



"""simple docstring"""






import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


__snake_case							:     Any   =					logging.getLogger()



@unittest.skip('Temporarily disable the doc tests.'							)
@require_torch
@require_tf
@slow
class     A__     (						unittest.TestCase							):




 '''simple docstring'''





 def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple ,    _SCREAMING_SNAKE_CASE:	Path ,    _SCREAMING_SNAKE_CASE:	Union[str, None] = None ,    _SCREAMING_SNAKE_CASE:	Union[List[str], None] = None ,    _SCREAMING_SNAKE_CASE:	Union[str, List[str], None] = None ,    _SCREAMING_SNAKE_CASE:	bool = True ,    )       -> Any:

    """simple docstring"""

    __lowerCAmelCase   :						Union[str, Any]				= [file for file in os.listdir(_SCREAMING_SNAKE_CASE) if os.path.isfile(os.path.join(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE))]

    if identifier is not None:
       __lowerCAmelCase   :						int				= [file for file in files if identifier in file]

    if n_identifier is not None:
       if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
          for n_ in n_identifier:
             __lowerCAmelCase   :						Dict				= [file for file in files if n_ not in file]
       else:
          __lowerCAmelCase   :						Union[str, Any]				= [file for file in files if n_identifier not in file]

    __lowerCAmelCase   :						List[str]				= ignore_files or []
    ignore_files.append("__init__.py")
    __lowerCAmelCase   :						Dict				= [file for file in files if file not in ignore_files]

    for file in files:
       # Open all files
       print("Testing" ,    _SCREAMING_SNAKE_CASE)

       if only_modules:
          __lowerCAmelCase   :						int				= file.split(".")[0]
          try:
             __lowerCAmelCase   :						List[Any]				= getattr(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
             __lowerCAmelCase   :						Optional[Any]				= doctest.DocTestSuite(_SCREAMING_SNAKE_CASE)
             __lowerCAmelCase   :						List[str]				= unittest.TextTestRunner().run(_SCREAMING_SNAKE_CASE)
             self.assertIs(len(result.failures) ,    0)
          except AttributeError:
             logger.info(F"""{module_identifier} is not a module.""")
       else:
          __lowerCAmelCase   :						Tuple				= doctest.testfile(str(".." / directory / file) ,    optionflags=doctest.ELLIPSIS)
          self.assertIs(result.failed ,    0)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Dict:

    """simple docstring"""

    __lowerCAmelCase   :						Dict				= Path("src/transformers")
    __lowerCAmelCase   :						Tuple				= "modeling"
    __lowerCAmelCase   :						Optional[int]				= [
        "modeling_ctrl.py",
        "modeling_tf_ctrl.py",
    ]
    self.analyze_directory(_SCREAMING_SNAKE_CASE ,    identifier=_SCREAMING_SNAKE_CASE ,    ignore_files=_SCREAMING_SNAKE_CASE)

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Optional[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						int				= Path("src/transformers")
    __lowerCAmelCase   :						Optional[Any]				= "tokenization"
    self.analyze_directory(_SCREAMING_SNAKE_CASE ,    identifier=_SCREAMING_SNAKE_CASE)

 def 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> int:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[int]				= Path("src/transformers")
    __lowerCAmelCase   :						int				= "configuration"
    self.analyze_directory(_SCREAMING_SNAKE_CASE ,    identifier=_SCREAMING_SNAKE_CASE)

 def 							_SCREAMING_SNAKE_CASE     (  self:	Dict)       -> int:

    """simple docstring"""

    __lowerCAmelCase   :						Optional[int]				= Path("src/transformers")
    __lowerCAmelCase   :						Union[str, Any]				= ["configuration", "modeling", "tokenization"]
    self.analyze_directory(_SCREAMING_SNAKE_CASE ,    n_identifier=_SCREAMING_SNAKE_CASE)







 def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> List[Any]:

    """simple docstring"""

    __lowerCAmelCase   :						Dict				= Path("docs/source")
    __lowerCAmelCase   :						Optional[int]				= ["favicon.ico"]
    self.analyze_directory(_SCREAMING_SNAKE_CASE ,    ignore_files=_SCREAMING_SNAKE_CASE ,    only_modules=_SCREAMING_SNAKE_CASE)




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,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      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					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.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)