not sure. i just feel like i've been feeling like i have to be in a certain place",
								    "i'm not sure. i just feel like i've been in a bad situation.",
								)


