





"""simple docstring"""


import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def        lowercase__     (       lowercase_      )       ->		str:




      """simple docstring"""

      _UpperCamelCase   :						List[Any]    =							[
          "decoder.version",
          "decoder.output_projection.weight",
          "_float_tensor",
          "decoder.embed_positions._float_tensor",
      ]
      for k in ignore_keys:
            state_dict.pop(lowercase_		,lowercase_      )




def        lowercase__     (       lowercase_      )       ->		Optional[Any]:




      """simple docstring"""

      _UpperCamelCase,	_UpperCamelCase   :						Optional[Any]    =							emb.weight.shape
      _UpperCamelCase   :						Optional[Any]    =							nn.Linear(lowercase_		,lowercase_		,bias=lowercase_      )
      _UpperCamelCase   :						Optional[Any]    =							emb.weight.data
      return lin_layer




def        lowercase__     (       lowercase_      )       ->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase   :						Optional[Any]    =							torch.load(lowercase_		,map_location="cpu"      )
      _UpperCamelCase   :						List[Any]    =							Namespace(**checkpoint["cfg"]["model"]      )
      _UpperCamelCase   :						Any    =							checkpoint["model"]
      remove_ignore_keys_(lowercase_      )
      _UpperCamelCase   :						Tuple    =							state_dict["decoder.embed_tokens.weight"].shape[0]

      _UpperCamelCase   :						List[str]    =							{key.replace("decoder"		,"model"      ): val for key, val in state_dict.items()}

      _UpperCamelCase   :						Any    =							XGLMConfig(
          vocab_size=lowercase_		,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		,)

      _UpperCamelCase   :						str    =							XGLMForCausalLM(lowercase_      )
      _UpperCamelCase   :						Any    =							model.load_state_dict(lowercase_		,strict=lowercase_      )
      print(lowercase_      )
      _UpperCamelCase   :						Any    =							make_linear_from_emb(model.model.embed_tokens      )

      return model


if __name__ == "__main__":
  lowerCamelCase__						= 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.")
  lowerCamelCase__						= parser.parse_args()
  lowerCamelCase__						= convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
  model.save_pretrained(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__						= {
    "google/mobilenet_v1_1.0_224": "https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json",
    "google/mobilenet_v1_0.75_192": "https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}



class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:int						  =       "mobilenet_v1"

  def __init__(		self :							Optional[int]       , __a :							List[str]=3       , __a :							Union[str, Any]=224       , __a :							Tuple=1.0       , __a :							List[Any]=8       , __a :							Union[str, Any]="relu6"       , __a :							Dict=True       , __a :							Tuple=0.9_99       , __a :							Dict=0.02       , __a :							Any=0.0_01       , **__a :							Any       , )		->					int:
        super().__init__(**__a						)

        if depth_multiplier <= 0:
              raise ValueError("depth_multiplier must be greater than zero."						)

        _UpperCamelCase   :						Any    =							num_channels
        _UpperCamelCase   :						Optional[Any]    =							image_size
        _UpperCamelCase   :						Optional[Any]    =							depth_multiplier
        _UpperCamelCase   :						int    =							min_depth
        _UpperCamelCase   :						List[str]    =							hidden_act
        _UpperCamelCase   :						Union[str, Any]    =							tf_padding
        _UpperCamelCase   :						int    =							classifier_dropout_prob
        _UpperCamelCase   :						Optional[int]    =							initializer_range
        _UpperCamelCase   :						Union[str, Any]    =							layer_norm_eps





class   __SCREAMING_SNAKE_CASE     (    _UpperCamelCase ):







  '''simple docstring'''


  SCREAMING_SNAKE_CASE__				:List[Any]						  =       version.parse("1.11" )

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							List[str]						)		->					Mapping[str, Mapping[int, str]]:
        return OrderedDict([("pixel_values", {0: "batch"})]						)

  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Optional[int]						)		->					Mapping[str, Mapping[int, str]]:
        if self.task == "image-classification":
              return OrderedDict([("logits", {0: "batch"})]						)
        else:
              return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]						)


  @property
  def  __SCREAMING_SNAKE_CASE					(		self :							Tuple						)		->					float:
        return 1e-4
