





'''simple docstring'''




import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import RobertaConfig, RobertaForMaskedLM, RobertaForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
							raise Exception("""requires fairseq >= 0.9.0""")


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

__UpperCAmelCase         =   """Hello world! cécé herlolip"""



def 						__A						(       lowerCamelCase_	,	lowerCamelCase_	,	lowerCamelCase_ ):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE				:							Tuple						=	FairseqRobertaModel.from_pretrained(lowerCamelCase_ )
		roberta.eval()  # disable dropout
		SCREAMING_SNAKE_CASE				:							int						=	roberta.model.encoder.sentence_encoder
		SCREAMING_SNAKE_CASE				:							Dict						=	RobertaConfig(
		    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings	,	hidden_size=roberta.args.encoder_embed_dim	,	num_hidden_layers=roberta.args.encoder_layers	,	num_attention_heads=roberta.args.encoder_attention_heads	,	intermediate_size=roberta.args.encoder_ffn_embed_dim	,	max_position_embeddings=5_14	,	type_vocab_size=1	,	layer_norm_eps=1E-5	,	)
		if classification_head:
				SCREAMING_SNAKE_CASE				:							Dict						=	roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
		print("""Our BERT config:"""	,	lowerCamelCase_ )

		SCREAMING_SNAKE_CASE				:							Tuple						=	RobertaForSequenceClassification(lowerCamelCase_ ) if classification_head else RobertaForMaskedLM(lowerCamelCase_ )
		model.eval()

		# Now let's copy all the weights.
		# Embeddings
		SCREAMING_SNAKE_CASE				:							Any						=	roberta_sent_encoder.embed_tokens.weight
		SCREAMING_SNAKE_CASE				:							List[Any]						=	roberta_sent_encoder.embed_positions.weight
		SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	torch.zeros_like(
		    model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c RoBERTa doesn't use them.
		SCREAMING_SNAKE_CASE				:							Any						=	roberta_sent_encoder.emb_layer_norm.weight
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	roberta_sent_encoder.emb_layer_norm.bias

		for i in range(config.num_hidden_layers ):
				# Encoder: start of layer
				SCREAMING_SNAKE_CASE				:							BertLayer						=	model.roberta.encoder.layer[i]
				SCREAMING_SNAKE_CASE				:							TransformerSentenceEncoderLayer						=	roberta_sent_encoder.layers[i]

				# self attention
				SCREAMING_SNAKE_CASE				:							BertSelfAttention						=	layer.attention.self
				assert (
				    roberta_layer.self_attn.k_proj.weight.data.shape
				    == roberta_layer.self_attn.q_proj.weight.data.shape
				    == roberta_layer.self_attn.v_proj.weight.data.shape
				    == torch.Size((config.hidden_size, config.hidden_size) )
				)

				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.self_attn.q_proj.weight
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	roberta_layer.self_attn.q_proj.bias
				SCREAMING_SNAKE_CASE				:							Dict						=	roberta_layer.self_attn.k_proj.weight
				SCREAMING_SNAKE_CASE				:							int						=	roberta_layer.self_attn.k_proj.bias
				SCREAMING_SNAKE_CASE				:							List[Any]						=	roberta_layer.self_attn.v_proj.weight
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.self_attn.v_proj.bias

				# self-attention output
				SCREAMING_SNAKE_CASE				:							BertSelfOutput						=	layer.attention.output
				assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
				SCREAMING_SNAKE_CASE				:							List[str]						=	roberta_layer.self_attn.out_proj.weight
				SCREAMING_SNAKE_CASE				:							str						=	roberta_layer.self_attn.out_proj.bias
				SCREAMING_SNAKE_CASE				:							List[Any]						=	roberta_layer.self_attn_layer_norm.weight
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.self_attn_layer_norm.bias

				# intermediate
				SCREAMING_SNAKE_CASE				:							BertIntermediate						=	layer.intermediate
				assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
				SCREAMING_SNAKE_CASE				:							List[str]						=	roberta_layer.fca.weight
				SCREAMING_SNAKE_CASE				:							str						=	roberta_layer.fca.bias

				# output
				SCREAMING_SNAKE_CASE				:							BertOutput						=	layer.output
				assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
				SCREAMING_SNAKE_CASE				:							Optional[Any]						=	roberta_layer.fca.weight
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta_layer.fca.bias
				SCREAMING_SNAKE_CASE				:							List[str]						=	roberta_layer.final_layer_norm.weight
				SCREAMING_SNAKE_CASE				:							Tuple						=	roberta_layer.final_layer_norm.bias
				# end of layer

		if classification_head:
				SCREAMING_SNAKE_CASE				:							int						=	roberta.model.classification_heads["""mnli"""].dense.weight
				SCREAMING_SNAKE_CASE				:							str						=	roberta.model.classification_heads["""mnli"""].dense.bias
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta.model.classification_heads["""mnli"""].out_proj.weight
				SCREAMING_SNAKE_CASE				:							Dict						=	roberta.model.classification_heads["""mnli"""].out_proj.bias
		else:
				# LM Head
				SCREAMING_SNAKE_CASE				:							Any						=	roberta.model.encoder.lm_head.dense.weight
				SCREAMING_SNAKE_CASE				:							List[str]						=	roberta.model.encoder.lm_head.dense.bias
				SCREAMING_SNAKE_CASE				:							List[Any]						=	roberta.model.encoder.lm_head.layer_norm.weight
				SCREAMING_SNAKE_CASE				:							Tuple						=	roberta.model.encoder.lm_head.layer_norm.bias
				SCREAMING_SNAKE_CASE				:							Optional[int]						=	roberta.model.encoder.lm_head.weight
				SCREAMING_SNAKE_CASE				:							List[str]						=	roberta.model.encoder.lm_head.bias

		# Let's check that we get the same results.
		SCREAMING_SNAKE_CASE				:							torch.Tensor						=	roberta.encode(lowerCamelCase_ ).unsqueeze(0 )  # batch of size 1

		SCREAMING_SNAKE_CASE				:							Dict						=	model(lowerCamelCase_ )[0]
		if classification_head:
				SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase_ ) )
		else:
				SCREAMING_SNAKE_CASE				:							Tuple						=	roberta.model(lowerCamelCase_ )[0]
		print(our_output.shape	,	their_output.shape )
		SCREAMING_SNAKE_CASE				:							Optional[Any]						=	torch.max(torch.abs(our_output - their_output ) ).item()
		print(f'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
		SCREAMING_SNAKE_CASE				:							Optional[int]						=	torch.allclose(lowerCamelCase_	,	lowerCamelCase_	,	atol=1E-3 )
		print("""Do both models output the same tensors?"""	,	"""🔥""" if success else """💩""" )
		if not success:
				raise Exception("""Something went wRoNg""" )

		pathlib.Path(lowerCamelCase_ ).mkdir(parents=lowerCamelCase_	,	exist_ok=lowerCamelCase_ )
		print(f'''Saving model to {pytorch_dump_folder_path}''' )
		model.save_pretrained(lowerCamelCase_ )


if __name__ == "__main__":
							__UpperCAmelCase         =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
							)
							parser.add_argument(
							    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
							)
							parser.add_argument(
							    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
							)
							__UpperCAmelCase         =   parser.parse_args()
							convert_roberta_checkpoint_to_pytorch(
							    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
							)










'''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         =   logging.get_logger(__name__)

__UpperCAmelCase         =   {
    """facebook/mbart-large-cc25""": """https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json""",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}




class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__						=					'''mbart'''
					SCREAMING_SNAKE_CASE__						=					['''past_key_values''']
					SCREAMING_SNAKE_CASE__						=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


					def __init__(		self							:	Union[str, Any]     ,		lowerCamelCase_							:	List[str]=5_02_65     ,		lowerCamelCase_							:	Tuple=10_24     ,		lowerCamelCase_							:	Union[str, Any]=12     ,		lowerCamelCase_							:	int=40_96     ,		lowerCamelCase_							:	Dict=16     ,		lowerCamelCase_							:	Any=12     ,		lowerCamelCase_							:	Any=40_96     ,		lowerCamelCase_							:	Tuple=16     ,		lowerCamelCase_							:	List[Any]=0.0     ,		lowerCamelCase_							:	Any=0.0     ,		lowerCamelCase_							:	Any=True     ,		lowerCamelCase_							:	Dict=True     ,		lowerCamelCase_							:	List[str]="gelu"     ,		lowerCamelCase_							:	List[str]=10_24     ,		lowerCamelCase_							:	int=0.1     ,		lowerCamelCase_							:	List[Any]=0.0     ,		lowerCamelCase_							:	Tuple=0.0     ,		lowerCamelCase_							:	Optional[int]=0.02     ,		lowerCamelCase_							:	Any=0.0     ,		lowerCamelCase_							:	Tuple=False     ,		lowerCamelCase_							:	Tuple=1     ,		lowerCamelCase_							:	Tuple=0     ,		lowerCamelCase_							:	List[str]=2     ,		lowerCamelCase_							:	Optional[int]=2     ,		**lowerCamelCase_							:	int     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	vocab_size
							SCREAMING_SNAKE_CASE				:							Any						=	max_position_embeddings
							SCREAMING_SNAKE_CASE				:							Optional[int]						=	d_model
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	encoder_ffn_dim
							SCREAMING_SNAKE_CASE				:							Tuple						=	encoder_layers
							SCREAMING_SNAKE_CASE				:							Dict						=	encoder_attention_heads
							SCREAMING_SNAKE_CASE				:							str						=	decoder_ffn_dim
							SCREAMING_SNAKE_CASE				:							List[Any]						=	decoder_layers
							SCREAMING_SNAKE_CASE				:							List[str]						=	decoder_attention_heads
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	dropout
							SCREAMING_SNAKE_CASE				:							Dict						=	attention_dropout
							SCREAMING_SNAKE_CASE				:							Any						=	activation_dropout
							SCREAMING_SNAKE_CASE				:							Dict						=	activation_function
							SCREAMING_SNAKE_CASE				:							str						=	init_std
							SCREAMING_SNAKE_CASE				:							Tuple						=	encoder_layerdrop
							SCREAMING_SNAKE_CASE				:							Tuple						=	decoder_layerdrop
							SCREAMING_SNAKE_CASE				:							List[str]						=	classifier_dropout
							SCREAMING_SNAKE_CASE				:							int						=	use_cache
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	encoder_layers
							SCREAMING_SNAKE_CASE				:							int						=	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_     ,		forced_eos_token_id=lowerCamelCase_     ,		**lowerCamelCase_     ,		)







class       UpperCamelCase__			(			lowercase_	):




					"""simple docstring"""


					@property
					def lowerCamelCase_	(		self							:	Optional[int]		):

							'''simple docstring'''



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

									if self.use_past:
											SCREAMING_SNAKE_CASE				:							str						=	{0: """batch"""}
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{0: """batch""", 1: """past_decoder_sequence + sequence"""}
									else:
											SCREAMING_SNAKE_CASE				:							Dict						=	{0: """batch""", 1: """decoder_sequence"""}
											SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{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.
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	OrderedDict(
									    [
									        ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
									        ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
									    ]		)
									if self.use_past:
											SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[Any]						=	self.num_layers
											for i in range(lowerCamelCase_		):
													SCREAMING_SNAKE_CASE				:							Any						=	{0: """batch""", 2: """past_sequence + sequence"""}
													SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	{0: """batch""", 2: """past_sequence + sequence"""}
							else:
									SCREAMING_SNAKE_CASE				:							List[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 lowerCamelCase_	(		self							:	List[str]		):

							'''simple docstring'''



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


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Dict						=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)

							# Generate decoder inputs
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	seq_length if not self.use_past else 1
							SCREAMING_SNAKE_CASE				:							List[str]						=	self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							Any						=	{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
							SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	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
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Dict						=	common_inputs["""input_ids"""].shape
									SCREAMING_SNAKE_CASE				:							Dict						=	common_inputs["""decoder_input_ids"""].shape[1]
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	self.num_attention_heads
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	(
									    batch,
									    num_encoder_attention_heads,
									    encoder_seq_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)
									SCREAMING_SNAKE_CASE				:							Any						=	decoder_seq_length + 3
									SCREAMING_SNAKE_CASE				:							Dict						=	(
									    batch,
									    num_decoder_attention_heads,
									    decoder_past_length,
									    self._config.hidden_size // num_decoder_attention_heads,
									)

									SCREAMING_SNAKE_CASE				:							Union[str, Any]						=	torch.cat(
									    [common_inputs["""decoder_attention_mask"""], torch.ones(lowerCamelCase_     ,		lowerCamelCase_		)]     ,		dim=1		)

									SCREAMING_SNAKE_CASE				:							List[str]						=	[]
									# If the number of encoder and decoder layers are present in the model configuration, both are considered
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							int						=	self.num_layers
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	min(lowerCamelCase_     ,		lowerCamelCase_		)
									SCREAMING_SNAKE_CASE				:							str						=	max(lowerCamelCase_     ,		lowerCamelCase_		) - min_num_layers
									SCREAMING_SNAKE_CASE				:							List[str]						=	"""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.
									SCREAMING_SNAKE_CASE				:							List[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 lowerCamelCase_	(		self							:	Optional[int]     ,		lowerCamelCase_							:	PreTrainedTokenizer     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	int = -1     ,		lowerCamelCase_							:	bool = False     ,		lowerCamelCase_							:	Optional[TensorType] = None     ,		):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							Optional[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
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[Any]						=	common_inputs["""input_ids"""].shape
									# Not using the same length for past_key_values
									SCREAMING_SNAKE_CASE				:							Optional[int]						=	seqlen + 2
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							Optional[int]						=	self.num_layers
									SCREAMING_SNAKE_CASE,    SCREAMING_SNAKE_CASE				:							str						=	self.num_attention_heads
									SCREAMING_SNAKE_CASE				:							int						=	(
									    batch,
									    num_encoder_attention_heads,
									    past_key_values_length,
									    self._config.hidden_size // num_encoder_attention_heads,
									)

									SCREAMING_SNAKE_CASE				:							Dict						=	common_inputs["""attention_mask"""].dtype
									SCREAMING_SNAKE_CASE				:							Dict						=	torch.cat(
									    [common_inputs["""attention_mask"""], torch.ones(lowerCamelCase_     ,		lowerCamelCase_     ,		dtype=lowerCamelCase_		)]     ,		dim=1		)
									SCREAMING_SNAKE_CASE				:							Optional[Any]						=	[
									    (torch.zeros(lowerCamelCase_		), torch.zeros(lowerCamelCase_		)) for _ in range(lowerCamelCase_		)
									]
							return common_inputs


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

							'''simple docstring'''



							SCREAMING_SNAKE_CASE				:							List[Any]						=	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
							SCREAMING_SNAKE_CASE				:							int						=	tokenizer.num_special_tokens_to_add(lowerCamelCase_		)
							SCREAMING_SNAKE_CASE				:							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
							SCREAMING_SNAKE_CASE				:							Any						=	[""" """.join([tokenizer.unk_token]		) * seq_length] * batch_size
							SCREAMING_SNAKE_CASE				:							Optional[Any]						=	dict(tokenizer(lowerCamelCase_     ,		return_tensors=lowerCamelCase_		)		)
							return common_inputs


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

							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE				:							List[str]						=	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":
									SCREAMING_SNAKE_CASE				:							str						=	self._generate_dummy_inputs_for_causal_lm(
									    lowerCamelCase_     ,		batch_size=lowerCamelCase_     ,		seq_length=lowerCamelCase_     ,		is_pair=lowerCamelCase_     ,		framework=lowerCamelCase_		)
							else:
									SCREAMING_SNAKE_CASE				:							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 lowerCamelCase_	(		self							:	Union[str, Any]     ,		lowerCamelCase_							:	Union[str, Any]     ,		lowerCamelCase_							:	Tuple     ,		lowerCamelCase_							:	str     ,		lowerCamelCase_							:	List[Any]		):

							'''simple docstring'''



							if self.task in ["default", "seq2seq-lm"]:
									SCREAMING_SNAKE_CASE				:							Tuple						=	super()._flatten_past_key_values_(lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)
							else:
									SCREAMING_SNAKE_CASE				:							List[Any]						=	super(lowerCamelCase_     ,		self		)._flatten_past_key_values_(
									    lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_     ,		lowerCamelCase_		)



