





'''simple docstring'''


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def 						__a(SCREAMING_SNAKE_CASE_	:       Dict     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   [
        "decoder.version",
        "decoder.output_projection.weight",
        "_float_tensor",
        "decoder.embed_positions._float_tensor",
    ]
    for k in ignore_keys:
        state_dict.pop(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_     )


def 						__a(SCREAMING_SNAKE_CASE_	:       List[str]     ):



    '''simple docstring'''





    _lowerCAmelCase      ,      _lowerCAmelCase						      =   emb.weight.shape
    _lowerCAmelCase						      =   nn.Linear(SCREAMING_SNAKE_CASE_		,							SCREAMING_SNAKE_CASE_		,							bias=SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   emb.weight.data
    return lin_layer


def 						__a(SCREAMING_SNAKE_CASE_	:       Union[str, Any]     ):



    '''simple docstring'''





    _lowerCAmelCase						      =   torch.load(SCREAMING_SNAKE_CASE_		,							map_location="cpu"     )
    _lowerCAmelCase						      =   Namespace(**checkpoint["cfg"]["model"]     )
    _lowerCAmelCase						      =   checkpoint["model"]
    remove_ignore_keys_(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   state_dict["decoder.embed_tokens.weight"].shape[0]

    _lowerCAmelCase						      =   {key.replace("decoder"		,							"model"     ): val for key, val in state_dict.items()}

    _lowerCAmelCase						      =   XGLMConfig(
        vocab_size=SCREAMING_SNAKE_CASE_		,							max_position_embeddings=args.max_target_positions		,							num_layers=args.decoder_layers		,							attention_heads=args.decoder_attention_heads		,							ffn_dim=args.decoder_ffn_embed_dim		,							d_model=args.decoder_embed_dim		,							layerdrop=args.decoder_layerdrop		,							dropout=args.dropout		,							attention_dropout=args.attention_dropout		,							activation_dropout=args.activation_dropout		,							activation_function="gelu"		,							scale_embedding=not args.no_scale_embedding		,							tie_word_embeddings=args.share_decoder_input_output_embed		,							)

    _lowerCAmelCase						      =   XGLMForCausalLM(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   model.load_state_dict(SCREAMING_SNAKE_CASE_		,							strict=SCREAMING_SNAKE_CASE_     )
    print(SCREAMING_SNAKE_CASE_     )
    _lowerCAmelCase						      =   make_linear_from_emb(model.model.embed_tokens     )

    return model


if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE									=				argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
       parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       _SCREAMING_SNAKE_CASE									=				parser.parse_args()
       _SCREAMING_SNAKE_CASE									=				convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
       model.save_pretrained(args.pytorch_dump_folder_path)









'''simple docstring'''


import math

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/data2vec-base-960h": "https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json",
    # See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[Any]								=       "data2vec-audio"




 def __init__(						self      ,  _lowerCAmelCase=32      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.1      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-5      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=(512, 512, 512, 512, 512, 512, 512)      ,  _lowerCAmelCase=(5, 2, 2, 2, 2, 2, 2)      ,  _lowerCAmelCase=(10, 3, 3, 3, 3, 2, 2)      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=19      ,  _lowerCAmelCase=5      ,  _lowerCAmelCase=0.05      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=10      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase="sum"      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=256      ,  _lowerCAmelCase=(512, 512, 512, 512, 1500)      ,  _lowerCAmelCase=(5, 3, 3, 1, 1)      ,  _lowerCAmelCase=(1, 2, 3, 1, 1)      ,  _lowerCAmelCase=512      ,  _lowerCAmelCase=0      ,  _lowerCAmelCase=1      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=False      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=2      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=None      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase      ,  pad_token_id=_lowerCAmelCase      ,  bos_token_id=_lowerCAmelCase      ,  eos_token_id=_lowerCAmelCase		)
     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   feat_extract_activation
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   conv_bias
     _lowerCAmelCase						      =   num_conv_pos_embeddings
     _lowerCAmelCase						      =   num_conv_pos_embedding_groups
     _lowerCAmelCase						      =   conv_pos_kernel_size
     _lowerCAmelCase						      =   len(self.conv_dim		)
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   hidden_dropout
     _lowerCAmelCase						      =   attention_dropout
     _lowerCAmelCase						      =   activation_dropout
     _lowerCAmelCase						      =   feat_proj_dropout
     _lowerCAmelCase						      =   final_dropout
     _lowerCAmelCase						      =   layerdrop
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   vocab_size
     _lowerCAmelCase						      =   use_weighted_layer_sum

     if (
         (len(self.conv_stride		) != self.num_feat_extract_layers)
         or (len(self.conv_kernel		) != self.num_feat_extract_layers)
         or (len(self.conv_dim		) != self.num_feat_extract_layers)
     ):
         raise ValueError(
             "Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
             " `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
             f''' {len(self.conv_dim		)}`, `len(config.conv_stride) = {len(self.conv_stride		)}`,'''
             f''' `len(config.conv_kernel) = {len(self.conv_kernel		)}`.'''		)

     # fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
     _lowerCAmelCase						      =   mask_time_prob
     _lowerCAmelCase						      =   mask_time_length
     _lowerCAmelCase						      =   mask_time_min_masks
     _lowerCAmelCase						      =   mask_feature_prob
     _lowerCAmelCase						      =   mask_feature_length
     _lowerCAmelCase						      =   mask_feature_min_masks

     # ctc loss
     _lowerCAmelCase						      =   ctc_loss_reduction
     _lowerCAmelCase						      =   ctc_zero_infinity

     # adapter
     _lowerCAmelCase						      =   add_adapter
     _lowerCAmelCase						      =   adapter_kernel_size
     _lowerCAmelCase						      =   adapter_stride
     _lowerCAmelCase						      =   num_adapter_layers
     _lowerCAmelCase						      =   output_hidden_size or hidden_size

     # SequenceClassification-specific parameter. Feel free to ignore for other classes.
     _lowerCAmelCase						      =   classifier_proj_size

     # XVector-specific parameters. Feel free to ignore for other classes.
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   list(_lowerCAmelCase		)
     _lowerCAmelCase						      =   xvector_output_dim





 @property
 def 				_snake_case (						self		)     ->				str:
     return math.prod(self.conv_stride		)


