



import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


logging.set_verbosity_info()
__UpperCAmelCase    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__UpperCAmelCase    	=  [
    '''lm_head''',
]





def     __lowerCamelCase							(   __magic_name__						:	Any  ,			__magic_name__						:	List[str]  ,			__magic_name__						:	int  ,			__magic_name__						:	Any  ,			__magic_name__						:	Union[str, Any]    ):
							for attribute in key.split("."    ):
														a__:							Union[str, Any]									=getattr(__magic_name__  ,			__magic_name__    )

							if weight_type is not None:
														a__:							Optional[Any]									=getattr(__magic_name__  ,			__magic_name__    ).shape
							else:
														a__:							str									=hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
														    F" {value.shape} for {full_name}"    )

							if weight_type == "weight":
														a__:							int									=value
							elif weight_type == "weight_g":
														a__:							Any									=value
							elif weight_type == "weight_v":
														a__:							Union[str, Any]									=value
							elif weight_type == "bias":
														a__:							List[str]									=value
							else:
														a__:							List[Any]									=value

							logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."    )





def     __lowerCamelCase							(   __magic_name__						:	Optional[int]  ,			__magic_name__						:	Tuple  ,			__magic_name__						:	Any    ):
							a__:							Optional[Any]									=[]
							a__:							int									=fairseq_model.state_dict()

							if not is_headless:
														a__:							Any									=hf_model.dataavec_audio.feature_extractor
														a__:							Union[str, Any]									=hf_model.dataavec_audio.encoder.pos_conv_embed

							else:
														a__:							Optional[int]									=hf_model.feature_extractor
														a__:							List[str]									=hf_model.encoder.pos_conv_embed

							for name, value in fairseq_dict.items():
														a__:							Optional[Any]									=False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __magic_name__  ,			__magic_name__  ,			__magic_name__  ,			__magic_name__  ,			)
																					a__:							List[str]									=True
														elif "pos_conv" in name:
																					load_pos_conv_layer(
																					    __magic_name__  ,			__magic_name__  ,			__magic_name__  ,			__magic_name__  ,			)
																					a__:							Optional[int]									=True
														else:
																					for key, mapped_key in MAPPING.items():
																												if not is_headless:
																																			a__:							Optional[Any]									="data2vec_audio." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split("w2v_model."    )[-1] == name.split("."    )[0]:
																																			a__:							List[str]									=True
																																			if "*" in mapped_key:
																																										a__:							Tuple									=name.split(__magic_name__    )[0].split("."    )[-2]
																																										a__:							Dict									=mapped_key.replace("*"  ,			__magic_name__    )
																																			if "weight_g" in name:
																																										a__:							str									="weight_g"
																																			elif "weight_v" in name:
																																										a__:							List[Any]									="weight_v"
																																			elif "bias" in name:
																																										a__:							Any									="bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										a__:							Union[str, Any]									="weight"
																																			else:
																																										a__:							Optional[Any]									=None
																																			set_recursively(__magic_name__  ,			__magic_name__  ,			__magic_name__  ,			__magic_name__  ,			__magic_name__    )
																												continue
														if not is_used:
																					unused_weights.append(__magic_name__    )

							logger.warning(F"Unused weights: {unused_weights}"    )





def     __lowerCamelCase							(   __magic_name__						:	Union[str, Any]  ,			__magic_name__						:	Tuple    ):
							a__:							int									=path.split("."    )
							return reduce(__magic_name__  ,			__magic_name__  ,			__magic_name__    )





def     __lowerCamelCase							(   __magic_name__						:	Optional[int]  ,			__magic_name__						:	List[Any]  ,			__magic_name__						:	List[Any]  ,			__magic_name__						:	Dict    ):
							a__:							str									=access_by_string(__magic_name__  ,			__magic_name__    )
							a__:							int									=hf_weight.data

							if fsq_value.shape != hf_value.shape:
														raise ValueError(F"{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."    )
							a__:							Any									=fsq_value
							logger.info(F"{full_name} was correctly initialized from {hf_weight_path}."    )





def     __lowerCamelCase							(   __magic_name__						:	int  ,			__magic_name__						:	Union[str, Any]  ,			__magic_name__						:	Union[str, Any]  ,			__magic_name__						:	Tuple    ):
							a__:							Optional[Any]									=full_name.split("conv_layers."    )[-1]
							a__:							Any									=name.split("."    )
							a__:							Tuple									=int(items[0]    )
							a__:							Optional[int]									=int(items[1]    )

							a__:							List[str]									=name.split("."    )[-1]
							if type_id == 0:
														a__:							Any									="conv"
							elif type_id == 2:
														a__:							Dict									="layer_norm"
							else:
														unused_weights.append(__magic_name__    )
														return

							set_weights(__magic_name__  ,			__magic_name__  ,			__magic_name__  ,			F"conv_layers.{layer_id}.{layer_type}.{weight_type}"    )





def     __lowerCamelCase							(   __magic_name__						:	List[str]  ,			__magic_name__						:	List[Any]  ,			__magic_name__						:	Any  ,			__magic_name__						:	Optional[int]    ):
							a__:							Any									=full_name.split("pos_conv."    )[-1]
							a__:							List[str]									=name.split("."    )
							a__:							Optional[Any]									=int(items[0]    )
							a__:							Any									=int(items[1]    )

							a__:							List[Any]									=name.split("."    )[-1]
							if type_id != 0:
														unused_weights.append(__magic_name__    )
														return
							else:
														a__:							Optional[int]									="conv"

							set_weights(__magic_name__  ,			__magic_name__  ,			__magic_name__  ,			F"layers.{layer_id}.{layer_type}.{weight_type}"    )





@torch.no_grad()
def     __lowerCamelCase							(   __magic_name__						:	Optional[int]  ,			__magic_name__						:	str  ,			__magic_name__						:	Tuple=None  ,			__magic_name__						:	Optional[Any]=None  ,			__magic_name__						:	Union[str, Any]=True    ):
							if config_path is not None:
														a__:							List[str]									=DataaVecAudioConfig.from_pretrained(__magic_name__    )
							else:
														a__:							str									=DataaVecAudioConfig()

							if not is_finetuned:
														# Modify final_proj layer name
														a__:							str									=DataaVecAudioModel(__magic_name__    )
														a__:							Union[str, Any]									=os.path.dirname(__magic_name__    )

														a__:							Optional[int]									=torch.load(__magic_name__    )
														a__:							List[str]									=state_dict["model"].pop("final_proj.0.weight"    )
														a__:							List[Any]									=state_dict["model"].pop("final_proj.0.bias"    )
														a__:							Optional[Any]									=os.path.join(__magic_name__  ,			"converted.pt"    )
														torch.save(__magic_name__  ,			__magic_name__    )
							else:
														a__:							List[Any]									=DataaVecAudioForCTC(__magic_name__    )
														a__:							Optional[Any]									=checkpoint_path

							def load_dataavec(__magic_name__						:	Optional[Any]    ):
														a__				,      a__				,      a__:							Optional[Any]									=fairseq.checkpoint_utils.load_model_ensemble_and_task([path]    )
														return model[0].eval()

							a__:							str									=load_dataavec(__magic_name__    )

							recursively_load_weights(__magic_name__  ,			__magic_name__  ,			not is_finetuned    )

							a__:							List[Any]									=WavaVecaProcessor.from_pretrained("facebook/wav2vec2-large-lv60"    )

							a__:							Optional[Any]									=load_dataset("patrickvonplaten/librispeech_asr_dummy"  ,			"clean"  ,			split="validation"    )
							a__:							Any									=[x["array"] for x in ds[:4]["audio"]]

							a__:							Dict									=processor(__magic_name__  ,			return_tensors="pt"  ,			padding=__magic_name__    )

							a__:							List[str]									=inputs.input_values
							a__:							List[Any]									=inputs.attention_mask
							#    input_values = inputs.input_values[:, :-1]
							#    attention_mask = inputs.attention_mask[:, :-1]

							hf_wavavec.eval()
							model.eval()
							if is_finetuned:
														a__:							int									=model(source=__magic_name__  ,			padding_mask=(1 - attention_mask)  ,			mask=__magic_name__  ,			features_only=__magic_name__    )[
														    "encoder_out"
														].transpose(0  ,			1    )
														a__:							Dict									=hf_wavavec(__magic_name__  ,			attention_mask=__magic_name__    )["logits"]

														a__:							List[str]									=torch.argmax(__magic_name__  ,			dim=-1    )
														a__:							Optional[int]									=processor.batch_decode(__magic_name__    )

														print(F"Expected Output: {ds[:4]['text']}, Pred: {output_string}"    )
							else:
														a__:							str									=model(source=__magic_name__  ,			padding_mask=(1 - attention_mask)  ,			mask=__magic_name__  ,			features_only=__magic_name__    )[
														    "layer_results"
														][-1][0].transpose(0  ,			1    )
														a__:							List[str]									=hf_wavavec(__magic_name__  ,			attention_mask=__magic_name__    )["last_hidden_state"]

							print(our_output.shape  ,			their_output.shape    )
							a__:							Union[str, Any]									=torch.max(torch.abs(our_output - their_output    )    ).item()
							print(F"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-7
							a__:							Any									=torch.allclose(__magic_name__  ,			__magic_name__  ,			atol=1e-3    )
							print("Do both models output the same tensors?"  ,			"🔥" if success else "💩"    )
							if not success:
														raise Exception("Something went wRoNg"    )

							hf_wavavec.save_pretrained(__magic_name__    )

							if is_finetuned:
														processor.save_pretrained(__magic_name__    )
							else:
														processor.feature_extractor.save_pretrained(__magic_name__    )


if __name__ == "__main__":
		__UpperCAmelCase    	=  argparse.ArgumentParser()
		parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
		parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
		parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
		parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
		parser.add_argument(
		    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
		)
		__UpperCAmelCase    	=  parser.parse_args()
		convert_wavaveca_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)










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    	=  logging.get_logger(__name__)

__UpperCAmelCase    	=  {
    '''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 						lowerCamelCase__			(		_a ):
						_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    :	int   ,							_a    :	List[Any]=9_6_1_0_3   ,							_a    :	Tuple=4_0_9_6   ,							_a    :	int=1_6   ,							_a    :	Dict=4_0_9_6   ,							_a    :	List[str]=1_6   ,							_a    :	Tuple=1_6   ,							_a    :	Union[str, Any]=4_0_9_6   ,							_a    :	int=1_6   ,							_a    :	Any=0.0   ,							_a    :	Union[str, Any]=0.0   ,							_a    :	Tuple=True   ,							_a    :	List[str]=True   ,							_a    :	Union[str, Any]="gelu_new"   ,							_a    :	Tuple=1_0_2_4   ,							_a    :	List[str]=0.1   ,							_a    :	Tuple=0.0   ,							_a    :	Any=0.0   ,							_a    :	int=0.0_2   ,							_a    :	str=2   ,							_a    :	Dict=0.0   ,							_a    :	Dict=True   ,							_a    :	List[str]=0   ,							_a    :	Dict=2   ,							_a    :	Any=1   ,							_a    :	Union[str, Any]="block_sparse"   ,							_a    :	Tuple=6_4   ,							_a    :	Tuple=3   ,							_a    :	List[str]=False   ,							**_a    :	Optional[Any]   ,							):
													a__:							Any									=vocab_size
													a__:							str									=max_position_embeddings
													a__:							str									=d_model
													a__:							int									=encoder_ffn_dim
													a__:							Optional[Any]									=encoder_layers
													a__:							Any									=encoder_attention_heads
													a__:							int									=decoder_ffn_dim
													a__:							Dict									=decoder_layers
													a__:							Dict									=decoder_attention_heads
													a__:							Union[str, Any]									=dropout
													a__:							List[Any]									=attention_dropout
													a__:							int									=activation_dropout
													a__:							Union[str, Any]									=activation_function
													a__:							Optional[int]									=init_std
													a__:							List[Any]									=encoder_layerdrop
													a__:							Dict									=decoder_layerdrop
													a__:							int									=classifier_dropout
													a__:							List[Any]									=use_cache
													a__:							int									=encoder_layers
													a__:							List[Any]									=scale_embedding  # scale factor will be sqrt(d_model) if True

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

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


class 						lowerCamelCase__			(		_a ):


						@property
						def 	_lowerCamelCase      (			self    :	int      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Optional[Any]									=OrderedDict(
																				    [
																				        ("input_ids", {0: "batch", 1: "encoder_sequence"}),
																				        ("attention_mask", {0: "batch", 1: "encoder_sequence"}),
																				    ]      )

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

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


						def 	_lowerCamelCase      (			self    :	Union[str, Any]   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													a__:							Union[str, Any]									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )

													# Generate decoder inputs
													a__:							str									=seq_length if not self.use_past else 1
													a__:							Dict									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )
													a__:							List[str]									={F"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
													a__:							str									=dict(**_a   ,							**_a      )

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

																				a__:							List[str]									=torch.cat(
																				    [common_inputs["decoder_attention_mask"], torch.ones(_a   ,							_a      )]   ,							dim=1      )

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

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


						def 	_lowerCamelCase      (			self    :	Any   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													a__:							Tuple									=self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
													    _a   ,							_a   ,							_a   ,							_a   ,							_a      )

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

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


						def 	_lowerCamelCase      (			self    :	Tuple   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													# Copied from OnnxConfig.generate_dummy_inputs
													# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
													# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
													a__:							List[str]									=compute_effective_axis_dimension(
													    _a   ,							fixed_dimension=OnnxConfig.default_fixed_batch   ,							num_token_to_add=0      )

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

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


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	PreTrainedTokenizer   ,							_a    :	int = -1   ,							_a    :	int = -1   ,							_a    :	bool = False   ,							_a    :	Optional[TensorType] = None   ,							):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Any									=self._generate_dummy_inputs_for_default_and_seqaseq_lm(
																				    _a   ,							batch_size=_a   ,							seq_length=_a   ,							is_pair=_a   ,							framework=_a      )

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

													return common_inputs







						def 	_lowerCamelCase      (			self    :	List[str]   ,							_a    :	int   ,							_a    :	str   ,							_a    :	Any   ,							_a    :	str      ):
													if self.task in ["default", "seq2seq-lm"]:
																				a__:							Tuple									=super()._flatten_past_key_values_(_a   ,							_a   ,							_a   ,							_a      )
													else:
																				a__:							Tuple									=super(_a   ,							self      )._flatten_past_key_values_(
																				    _a   ,							_a   ,							_a   ,							_a      )





