


"""simple docstring"""







import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration
def 					a__			(				SCREAMING_SNAKE_CASE    :    Dict				):


  '''simple docstring'''







  lowerCAmelCase     :     Tuple								=							[
      "encoder.version",
      "decoder.version",
      "model.encoder.version",
      "model.decoder.version",
      "decoder.output_projection.weight",
      "_float_tensor",
      "encoder.embed_positions._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     :     Optional[Any]								=							list(s_dict.keys()				)
  for key in keys:
    if "transformer_layers" in key:
      lowerCAmelCase     :     Any								=							s_dict.pop(SCREAMING_SNAKE_CASE				)
    elif "subsample" in key:
      lowerCAmelCase     :     str								=							s_dict.pop(SCREAMING_SNAKE_CASE				)
def 					a__			(				SCREAMING_SNAKE_CASE    :    Any				):


  '''simple docstring'''







  lowerCAmelCase						,  lowerCAmelCase     :     Union[str, Any]								=							emb.weight.shape
  lowerCAmelCase     :     Union[str, Any]								=							nn.Linear(SCREAMING_SNAKE_CASE				,      SCREAMING_SNAKE_CASE				,      bias=SCREAMING_SNAKE_CASE				)
  lowerCAmelCase     :     List[str]								=							emb.weight.data
  return lin_layer
def 					a__			(				SCREAMING_SNAKE_CASE    :    int				,      SCREAMING_SNAKE_CASE    :    Optional[Any]				):


  '''simple docstring'''







  lowerCAmelCase     :     Optional[int]								=							torch.load(SCREAMING_SNAKE_CASE				,      map_location="cpu"				)
  lowerCAmelCase     :     List[Any]								=							mam_aaa["args"]
  lowerCAmelCase     :     str								=							mam_aaa["model"]
  lowerCAmelCase     :     Union[str, Any]								=							state_dict["decoder.output_projection.weight"]

  remove_ignore_keys_(SCREAMING_SNAKE_CASE				)
  rename_keys(SCREAMING_SNAKE_CASE				)

  lowerCAmelCase     :     Optional[Any]								=							state_dict["decoder.embed_tokens.weight"].shape[0]

  lowerCAmelCase     :     Any								=							args.share_decoder_input_output_embed

  lowerCAmelCase     :     Union[str, Any]								=							[int(SCREAMING_SNAKE_CASE				) for i in args.conv_kernel_sizes.split(","				)]
  lowerCAmelCase     :     Optional[Any]								=							SpeechaTextConfig(
      vocab_size=SCREAMING_SNAKE_CASE				,      max_source_positions=args.max_source_positions				,      max_target_positions=args.max_target_positions				,      encoder_layers=args.encoder_layers				,      decoder_layers=args.decoder_layers				,      encoder_attention_heads=args.encoder_attention_heads				,      decoder_attention_heads=args.decoder_attention_heads				,      encoder_ffn_dim=args.encoder_ffn_embed_dim				,      decoder_ffn_dim=args.decoder_ffn_embed_dim				,      d_model=args.encoder_embed_dim				,      dropout=args.dropout				,      attention_dropout=args.attention_dropout				,      activation_dropout=args.activation_dropout				,      activation_function="relu"				,      num_conv_layers=len(SCREAMING_SNAKE_CASE				)				,      conv_channels=args.conv_channels				,      conv_kernel_sizes=SCREAMING_SNAKE_CASE				,      input_feat_per_channel=args.input_feat_per_channel				,      input_channels=args.input_channels				,      tie_word_embeddings=SCREAMING_SNAKE_CASE				,      num_beams=5				,      max_length=2_0_0				,      use_cache=SCREAMING_SNAKE_CASE				,      decoder_start_token_id=2				,      early_stopping=SCREAMING_SNAKE_CASE				,      )

  lowerCAmelCase     :     Dict								=							SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE				)
  lowerCAmelCase						,  lowerCAmelCase     :     Dict								=							model.model.load_state_dict(SCREAMING_SNAKE_CASE				,      strict=SCREAMING_SNAKE_CASE				)
  if len(SCREAMING_SNAKE_CASE				) > 0 and not set(SCREAMING_SNAKE_CASE				) <= {
      "encoder.embed_positions.weights",
      "decoder.embed_positions.weights",
  }:
    raise ValueError(
        "Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"
        f""" but all the following weights are missing {missing}"""				)

  if tie_embeds:
    lowerCAmelCase     :     int								=							make_linear_from_emb(model.model.decoder.embed_tokens				)
  else:
    lowerCAmelCase     :     List[Any]								=							lm_head_weights

  model.save_pretrained(SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
     lowerCAmelCase__      				=   argparse.ArgumentParser()
     # Required parameters
     parser.add_argument('''--fairseq_path''', type=str, help='''Path to the fairseq model (.pt) file.''')
     parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
     lowerCAmelCase__      				=   parser.parse_args()
     convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)









"""simple docstring"""







from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase__      				=   logging.get_logger(__name__)

lowerCAmelCase__      				=   {
    '''facebook/data2vec-vision-base-ft''': (
        '''https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'''
    ),
}


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				List[Any]   ="data2vec-vision"

       def __init__(						self		,     snake_case__=768		,     snake_case__=12		,     snake_case__=12		,     snake_case__=3_072		,     snake_case__="gelu"		,     snake_case__=0.0		,     snake_case__=0.0		,     snake_case__=0.02		,     snake_case__=1e-12		,     snake_case__=224		,     snake_case__=16		,     snake_case__=3		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=False		,     snake_case__=0.1		,     snake_case__=0.1		,     snake_case__=True		,     snake_case__=[3, 5, 7, 11]		,     snake_case__=[1, 2, 3, 6]		,     snake_case__=True		,     snake_case__=0.4		,     snake_case__=256		,     snake_case__=1		,     snake_case__=False		,     snake_case__=255		,     **snake_case__		,     ):






         """simple docstring"""





         super().__init__(**snake_case__						)

         lowerCAmelCase     :     Tuple								=							hidden_size
         lowerCAmelCase     :     List[Any]								=							num_hidden_layers
         lowerCAmelCase     :     Tuple								=							num_attention_heads
         lowerCAmelCase     :     Optional[int]								=							intermediate_size
         lowerCAmelCase     :     Optional[int]								=							hidden_act
         lowerCAmelCase     :     Dict								=							hidden_dropout_prob
         lowerCAmelCase     :     Optional[Any]								=							attention_probs_dropout_prob
         lowerCAmelCase     :     int								=							initializer_range
         lowerCAmelCase     :     Dict								=							layer_norm_eps

         lowerCAmelCase     :     Optional[int]								=							image_size
         lowerCAmelCase     :     Optional[Any]								=							patch_size
         lowerCAmelCase     :     Optional[Any]								=							num_channels
         lowerCAmelCase     :     Union[str, Any]								=							use_mask_token
         lowerCAmelCase     :     str								=							use_absolute_position_embeddings
         lowerCAmelCase     :     Any								=							use_relative_position_bias
         lowerCAmelCase     :     List[str]								=							use_shared_relative_position_bias
         lowerCAmelCase     :     str								=							layer_scale_init_value
         lowerCAmelCase     :     Union[str, Any]								=							drop_path_rate
         lowerCAmelCase     :     Any								=							use_mean_pooling
         # decode head attributes (semantic segmentation)
         lowerCAmelCase     :     Optional[int]								=							out_indices
         lowerCAmelCase     :     Union[str, Any]								=							pool_scales
         # auxiliary head attributes (semantic segmentation)
         lowerCAmelCase     :     str								=							use_auxiliary_head
         lowerCAmelCase     :     int								=							auxiliary_loss_weight
         lowerCAmelCase     :     Tuple								=							auxiliary_channels
         lowerCAmelCase     :     List[str]								=							auxiliary_num_convs
         lowerCAmelCase     :     Tuple								=							auxiliary_concat_input
         lowerCAmelCase     :     List[str]								=							semantic_loss_ignore_index


class       SCREAMING_SNAKE_CASE__				(		lowercase				):
       """simple docstring"""


       a  :				Union[str, Any]   =version.parse("1.11"				)

       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]						)







       @property
       def        lowercase__							(						self						):






         """simple docstring"""





         return 1e-4





