





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
    lowercase__			    =						[
        '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     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     Tuple:
    lowercase__			    =						list(s_dict.keys()	)
    for key in keys:
        if "transformer_layers" in key:
            lowercase__			    =						s_dict.pop(_SCREAMING_SNAKE_CASE	)
        elif "subsample" in key:
            lowercase__			    =						s_dict.pop(_SCREAMING_SNAKE_CASE	)




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	)       ->     List[str]:
    lowercase__    ,					lowercase__			    =						emb.weight.shape
    lowercase__			    =						nn.Linear(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	,		bias=_SCREAMING_SNAKE_CASE	)
    lowercase__			    =						emb.weight.data
    return lin_layer




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     Optional[Any]:
    lowercase__			    =						torch.load(_SCREAMING_SNAKE_CASE	,		map_location='cpu'	)
    lowercase__			    =						mam_aaa['args']
    lowercase__			    =						mam_aaa['model']
    lowercase__			    =						state_dict['decoder.output_projection.weight']

    remove_ignore_keys_(_SCREAMING_SNAKE_CASE	)
    rename_keys(_SCREAMING_SNAKE_CASE	)

    lowercase__			    =						state_dict['decoder.embed_tokens.weight'].shape[0]

    lowercase__			    =						args.share_decoder_input_output_embed

    lowercase__			    =						[int(_SCREAMING_SNAKE_CASE	) for i in args.conv_kernel_sizes.split(','	)]
    lowercase__			    =						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=200	,		use_cache=_SCREAMING_SNAKE_CASE	,		decoder_start_token_id=2	,		early_stopping=_SCREAMING_SNAKE_CASE	,		)

    lowercase__			    =						SpeechaTextForConditionalGeneration(_SCREAMING_SNAKE_CASE	)
    lowercase__    ,					lowercase__			    =						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:
        lowercase__			    =						make_linear_from_emb(model.model.decoder.embed_tokens	)
    else:
        lowercase__			    =						lm_head_weights

    model.save_pretrained(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
      lowercase_					     =	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.""")
      lowercase_					     =	parser.parse_args()
      convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowercase_					     =	{
    """configuration_squeezebert""": [
        """SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """SqueezeBertConfig""",
        """SqueezeBertOnnxConfig""",
    ],
    """tokenization_squeezebert""": ["""SqueezeBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	["""SqueezeBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      lowercase_					     =	[
          """SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """SqueezeBertForMaskedLM""",
          """SqueezeBertForMultipleChoice""",
          """SqueezeBertForQuestionAnswering""",
          """SqueezeBertForSequenceClassification""",
          """SqueezeBertForTokenClassification""",
          """SqueezeBertModel""",
          """SqueezeBertModule""",
          """SqueezeBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_squeezebert import (
          SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
          SqueezeBertConfig,
          SqueezeBertOnnxConfig,
      )
      from .tokenization_squeezebert import SqueezeBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_squeezebert import (
                SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                SqueezeBertForMaskedLM,
                SqueezeBertForMultipleChoice,
                SqueezeBertForQuestionAnswering,
                SqueezeBertForSequenceClassification,
                SqueezeBertForTokenClassification,
                SqueezeBertModel,
                SqueezeBertModule,
                SqueezeBertPreTrainedModel,
            )

else:
      import sys

      lowercase_					     =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


