




import argparse
import json

import numpy
import torch

from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()


def    A						(__A   :    Union[str, Any]   ,				__A   :    str       )   ->      Union[str, Any]:







     """simple docstring"""


     UpperCAmelCase_          =							torch.load(__snake_case   ,				map_location='''cpu'''       )

     UpperCAmelCase_          =							chkpt['''model''']

     # We have the base model one level deeper than the original XLM repository
     UpperCAmelCase_          =							{}
     for k, v in state_dict.items():
          if "pred_layer" in k:
               UpperCAmelCase_          =							v
          else:
               UpperCAmelCase_          =							v

     UpperCAmelCase_          =							chkpt['''params''']
     UpperCAmelCase_          =							{n: v for n, v in config.items() if not isinstance(__snake_case   ,				(torch.FloatTensor, numpy.ndarray)       )}

     UpperCAmelCase_          =							chkpt['''dico_word2id''']
     UpperCAmelCase_          =							{s + '''</w>''' if s.find('''@@'''       ) == -1 and i > 13 else s.replace('''@@'''   ,				''''''       ): i for s, i in vocab.items()}

     # Save pytorch-model
     UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + WEIGHTS_NAME
     UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + CONFIG_NAME
     UpperCAmelCase_          =							pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''vocab_file''']

     print(F"""Save PyTorch model to {pytorch_weights_dump_path}"""       )
     torch.save(__snake_case   ,				__snake_case       )

     print(F"""Save configuration file to {pytorch_config_dump_path}"""       )
     with open(__snake_case   ,				'''w'''   ,				encoding='''utf-8'''       ) as f:
          f.write(json.dumps(__snake_case   ,				indent=2       ) + '''\n'''       )

     print(F"""Save vocab file to {pytorch_config_dump_path}"""       )
     with open(__snake_case   ,				'''w'''   ,				encoding='''utf-8'''       ) as f:
          f.write(json.dumps(__snake_case   ,				indent=2       ) + '''\n'''       )


if __name__ == "__main__":
    snake_case_ : Any    		=       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--xlm_checkpoint_path", default=None, type=str, required=True, help="Path the official PyTorch dump."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    snake_case_ : Tuple    		=       parser.parse_args()
    convert_xlm_checkpoint_to_pytorch(args.xlm_checkpoint_path, args.pytorch_dump_folder_path)










from typing import Optional

from torch import nn

from .transformer_ad import TransformeraDModel, TransformeraDModelOutput







class       lowerCamelCase__   (       nn.Module):





     def __init__(self		,     UpperCAmelCase = 1_6		,     UpperCAmelCase = 8_8		,     UpperCAmelCase = None		,     UpperCAmelCase = 1		,     UpperCAmelCase = 0.0		,     UpperCAmelCase = 3_2		,     UpperCAmelCase = None		,     UpperCAmelCase = False		,     UpperCAmelCase = None		,     UpperCAmelCase = None		,     UpperCAmelCase = "geglu"		,     UpperCAmelCase = None		,     )     ->   Any:
      super().__init__()
      _lowercase							      =nn.ModuleList(
          [
              TransformeraDModel(
                  num_attention_heads=UpperCAmelCase		,     attention_head_dim=UpperCAmelCase		,     in_channels=UpperCAmelCase		,     num_layers=UpperCAmelCase		,     dropout=UpperCAmelCase		,     norm_num_groups=UpperCAmelCase		,     cross_attention_dim=UpperCAmelCase		,     attention_bias=UpperCAmelCase		,     sample_size=UpperCAmelCase		,     num_vector_embeds=UpperCAmelCase		,     activation_fn=UpperCAmelCase		,     num_embeds_ada_norm=UpperCAmelCase		,     )
              for _ in range(2       )
          ]       )

      # Variables that can be set by a pipeline:

      # The ratio of transformer1 to transformer2's output states to be combined during inference
      _lowercase							      =0.5

      # The shape of `encoder_hidden_states` is expected to be
      # `(batch_size, condition_lengths[0]+condition_lengths[1], num_features)`
      _lowercase							      =[7_7, 2_5_7]

      # Which transformer to use to encode which condition.
      # E.g. `(1, 0)` means that we'll use `transformers[1](conditions[0])` and `transformers[0](conditions[1])`
      _lowercase							      =[1, 0]





     def      __A					(self		,     UpperCAmelCase		,     UpperCAmelCase		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase=None		,     UpperCAmelCase = True		,     )     ->   str:
      _lowercase							      =hidden_states

      _lowercase							      =[]
      _lowercase							      =0
      # attention_mask is not used yet
      for i in range(2       ):
       # for each of the two transformers, pass the corresponding condition tokens
       _lowercase							      =encoder_hidden_states[:, tokens_start : tokens_start + self.condition_lengths[i]]
       _lowercase							      =self.transformer_index_for_condition[i]
       _lowercase							      =self.transformers[transformer_index](
           UpperCAmelCase		,     encoder_hidden_states=UpperCAmelCase		,     timestep=UpperCAmelCase		,     cross_attention_kwargs=UpperCAmelCase		,     return_dict=UpperCAmelCase		,     )[0]
       encoded_states.append(encoded_state - input_states       )
       tokens_start += self.condition_lengths[i]

      _lowercase							      =encoded_states[0] * self.mix_ratio + encoded_states[1] * (1 - self.mix_ratio)
      _lowercase							      =output_states + input_states

      if not return_dict:
       return (output_states,)

      return TransformeraDModelOutput(sample=UpperCAmelCase       )

