
'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import OpenAIGPTConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
	import tensorflow as tf

	from transformers.models.openai.modeling_tf_openai import (
	    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
	    TFOpenAIGPTDoubleHeadsModel,
	    TFOpenAIGPTForSequenceClassification,
	    TFOpenAIGPTLMHeadModel,
	    TFOpenAIGPTModel,
	)

class 			UpperCAmelCase				:





		"""simple docstring"""





		def __init__(    self  ,					_snake_case  ,					)				->    int:
				_UpperCamelCase       : Any    =      parent
				_UpperCamelCase       : Dict    =      13
				_UpperCamelCase       : Union[str, Any]    =      7
				_UpperCamelCase       : str    =      True
				_UpperCamelCase       : int    =      True
				_UpperCamelCase       : Tuple    =      True
				_UpperCamelCase       : Optional[Any]    =      True
				_UpperCamelCase       : Any    =      True
				_UpperCamelCase       : Any    =      99
				_UpperCamelCase       : Tuple    =      32
				_UpperCamelCase       : List[Any]    =      2
				_UpperCamelCase       : int    =      4
				_UpperCamelCase       : Dict    =      37
				_UpperCamelCase       : Optional[Any]    =      '''gelu'''
				_UpperCamelCase       : str    =      0.1
				_UpperCamelCase       : List[str]    =      0.1
				_UpperCamelCase       : Optional[Any]    =      512
				_UpperCamelCase       : Tuple    =      16
				_UpperCamelCase       : List[str]    =      2
				_UpperCamelCase       : Dict    =      0.02
				_UpperCamelCase       : List[str]    =      3
				_UpperCamelCase       : Optional[Any]    =      4
				_UpperCamelCase       : int    =      None
				_UpperCamelCase       : List[Any]    =      self.vocab_size - 1
		def _lowercase  (    self     )				->    Any:
				_UpperCamelCase       : Tuple    =      ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size     )

				_UpperCamelCase       : Tuple    =      None
				if self.use_input_mask:
						_UpperCamelCase       : Any    =      random_attention_mask([self.batch_size, self.seq_length]     )

				_UpperCamelCase       : Any    =      None
				if self.use_token_type_ids:
						_UpperCamelCase       : List[str]    =      ids_tensor([self.batch_size, self.seq_length]  ,					self.type_vocab_size     )

				_UpperCamelCase       : Any    =      None
				if self.use_mc_token_ids:
						_UpperCamelCase       : List[Any]    =      ids_tensor([self.batch_size, self.num_choices]  ,					self.seq_length     )

				_UpperCamelCase       : List[Any]    =      None
				_UpperCamelCase       : Union[str, Any]    =      None
				_UpperCamelCase       : Tuple    =      None
				if self.use_labels:
						_UpperCamelCase       : Optional[int]    =      ids_tensor([self.batch_size]  ,					self.type_sequence_label_size     )
						_UpperCamelCase       : List[str]    =      ids_tensor([self.batch_size, self.seq_length]  ,					self.num_labels     )
						_UpperCamelCase       : str    =      ids_tensor([self.batch_size]  ,					self.num_choices     )

				_UpperCamelCase       : int    =      OpenAIGPTConfig(
				    vocab_size=self.vocab_size  ,					n_embd=self.hidden_size  ,					n_layer=self.num_hidden_layers  ,					n_head=self.num_attention_heads  ,					n_positions=self.max_position_embeddings  ,					pad_token_id=self.pad_token_id  ,					)

				_UpperCamelCase       : Union[str, Any]    =      ids_tensor([self.num_hidden_layers, self.num_attention_heads]  ,					2     )

				return (
				    config,
				    input_ids,
				    input_mask,
				    head_mask,
				    token_type_ids,
				    mc_token_ids,
				    sequence_labels,
				    token_labels,
				    choice_labels,
				)
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					*_snake_case     )				->    List[str]:
				_UpperCamelCase       : List[Any]    =      TFOpenAIGPTModel(config=_snake_case     )
				_UpperCamelCase       : Union[str, Any]    =      {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
				_UpperCamelCase       : List[str]    =      model(_snake_case     )

				_UpperCamelCase       : List[str]    =      [input_ids, input_mask]
				_UpperCamelCase       : Dict    =      model(_snake_case     )

				_UpperCamelCase       : Optional[Any]    =      model(_snake_case     )

				self.parent.assertEqual(result.last_hidden_state.shape  ,					(self.batch_size, self.seq_length, self.hidden_size)     )
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					*_snake_case     )				->    Any:
				_UpperCamelCase       : Optional[int]    =      TFOpenAIGPTLMHeadModel(config=_snake_case     )
				_UpperCamelCase       : Tuple    =      {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
				_UpperCamelCase       : Union[str, Any]    =      model(_snake_case     )
				self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.vocab_size)     )
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					*_snake_case     )				->    Optional[int]:
				_UpperCamelCase       : List[Any]    =      TFOpenAIGPTDoubleHeadsModel(config=_snake_case     )

				_UpperCamelCase       : List[Any]    =      tf.tile(tf.expand_dims(_snake_case  ,					1     )  ,					(1, self.num_choices, 1)     )
				_UpperCamelCase       : str    =      tf.tile(tf.expand_dims(_snake_case  ,					1     )  ,					(1, self.num_choices, 1)     )
				_UpperCamelCase       : int    =      tf.tile(tf.expand_dims(_snake_case  ,					1     )  ,					(1, self.num_choices, 1)     )

				_UpperCamelCase       : List[str]    =      {
				    '''input_ids''': multiple_choice_inputs_ids,
				    '''mc_token_ids''': mc_token_ids,
				    '''attention_mask''': multiple_choice_input_mask,
				    '''token_type_ids''': multiple_choice_token_type_ids,
				}
				_UpperCamelCase       : Tuple    =      model(_snake_case     )
				self.parent.assertEqual(
				    result.logits.shape  ,					(self.batch_size, self.num_choices, self.seq_length, self.vocab_size)     )
				self.parent.assertEqual(result.mc_logits.shape  ,					(self.batch_size, self.num_choices)     )
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					*_snake_case     )				->    str:
				_UpperCamelCase       : List[str]    =      self.num_labels
				_UpperCamelCase       : Dict    =      ids_tensor([self.batch_size]  ,					self.type_sequence_label_size     )
				_UpperCamelCase       : str    =      {
				    '''input_ids''': input_ids,
				    '''attention_mask''': input_mask,
				    '''token_type_ids''': token_type_ids,
				    '''labels''': sequence_labels,
				}
				_UpperCamelCase       : Dict    =      TFOpenAIGPTForSequenceClassification(_snake_case     )
				_UpperCamelCase       : str    =      model(_snake_case     )
				self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.num_labels)     )




		def _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : Tuple    =      self.prepare_config_and_inputs()

				(
				    (
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							(
				    _UpperCamelCase
				),							
				)       : List[str]    =      config_and_inputs

				_UpperCamelCase       : int    =      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
				return config, inputs_dict







@require_tf
class 			UpperCAmelCase				(					a_  ,		a_  ,		unittest.TestCase ):





		"""simple docstring"""





		A__  :   Union[str, Any]          =    (
		    (TFOpenAIGPTModel, TFOpenAIGPTLMHeadModel, TFOpenAIGPTDoubleHeadsModel, TFOpenAIGPTForSequenceClassification)
		    if is_tf_available()
		    else ()
		)
		A__  :   Tuple          =    (
		    (TFOpenAIGPTLMHeadModel,) if is_tf_available() else ()
		)  # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
		A__  :   Tuple          =    (
		    {
		        'feature-extraction': TFOpenAIGPTModel,
		        'text-classification': TFOpenAIGPTForSequenceClassification,
		        'text-generation': TFOpenAIGPTLMHeadModel,
		        'zero-shot': TFOpenAIGPTForSequenceClassification,
		    }
		    if is_tf_available()
		    else {}
		)
		A__  :   Optional[Any]          =    False
		A__  :   Optional[Any]          =    False
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Optional[int]:
				if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
						# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
						# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
						# tiny config could not be created.
						return True

				return False
		def _lowercase  (    self     )				->    Optional[Any]:
				_UpperCamelCase       : Dict    =      TFOpenAIGPTModelTester(self     )
				_UpperCamelCase       : Optional[int]    =      ConfigTester(self  ,					config_class=_snake_case  ,					n_embd=37     )
		def _lowercase  (    self     )				->    int:
				self.config_tester.run_common_tests()
		def _lowercase  (    self     )				->    List[str]:
				_UpperCamelCase       : int    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_model(*_snake_case     )
		def _lowercase  (    self     )				->    List[Any]:
				_UpperCamelCase       : Optional[int]    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_lm_head(*_snake_case     )
		def _lowercase  (    self     )				->    int:
				_UpperCamelCase       : Dict    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_double_head(*_snake_case     )
		def _lowercase  (    self     )				->    Optional[int]:
				_UpperCamelCase       : Optional[int]    =      self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*_snake_case     )




		@slow
		def _lowercase  (    self     )				->    int:
				for model_name in TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						_UpperCamelCase       : int    =      TFOpenAIGPTModel.from_pretrained(_snake_case     )
						self.assertIsNotNone(_snake_case     )







@require_tf
class 			UpperCAmelCase				(					unittest.TestCase ):





		"""simple docstring"""





		@slow
		def _lowercase  (    self     )				->    List[Any]:
				_UpperCamelCase       : Union[str, Any]    =      TFOpenAIGPTLMHeadModel.from_pretrained('''openai-gpt'''     )
				_UpperCamelCase       : Optional[Any]    =      tf.convert_to_tensor([[481, 4735, 544]]  ,					dtype=tf.intaa     )  # the president is
				_UpperCamelCase       : Union[str, Any]    =      [
				    481,
				    4735,
				    544,
				    246,
				    963,
				    870,
				    762,
				    239,
				    244,
				    40477,
				    244,
				    249,
				    719,
				    881,
				    487,
				    544,
				    240,
				    244,
				    603,
				    481,
				]  # the president is a very good man. " \n " i\'m sure he is, " said the

				_UpperCamelCase       : Union[str, Any]    =      model.generate(_snake_case  ,					do_sample=_snake_case     )
				self.assertListEqual(output_ids[0].numpy().tolist()  ,					_snake_case     )







'''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


_UpperCAmelCase   :      Optional[int] 						=  logging.get_logger(__name__)

_UpperCAmelCase   :      Any 						=  {
    """google/bigbird-pegasus-large-arxiv""": (
        """https://huggingface.co/google/bigbird-pegasus-large-arxiv/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-pubmed""": (
        """https://huggingface.co/google/bigbird-pegasus-large-pubmed/resolve/main/config.json"""
    ),
    """google/bigbird-pegasus-large-bigpatent""": (
        """https://huggingface.co/google/bigbird-pegasus-large-bigpatent/resolve/main/config.json"""
    ),
    # See all BigBirdPegasus models at https://huggingface.co/models?filter=bigbird_pegasus
}

class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





		A__  :   List[str]          =    'bigbird_pegasus'
		A__  :   Union[str, Any]          =    ['past_key_values']
		A__  :   Optional[int]          =    {
		    'num_attention_heads': 'encoder_attention_heads',
		    'hidden_size': 'd_model',
		    'attention_probs_dropout_prob': 'attention_dropout',
		}
		def __init__(    self  ,					_snake_case=96103  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=16  ,					_snake_case=4096  ,					_snake_case=16  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=True  ,					_snake_case="gelu_new"  ,					_snake_case=1024  ,					_snake_case=0.1  ,					_snake_case=0.0  ,					_snake_case=0.0  ,					_snake_case=0.02  ,					_snake_case=2  ,					_snake_case=0.0  ,					_snake_case=True  ,					_snake_case=0  ,					_snake_case=2  ,					_snake_case=1  ,					_snake_case="block_sparse"  ,					_snake_case=64  ,					_snake_case=3  ,					_snake_case=False  ,					**_snake_case  ,					)				->    Optional[Any]:
				_UpperCamelCase       : str    =      vocab_size
				_UpperCamelCase       : Tuple    =      max_position_embeddings
				_UpperCamelCase       : int    =      d_model
				_UpperCamelCase       : List[str]    =      encoder_ffn_dim
				_UpperCamelCase       : str    =      encoder_layers
				_UpperCamelCase       : Dict    =      encoder_attention_heads
				_UpperCamelCase       : str    =      decoder_ffn_dim
				_UpperCamelCase       : List[str]    =      decoder_layers
				_UpperCamelCase       : Any    =      decoder_attention_heads
				_UpperCamelCase       : str    =      dropout
				_UpperCamelCase       : int    =      attention_dropout
				_UpperCamelCase       : Optional[int]    =      activation_dropout
				_UpperCamelCase       : Optional[Any]    =      activation_function
				_UpperCamelCase       : Tuple    =      init_std
				_UpperCamelCase       : Any    =      encoder_layerdrop
				_UpperCamelCase       : List[str]    =      decoder_layerdrop
				_UpperCamelCase       : Any    =      classifier_dropout
				_UpperCamelCase       : int    =      use_cache
				_UpperCamelCase       : Union[str, Any]    =      encoder_layers
				_UpperCamelCase       : str    =      scale_embedding  # scale factor will be sqrt(d_model) if True

				# extra config
				_UpperCamelCase       : str    =      attention_type
				_UpperCamelCase       : Tuple    =      block_size
				_UpperCamelCase       : Optional[Any]    =      num_random_blocks
				_UpperCamelCase       : Dict    =      use_bias

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







class 			UpperCAmelCase				(					a_ ):





		"""simple docstring"""





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

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

						if self.use_past:
								self.fill_with_past_key_values_(_snake_case  ,					direction='''inputs'''     )
				elif self.task == "causal-lm":
						# TODO: figure this case out.
						_UpperCamelCase       : Optional[int]    =      OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]     )
						if self.use_past:
								_UpperCamelCase,							_UpperCamelCase       : List[str]    =      self.num_layers
								for i in range(_snake_case     ):
										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
										_UpperCamelCase       : Dict    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
						_UpperCamelCase       : Optional[int]    =      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 _lowercase  (    self     )				->    Mapping[str, Mapping[int, str]]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : int    =      super().outputs
				else:
						_UpperCamelCase       : Optional[int]    =      super(_snake_case  ,					self     ).outputs
						if self.use_past:
								_UpperCamelCase,							_UpperCamelCase       : Tuple    =      self.num_layers
								for i in range(_snake_case     ):
										_UpperCamelCase       : int    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
										_UpperCamelCase       : Optional[int]    =      {0: '''batch''', 2: '''past_sequence + sequence'''}
				return common_outputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				_UpperCamelCase       : List[str]    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )

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

				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       : Dict    =      common_inputs['''input_ids'''].shape
						_UpperCamelCase       : Dict    =      common_inputs['''decoder_input_ids'''].shape[1]
						_UpperCamelCase,							_UpperCamelCase       : List[Any]    =      self.num_attention_heads
						_UpperCamelCase       : List[Any]    =      (
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						_UpperCamelCase       : int    =      decoder_seq_length + 3
						_UpperCamelCase       : Union[str, Any]    =      (
						    batch,
						    num_decoder_attention_heads,
						    decoder_past_length,
						    self._config.hidden_size // num_decoder_attention_heads,
						)

						_UpperCamelCase       : Any    =      torch.cat(
						    [common_inputs['''decoder_attention_mask'''], torch.ones(_snake_case  ,					_snake_case     )]  ,					dim=1     )

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

						for _ in range(_snake_case     ):
								common_inputs["past_key_values"].append(
								    (
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								        torch.zeros(_snake_case     ),
								    )     )
						# TODO: test this.
						_UpperCamelCase       : Optional[Any]    =      encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
						for _ in range(_snake_case  ,					_snake_case     ):
								common_inputs["past_key_values"].append((torch.zeros(_snake_case     ), torch.zeros(_snake_case     ))     )
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				_UpperCamelCase       : Dict    =      self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
				    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )

				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       : Union[str, Any]    =      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       : str    =      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       : Optional[int]    =      common_inputs['''attention_mask'''].dtype
						_UpperCamelCase       : Union[str, Any]    =      torch.cat(
						    [common_inputs['''attention_mask'''], torch.ones(_snake_case  ,					_snake_case  ,					dtype=_snake_case     )]  ,					dim=1     )
						_UpperCamelCase       : int    =      [
						    (torch.zeros(_snake_case     ), torch.zeros(_snake_case     )) for _ in range(_snake_case     )
						]
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = 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
				_UpperCamelCase       : Union[str, Any]    =      compute_effective_axis_dimension(
				    _snake_case  ,					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       : List[Any]    =      tokenizer.num_special_tokens_to_add(_snake_case     )
				_UpperCamelCase       : List[str]    =      compute_effective_axis_dimension(
				    _snake_case  ,					fixed_dimension=OnnxConfig.default_fixed_sequence  ,					num_token_to_add=_snake_case     )

				# Generate dummy inputs according to compute batch and sequence
				_UpperCamelCase       : Optional[Any]    =      [''' '''.join([tokenizer.unk_token]     ) * seq_length] * batch_size
				_UpperCamelCase       : Any    =      dict(tokenizer(_snake_case  ,					return_tensors=_snake_case     )     )
				return common_inputs
		def _lowercase  (    self  ,					_snake_case  ,					_snake_case = -1  ,					_snake_case = -1  ,					_snake_case = False  ,					_snake_case = None  ,					)				->    Mapping[str, Any]:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : List[Any]    =      self._generate_dummy_inputs_for_default_and_seqaseq_lm(
						    _snake_case  ,					batch_size=_snake_case  ,					seq_length=_snake_case  ,					is_pair=_snake_case  ,					framework=_snake_case     )

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

				return common_inputs




		def _lowercase  (    self  ,					_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )				->    Tuple:
				if self.task in ["default", "seq2seq-lm"]:
						_UpperCamelCase       : List[Any]    =      super()._flatten_past_key_values_(_snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )
				else:
						_UpperCamelCase       : int    =      super(_snake_case  ,					self     )._flatten_past_key_values_(
						    _snake_case  ,					_snake_case  ,					_snake_case  ,					_snake_case     )





