import unittest

from transformers.testing_utils import is_torch_available, require_torch, tooslow

from ...generation.test_utils import torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch

				from transformers import (
				    CpmAntConfig,
				    CpmAntForCausalLM,
				    CpmAntModel,
				    CpmAntTokenizer,
				)





@require_torch
class 					__snake_case    :




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=2       ,			__UpperCamelCase=8       ,			__UpperCamelCase=True       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			__UpperCamelCase=False       ,			__UpperCamelCase=99       ,			__UpperCamelCase=32       ,			__UpperCamelCase=3       ,			__UpperCamelCase=4       ,			__UpperCamelCase=37       ,			__UpperCamelCase=32       ,			__UpperCamelCase=128       ,			__UpperCamelCase=8       ,			__UpperCamelCase=8       ,			__UpperCamelCase=8       ,			__UpperCamelCase=1.0       ,			__UpperCamelCase=True       ,			)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= parent
									snake_case__   :      List[str]   				= batch_size
									snake_case__   :      Optional[Any]   				= seq_length
									snake_case__   :      Dict   				= is_training
									snake_case__   :      Any   				= use_token_type_ids
									snake_case__   :      str   				= use_input_mask
									snake_case__   :      List[Any]   				= use_labels
									snake_case__   :      List[str]   				= use_mc_token_ids
									snake_case__   :      Any   				= vocab_size
									snake_case__   :      Optional[Any]   				= hidden_size
									snake_case__   :      Dict   				= num_hidden_layers
									snake_case__   :      List[Any]   				= num_attention_heads
									snake_case__   :      Union[str, Any]   				= intermediate_size
									snake_case__   :      Tuple   				= num_buckets
									snake_case__   :      List[Any]   				= max_distance
									snake_case__   :      List[str]   				= prompt_length
									snake_case__   :      List[str]   				= prompt_types
									snake_case__   :      Tuple   				= segment_types
									snake_case__   :      Optional[int]   				= init_std
									snake_case__   :      List[str]   				= return_dict




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= {}
									snake_case__   :      Dict   				= ids_tensor([self.batch_size, self.seq_length]       ,			self.vocab_size						).type(torch.intaa						)
									snake_case__   :      Tuple   				= False

									snake_case__   :      Any   				= self.get_config()

									return (config, input_ids)




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									return CpmAntConfig(
									    vocab_size=self.vocab_size       ,			hidden_size=self.hidden_size       ,			num_hidden_layers=self.num_hidden_layers       ,			num_attention_heads=self.num_attention_heads       ,			dim_ff=self.intermediate_size       ,			position_bias_num_buckets=self.num_buckets       ,			position_bias_max_distance=self.max_distance       ,			prompt_types=self.prompt_types       ,			prompt_length=self.prompt_length       ,			segment_types=self.segment_types       ,			use_cache=__UpperCamelCase       ,			init_std=self.init_std       ,			return_dict=self.return_dict       ,			)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			*__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      int   				= CpmAntModel(config=__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									model.eval()

									snake_case__   :      List[Any]   				= model(**__UpperCamelCase						).last_hidden_state

									self.parent.assertEqual(hidden_states.shape       ,			(self.batch_size, self.seq_length, config.hidden_size)						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			*__UpperCamelCase						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      int   				= CpmAntForCausalLM(__UpperCamelCase						)
									model.to(__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= input_ids['input_ids'].to(__UpperCamelCase						)
									model.eval()

									snake_case__   :      str   				= model(**__UpperCamelCase						)
									self.parent.assertEqual(
									    model_output.logits.shape       ,			(self.batch_size, self.seq_length, config.vocab_size + config.prompt_types * config.prompt_length)       ,			)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      List[str]   				= self.prepare_config_and_inputs()
									return config, inputs_dict





@require_torch
class 					__snake_case    (     _lowerCamelCase       ,_lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  (CpmAntModel, CpmAntForCausalLM) if is_torch_available() else ()
					__lowerCamelCase			   =  (
					    {"""feature-extraction""": CpmAntModel, """text-generation""": CpmAntForCausalLM} if is_torch_available() else {}
					)

					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False
					__lowerCamelCase			   =  False




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      str   				= CpmAntModelTester(self						)
									snake_case__   :      Any   				= ConfigTester(self       ,			config_class=__UpperCamelCase						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									self.config_tester.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()




					def      __a     (  self						)	->    int:



									'''simple docstring'''







									unittest.skip('CPMAnt doesn\'t support input_embeds.'						)(self.test_inputs_embeds						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									unittest.skip(
									    'CPMAnt doesn\'t support retain grad in hidden_states or attentions, because prompt management will peel off the output.hidden_states from graph.\
                 So is attentions. We strongly recommand you use loss to tune model.'						)(self.test_retain_grad_hidden_states_attentions						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Union[str, Any]   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_cpmant_model(__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      str   				= self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_lm_head_model(__UpperCamelCase       ,			__UpperCamelCase						)





@require_torch
class 					__snake_case    (     unittest.TestCase      ):




					@tooslow
					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= '今天天气真好！'
									snake_case__   :      Dict   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      Any   				= CpmAntModel.from_pretrained(__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= CpmAntTokenizer.from_pretrained(__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= tokenizer(__UpperCamelCase       ,			return_tensors='pt'						)
									snake_case__   :      int   				= model(**__UpperCamelCase						).last_hidden_state

									snake_case__   :      List[Any]   				= torch.tensor(
									    [[[6.1_7_0_8, 5.9_2_4_4, 1.0_8_3_5], [6.5_2_0_7, 6.2_8_9_3, -1_1.3_3_2_4], [-1.0_1_0_7, -0.0_5_7_6, -5.9_5_7_7]]]       ,			)
									self.assertTrue(torch.allclose(hidden_states[:, :3, :3]       ,			__UpperCamelCase       ,			atol=1E-2						)						)





@require_torch
class 					__snake_case    (     unittest.TestCase      ):




					@tooslow
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= '今天天气真好！'
									snake_case__   :      str   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      str   				= CpmAntForCausalLM.from_pretrained(__UpperCamelCase						)
									snake_case__   :      str   				= CpmAntTokenizer.from_pretrained(__UpperCamelCase						)
									snake_case__   :      Dict   				= tokenizer(__UpperCamelCase       ,			return_tensors='pt'						)
									snake_case__   :      Tuple   				= model(**__UpperCamelCase						).logits

									snake_case__   :      Dict   				= torch.tensor(
									    [[[-6.4_2_6_7, -6.4_0_8_3, -6.3_9_5_8], [-5.8_8_0_2, -5.9_4_4_7, -5.7_8_1_1], [-5.3_8_9_6, -5.4_8_2_0, -5.4_2_9_5]]]       ,			)
									self.assertTrue(torch.allclose(hidden_states[:, :3, :3]       ,			__UpperCamelCase       ,			atol=1E-2						)						)




					@tooslow
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      Union[str, Any]   				= CpmAntForCausalLM.from_pretrained(__UpperCamelCase						)
									snake_case__   :      Any   				= CpmAntTokenizer.from_pretrained(__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= '今天天气不错，'
									snake_case__   :      Dict   				= '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的'
									snake_case__   :      int   				= tokenizer(__UpperCamelCase       ,			return_tensors='pt'						)
									snake_case__   :      Union[str, Any]   				= model.generate(**__UpperCamelCase						)
									snake_case__   :      Tuple   				= tokenizer.batch_decode(__UpperCamelCase						)
									self.assertEqual(__UpperCamelCase       ,			__UpperCamelCase						)




					@tooslow
					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Tuple   				= 'openbmb/cpm-ant-10b'
									snake_case__   :      int   				= CpmAntForCausalLM.from_pretrained(__UpperCamelCase						)
									snake_case__   :      Optional[Any]   				= CpmAntTokenizer.from_pretrained(__UpperCamelCase						)
									snake_case__   :      Any   				= ['今天天气不错，', '新年快乐，万事如意！']
									snake_case__   :      Any   				= [
									    '今天天气不错，阳光明媚，我和妈妈一起去超市买东西。\n在超市里，我看到了一个很好玩的玩具，它的名字叫“机器人”。它有一个圆圆的脑袋，两只圆圆的眼睛，还有一个圆圆的',
									    '新年快乐，万事如意！在这辞旧迎新的美好时刻，我谨代表《农村新技术》杂志社全体同仁，向一直以来关心、支持《农村新技术》杂志发展的各级领导、各界朋友和广大读者致以最诚挚的',
									]
									snake_case__   :      int   				= tokenizer(__UpperCamelCase       ,			return_tensors='pt'       ,			padding=__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= model.generate(**__UpperCamelCase						)
									snake_case__   :      int   				= tokenizer.batch_decode(__UpperCamelCase						)
									self.assertEqual(__UpperCamelCase       ,			__UpperCamelCase						)



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


lowerCAmelCase__	:  List[Any]						=  logging.get_logger(__name__)

lowerCAmelCase__	:  str						=  {
    '''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 					__snake_case    (     _lowerCamelCase      ):
					__lowerCamelCase			   =  """bigbird_pegasus"""
					__lowerCamelCase			   =  ["""past_key_values"""]
					__lowerCamelCase			   =  {
					    """num_attention_heads""": """encoder_attention_heads""",
					    """hidden_size""": """d_model""",
					    """attention_probs_dropout_prob""": """attention_dropout""",
					}




					def __init__(  self       ,			__UpperCamelCase=96103       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=16       ,			__UpperCamelCase=4096       ,			__UpperCamelCase=16       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=True       ,			__UpperCamelCase="gelu_new"       ,			__UpperCamelCase=1024       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=0.0_2       ,			__UpperCamelCase=2       ,			__UpperCamelCase=0.0       ,			__UpperCamelCase=True       ,			__UpperCamelCase=0       ,			__UpperCamelCase=2       ,			__UpperCamelCase=1       ,			__UpperCamelCase="block_sparse"       ,			__UpperCamelCase=64       ,			__UpperCamelCase=3       ,			__UpperCamelCase=False       ,			**__UpperCamelCase       ,			)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= vocab_size
									snake_case__   :      Optional[Any]   				= max_position_embeddings
									snake_case__   :      Tuple   				= d_model
									snake_case__   :      str   				= encoder_ffn_dim
									snake_case__   :      List[str]   				= encoder_layers
									snake_case__   :      Any   				= encoder_attention_heads
									snake_case__   :      Any   				= decoder_ffn_dim
									snake_case__   :      List[Any]   				= decoder_layers
									snake_case__   :      Dict   				= decoder_attention_heads
									snake_case__   :      List[Any]   				= dropout
									snake_case__   :      List[Any]   				= attention_dropout
									snake_case__   :      List[Any]   				= activation_dropout
									snake_case__   :      str   				= activation_function
									snake_case__   :      Optional[Any]   				= init_std
									snake_case__   :      Optional[int]   				= encoder_layerdrop
									snake_case__   :      List[Any]   				= decoder_layerdrop
									snake_case__   :      Tuple   				= classifier_dropout
									snake_case__   :      Optional[Any]   				= use_cache
									snake_case__   :      List[Any]   				= encoder_layers
									snake_case__   :      Union[str, Any]   				= scale_embedding  # scale factor will be sqrt(d_model) if True

									# extra config
									snake_case__   :      Optional[Any]   				= attention_type
									snake_case__   :      Optional[int]   				= block_size
									snake_case__   :      Optional[Any]   				= num_random_blocks
									snake_case__   :      List[str]   				= use_bias

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





class 					__snake_case    (     _lowerCamelCase      ):




					@property
					def      __a     (  self						)	->    Mapping[str, Mapping[int, str]]:



									'''simple docstring'''







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

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

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



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

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

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

													snake_case__   :      Union[str, Any]   				= torch.cat(
													    [common_inputs['decoder_attention_mask'], torch.ones(__UpperCamelCase       ,			__UpperCamelCase						)]       ,			dim=1						)

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

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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      List[str]   				= self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
									    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)

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

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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= compute_effective_axis_dimension(
									    __UpperCamelCase       ,			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
									snake_case__   :      str   				= tokenizer.num_special_tokens_to_add(__UpperCamelCase						)
									snake_case__   :      List[str]   				= compute_effective_axis_dimension(
									    __UpperCamelCase       ,			fixed_dimension=OnnxConfig.default_fixed_sequence       ,			num_token_to_add=__UpperCamelCase						)

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




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = -1       ,			__UpperCamelCase = False       ,			__UpperCamelCase = None       ,			)	->    Mapping[str, Any]:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Dict   				= self._generate_dummy_inputs_for_default_and_seqaseq_lm(
													    __UpperCamelCase       ,			batch_size=__UpperCamelCase       ,			seq_length=__UpperCamelCase       ,			is_pair=__UpperCamelCase       ,			framework=__UpperCamelCase						)

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

									return common_inputs




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									if self.task in ["default", "seq2seq-lm"]:
													snake_case__   :      Optional[int]   				= super()._flatten_past_key_values_(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									else:
													snake_case__   :      Optional[Any]   				= super(__UpperCamelCase       ,			self						)._flatten_past_key_values_(
													    __UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)


