'''simple docstring'''
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 			_UpperCamelCase			(  _a						:						Dict					,  _a						:						Any					,  _a						:						int					,  _a						:						int					,  _a						:						List[Any]      ):




    """simple docstring"""



    for attribute in key.split('.'      ):
        __UpperCamelCase						:						List[Any]		     =	getattr(lowercase_					,  lowercase_      )

    if weight_type is not None:
        __UpperCamelCase						:						Dict		     =	getattr(lowercase_					,  lowercase_      ).shape
    else:
        __UpperCamelCase						:						Union[str, Any]		     =	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						:						Optional[int]		     =	value
    elif weight_type == "weight_g":
        __UpperCamelCase						:						List[str]		     =	value
    elif weight_type == "weight_v":
        __UpperCamelCase						:						List[Any]		     =	value
    elif weight_type == "bias":
        __UpperCamelCase						:						Union[str, Any]		     =	value
    else:
        __UpperCamelCase						:						Union[str, Any]		     =	value

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







def 			_UpperCamelCase			(  _a						:						Optional[Any]					,  _a						:						List[str]      ):




    """simple docstring"""



    __UpperCamelCase						:						int		     =	[]
    __UpperCamelCase						:						Optional[int]		     =	fairseq_model.state_dict()

    __UpperCamelCase						:						str		     =	hf_model.feature_extractor

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

                if key in name:
                    __UpperCamelCase						:						Tuple		     =	True
                    if "*" in mapped_key:
                        __UpperCamelCase						:						Optional[Any]		     =	name.split(lowercase_      )[0].split('.'      )[-2]
                        __UpperCamelCase						:						Tuple		     =	mapped_key.replace('*'					,  lowercase_      )
                    if "weight_g" in name:
                        __UpperCamelCase						:						Tuple		     =	'weight_g'
                    elif "weight_v" in name:
                        __UpperCamelCase						:						Optional[int]		     =	'weight_v'
                    elif "weight" in name:
                        __UpperCamelCase						:						Union[str, Any]		     =	'weight'
                    elif "bias" in name:
                        __UpperCamelCase						:						Union[str, Any]		     =	'bias'
                    else:
                        __UpperCamelCase						:						Any		     =	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 			_UpperCamelCase			(  _a						:						Optional[Any]					,  _a						:						Optional[Any]					,  _a						:						List[str]					,  _a						:						Optional[int]					,  _a						:						Any      ):




    """simple docstring"""



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







def 			_UpperCamelCase			(  _a						:						Dict      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	HubertConfig()
    __UpperCamelCase						:						List[str]		     =	model.config

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

    return config







@torch.no_grad()
def 			_UpperCamelCase			(  _a						:						Dict					,  _a						:						Union[str, Any]=None      ):




    """simple docstring"""



    __UpperCamelCase						:						Tuple		     =	distilhubert().model.model

    if config_path is not None:
        __UpperCamelCase						:						Optional[Any]		     =	HubertConfig.from_pretrained(lowercase_      )
    else:
        __UpperCamelCase						:						Dict		     =	convert_config(lowercase_      )
    __UpperCamelCase						:						List[Any]		     =	model.eval()

    __UpperCamelCase						:						Optional[int]		     =	WavaVecaFeatureExtractor(
        feature_size=1					,  sampling_rate=1_6_0_0_0					,  padding_value=0					,  do_normalize=lowercase_					,  return_attention_mask=lowercase_					,  )
    __UpperCamelCase						:						Dict		     =	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)








"""simple docstring"""


from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_clip import CLIPTokenizer


snake_case_								=			logging.get_logger(__name__)

snake_case_								=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

snake_case_								=			{
    """vocab_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """openai/clip-vit-base-patch32""": """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """openai/clip-vit-base-patch32""": (
            """https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/tokenizer.json"""
        ),
    },
}

snake_case_								=			{
    """openai/clip-vit-base-patch32""": 77,
}







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""
						__UpperCamelCase													=				VOCAB_FILES_NAMES
						__UpperCamelCase													=				PRETRAINED_VOCAB_FILES_MAP
						__UpperCamelCase													=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						__UpperCamelCase													=				["""input_ids""", """attention_mask"""]
						__UpperCamelCase													=				CLIPTokenizer






						def __init__(   self					:int  ,    lowercase_					:Optional[int]=None  ,    lowercase_					:int=None  ,    lowercase_					:Optional[Any]=None  ,    lowercase_					:Dict="<|endoftext|>"  ,    lowercase_					:List[Any]="<|startoftext|>"  ,    lowercase_					:Any="<|endoftext|>"  ,    lowercase_					:List[str]="<|endoftext|>"  ,    **lowercase_					:Tuple  ,    )							->     List[Any]:
												super().__init__(
												    lowercase_  ,    lowercase_  ,    tokenizer_file=lowercase_  ,    unk_token=lowercase_  ,    bos_token=lowercase_  ,    eos_token=lowercase_  ,    pad_token=lowercase_  ,    **lowercase_  ,    )

												if not isinstance(self.backend_tokenizer.pre_tokenizer  ,    pre_tokenizers.Sequence   ):
																		raise ValueError(
																		    'The `backend_tokenizer` provided does not match the expected format. The CLIP tokenizer has been'
																		    ' heavily modified from transformers version 4.17.0. You need to convert the tokenizer you are using'
																		    ' to be compatible with this version.The easiest way to do so is'
																		    ' `CLIPTokenizerFast.from_pretrained("path_to_local_folder_or_hub_repo, from_slow=True)`. If you want'
																		    ' to use your existing tokenizer, you will have to revert to a version prior to 4.17.0 of'
																		    ' transformers.'   )

												self._wrap_decode_method_backend_tokenizer()






						def       UpperCAmelCase__	(   self					:Dict   )							->     List[Any]:
												UpperCAmelCase			 =					self.backend_tokenizer.decode

												def new_decode_method(*lowercase_					:List[Any]  ,    **lowercase_					:Optional[Any]   ):
																		UpperCAmelCase			 =					orig_decode_method(*lowercase_  ,    **lowercase_   )
																		UpperCAmelCase			 =					text.replace(self.backend_tokenizer.model.end_of_word_suffix  ,    ' '   ).strip()
																		return text

												UpperCAmelCase			 =					new_decode_method






						def       UpperCAmelCase__	(   self					:Optional[int]  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return bos_token + token_ids_a + eos_token
												return bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token






						def       UpperCAmelCase__	(   self					:Dict  ,    lowercase_					:List[int]  ,    lowercase_					:Optional[List[int]] = None   )							->     List[int]:
												UpperCAmelCase			 =					[self.bos_token_id]
												UpperCAmelCase			 =					[self.eos_token_id]

												if token_ids_a is None:
																		return len(bos_token + token_ids_a + eos_token   ) * [0]
												return len(bos_token + token_ids_a + eos_token + eos_token + token_ids_a + eos_token   ) * [0]






						def       UpperCAmelCase__	(   self					:Optional[Any]  ,    lowercase_					:str  ,    lowercase_					:Optional[str] = None   )							->     Tuple[str]:
												UpperCAmelCase			 =					self._tokenizer.model.save(lowercase_  ,    name=lowercase_   )
												return tuple(lowercase_   )





