




'''simple docstring'''


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     SCREAMING_SNAKE_CASE							(   a_						:				Union[str, Any]      ):
  __a								=       [
      'decoder.version',
      'decoder.output_projection.weight',
      '_float_tensor',
      'decoder.embed_positions._float_tensor',
  ]
  for k in ignore_keys:
    state_dict.pop(__lowerCAmelCase					,       __lowerCAmelCase      )


def     SCREAMING_SNAKE_CASE							(   a_						:				Dict      ):
  __a						,							__a								=       emb.weight.shape
  __a								=       nn.Linear(__lowerCAmelCase					,       __lowerCAmelCase					,       bias=__lowerCAmelCase      )
  __a								=       emb.weight.data
  return lin_layer


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]      ):
  __a								=       torch.load(__lowerCAmelCase					,       map_location='cpu'      )
  __a								=       Namespace(**checkpoint['cfg']['model']      )
  __a								=       checkpoint['model']
  remove_ignore_keys_(__lowerCAmelCase      )
  __a								=       state_dict['decoder.embed_tokens.weight'].shape[0]

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

  __a								=       XGLMConfig(
      vocab_size=__lowerCAmelCase					,       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					,       )

  __a								=       XGLMForCausalLM(__lowerCAmelCase      )
  __a								=       model.load_state_dict(__lowerCAmelCase					,       strict=__lowerCAmelCase      )
  print(__lowerCAmelCase      )
  __a								=       make_linear_from_emb(model.model.embed_tokens      )

  return model


if __name__ == "__main__":
  UpperCAmelCase_					=   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.")
  UpperCAmelCase_					=   parser.parse_args()
  UpperCAmelCase_					=   convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
  model.save_pretrained(args.pytorch_dump_folder_path)









from typing import TYPE_CHECKING

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


__magic_name__  :  Union[str, Any]					=       {
    """configuration_altclip""": [
        """ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AltCLIPConfig""",
        """AltCLIPTextConfig""",
        """AltCLIPVisionConfig""",
    ],
    """processing_altclip""": ["""AltCLIPProcessor"""],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__  :  str					=       [
          """ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """AltCLIPPreTrainedModel""",
          """AltCLIPModel""",
          """AltCLIPTextModel""",
          """AltCLIPVisionModel""",
      ]


if TYPE_CHECKING:
      from .configuration_altclip import (
          ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
          AltCLIPConfig,
          AltCLIPTextConfig,
          AltCLIPVisionConfig,
      )
      from .processing_altclip import AltCLIPProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_altclip import (
                ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                AltCLIPModel,
                AltCLIPPreTrainedModel,
                AltCLIPTextModel,
                AltCLIPVisionModel,
            )


else:
      import sys

      __magic_name__  :  str					=       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

