
import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Any:
    __lowercase						:	List[str]					=				[
        '''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(UpperCamelCase__		,    UpperCamelCase__		)


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Tuple:
    __lowercase						:	Optional[int]					=				list(s_dict.keys()		)
    for key in keys:
        if "transformer_layers" in key:
            __lowercase						:	Any					=				s_dict.pop(UpperCamelCase__		)
        elif "subsample" in key:
            __lowercase						:	str					=				s_dict.pop(UpperCamelCase__		)


def 			UpperCAmelCase_     (  __lowerCAmelCase		)       ->  Dict:
    __lowercase						:	Optional[Any]					=				emb.weight.shape
    __lowercase						:	Optional[int]					=				nn.Linear(UpperCamelCase__		,    UpperCamelCase__		,    bias=UpperCamelCase__		)
    __lowercase						:	Union[str, Any]					=				emb.weight.data
    return lin_layer


def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  Optional[Any]:
    __lowercase						:	Dict					=				torch.load(UpperCamelCase__		,    map_location='''cpu'''		)
    __lowercase						:	Optional[int]					=				mam_aaa['''args''']
    __lowercase						:	Dict					=				mam_aaa['''model''']
    __lowercase						:	Any					=				state_dict['''decoder.output_projection.weight''']

    remove_ignore_keys_(UpperCamelCase__		)
    rename_keys(UpperCamelCase__		)

    __lowercase						:	str					=				state_dict['''decoder.embed_tokens.weight'''].shape[0]

    __lowercase						:	Optional[int]					=				args.share_decoder_input_output_embed

    __lowercase						:	Any					=				[int(UpperCamelCase__		) for i in args.conv_kernel_sizes.split(''','''		)]
    __lowercase						:	Dict					=				SpeechaTextConfig(
        vocab_size=UpperCamelCase__		,    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(UpperCamelCase__		)		,    conv_channels=args.conv_channels		,    conv_kernel_sizes=UpperCamelCase__		,    input_feat_per_channel=args.input_feat_per_channel		,    input_channels=args.input_channels		,    tie_word_embeddings=UpperCamelCase__		,    num_beams=5		,    max_length=200		,    use_cache=UpperCamelCase__		,    decoder_start_token_id=2		,    early_stopping=UpperCamelCase__		,    )

    __lowercase						:	Tuple					=				SpeechaTextForConditionalGeneration(UpperCamelCase__		)
    __lowercase						:	Optional[Any]					=				model.model.load_state_dict(UpperCamelCase__		,    strict=UpperCamelCase__		)
    if len(UpperCamelCase__		) > 0 and not set(UpperCamelCase__		) <= {
        "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						:	List[Any]					=				make_linear_from_emb(model.model.decoder.embed_tokens		)
    else:
        __lowercase						:	Any					=				lm_head_weights

    model.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
 __lowerCAmelCase    :	Dict						=						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    :	Optional[Any]						=						parser.parse_args()
 convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)







"""simple docstring"""
from typing import TYPE_CHECKING

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


_lowerCAmelCase    :str     =					{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :Optional[int]     =					['SqueezeBertTokenizerFast']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase    :str     =					[
      '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

  _lowerCAmelCase    :Optional[Any]     =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


