





import argparse

import torch

from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCamelCase 					=      logging.get_logger(__name__)

_lowerCamelCase 					=      [
    ['attention', 'attn'],
    ['encoder_attention', 'encoder_attn'],
    ['q_lin', 'q_proj'],
    ['k_lin', 'k_proj'],
    ['v_lin', 'v_proj'],
    ['out_lin', 'out_proj'],
    ['norm_embeddings', 'layernorm_embedding'],
    ['position_embeddings', 'embed_positions'],
    ['embeddings', 'embed_tokens'],
    ['ffn.lin', 'fc'],
]






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		if k == "embeddings.weight":
				return "shared.weight"

		for parlai_name, hf_name in PATTERNS:
				SCREAMING_SNAKE_CASE__								=   k.replace(UpperCamelCase__       , UpperCamelCase__     )

		if k.startswith("""encoder"""     ):
				SCREAMING_SNAKE_CASE__								=   k.replace(""".attn"""       , """.self_attn"""     )
				SCREAMING_SNAKE_CASE__								=   k.replace("""norm1"""       , """self_attn_layer_norm"""     )
				SCREAMING_SNAKE_CASE__								=   k.replace("""norm2"""       , """final_layer_norm"""     )
		elif k.startswith("""decoder"""     ):
				SCREAMING_SNAKE_CASE__								=   k.replace("""norm1"""       , """self_attn_layer_norm"""     )
				SCREAMING_SNAKE_CASE__								=   k.replace("""norm2"""       , """encoder_attn_layer_norm"""     )
				SCREAMING_SNAKE_CASE__								=   k.replace("""norm3"""       , """final_layer_norm"""     )
		return k






def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				Any     ):
		SCREAMING_SNAKE_CASE__								=   [
		    """model.encoder.layernorm_embedding.weight""",
		    """model.encoder.layernorm_embedding.bias""",
		    """model.decoder.layernorm_embedding.weight""",
		    """model.decoder.layernorm_embedding.bias""",
		]
		for k in keys:
				SCREAMING_SNAKE_CASE__								=   sd.pop(UpperCamelCase__     )
				SCREAMING_SNAKE_CASE__								=   k.replace("""layernorm_embedding"""       , """layer_norm"""     )
				assert new_k not in sd
				SCREAMING_SNAKE_CASE__								=   v


_lowerCamelCase 					=      ['START']






@torch.no_grad()
def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				int       , UpperCamelCase__:				Any       , UpperCamelCase__:				str     ):
		SCREAMING_SNAKE_CASE__								=   torch.load(UpperCamelCase__       , map_location="""cpu"""     )
		SCREAMING_SNAKE_CASE__								=   model["""model"""]
		SCREAMING_SNAKE_CASE__								=   BlenderbotConfig.from_json_file(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   BlenderbotForConditionalGeneration(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   m.model.state_dict().keys()
		SCREAMING_SNAKE_CASE__								=   []
		SCREAMING_SNAKE_CASE__								=   {}
		for k, v in sd.items():
				if k in IGNORE_KEYS:
						continue

				SCREAMING_SNAKE_CASE__								=   rename_state_dict_key(UpperCamelCase__     )
				if new_k not in valid_keys:
						failures.append([k, new_k]     )
				else:
						SCREAMING_SNAKE_CASE__								=   v
		if cfg.normalize_before:  # Blenderbot-3B checkpoints. Rename layernorm_embedding -> layer_norm
				rename_layernorm_keys(UpperCamelCase__     )
		m.model.load_state_dict(UpperCamelCase__       , strict=UpperCamelCase__     )
		m.half()
		m.save_pretrained(UpperCamelCase__     )


if __name__ == "__main__":
	_lowerCamelCase 					=      argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('--src_path', type=str, help='like blenderbot-model.bin')
	parser.add_argument('--save_dir', default='hf_blenderbot', type=str, help='Where to save converted model.')
	parser.add_argument(
	    '--hf_config_json', default='blenderbot-3b-config.json', type=str, help='Path to config to use'
	)
	_lowerCamelCase 					=      parser.parse_args()
	convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__magic_name__  :  Tuple					=       data_utils.TransfoXLTokenizer
__magic_name__  :  int					=       data_utils.TransfoXLCorpus
__magic_name__  :  List[str]					=       data_utils
__magic_name__  :  str					=       data_utils
def 		a_							( __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				):
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(__lowerCAmelCase   , '''rb'''				) as fp:
      lowerCAmelCase__										=      pickle.load(__lowerCAmelCase   , encoding='''latin1'''				)
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + VOCAB_FILES_NAMES['''pretrained_vocab_file''']
    print(F"""Save vocabulary to {pytorch_vocab_dump_path}"""				)
    lowerCAmelCase__										=      corpus.vocab.__dict__
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

    lowerCAmelCase__										=      corpus.__dict__
    corpus_dict_no_vocab.pop('''vocab'''   , __lowerCAmelCase				)
    lowerCAmelCase__										=      pytorch_dump_folder_path + '''/''' + CORPUS_NAME
    print(F"""Save dataset to {pytorch_dataset_dump_path}"""				)
    torch.save(__lowerCAmelCase   , __lowerCAmelCase				)

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.abspath(__lowerCAmelCase				)

    print(F"""Converting Transformer XL checkpoint from {tf_path} with config at {config_path}."""				)
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      lowerCAmelCase__										=      TransfoXLConfig()
    else:
      lowerCAmelCase__										=      TransfoXLConfig.from_json_file(__lowerCAmelCase				)
    print(F"""Building PyTorch model from configuration: {config}"""				)
    lowerCAmelCase__										=      TransfoXLLMHeadModel(__lowerCAmelCase				)

    lowerCAmelCase__										=      load_tf_weights_in_transfo_xl(__lowerCAmelCase   , __lowerCAmelCase   , __lowerCAmelCase				)
    # Save pytorch-model
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    lowerCAmelCase__										=      os.path.join(__lowerCAmelCase   , __lowerCAmelCase				)
    print(F"""Save PyTorch model to {os.path.abspath(__lowerCAmelCase				)}"""				)
    torch.save(model.state_dict()   , __lowerCAmelCase				)
    print(F"""Save configuration file to {os.path.abspath(__lowerCAmelCase				)}"""				)
    with open(__lowerCAmelCase   , '''w'''   , encoding='''utf-8'''				) as f:
      f.write(config.to_json_string()				)


if __name__ == "__main__":
      __magic_name__  :  int					=       argparse.ArgumentParser()
      parser.add_argument(
          """--pytorch_dump_folder_path""",
          default=None,
          type=str,
          required=True,
          help="""Path to the folder to store the PyTorch model or dataset/vocab.""",
      )
      parser.add_argument(
          """--tf_checkpoint_path""",
          default="""""",
          type=str,
          help="""An optional path to a TensorFlow checkpoint path to be converted.""",
      )
      parser.add_argument(
          """--transfo_xl_config_file""",
          default="""""",
          type=str,
          help=(
              """An optional config json file corresponding to the pre-trained BERT model. \n"""
              """This specifies the model architecture."""
          ),
      )
      parser.add_argument(
          """--transfo_xl_dataset_file""",
          default="""""",
          type=str,
          help="""An optional dataset file to be converted in a vocabulary.""",
      )
      __magic_name__  :  Optional[Any]					=       parser.parse_args()
      convert_transfo_xl_checkpoint_to_pytorch(
          args.tf_checkpoint_path,
          args.transfo_xl_config_file,
          args.pytorch_dump_folder_path,
          args.transfo_xl_dataset_file,
      )

