
'''simple docstring'''
import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

lowercase__						       =      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "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.grep_linear": "encoder.layers.*.attention.gru_rel_pos_linear",
    "self_attn.relative_attention_bias": "encoder.layers.*.attention.rel_attn_embed",
    "self_attn.grep_a": "encoder.layers.*.attention.gru_rel_pos_const",
    "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",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "ctc_proj",
    "mask_emb": "masked_spec_embed",
}
lowercase__						       =      [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Optional[int]:


		'''simple docstring'''


		for attribute in key.split('''.'''			):
				snake_case  :       Optional[int]     			=						getattr(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

		if weight_type is not None:
				snake_case  :       List[str]     			=						getattr(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			).shape
		else:
				snake_case  :       int     			=						hf_pointer.shape

		assert hf_shape == value.shape, (
		    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":
				snake_case  :       Dict     			=						value
		elif weight_type == "weight_g":
				snake_case  :       List[str]     			=						value
		elif weight_type == "weight_v":
				snake_case  :       int     			=						value
		elif weight_type == "bias":
				snake_case  :       str     			=						value
		else:
				snake_case  :       List[Any]     			=						value

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Optional[Any]:


		'''simple docstring'''


		snake_case  :       Any     			=						[]
		snake_case  :       List[str]     			=						fairseq_model.state_dict()

		snake_case  :       List[str]     			=						hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				snake_case  :       Dict     			=						False
				if "conv_layers" in name:
						load_conv_layer(
						    SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       hf_model.config.feat_extract_norm == '''group''' ,       )
						snake_case  :       List[Any]     			=						True
				else:
						for key, mapped_key in MAPPING.items():
								if key in name or key.split('''w2v_model.'''			)[-1] == name.split('''.'''			)[0]:
										snake_case  :       List[Any]     			=						True
										if "*" in mapped_key:
												snake_case  :       Dict     			=						name.split(SCREAMING_SNAKE_CASE__			)[0].split('''.'''			)[-2]
												snake_case  :       List[Any]     			=						mapped_key.replace('''*''' ,       SCREAMING_SNAKE_CASE__			)
										if "weight_g" in name:
												snake_case  :       Optional[Any]     			=						'''weight_g'''
										elif "weight_v" in name:
												snake_case  :       int     			=						'''weight_v'''
										elif "bias" in name and "relative_attention_bias" not in name:
												snake_case  :       int     			=						'''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												snake_case  :       Dict     			=						'''weight'''
										else:
												snake_case  :       Tuple     			=						None

										set_recursively(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)
								continue
				if not is_used:
						unused_weights.append(SCREAMING_SNAKE_CASE__			)

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




def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)  ->							Dict:


		'''simple docstring'''


		snake_case  :       str     			=						full_name.split('''conv_layers.'''			)[-1]
		snake_case  :       int     			=						name.split('''.'''			)
		snake_case  :       int     			=						int(items[0]			)
		snake_case  :       Any     			=						int(items[1]			)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    F'{full_name} has size {value.shape}, but'
						    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
						)
						snake_case  :       Tuple     			=						value
						logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    F'{full_name} has size {value.shape}, but'
						    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
						)
						snake_case  :       List[Any]     			=						value
						logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'			)
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
						    " found."
						)
						snake_case  :       List[Any]     			=						value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    F'{full_name} has size {value.shape}, but'
						    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
						)
						snake_case  :       Dict     			=						value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
		else:
				unused_weights.append(SCREAMING_SNAKE_CASE__			)




@torch.no_grad()
def    _UpperCamelCase						(       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__=None			)  ->							Union[str, Any]:


		'''simple docstring'''


		snake_case  :       str     			=						torch.load(SCREAMING_SNAKE_CASE__			)
		snake_case  :       Dict     			=						WavLMConfigOrig(checkpoint['''cfg''']			)
		snake_case  :       Any     			=						WavLMOrig(SCREAMING_SNAKE_CASE__			)
		model.load_state_dict(checkpoint['''model''']			)
		model.eval()

		if config_path is not None:
				snake_case  :       Union[str, Any]     			=						WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE__			)
		else:
				snake_case  :       List[str]     			=						WavLMConfig()

		snake_case  :       List[Any]     			=						WavLMModel(SCREAMING_SNAKE_CASE__			)

		recursively_load_weights(SCREAMING_SNAKE_CASE__ ,       SCREAMING_SNAKE_CASE__			)

		hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
						lowercase__						       =      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("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
						lowercase__						       =      parser.parse_args()
						convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




'''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


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "Helsinki-NLP/opus-mt-en-de": "https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json",
    # See all Marian models at https://huggingface.co/models?filter=marian
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""marian"""
		lowerCamelCase     							=						["""past_key_values"""]
		lowerCamelCase     							=						{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(      self				:			Any  ,	UpperCamelCase__				:			Dict=5_8101  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			Union[str, Any]=1024  ,	UpperCamelCase__				:			List[Any]=12  ,	UpperCamelCase__				:			str=4096  ,	UpperCamelCase__				:			Optional[Any]=16  ,	UpperCamelCase__				:			str=12  ,	UpperCamelCase__				:			Tuple=4096  ,	UpperCamelCase__				:			Any=16  ,	UpperCamelCase__				:			Tuple=0.0  ,	UpperCamelCase__				:			Dict=0.0  ,	UpperCamelCase__				:			Dict=True  ,	UpperCamelCase__				:			Any=True  ,	UpperCamelCase__				:			List[Any]="gelu"  ,	UpperCamelCase__				:			Tuple=1024  ,	UpperCamelCase__				:			Optional[Any]=0.1  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[int]=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.02  ,	UpperCamelCase__				:			Tuple=5_8100  ,	UpperCamelCase__				:			Union[str, Any]=False  ,	UpperCamelCase__				:			str=5_8100  ,	UpperCamelCase__				:			List[str]=0  ,	UpperCamelCase__				:			int=0  ,	UpperCamelCase__				:			Dict=True  ,	**UpperCamelCase__				:			int  ,	)						->			int:






				"""simple docstring"""







				snake_case  :       List[Any]     			=						vocab_size
				snake_case  :       Optional[int]     			=						decoder_vocab_size or vocab_size
				snake_case  :       int     			=						max_position_embeddings
				snake_case  :       Tuple     			=						d_model
				snake_case  :       int     			=						encoder_ffn_dim
				snake_case  :       Optional[int]     			=						encoder_layers
				snake_case  :       Union[str, Any]     			=						encoder_attention_heads
				snake_case  :       List[str]     			=						decoder_ffn_dim
				snake_case  :       List[str]     			=						decoder_layers
				snake_case  :       List[str]     			=						decoder_attention_heads
				snake_case  :       Any     			=						dropout
				snake_case  :       Optional[Any]     			=						attention_dropout
				snake_case  :       Tuple     			=						activation_dropout
				snake_case  :       Union[str, Any]     			=						activation_function
				snake_case  :       int     			=						init_std
				snake_case  :       Dict     			=						encoder_layerdrop
				snake_case  :       Dict     			=						decoder_layerdrop
				snake_case  :       List[Any]     			=						use_cache
				snake_case  :       int     			=						encoder_layers
				snake_case  :       List[str]     			=						scale_embedding  # scale factor will be sqrt(d_model) if True
				snake_case  :       str     			=						share_encoder_decoder_embeddings
				super().__init__(
				    pad_token_id=UpperCamelCase__  ,	eos_token_id=UpperCamelCase__  ,	is_encoder_decoder=UpperCamelCase__  ,	decoder_start_token_id=UpperCamelCase__  ,	forced_eos_token_id=UpperCamelCase__  ,	**UpperCamelCase__  ,	)







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		@property
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
		def       lowerCAmelCase				(      self				:			Optional[int]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







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

						if self.use_past:
								snake_case  :       List[Any]     			=						{0: '''batch'''}
								snake_case  :       Optional[Any]     			=						{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
								snake_case  :       List[Any]     			=						{0: '''batch''', 1: '''decoder_sequence'''}
								snake_case  :       Any     			=						{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  :       Union[str, Any]     			=						self.num_layers
								for i in range(UpperCamelCase__      ):
										snake_case  :       Optional[Any]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
										snake_case  :       List[str]     			=						{0: '''batch''', 2: '''past_sequence + sequence'''}
				else:
						snake_case  :       Dict     			=						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
		# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
		def       lowerCAmelCase				(      self				:			Any      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







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



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






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )

				# Generate decoder inputs
				snake_case  :       Optional[int]     			=						seq_length if not self.use_past else 1
				snake_case  :       Optional[int]     			=						self._generate_dummy_inputs_for_encoder_and_decoder(
				    UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__  ,	UpperCamelCase__      )
				snake_case  :       Dict     			=						{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  :       Dict     			=						common_inputs['''input_ids'''].shape
						snake_case  :       Any     			=						common_inputs['''decoder_input_ids'''].shape[1]
						snake_case			,snake_case  :       Tuple     			=						self.num_attention_heads
						snake_case  :       str     			=						(
						    batch,
						    num_encoder_attention_heads,
						    encoder_seq_length,
						    self._config.hidden_size // num_encoder_attention_heads,
						)
						snake_case  :       Union[str, Any]     			=						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  :       Optional[int]     			=						torch.cat(
						    [common_inputs['''decoder_attention_mask'''], torch.ones(UpperCamelCase__  ,	UpperCamelCase__      )]  ,	dim=1      )

						snake_case  :       Dict     			=						[]
						# 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  :       Union[str, Any]     			=						min(UpperCamelCase__  ,	UpperCamelCase__      )
						snake_case  :       Union[str, Any]     			=						max(UpperCamelCase__  ,	UpperCamelCase__      ) - min_num_layers
						snake_case  :       str     			=						'''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  :       Optional[int]     			=						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       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = None  ,	)						->			Mapping[str, Any]:






				"""simple docstring"""







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

						snake_case  :       Tuple     			=						common_inputs['''attention_mask'''].dtype
						snake_case  :       Optional[int]     			=						torch.cat(
						    [common_inputs['''attention_mask'''], torch.ones(UpperCamelCase__  ,	UpperCamelCase__  ,	dtype=UpperCamelCase__      )]  ,	dim=1      )
						snake_case  :       str     			=						[
						    (torch.zeros(UpperCamelCase__      ), torch.zeros(UpperCamelCase__      )) for _ in range(UpperCamelCase__      )
						]
				return common_inputs



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






				"""simple docstring"""







				snake_case  :       Tuple     			=						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  :       List[str]     			=						tokenizer.num_special_tokens_to_add(UpperCamelCase__      )
				snake_case  :       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  :       str     			=						[''' '''.join([tokenizer.unk_token]      ) * seq_length] * batch_size
				snake_case  :       Union[str, Any]     			=						dict(tokenizer(UpperCamelCase__  ,	return_tensors=UpperCamelCase__      )      )
				return common_inputs



		def       lowerCAmelCase				(      self				:			List[Any]  ,	UpperCamelCase__				:			PreTrainedTokenizer  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			int = -1  ,	UpperCamelCase__				:			bool = False  ,	UpperCamelCase__				:			Optional[TensorType] = 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__      )

				else:
						snake_case  :       Tuple     			=						self._generate_dummy_inputs_for_causal_lm(
						    UpperCamelCase__  ,	batch_size=UpperCamelCase__  ,	seq_length=UpperCamelCase__  ,	is_pair=UpperCamelCase__  ,	framework=UpperCamelCase__      )

				return common_inputs



		def       lowerCAmelCase				(      self				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			int  ,	UpperCamelCase__				:			Union[str, Any]      )						->			Union[str, Any]:






				"""simple docstring"""







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



		@property
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


