





"""simple docstring"""




from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


a						: List[str]     	=			logging.get_logger(__name__)

a						: int     	=			{
    '''t5-small''': '''https://huggingface.co/t5-small/resolve/main/config.json''',
    '''t5-base''': '''https://huggingface.co/t5-base/resolve/main/config.json''',
    '''t5-large''': '''https://huggingface.co/t5-large/resolve/main/config.json''',
    '''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/config.json''',
    '''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/config.json''',
}


class        __UpperCamelCase				(						a__						):
							lowerCamelCase   :					Optional[Any]      					="""t5"""
							lowerCamelCase   :					Dict      					=["""past_key_values"""]
							lowerCamelCase   :					Optional[int]      					={"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






							def __init__(   self      ,					lowerCAmelCase__=3_2128      ,					lowerCAmelCase__=512      ,					lowerCAmelCase__=64      ,					lowerCAmelCase__=2048      ,					lowerCAmelCase__=6      ,					lowerCAmelCase__=None      ,					lowerCAmelCase__=8      ,					lowerCAmelCase__=32      ,					lowerCAmelCase__=128      ,					lowerCAmelCase__=0.1      ,					lowerCAmelCase__=1E-6      ,					lowerCAmelCase__=1.0      ,					lowerCAmelCase__="relu"      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=True      ,					lowerCAmelCase__=0      ,					lowerCAmelCase__=1      ,					**lowerCAmelCase__      ,					)	-> str:
								a      :		List[Any]	=				vocab_size
								a      :		Any	=				d_model
								a      :		Optional[int]	=				d_kv
								a      :		Optional[int]	=				d_ff
								a      :		Any	=				num_layers
								a      :		Any	=				(
								    num_decoder_layers if num_decoder_layers is not None else self.num_layers
								)  # default = symmetry
								a      :		Optional[Any]	=				num_heads
								a      :		List[str]	=				relative_attention_num_buckets
								a      :		int	=				relative_attention_max_distance
								a      :		List[str]	=				dropout_rate
								a      :		Optional[Any]	=				layer_norm_epsilon
								a      :		Optional[Any]	=				initializer_factor
								a      :		Union[str, Any]	=				feed_forward_proj
								a      :		List[Any]	=				use_cache

								a      :		Dict	=				self.feed_forward_proj.split("-"      )
								a      :		Tuple	=				act_info[-1]
								a      :		Optional[Any]	=				act_info[0] == "gated"

								if len(lowerCAmelCase__      ) > 1 and act_info[0] != "gated" or len(lowerCAmelCase__      ) > 2:
									raise ValueError(
									    f"""`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."""
									    "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. "
									    "'gated-gelu' or 'relu'"      )

								# for backwards compatibility
								if feed_forward_proj == "gated-gelu":
									a      :		Union[str, Any]	=				"gelu_new"

								super().__init__(
								    pad_token_id=lowerCAmelCase__      ,					eos_token_id=lowerCAmelCase__      ,					is_encoder_decoder=lowerCAmelCase__      ,					**lowerCAmelCase__      ,					)


class        __UpperCamelCase				(						a__						):






							@property
							def     __a   (   self      )	-> Mapping[str, Mapping[int, str]]:
								a      :		Optional[Any]	=				{
								    "input_ids": {0: "batch", 1: "encoder_sequence"},
								    "attention_mask": {0: "batch", 1: "encoder_sequence"},
								}
								if self.use_past:
									a      :		str	=				"past_encoder_sequence + sequence"
									a      :		Optional[Any]	=				{0: "batch"}
									a      :		Optional[Any]	=				{0: "batch", 1: "past_decoder_sequence + sequence"}
								else:
									a      :		Tuple	=				{0: "batch", 1: "decoder_sequence"}
									a      :		Any	=				{0: "batch", 1: "decoder_sequence"}

								if self.use_past:
									self.fill_with_past_key_values_(lowerCAmelCase__      ,					direction="inputs"      )

								return common_inputs






							@property
							def     __a   (   self      )	-> int:
								return 13








import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  [
						    '''decoder.version''',
						    '''decoder.output_projection.weight''',
						    '''_float_tensor''',
						    '''decoder.embed_positions._float_tensor''',
						]
						for k in ignore_keys:
												state_dict.pop(_A ,  _A  )





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_			,					lowerCAmelCase_      			=  emb.weight.shape
						lowerCAmelCase_      			=  nn.Linear(_A ,  _A ,  bias=_A  )
						lowerCAmelCase_      			=  emb.weight.data
						return lin_layer





def __UpperCamelCase			(       _A  ):
						lowerCAmelCase_      			=  torch.load(_A ,  map_location='''cpu'''  )
						lowerCAmelCase_      			=  Namespace(**checkpoint['''cfg''']['''model''']  )
						lowerCAmelCase_      			=  checkpoint['''model''']
						remove_ignore_keys_(_A  )
						lowerCAmelCase_      			=  state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

						lowerCAmelCase_      			=  XGLMConfig(
						    vocab_size=_A ,  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_      			=  XGLMForCausalLM(_A  )
						lowerCAmelCase_      			=  model.load_state_dict(_A ,  strict=_A  )
						print(_A  )
						lowerCAmelCase_      			=  make_linear_from_emb(model.model.embed_tokens  )

						return model


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




