

'''simple docstring'''



import argparse

import torch

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


logging.set_verbosity_info()
UpperCAmelCase					:			Tuple       =	logging.get_logger(__name__)

UpperCAmelCase					:			Tuple       =	[
    ['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   a__			(    a__		):




     """simple docstring"""


     if k == "embeddings.weight":
          return "shared.weight"

     for parlai_name, hf_name in PATTERNS:
          __SCREAMING_SNAKE_CASE      = k.replace(a__ ,       a__		)

     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   a__			(    a__		):




     """simple docstring"""


     __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(a__		)
          __SCREAMING_SNAKE_CASE      = k.replace("""layernorm_embedding""" ,       """layer_norm"""		)
          assert new_k not in sd
          __SCREAMING_SNAKE_CASE      = v


UpperCAmelCase					:			Any       =	['START']







@torch.no_grad()
def   a__			(    a__ ,       a__ ,       a__		):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = torch.load(a__ ,       map_location="""cpu"""		)
     __SCREAMING_SNAKE_CASE      = model["""model"""]
     __SCREAMING_SNAKE_CASE      = BlenderbotConfig.from_json_file(a__		)
     __SCREAMING_SNAKE_CASE      = BlenderbotForConditionalGeneration(a__		)
     __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(a__		)
          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(a__		)
     m.model.load_state_dict(a__ ,       strict=a__		)
     m.half()
     m.save_pretrained(a__		)


if __name__ == "__main__":
  UpperCAmelCase					:			int       =	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'
  )
  UpperCAmelCase					:			Tuple       =	parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)









'''simple docstring'''



import importlib
import inspect
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
UpperCAmelCase					:			Any       =	'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase					:			Optional[int]       =	importlib.util.spec_from_file_location(
    'transformers',
    os.path.join(PATH_TO_TRANSFORMERS, '__init__.py'),
    submodule_search_locations=[PATH_TO_TRANSFORMERS],
)
UpperCAmelCase					:			Dict       =	spec.loader.load_module()

UpperCAmelCase					:			Dict       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
UpperCAmelCase					:			Dict       =	re.compile('\[(.+?)\]\((https://huggingface\.co/.+?)\)')


UpperCAmelCase					:			Any       =	{
    'CLIPConfigMixin',
    'DecisionTransformerConfigMixin',
    'EncoderDecoderConfigMixin',
    'RagConfigMixin',
    'SpeechEncoderDecoderConfigMixin',
    'VisionEncoderDecoderConfigMixin',
    'VisionTextDualEncoderConfigMixin',
}







def   a__			(    ):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE      = []

     for config_class in list(CONFIG_MAPPING.values()		):
          __SCREAMING_SNAKE_CASE      = False

          # source code of `config_class`
          __SCREAMING_SNAKE_CASE      = inspect.getsource(a__		)
          __SCREAMING_SNAKE_CASE      = _re_checkpoint.findall(a__		)

          for checkpoint in checkpoints:
               # Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
               # For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
               __SCREAMING_SNAKE_CASE  ,     __SCREAMING_SNAKE_CASE      = checkpoint

               # verify the checkpoint name corresponds to the checkpoint link
               __SCREAMING_SNAKE_CASE      = F'https://huggingface.co/{ckpt_name}'
               if ckpt_link == ckpt_link_from_name:
                    __SCREAMING_SNAKE_CASE      = True
                    break

          __SCREAMING_SNAKE_CASE      = config_class.__name__
          if not checkpoint_found and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
               configs_without_checkpoint.append(a__		)

     if len(a__		) > 0:
          __SCREAMING_SNAKE_CASE      = """\n""".join(sorted(a__		)		)
          raise ValueError(F'The following configurations don\'t contain any valid checkpoint:\n{message}'		)


if __name__ == "__main__":
  check_config_docstrings_have_checkpoints()






