



'''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


SCREAMING_SNAKE_CASE__     : int				=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     : Dict				=   {
    """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(							lowerCAmelCase__      ):
			__snake_case  :		int		=      'bigbird_pegasus'
			__snake_case  :		Tuple		=      ['past_key_values']
			__snake_case  :		Any		=      {
			    'num_attention_heads': 'encoder_attention_heads',
			    'hidden_size': 'd_model',
			    'attention_probs_dropout_prob': 'attention_dropout',
			}






			def __init__(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=96_103      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=4_096      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=16      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=4_096      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=16      ,       SCREAMING_SNAKE_CASE  :							Optional[int]=16      ,       SCREAMING_SNAKE_CASE  :							List[Any]=4_096      ,       SCREAMING_SNAKE_CASE  :							str=16      ,       SCREAMING_SNAKE_CASE  :							Dict=0.0      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]=0.0      ,       SCREAMING_SNAKE_CASE  :							Tuple=True      ,       SCREAMING_SNAKE_CASE  :							Dict=True      ,       SCREAMING_SNAKE_CASE  :							List[str]="gelu_new"      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=1_024      ,       SCREAMING_SNAKE_CASE  :							Dict=0.1      ,       SCREAMING_SNAKE_CASE  :							Any=0.0      ,       SCREAMING_SNAKE_CASE  :							List[Any]=0.0      ,       SCREAMING_SNAKE_CASE  :							List[Any]=0.02      ,       SCREAMING_SNAKE_CASE  :							Union[str, Any]=2      ,       SCREAMING_SNAKE_CASE  :							Any=0.0      ,       SCREAMING_SNAKE_CASE  :							int=True      ,       SCREAMING_SNAKE_CASE  :							List[str]=0      ,       SCREAMING_SNAKE_CASE  :							List[str]=2      ,       SCREAMING_SNAKE_CASE  :							int=1      ,       SCREAMING_SNAKE_CASE  :							int="block_sparse"      ,       SCREAMING_SNAKE_CASE  :							List[str]=64      ,       SCREAMING_SNAKE_CASE  :							Tuple=3      ,       SCREAMING_SNAKE_CASE  :							int=False      ,       **SCREAMING_SNAKE_CASE  :							int      ,       ):




					"""simple docstring"""







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

					# extra config
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      attention_type
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      block_size
					SCREAMING_SNAKE_CASE_						:int										=      num_random_blocks
					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      use_bias

					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      ,       **SCREAMING_SNAKE_CASE      ,       )




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






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




					"""simple docstring"""







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

							if self.use_past:
									SCREAMING_SNAKE_CASE_						:List[str]										=      {0: 'batch'}
									SCREAMING_SNAKE_CASE_						:Tuple										=      {0: 'batch', 1: 'past_decoder_sequence + sequence'}
							else:
									SCREAMING_SNAKE_CASE_						:List[str]										=      {0: 'batch', 1: 'decoder_sequence'}
									SCREAMING_SNAKE_CASE_						:List[str]										=      {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.
							SCREAMING_SNAKE_CASE_						:int										=      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_						:Dict										=      self.num_layers
									for i in range(SCREAMING_SNAKE_CASE	):
											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_						:Optional[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 				_lowercase	(   self  :							str	):




					"""simple docstring"""







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






			def 				_lowercase	(   self  :							Tuple      ,       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      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Any										=      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
					SCREAMING_SNAKE_CASE_						:List[str]										=      seq_length if not self.use_past else 1
					SCREAMING_SNAKE_CASE_						:List[Any]										=      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	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      {f'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
					SCREAMING_SNAKE_CASE_						:Tuple										=      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
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[Any]										=      common_inputs['input_ids'].shape
							SCREAMING_SNAKE_CASE_						:Tuple										=      common_inputs['decoder_input_ids'].shape[1]
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.num_attention_heads
							SCREAMING_SNAKE_CASE_						:Tuple										=      (
							    batch,
							    num_encoder_attention_heads,
							    encoder_seq_length,
							    self._config.hidden_size // num_encoder_attention_heads,
							)
							SCREAMING_SNAKE_CASE_						:List[str]										=      decoder_seq_length + 3
							SCREAMING_SNAKE_CASE_						:int										=      (
							    batch,
							    num_decoder_attention_heads,
							    decoder_past_length,
							    self._config.hidden_size // num_decoder_attention_heads,
							)

							SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.cat(
							    [common_inputs['decoder_attention_mask'], torch.ones(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)]      ,       dim=1	)

							SCREAMING_SNAKE_CASE_						:Optional[int]										=      []
							# If the number of encoder and decoder layers are present in the model configuration, both are considered
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      self.num_layers
							SCREAMING_SNAKE_CASE_						:Tuple										=      min(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      max(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	) - min_num_layers
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      '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.
							SCREAMING_SNAKE_CASE_						:Dict										=      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 				_lowercase	(   self  :							Optional[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      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      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
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      common_inputs['input_ids'].shape
							# Not using the same length for past_key_values
							SCREAMING_SNAKE_CASE_						:Any										=      seqlen + 2
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      self.num_layers
							SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Tuple										=      self.num_attention_heads
							SCREAMING_SNAKE_CASE_						:Dict										=      (
							    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_						:str										=      torch.cat(
							    [common_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       dtype=SCREAMING_SNAKE_CASE	)]      ,       dim=1	)
							SCREAMING_SNAKE_CASE_						:List[Any]										=      [
							    (torch.zeros(SCREAMING_SNAKE_CASE	), torch.zeros(SCREAMING_SNAKE_CASE	)) for _ in range(SCREAMING_SNAKE_CASE	)
							]
					return common_inputs






			def 				_lowercase	(   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      ,       ):




					"""simple docstring"""







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






			def 				_lowercase	(   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      ,       ):




					"""simple docstring"""







					if self.task in ["default", "seq2seq-lm"]:
							SCREAMING_SNAKE_CASE_						:Any										=      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":
							SCREAMING_SNAKE_CASE_						:Any										=      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:
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      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 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							Optional[Any]	):




					"""simple docstring"""







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






import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

a_ 		=  {
    "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_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",
    "mask_emb": "masked_spec_embed",
}




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


    for attribute in key.split('''.'''   ):
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   )

    if weight_type is not None:
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   ).shape
    else:
        __UpperCamelCase			=    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":
        __UpperCamelCase			=    value
    elif weight_type == "weight_g":
        __UpperCamelCase			=    value
    elif weight_type == "weight_v":
        __UpperCamelCase			=    value
    elif weight_type == "bias":
        __UpperCamelCase			=    value
    else:
        __UpperCamelCase			=    value

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    []
    __UpperCamelCase			=    fairseq_model.state_dict()

    __UpperCamelCase			=    hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase			=    False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  hf_model.config.feat_extract_norm == '''group'''       ,  )
            __UpperCamelCase			=    True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase			=    mapped_key

                if key in name:
                    __UpperCamelCase			=    True
                    if "*" in mapped_key:
                        __UpperCamelCase			=    name.split(lowercase_   )[0].split('''.'''   )[-2]
                        __UpperCamelCase			=    mapped_key.replace('''*'''       ,  lowercase_   )
                    if "weight_g" in name:
                        __UpperCamelCase			=    '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase			=    '''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase			=    '''weight'''
                    elif "bias" in name:
                        __UpperCamelCase			=    '''bias'''
                    else:
                        __UpperCamelCase			=    None
                    set_recursively(lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )
                continue
        if not is_used:
            unused_weights.append(lowercase_   )

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    __UpperCamelCase			=    full_name.split('''conv_layers.'''   )[-1]
    __UpperCamelCase			=    name.split('''.'''   )
    __UpperCamelCase			=    int(items[0]   )
    __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    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."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
    else:
        unused_weights.append(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    HubertConfig()
    __UpperCamelCase			=    model.config

    __UpperCamelCase			=    fs_config.activation_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    fs_config.attention_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    eval(fs_config.extractor_conv_feature_layers   )
    __UpperCamelCase			=    [x[0] for x in conv_layers]
    __UpperCamelCase			=    [x[1] for x in conv_layers]
    __UpperCamelCase			=    [x[2] for x in conv_layers]
    __UpperCamelCase			=    '''gelu'''
    __UpperCamelCase			=    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    __UpperCamelCase			=    False
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.activation_fn
    __UpperCamelCase			=    fs_config.dropout
    __UpperCamelCase			=    fs_config.encoder_embed_dim
    __UpperCamelCase			=    0.02
    __UpperCamelCase			=    fs_config.encoder_ffn_embed_dim
    __UpperCamelCase			=    1E-5
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.encoder_attention_heads
    __UpperCamelCase			=    fs_config.conv_pos_groups
    __UpperCamelCase			=    fs_config.conv_pos
    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    fs_config.encoder_layers

    return config




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=None   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    distilhubert().model.model

    if config_path is not None:
        __UpperCamelCase			=    HubertConfig.from_pretrained(lowercase_   )
    else:
        __UpperCamelCase			=    convert_config(lowercase_   )
    __UpperCamelCase			=    model.eval()

    __UpperCamelCase			=    WavaVecaFeatureExtractor(
        feature_size=1       ,  sampling_rate=1_60_00       ,  padding_value=0       ,  do_normalize=lowercase_       ,  return_attention_mask=lowercase_       ,  )
    __UpperCamelCase			=    HubertModel(lowercase_   )

    recursively_load_weights(lowercase_       ,  lowercase_   )

    feature_extractor.save_pretrained(lowercase_   )
    hf_model.save_pretrained(lowercase_   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 a_ 		=  parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






