


import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


SCREAMING_SNAKE_CASE			: int				   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			: Tuple				   =					{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}






class _lowerCamelCase(		_a  ):
       lowercase_					:      Dict       					=     """xlnet"""
       lowercase_					:      Tuple       					=     ["""mems"""]
       lowercase_					:      List[Any]       					=     {
           """n_token""": """vocab_size""",  # Backward compatibility
           """hidden_size""": """d_model""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }
       def __init__(   self, lowerCamelCase=3_20_00, lowerCamelCase=10_24, lowerCamelCase=24, lowerCamelCase=16, lowerCamelCase=40_96, lowerCamelCase="gelu", lowerCamelCase=True, lowerCamelCase="bi", lowerCamelCase=0.0_2, lowerCamelCase=1E-12, lowerCamelCase=0.1, lowerCamelCase=5_12, lowerCamelCase=None, lowerCamelCase=True, lowerCamelCase=False, lowerCamelCase=False, lowerCamelCase=-1, lowerCamelCase=False, lowerCamelCase="last", lowerCamelCase=True, lowerCamelCase="tanh", lowerCamelCase=0.1, lowerCamelCase=5, lowerCamelCase=5, lowerCamelCase=5, lowerCamelCase=1, lowerCamelCase=2, **lowerCamelCase, )			->  Union[str, Any]:




           """simple docstring"""

           _lowercase			:				List[Any] 						=						vocab_size
           _lowercase			:				List[Any] 						=						d_model
           _lowercase			:				List[str] 						=						n_layer
           _lowercase			:				List[str] 						=						n_head
           if d_model % n_head != 0:
               raise ValueError(F'''\'d_model % n_head\' ({d_model % n_head}) should be equal to 0''')
           if "d_head" in kwargs:
               if kwargs["d_head"] != d_model // n_head:
                   raise ValueError(
                       F'''`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})''')
           _lowercase			:				Optional[Any] 						=						d_model // n_head
           _lowercase			:				List[Any] 						=						ff_activation
           _lowercase			:				int 						=						d_inner
           _lowercase			:				Any 						=						untie_r
           _lowercase			:				Union[str, Any] 						=						attn_type

           _lowercase			:				Optional[Any] 						=						initializer_range
           _lowercase			:				List[Any] 						=						layer_norm_eps

           _lowercase			:				Dict 						=						dropout
           _lowercase			:				List[str] 						=						mem_len
           _lowercase			:				Optional[int] 						=						reuse_len
           _lowercase			:				str 						=						bi_data
           _lowercase			:				List[Any] 						=						clamp_len
           _lowercase			:				List[Any] 						=						same_length

           _lowercase			:				int 						=						summary_type
           _lowercase			:				str 						=						summary_use_proj
           _lowercase			:				Optional[int] 						=						summary_activation
           _lowercase			:				Tuple 						=						summary_last_dropout
           _lowercase			:				Dict 						=						start_n_top
           _lowercase			:				Union[str, Any] 						=						end_n_top

           _lowercase			:				int 						=						bos_token_id
           _lowercase			:				List[Any] 						=						pad_token_id
           _lowercase			:				Optional[int] 						=						eos_token_id

           if "use_cache" in kwargs:
               warnings.warn(
                   'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
                   ' instead.', lowerCamelCase, )
               _lowercase			:				Dict 						=						kwargs['use_cache']

           _lowercase			:				str 						=						use_mems_eval
           _lowercase			:				Tuple 						=						use_mems_train
           super().__init__(pad_token_id=lowerCamelCase, bos_token_id=lowerCamelCase, eos_token_id=lowerCamelCase, **lowerCamelCase)
       @property
       def      UpperCamelCase	(   self)			->  Optional[int]:




           """simple docstring"""

           logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''')
           return -1




       @max_position_embeddings.setter
       def      UpperCamelCase	(   self, lowerCamelCase)			->  Union[str, Any]:




           """simple docstring"""

           raise NotImplementedError(
               F'''The model {self.model_type} is one of the few models that has no sequence length limit.''')









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				List[Any]           =   [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(UpperCamelCase__       ,			UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   str					)      ->    int:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE       ,__SCREAMING_SNAKE_CASE:				str           =   emb.weight.shape
						__SCREAMING_SNAKE_CASE:				List[str]           =   nn.Linear(UpperCamelCase__       ,			UpperCamelCase__       ,			bias=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   emb.weight.data
						return lin_layer






def 		lowerCAmelCase  (	UpperCamelCase__   :   Optional[int]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				int           =   torch.load(UpperCamelCase__       ,			map_location='''cpu'''					)
						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Namespace(**checkpoint['''cfg''']['''model''']					)
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   checkpoint['''model''']
						remove_ignore_keys_(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				int           =   state_dict['''decoder.embed_tokens.weight'''].shape[0]

						__SCREAMING_SNAKE_CASE:				List[Any]           =   {key.replace('''decoder'''       ,			'''model'''					): val for key, val in state_dict.items()}

						__SCREAMING_SNAKE_CASE:				Dict           =   XGLMConfig(
						    vocab_size=UpperCamelCase__       ,			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       ,			)

						__SCREAMING_SNAKE_CASE:				int           =   XGLMForCausalLM(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   model.load_state_dict(UpperCamelCase__       ,			strict=UpperCamelCase__					)
						print(UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				List[str]           =   make_linear_from_emb(model.model.embed_tokens					)

						return model


if __name__ == "__main__":
							lowerCAmelCase							:       Union[str, Any]					 =	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							:       List[str]					 =	parser.parse_args()
							lowerCAmelCase							:       Optional[int]					 =	convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
							model.save_pretrained(args.pytorch_dump_folder_path)



