



"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import BlenderbotConfig, BlenderbotTokenizer, 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, TFBlenderbotForConditionalGeneration, TFBlenderbotModel

@require_tf
class 	__SCREAMING_SNAKE_CASE     :


					'''simple docstring'''





					_a             =							BlenderbotConfig
					_a             =							{}
					_a             =							'gelu'



					def __init__(	self							:  str,  lowerCamelCase							:  str,  lowerCamelCase							:  str=13,  lowerCamelCase							:  Union[str, Any]=7,  lowerCamelCase							:  List[str]=True,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  str=99,  lowerCamelCase							:  Tuple=32,  lowerCamelCase							:  Dict=2,  lowerCamelCase							:  Any=4,  lowerCamelCase							:  Union[str, Any]=37,  lowerCamelCase							:  int=0.1,  lowerCamelCase							:  str=0.1,  lowerCamelCase							:  List[Any]=20,  lowerCamelCase							:  str=2,  lowerCamelCase							:  List[Any]=1,  lowerCamelCase							:  str=0,  )-> List[str]:
								lowerCamelCase__		:		int		     =parent
								lowerCamelCase__		:		Optional[Any]		     =batch_size
								lowerCamelCase__		:		List[Any]		     =seq_length
								lowerCamelCase__		:		Optional[Any]		     =is_training
								lowerCamelCase__		:		Union[str, Any]		     =use_labels
								lowerCamelCase__		:		Optional[Any]		     =vocab_size
								lowerCamelCase__		:		Any		     =hidden_size
								lowerCamelCase__		:		Union[str, Any]		     =num_hidden_layers
								lowerCamelCase__		:		List[Any]		     =num_attention_heads
								lowerCamelCase__		:		Tuple		     =intermediate_size

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



					def 		snake_case	(	self							:  str					)-> Optional[int]:
								lowerCamelCase__		:		Optional[int]		     =ids_tensor([self.batch_size, self.seq_length - 1],  self.vocab_size					)
								lowerCamelCase__		:		Union[str, Any]		     =tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size					),  1					)
								lowerCamelCase__		:		Tuple		     =tf.concat([input_ids, eos_tensor],  axis=1					)

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

								lowerCamelCase__		:		Tuple		     =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,  )
								lowerCamelCase__		:		Optional[int]		     =prepare_blenderbot_inputs_dict(lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								return config, inputs_dict




					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  Any,  lowerCamelCase							:  Tuple					)-> Union[str, Any]:
								lowerCamelCase__		:		str		     =TFBlenderbotModel(config=lowerCamelCase					).get_decoder()
								lowerCamelCase__		:		Dict		     =inputs_dict['''input_ids''']

								lowerCamelCase__		:		Tuple		     =input_ids[:1, :]
								lowerCamelCase__		:		Optional[int]		     =inputs_dict['''attention_mask'''][:1, :]
								lowerCamelCase__		:		Dict		     =inputs_dict['''head_mask''']
								lowerCamelCase__		:		Tuple		     =1

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

								lowerCamelCase__     ,       lowerCamelCase__		:		List[Any]		     =outputs.to_tuple()

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

								# append to next input_ids and
								lowerCamelCase__		:		List[Any]		     =tf.concat([input_ids, next_tokens],  axis=-1					)
								lowerCamelCase__		:		Optional[int]		     =tf.concat([attention_mask, next_attn_mask],  axis=-1					)

								lowerCamelCase__		:		Dict		     =model(lowerCamelCase,  attention_mask=lowerCamelCase					)[0]
								lowerCamelCase__		:		Optional[Any]		     =model(lowerCamelCase,  attention_mask=lowerCamelCase,  past_key_values=lowerCamelCase					)[0]

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

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

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






def 					snake_case__       (		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   Optional[int]							,		__lowerCamelCase    :   List[str]							,		__lowerCamelCase    :   str=None							,		__lowerCamelCase    :   str=None							,		__lowerCamelCase    :   int=None							,		__lowerCamelCase    :   Tuple=None							,		__lowerCamelCase    :   Dict=None							,		):
			"""simple docstring"""
			if attention_mask is None:
						lowerCamelCase__		:		Union[str, Any]		     =tf.cast(tf.math.not_equal(__lowerCamelCase							,		config.pad_token_id     )							,		tf.inta     )
			if decoder_attention_mask is None:
						lowerCamelCase__		:		Dict		     =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:
						lowerCamelCase__		:		Any		     =tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
			if decoder_head_mask is None:
						lowerCamelCase__		:		Optional[int]		     =tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
			if cross_attn_head_mask is None:
						lowerCamelCase__		:		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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_		,       lowerCAmelCase_		,       unittest.TestCase       ):


					'''simple docstring'''





					_a             =							(TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
					_a             =							(TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
					_a             =							(
					    {
					        'conversational': TFBlenderbotForConditionalGeneration,
					        'feature-extraction': TFBlenderbotModel,
					        'summarization': TFBlenderbotForConditionalGeneration,
					        'text2text-generation': TFBlenderbotForConditionalGeneration,
					        'translation': TFBlenderbotForConditionalGeneration,
					    }
					    if is_tf_available()
					    else {}
					)
					_a             =							True
					_a             =							False
					_a             =							False



					def 		snake_case	(	self							:  Optional[Any]					)-> Tuple:
								lowerCamelCase__		:		Tuple		     =TFBlenderbotModelTester(self					)
								lowerCamelCase__		:		Optional[int]		     =ConfigTester(self,  config_class=lowerCamelCase					)



					def 		snake_case	(	self							:  str					)-> List[str]:
								self.config_tester.run_common_tests()




					def 		snake_case	(	self							:  str					)-> Union[str, Any]:
								lowerCamelCase__		:		Union[str, Any]		     =self.model_tester.prepare_config_and_inputs_for_common()
								self.model_tester.check_decoder_model_past_large_inputs(*lowerCamelCase					)





@require_tokenizers
@require_tf
class 	__SCREAMING_SNAKE_CASE     (				unittest.TestCase       ):


					'''simple docstring'''





					_a             =							['My friends are cool but they eat too many carbs.']
					_a             =							'facebook/blenderbot-400M-distill'



					@cached_property
					def 		snake_case	(	self							:  List[str]					)-> List[Any]:
								return BlenderbotTokenizer.from_pretrained(self.model_name					)



					@cached_property
					def 		snake_case	(	self							:  Any					)-> Dict:
								lowerCamelCase__		:		List[str]		     =TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
								return model




					@slow
					def 		snake_case	(	self							:  Optional[int]					)-> Union[str, Any]:
								lowerCamelCase__		:		Any		     =self.tokenizer(self.src_text,  return_tensors='''tf'''					)
								lowerCamelCase__		:		int		     =self.model.generate(
								    model_inputs.input_ids,  )
								lowerCamelCase__		:		List[Any]		     =self.tokenizer.batch_decode(generated_ids.numpy(),  skip_special_tokens=lowerCamelCase					)[0]
								assert (
								    generated_words
								    == " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
								)









"""simple docstring"""






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


_lowercase			:			Dict										=						logging.get_logger(__name__)

_lowercase			:			int										=						{
    "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 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					_a             =							'blenderbot-small'
					_a             =							['past_key_values']
					_a             =							{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}



					def __init__(	self							:  Tuple,  lowerCamelCase							:  Any=5_0265,  lowerCamelCase							:  Optional[Any]=512,  lowerCamelCase							:  Union[str, Any]=8,  lowerCamelCase							:  Dict=2048,  lowerCamelCase							:  str=16,  lowerCamelCase							:  List[Any]=8,  lowerCamelCase							:  List[str]=2048,  lowerCamelCase							:  int=16,  lowerCamelCase							:  Any=0.0,  lowerCamelCase							:  Dict=0.0,  lowerCamelCase							:  Tuple=True,  lowerCamelCase							:  Tuple=True,  lowerCamelCase							:  Optional[int]="gelu",  lowerCamelCase							:  Tuple=512,  lowerCamelCase							:  Tuple=0.1,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  Optional[int]=0.0,  lowerCamelCase							:  List[str]=0.02,  lowerCamelCase							:  Any=1,  lowerCamelCase							:  Union[str, Any]=False,  lowerCamelCase							:  Any=0,  lowerCamelCase							:  Tuple=1,  lowerCamelCase							:  Tuple=2,  lowerCamelCase							:  Dict=2,  **lowerCamelCase							:  Any,  )-> Dict:
								lowerCamelCase__		:		Dict		     =vocab_size
								lowerCamelCase__		:		Dict		     =max_position_embeddings
								lowerCamelCase__		:		Optional[Any]		     =d_model
								lowerCamelCase__		:		Union[str, Any]		     =encoder_ffn_dim
								lowerCamelCase__		:		Optional[Any]		     =encoder_layers
								lowerCamelCase__		:		Any		     =encoder_attention_heads
								lowerCamelCase__		:		Union[str, Any]		     =decoder_ffn_dim
								lowerCamelCase__		:		Optional[int]		     =decoder_layers
								lowerCamelCase__		:		Any		     =decoder_attention_heads
								lowerCamelCase__		:		Optional[int]		     =dropout
								lowerCamelCase__		:		str		     =attention_dropout
								lowerCamelCase__		:		Union[str, Any]		     =activation_dropout
								lowerCamelCase__		:		Tuple		     =activation_function
								lowerCamelCase__		:		str		     =init_std
								lowerCamelCase__		:		List[Any]		     =encoder_layerdrop
								lowerCamelCase__		:		List[str]		     =decoder_layerdrop
								lowerCamelCase__		:		Tuple		     =use_cache
								lowerCamelCase__		:		Optional[Any]		     =encoder_layers
								lowerCamelCase__		:		List[Any]		     =scale_embedding  # scale factor will be sqrt(d_model) if True

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





class 	__SCREAMING_SNAKE_CASE     (				lowerCAmelCase_       ):


					'''simple docstring'''





					@property
					def 		snake_case	(	self							:  Optional[int]					)-> Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		int		     =OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]					)

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

											if self.use_past:
														self.fill_with_past_key_values_(lowerCamelCase,  direction='''inputs'''					)
								elif self.task == "causal-lm":
											# TODO: figure this case out.
											lowerCamelCase__		:		Optional[Any]		     =OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]					)
											if self.use_past:
														lowerCamelCase__     ,       lowerCamelCase__		:		List[str]		     =self.num_layers
														for i in range(lowerCamelCase					):
																	lowerCamelCase__		:		Optional[Any]		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
																	lowerCamelCase__		:		Optional[int]		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
								else:
											lowerCamelCase__		:		str		     =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 		snake_case	(	self							:  Dict					)-> Mapping[str, Mapping[int, str]]:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		Dict		     =super().outputs
								else:
											lowerCamelCase__		:		Optional[Any]		     =super(lowerCamelCase,  self					).outputs
											if self.use_past:
														lowerCamelCase__     ,       lowerCamelCase__		:		str		     =self.num_layers
														for i in range(lowerCamelCase					):
																	lowerCamelCase__		:		Tuple		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
																	lowerCamelCase__		:		Dict		     ={0: '''batch''', 2: '''past_sequence + sequence'''}
								return common_outputs



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								lowerCamelCase__		:		Any		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

								# Generate decoder inputs
								lowerCamelCase__		:		str		     =seq_length if not self.use_past else 1
								lowerCamelCase__		:		Tuple		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								lowerCamelCase__		:		str		     ={F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
								lowerCamelCase__		:		Tuple		     =dict(**lowerCamelCase,  **lowerCamelCase					)

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

											lowerCamelCase__		:		Optional[Any]		     =torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(lowerCamelCase,  lowerCamelCase					)],  dim=1					)

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

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



					def 		snake_case	(	self							:  List[str],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								lowerCamelCase__		:		int		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
								    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)

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

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



					def 		snake_case	(	self							:  Optional[int],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								# 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
								lowerCamelCase__		:		int		     =compute_effective_axis_dimension(
								    lowerCamelCase,  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
								lowerCamelCase__		:		Optional[int]		     =tokenizer.num_special_tokens_to_add(lowerCamelCase					)
								lowerCamelCase__		:		Optional[int]		     =compute_effective_axis_dimension(
								    lowerCamelCase,  fixed_dimension=OnnxConfig.default_fixed_sequence,  num_token_to_add=lowerCamelCase					)

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



					def 		snake_case	(	self							:  List[Any],  lowerCamelCase							:  PreTrainedTokenizer,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  int = -1,  lowerCamelCase							:  bool = False,  lowerCamelCase							:  Optional[TensorType] = None,  )-> Mapping[str, Any]:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		Union[str, Any]		     =self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    lowerCamelCase,  batch_size=lowerCamelCase,  seq_length=lowerCamelCase,  is_pair=lowerCamelCase,  framework=lowerCamelCase					)

								elif self.task == "causal-lm":
											lowerCamelCase__		:		Union[str, Any]		     =self._generate_dummy_inputs_for_causal_lm(
											    lowerCamelCase,  batch_size=lowerCamelCase,  seq_length=lowerCamelCase,  is_pair=lowerCamelCase,  framework=lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[Any]		     =self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    lowerCamelCase,  batch_size=lowerCamelCase,  seq_length=lowerCamelCase,  is_pair=lowerCamelCase,  framework=lowerCamelCase					)

								return common_inputs




					def 		snake_case	(	self							:  Optional[Any],  lowerCamelCase							:  Tuple,  lowerCamelCase							:  Union[str, Any],  lowerCamelCase							:  Dict,  lowerCamelCase							:  Any					)-> str:
								if self.task in ["default", "seq2seq-lm"]:
											lowerCamelCase__		:		Any		     =super()._flatten_past_key_values_(lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)
								else:
											lowerCamelCase__		:		List[str]		     =super(lowerCamelCase,  self					)._flatten_past_key_values_(
											    lowerCamelCase,  lowerCamelCase,  lowerCamelCase,  lowerCamelCase					)




