


import argparse
from pathlib import Path

import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version

from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('0.12.2'):
 raise Exception('requires fairseq >= 0.12.2')
if version.parse(fairseq.__version__) > version.parse('2'):
 raise Exception('requires fairseq < v2')

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

A							:					Any     						=     'Hello, World!'
A							:					Dict     						=     'en_XX'





def 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Tuple:


      '''simple docstring'''






      __snake_case				=		Path("data_bin" )
      __snake_case				=		FairseqXmodModel.from_pretrained(
          model_name_or_path=str(Path(snake_case__ ).parent )      ,	checkpoint_file=Path(snake_case__ ).name      ,	_name="xmod_base"      ,	arch="xmod_base"      ,	task="multilingual_masked_lm"      ,	data_name_or_path=str(snake_case__ )      ,	bpe="sentencepiece"      ,	sentencepiece_model=str(Path(snake_case__ ).parent / "sentencepiece.bpe.model" )      ,	src_dict=str(data_dir / "dict.txt" )      ,	)
      xmod.eval()  # disable dropout
      print(snake_case__ )

      __snake_case				=		xmod.model.encoder.sentence_encoder
      __snake_case				=		XmodConfig(
          vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings      ,	hidden_size=xmod.cfg.model.encoder_embed_dim      ,	num_hidden_layers=xmod.cfg.model.encoder_layers      ,	num_attention_heads=xmod.cfg.model.encoder_attention_heads      ,	intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim      ,	max_position_embeddings=514      ,	type_vocab_size=1      ,	layer_norm_eps=1E-5      ,	pre_norm=xmod.cfg.model.encoder_normalize_before      ,	adapter_reduction_factor=getattr(xmod.cfg.model      ,	"bottleneck"      ,	2 )      ,	adapter_layer_norm=xmod.cfg.model.adapter_layer_norm      ,	adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm      ,	ln_before_adapter=xmod.cfg.model.ln_before_adapter      ,	languages=xmod.cfg.model.languages      ,	)
      if classification_head:
            __snake_case				=		xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]

      print("Our X-MOD config:"      ,	snake_case__ )

      __snake_case				=		XmodForSequenceClassification(snake_case__ ) if classification_head else XmodForMaskedLM(snake_case__ )
      model.eval()

      # Now let's copy all the weights.
      # Embeddings
      __snake_case				=		xmod_sent_encoder.embed_tokens.weight
      __snake_case				=		xmod_sent_encoder.embed_positions.weight
      __snake_case				=		torch.zeros_like(
          model.roberta.embeddings.token_type_embeddings.weight )  # just zero them out b/c xmod doesn't use them.

      __snake_case				=		xmod_sent_encoder.layernorm_embedding.weight
      __snake_case				=		xmod_sent_encoder.layernorm_embedding.bias

      for i in range(config.num_hidden_layers ):
            # Encoder: start of layer
            __snake_case				=		model.roberta.encoder.layer[i]
            __snake_case				=		xmod_sent_encoder.layers[i]

            # self attention
            __snake_case				=		layer.attention.self
            if not (
                xmod_layer.self_attn.k_proj.weight.data.shape
                == xmod_layer.self_attn.q_proj.weight.data.shape
                == xmod_layer.self_attn.v_proj.weight.data.shape
                == torch.Size((config.hidden_size, config.hidden_size) )
            ):
                  raise AssertionError("Dimensions of self-attention weights do not match." )

            __snake_case				=		xmod_layer.self_attn.q_proj.weight
            __snake_case				=		xmod_layer.self_attn.q_proj.bias
            __snake_case				=		xmod_layer.self_attn.k_proj.weight
            __snake_case				=		xmod_layer.self_attn.k_proj.bias
            __snake_case				=		xmod_layer.self_attn.v_proj.weight
            __snake_case				=		xmod_layer.self_attn.v_proj.bias

            # self-attention output
            __snake_case				=		layer.attention.output
            if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
                  raise AssertionError("Dimensions of self-attention output weights do not match." )
            __snake_case				=		xmod_layer.self_attn.out_proj.weight
            __snake_case				=		xmod_layer.self_attn.out_proj.bias
            __snake_case				=		xmod_layer.self_attn_layer_norm.weight
            __snake_case				=		xmod_layer.self_attn_layer_norm.bias

            # intermediate
            __snake_case				=		layer.intermediate
            if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("Dimensions of intermediate weights do not match." )
            __snake_case				=		xmod_layer.fca.weight
            __snake_case				=		xmod_layer.fca.bias

            # output
            __snake_case				=		layer.output
            if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
                  raise AssertionError("Dimensions of feed-forward weights do not match." )
            __snake_case				=		xmod_layer.fca.weight
            __snake_case				=		xmod_layer.fca.bias
            __snake_case				=		xmod_layer.final_layer_norm.weight
            __snake_case				=		xmod_layer.final_layer_norm.bias
            if bert_output.adapter_layer_norm is not None:
                  __snake_case				=		xmod_layer.adapter_layer_norm.weight
                  __snake_case				=		xmod_layer.adapter_layer_norm.bias

            if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
                  raise AssertionError("Lists of language adapters do not match." )
            for lang_code, adapter in xmod_layer.adapter_modules.items():
                  __snake_case				=		bert_output.adapter_modules[lang_code]
                  __snake_case				=		xmod_layer.adapter_modules[lang_code]
                  __snake_case				=		from_adapter.fca.weight
                  __snake_case				=		from_adapter.fca.bias
                  __snake_case				=		from_adapter.fca.weight
                  __snake_case				=		from_adapter.fca.bias

            # end of layer

      if xmod_sent_encoder.layer_norm is not None:
            __snake_case				=		xmod_sent_encoder.layer_norm.weight
            __snake_case				=		xmod_sent_encoder.layer_norm.bias

      if classification_head:
            __snake_case				=		xmod.model.classification_heads["mnli"].dense.weight
            __snake_case				=		xmod.model.classification_heads["mnli"].dense.bias
            __snake_case				=		xmod.model.classification_heads["mnli"].out_proj.weight
            __snake_case				=		xmod.model.classification_heads["mnli"].out_proj.bias
      else:
            # LM Head
            __snake_case				=		xmod.model.encoder.lm_head.dense.weight
            __snake_case				=		xmod.model.encoder.lm_head.dense.bias
            __snake_case				=		xmod.model.encoder.lm_head.layer_norm.weight
            __snake_case				=		xmod.model.encoder.lm_head.layer_norm.bias
            __snake_case				=		xmod.model.encoder.lm_head.weight
            __snake_case				=		xmod.model.encoder.lm_head.bias

      # Let's check that we get the same results.
      __snake_case				=		xmod.encode(snake_case__ ).unsqueeze(0 )  # batch of size 1
      model.roberta.set_default_language(snake_case__ )

      __snake_case				=		model(snake_case__ )[0]
      if classification_head:
            __snake_case				=		xmod.model.classification_heads["mnli"](xmod.extract_features(snake_case__ ) )
      else:
            __snake_case				=		xmod.model(snake_case__      ,	lang_id=[SAMPLE_LANGUAGE] )[0]
      print(our_output.shape      ,	their_output.shape )
      __snake_case				=		torch.max(torch.abs(our_output - their_output ) ).item()
      print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
      __snake_case				=		torch.allclose(snake_case__      ,	snake_case__      ,	atol=1E-3 )
      print("Do both models output the same tensors?"      ,	"🔥" if success else "💩" )
      if not success:
            raise Exception("Something went wRoNg" )

      Path(snake_case__ ).mkdir(parents=snake_case__      ,	exist_ok=snake_case__ )
      print(F'''Saving model to {pytorch_dump_folder_path}''' )
      model.save_pretrained(snake_case__ )


if __name__ == "__main__":
 A							:					int     						=     argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--xmod_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.'
 )
 A							:					List[str]     						=     parser.parse_args()
 convert_xmod_checkpoint_to_pytorch(
     args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
 )









from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


A							:					Tuple     						=     logging.get_logger(__name__)

A							:					int     						=     {
    'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json',
    # See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				str										=	"""blenderbot-small"""
					snake_case_		:				List[Any]										=	["""past_key_values"""]
					snake_case_		:				Any										=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}


					def __init__(						self	:		Optional[Any]     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=5_0_2_6_5     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=8     ,    SCREAMING_SNAKE_CASE	:		int=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Any=1_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=8     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_0_4_8     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		str=0.0     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=True     ,    SCREAMING_SNAKE_CASE	:		int=True     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]="gelu"     ,    SCREAMING_SNAKE_CASE	:		Dict=5_1_2     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Dict=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.0     ,    SCREAMING_SNAKE_CASE	:		int=0.02     ,    SCREAMING_SNAKE_CASE	:		List[Any]=1     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Union[str, Any]=0     ,    SCREAMING_SNAKE_CASE	:		str=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2     ,    SCREAMING_SNAKE_CASE	:		Any=2     ,    **SCREAMING_SNAKE_CASE	:		Dict     ,    )			->	Optional[int]:




											'''simple docstring'''

											__snake_case				=		vocab_size
											__snake_case				=		max_position_embeddings
											__snake_case				=		d_model
											__snake_case				=		encoder_ffn_dim
											__snake_case				=		encoder_layers
											__snake_case				=		encoder_attention_heads
											__snake_case				=		decoder_ffn_dim
											__snake_case				=		decoder_layers
											__snake_case				=		decoder_attention_heads
											__snake_case				=		dropout
											__snake_case				=		attention_dropout
											__snake_case				=		activation_dropout
											__snake_case				=		activation_function
											__snake_case				=		init_std
											__snake_case				=		encoder_layerdrop
											__snake_case				=		decoder_layerdrop
											__snake_case				=		use_cache
											__snake_case				=		encoder_layers
											__snake_case				=		scale_embedding  # scale factor will be sqrt(d_model) if True

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






class     UpperCamelCase(  _a   ):


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




											'''simple docstring'''

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

																	if self.use_past:
																							__snake_case				=		{0: "batch"}
																							__snake_case				=		{0: "batch", 1: "past_decoder_sequence + sequence"}
																	else:
																							__snake_case				=		{0: "batch", 1: "decoder_sequence"}
																							__snake_case				=		{0: "batch", 1: "decoder_sequence"}

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




											'''simple docstring'''

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)

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

																	__snake_case				=		torch.cat(
																	    [common_inputs["decoder_attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)]     ,    dim=1						)

																	__snake_case				=		[]
																	# If the number of encoder and decoder layers are present in the model configuration, both are considered
																	__snake_case		,					__snake_case				=		self.num_layers
																	__snake_case				=		min(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						)
																	__snake_case				=		max(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE						) - min_num_layers
																	__snake_case				=		"encoder" if num_encoder_layers > num_decoder_layers else "decoder"

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

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

																	__snake_case				=		common_inputs["attention_mask"].dtype
																	__snake_case				=		torch.cat(
																	    [common_inputs["attention_mask"], torch.ones(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE     ,    dtype=SCREAMING_SNAKE_CASE						)]     ,    dim=1						)
																	__snake_case				=		[
																	    (torch.zeros(SCREAMING_SNAKE_CASE						), torch.zeros(SCREAMING_SNAKE_CASE						)) for _ in range(SCREAMING_SNAKE_CASE						)
																	]
											return common_inputs


					def 							SCREAMING_SNAKE_CASE_  (						self	:		Dict     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

											__snake_case				=		compute_effective_axis_dimension(
											    SCREAMING_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
											__snake_case				=		tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE						)
											__snake_case				=		compute_effective_axis_dimension(
											    SCREAMING_SNAKE_CASE     ,    fixed_dimension=OnnxConfig.default_fixed_sequence     ,    num_token_to_add=SCREAMING_SNAKE_CASE						)

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


					def 							SCREAMING_SNAKE_CASE_  (						self	:		str     ,    SCREAMING_SNAKE_CASE	:		PreTrainedTokenizer     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		int = -1     ,    SCREAMING_SNAKE_CASE	:		bool = False     ,    SCREAMING_SNAKE_CASE	:		Optional[TensorType] = None     ,    )			->	Mapping[str, Any]:




											'''simple docstring'''

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

											elif self.task == "causal-lm":
																	__snake_case				=		self._generate_dummy_inputs_for_causal_lm(
																	    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)
											else:
																	__snake_case				=		self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
																	    SCREAMING_SNAKE_CASE     ,    batch_size=SCREAMING_SNAKE_CASE     ,    seq_length=SCREAMING_SNAKE_CASE     ,    is_pair=SCREAMING_SNAKE_CASE     ,    framework=SCREAMING_SNAKE_CASE						)

											return common_inputs






					def 							SCREAMING_SNAKE_CASE_  (						self	:		int     ,    SCREAMING_SNAKE_CASE	:		Any     ,    SCREAMING_SNAKE_CASE	:		int     ,    SCREAMING_SNAKE_CASE	:		List[Any]     ,    SCREAMING_SNAKE_CASE	:		int						)			->	Union[str, Any]:




											'''simple docstring'''

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





