





"""simple docstring"""






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


lowerCAmelCase_			=							logging.get_logger(__name__)

lowerCAmelCase_			=							{
    'EleutherAI/gpt-neo-1.3B': 'https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json',
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}



class 	lowerCAmelCase					(      __lowercase							):
       lowerCAmelCase__						 =  """gpt_neo"""
       lowerCAmelCase__						 =  ["""past_key_values"""]
       lowerCAmelCase__						 =  {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






       def __init__(   self    ,  a__=5_02_57    ,  a__=20_48    ,  a__=20_48    ,  a__=24    ,  a__=[[["global", "local"], 12]]    ,  a__=16    ,  a__=None    ,  a__=2_56    ,  a__="gelu_new"    ,  a__=0.0    ,  a__=0.0    ,  a__=0.0    ,  a__=0.1    ,  a__=1E-5    ,  a__=0.02    ,  a__=True    ,  a__=5_02_56    ,  a__=5_02_56    ,  **a__    ,  ):
              _UpperCAmelCase     =     vocab_size
              _UpperCAmelCase     =     max_position_embeddings
              _UpperCAmelCase     =     hidden_size
              _UpperCAmelCase     =     num_layers
              _UpperCAmelCase     =     num_heads
              _UpperCAmelCase     =     intermediate_size
              _UpperCAmelCase     =     window_size
              _UpperCAmelCase     =     activation_function
              _UpperCAmelCase     =     resid_dropout
              _UpperCAmelCase     =     embed_dropout
              _UpperCAmelCase     =     attention_dropout
              _UpperCAmelCase     =     classifier_dropout
              _UpperCAmelCase     =     layer_norm_epsilon
              _UpperCAmelCase     =     initializer_range
              _UpperCAmelCase     =     use_cache

              _UpperCAmelCase     =     bos_token_id
              _UpperCAmelCase     =     eos_token_id

              _UpperCAmelCase     =     attention_types
              _UpperCAmelCase     =     self.expand_attention_types_params(a__     )

              if len(self.attention_layers     ) != self.num_layers:
                     raise ValueError(
                         'Configuration for convolutional module is incorrect. '
                         'It is required that `len(config.attention_layers)` == `config.num_layers` '
                         f"""but is `len(config.attention_layers) = {len(self.attention_layers     )}`, """
                         f"""`config.num_layers = {self.num_layers}`. """
                         '`config.attention_layers` is prepared using `config.attention_types`. '
                         'Please verify the value of `config.attention_types` argument.'     )

              super().__init__(bos_token_id=a__    ,  eos_token_id=a__    ,  **a__     )






       @staticmethod
       def 							__A (   a__     ):
              _UpperCAmelCase     =     []
              for item in attention_types:
                     for _ in range(item[1]     ):
                            attentions.extend(item[0]     )
              return attentions


def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       import torch

       _UpperCAmelCase     =     input.size()
       _UpperCAmelCase     =     len(_lowerCamelCase							)
       _UpperCAmelCase     =     shape[dimension]

       _UpperCAmelCase     =     torch.arange(0,_lowerCamelCase,_lowerCamelCase							)
       _UpperCAmelCase     =     torch.div(sizedim - size,_lowerCamelCase,rounding_mode='floor'							) + 1
       _UpperCAmelCase     =     torch.arange(_lowerCamelCase							) + low_indices[:min_length][:, None]

       _UpperCAmelCase     =     [slice(_lowerCamelCase							)] * rank
       _UpperCAmelCase     =     indices
       _UpperCAmelCase     =     input[s]

       _UpperCAmelCase     =     list(range(0,rank + 1							)							)
       perm.append(perm.pop(dimension + 1							)							)

       return sliced.permute(_lowerCamelCase							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)		->       Optional[Any]:






       """simple docstring"""




       import torch

       _UpperCAmelCase     =     torch.arange(1,_lowerCamelCase							)
       _UpperCAmelCase     =     torch.remainder(_lowerCamelCase,_lowerCamelCase							)
       _UpperCAmelCase     =     remainders == 0
       _UpperCAmelCase     =     candidates[divisor_indices]
       _UpperCAmelCase     =     torch.max(_lowerCamelCase							)
       return largest_divisor, torch.div(_lowerCamelCase,_lowerCamelCase,rounding_mode='floor'							)



class 	lowerCAmelCase					(      __lowercase							):






       @property
       def 							__A (   self     ):
              _UpperCAmelCase     =     OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}     )
              if self.use_past:
                     self.fill_with_past_key_values_(a__    ,  direction='inputs'     )
                     _UpperCAmelCase     =     {0: "batch", 1: "past_sequence + sequence"}
              else:
                     _UpperCAmelCase     =     {0: "batch", 1: "sequence"}

              return common_inputs






       @property
       def 							__A (   self     ):
              return self._config.num_heads






       def 							__A (   self    ,  a__    ,  a__ = -1    ,  a__ = -1    ,  a__ = False    ,  a__ = None    ,  ):
              _UpperCAmelCase     =     super(a__    ,  self     ).generate_dummy_inputs(
                  a__    ,  batch_size=a__    ,  seq_length=a__    ,  is_pair=a__    ,  framework=a__     )

              # We need to order the input in the way they appears in the forward()
              _UpperCAmelCase     =     OrderedDict({'input_ids': common_inputs['input_ids']}     )

              # Need to add the past_keys
              if self.use_past:
                     if not is_torch_available():
                            raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'     )
                     else:
                            import torch

                            _UpperCAmelCase     =     common_inputs["input_ids"].shape
                            # Not using the same length for past_key_values
                            _UpperCAmelCase     =     seqlen + 2
                            _UpperCAmelCase     =     (
                                batch,
                                self.num_attention_heads,
                                past_key_values_length,
                                self._config.hidden_size // self.num_attention_heads,
                            )
                            _UpperCAmelCase     =     [
                                (torch.zeros(a__     ), torch.zeros(a__     )) for _ in range(self.num_layers     )
                            ]

              _UpperCAmelCase     =     common_inputs["attention_mask"]
              if self.use_past:
                     _UpperCAmelCase     =     ordered_inputs["attention_mask"].dtype
                     _UpperCAmelCase     =     torch.cat(
                         [ordered_inputs['attention_mask'], torch.ones(a__    ,  a__    ,  dtype=a__     )]    ,  dim=1     )

              return ordered_inputs






       @property
       def 							__A (   self     ):
              return 13









"""simple docstring"""






import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       str:






       """simple docstring"""




       _UpperCAmelCase     =     [
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE							)

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       List[Any]:






       """simple docstring"""




       _UpperCAmelCase    ,							_UpperCAmelCase     =     emb.weight.shape
       _UpperCAmelCase     =     nn.Linear(SCREAMING_SNAKE_CASE,SCREAMING_SNAKE_CASE,bias=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     emb.weight.data
       return lin_layer

def       __lowerCamelCase    (       SCREAMING_SNAKE_CASE							)		->       int:






       """simple docstring"""




       _UpperCAmelCase     =     torch.load(SCREAMING_SNAKE_CASE,map_location='cpu'							)
       _UpperCAmelCase     =     mam_aaa['args'] or mam_aaa['cfg']['model']
       _UpperCAmelCase     =     mam_aaa['model']
       remove_ignore_keys_(SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     state_dict['encoder.embed_tokens.weight'].shape[0]

       _UpperCAmelCase     =     MaMaaaConfig(
           vocab_size=SCREAMING_SNAKE_CASE,max_position_embeddings=1024,encoder_layers=args.encoder_layers,decoder_layers=args.decoder_layers,encoder_attention_heads=args.encoder_attention_heads,decoder_attention_heads=args.decoder_attention_heads,encoder_ffn_dim=args.encoder_ffn_embed_dim,decoder_ffn_dim=args.decoder_ffn_embed_dim,d_model=args.encoder_embed_dim,encoder_layerdrop=args.encoder_layerdrop,decoder_layerdrop=args.decoder_layerdrop,dropout=args.dropout,attention_dropout=args.attention_dropout,activation_dropout=args.activation_dropout,activation_function='relu',)

       _UpperCAmelCase     =     state_dict['decoder.embed_tokens.weight']
       _UpperCAmelCase     =     MaMaaaForConditionalGeneration(SCREAMING_SNAKE_CASE							)
       model.model.load_state_dict(SCREAMING_SNAKE_CASE,strict=SCREAMING_SNAKE_CASE							)
       _UpperCAmelCase     =     make_linear_from_emb(model.model.shared							)

       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_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
    model.save_pretrained(args.pytorch_dump_folder_path)






