
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   int:
       lowercase							:					int	     =				[
           'decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(__a ,     __a    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Tuple:
       lowercase							:					Optional[Any]	     =				emb.weight.shape
       lowercase							:					str	     =				nn.Linear(__a ,     __a ,     bias=__a    )
       lowercase							:					List[str]	     =				emb.weight.data
       return lin_layer







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   Any:
       lowercase							:					Dict	     =				torch.load(__a ,     map_location="""cpu"""    )
       lowercase							:					Optional[Any]	     =				Namespace(**checkpoint["""cfg"""]["""model"""]    )
       lowercase							:					List[Any]	     =				checkpoint['model']
       remove_ignore_keys_(__a    )
       lowercase							:					Dict	     =				state_dict['decoder.embed_tokens.weight'].shape[0]

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

       lowercase							:					List[str]	     =				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 ,     )

       lowercase							:					Tuple	     =				XGLMForCausalLM(__a    )
       lowercase							:					Optional[Any]	     =				model.load_state_dict(__a ,     strict=__a    )
       print(__a    )
       lowercase							:					int	     =				make_linear_from_emb(model.model.embed_tokens    )

       return model


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



from typing import Mapping

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


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

lowercase  : Tuple						   =							{
    """google/umt5-small""": """https://huggingface.co/google/umt5-small/resolve/main/config.json""",
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}

class __snake_case		(				lowerCAmelCase					):
    _a		:						List[str]=    "umt5"
    _a		:						Optional[Any]=    ["past_key_values"]
    def __init__(  self		,snake_case=250112		,snake_case=512		,snake_case=64		,snake_case=1024		,snake_case=8		,snake_case=None		,snake_case=6		,snake_case=32		,snake_case=128		,snake_case=0.1		,snake_case=1e-6		,snake_case=1.0		,snake_case="gated-gelu"		,snake_case=True		,snake_case=True		,snake_case="T5Tokenizer"		,snake_case=True		,snake_case=0		,snake_case=1		,snake_case=0		,**snake_case		,):



           '''simple docstring'''







           super().__init__(
               is_encoder_decoder=snake_case		,tokenizer_class=snake_case		,tie_word_embeddings=snake_case		,pad_token_id=snake_case		,eos_token_id=snake_case		,decoder_start_token_id=snake_case		,**snake_case		,)
           lowercase							:					List[str]	     =				vocab_size
           lowercase							:					Optional[Any]	     =				d_model
           lowercase							:					int	     =				d_kv
           lowercase							:					List[Any]	     =				d_ff
           lowercase							:					Dict	     =				num_layers
           lowercase							:					int	     =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           lowercase							:					List[Any]	     =				num_heads
           lowercase							:					Optional[Any]	     =				relative_attention_num_buckets
           lowercase							:					Dict	     =				relative_attention_max_distance
           lowercase							:					Dict	     =				dropout_rate
           lowercase							:					Any	     =				layer_norm_epsilon
           lowercase							:					Any	     =				initializer_factor
           lowercase							:					Union[str, Any]	     =				feed_forward_proj
           lowercase							:					Optional[Any]	     =				use_cache

           lowercase							:					Dict	     =				self.feed_forward_proj.split("""-"""				)
           lowercase							:					List[Any]	     =				act_info[-1]
           lowercase							:					Any	     =				act_info[0] == """gated"""

           if len(snake_case				) > 1 and act_info[0] != "gated" or len(snake_case				) > 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'"""				)

           if feed_forward_proj == "gated-gelu":
                  lowercase							:					int	     =				"""gelu_new"""
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.d_model
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_heads
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return self.num_layers






class __snake_case		(				lowerCAmelCase					):
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           lowercase							:					int	     =				{
               """input_ids""": {0: """batch""", 1: """encoder_sequence"""},
               """attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
           }
           if self.use_past:
                  lowercase							:					List[Any]	     =				"""past_encoder_sequence + sequence"""
                  lowercase							:					Dict	     =				{0: """batch"""}
                  lowercase							:					Union[str, Any]	     =				{0: """batch""", 1: """past_decoder_sequence + sequence"""}
           else:
                  lowercase							:					Optional[Any]	     =				{0: """batch""", 1: """decoder_sequence"""}
                  lowercase							:					List[Any]	     =				{0: """batch""", 1: """decoder_sequence"""}

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

           return common_inputs
    @property
    # Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 13
    @property
    def 				_SCREAMING_SNAKE_CASE	(  self				):



           '''simple docstring'''







           return 5e-4
