





"""simple docstring"""






from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
    import tensorflow as tf


lowerCAmelCase_			=							logging.get_logger(__name__)



@dataclass
class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  [
           """no_inference""",
           """no_cuda""",
           """no_tpu""",
           """no_speed""",
           """no_memory""",
           """no_env_print""",
           """no_multi_process""",
       ]






       def __init__(   self    ,  **a__     ):
              for deprecated_arg in self.deprecated_args:
                     if deprecated_arg in kwargs:
                            _UpperCAmelCase     =     deprecated_arg[3:]
                            _UpperCAmelCase     =     not kwargs.pop(a__     )
                            logger.warning(
                                f"""{deprecated_arg} is depreciated. Please use --no-{positive_arg} or"""
                                f""" {positive_arg}={kwargs[positive_arg]}"""     )
              _UpperCAmelCase     =     kwargs.pop('tpu_name'    ,  self.tpu_name     )
              _UpperCAmelCase     =     kwargs.pop('device_idx'    ,  self.device_idx     )
              _UpperCAmelCase     =     kwargs.pop('eager_mode'    ,  self.eager_mode     )
              _UpperCAmelCase     =     kwargs.pop('use_xla'    ,  self.use_xla     )
              super().__init__(**a__     )

       lowerCAmelCase__						 =  field(
           default=snake_case			,	metadata={"""help""": """Name of TPU"""}			,	)
       lowerCAmelCase__						 =  field(
           default=0			,	metadata={"""help""": """CPU / GPU device index. Defaults to 0."""}			,	)
       lowerCAmelCase__						 =  field(default=snake_case			,	metadata={"""help""": """Benchmark models in eager model."""}							)
       lowerCAmelCase__						 =  field(
           default=snake_case			,	metadata={
               """help""": """Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`."""
           }			,	)






       @cached_property
       def 							__A (   self     ):
              requires_backends(self    ,  ['tf']     )
              _UpperCAmelCase     =     None
              if self.tpu:
                     try:
                            if self.tpu_name:
                                   _UpperCAmelCase     =     tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name     )
                            else:
                                   _UpperCAmelCase     =     tf.distribute.cluster_resolver.TPUClusterResolver()
                     except ValueError:
                            _UpperCAmelCase     =     None
              return tpu






       @cached_property
       def 							__A (   self     ):
              requires_backends(self    ,  ['tf']     )
              if self.is_tpu:
                     tf.config.experimental_connect_to_cluster(self._setup_tpu     )
                     tf.tpu.experimental.initialize_tpu_system(self._setup_tpu     )

                     _UpperCAmelCase     =     tf.distribute.TPUStrategy(self._setup_tpu     )
              else:
                     # currently no multi gpu is allowed
                     if self.is_gpu:
                            # TODO: Currently only single GPU is supported
                            tf.config.set_visible_devices(self.gpu_list[self.device_idx]    ,  'GPU'     )
                            _UpperCAmelCase     =     tf.distribute.OneDeviceStrategy(device=f"""/gpu:{self.device_idx}"""     )
                     else:
                            tf.config.set_visible_devices([]    ,  'GPU'     )  # disable GPU
                            _UpperCAmelCase     =     tf.distribute.OneDeviceStrategy(device=f"""/cpu:{self.device_idx}"""     )

              return strategy






       @property
       def 							__A (   self     ):
              requires_backends(self    ,  ['tf']     )
              return self._setup_tpu is not None






       @property
       def 							__A (   self     ):
              requires_backends(self    ,  ['tf']     )
              return self._setup_strategy






       @property
       def 							__A (   self     ):
              requires_backends(self    ,  ['tf']     )
              return tf.config.list_physical_devices('GPU'     )






       @property
       def 							__A (   self     ):
              requires_backends(self    ,  ['tf']     )
              if self.cuda:
                     return len(self.gpu_list     )
              return 0






       @property
       def 							__A (   self     ):
              return self.n_gpu > 0













"""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)






