






from pickle import UnpicklingError

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict

from ..utils import logging


UpperCAmelCase__							  =						logging.get_logger(__name__)
def 		_a       ( a					:Tuple      ,				a					:List[Any]					)				->     int:
 try:
  with open(a      ,				'''rb'''					) as flax_state_f:
   a						    = from_bytes(a      ,				flax_state_f.read()					)
 except UnpicklingError as e:
  try:
   with open(a					) as f:
    if f.read().startswith('''version'''					):
     raise OSError(
         '''You seem to have cloned a repository without having git-lfs installed. Please'''
         ''' install git-lfs and run `git lfs install` followed by `git lfs pull` in the'''
         ''' folder you cloned.'''					)
    else:
     raise ValueError from e
  except (UnicodeDecodeError, ValueError):
   raise EnvironmentError(F"""Unable to convert {model_file} to Flax deserializable object. """					)

 return load_flax_weights_in_pytorch_model(a      ,				a					)

def 		_a       ( a					:Union[str, Any]      ,				a					:int					)				->     str:

 try:
  import torch  # noqa: F401
 except ImportError:
  logger.error(
      '''Loading Flax weights in PyTorch requires both PyTorch and Flax to be installed. Please see'''
      ''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
      ''' instructions.'''					)
  raise

 # check if we have bf16 weights
 a						    = flatten_dict(jax.tree_util.tree_map(lambda a					: x.dtype == jnp.bfloataa      ,				a					)					).values()
 if any(a					):
  # convert all weights to fp32 if they are bf16 since torch.from_numpy can-not handle bf16

  # and bf16 is not fully supported in PT yet.
  logger.warning(
      '''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
      '''before loading those in PyTorch model.'''					)
  a						    = jax.tree_util.tree_map(
      lambda a					: params.astype(np.floataa					) if params.dtype == jnp.bfloataa else params      ,				a					)

 a						    = ''''''

 a						    = flatten_dict(a      ,				sep='''.'''					)
 a						    = pt_model.state_dict()

 # keep track of unexpected & missing keys
 a						    = []
 a						    = set(pt_model_dict.keys()					)

 for flax_key_tuple, flax_tensor in flax_state_dict.items():
  a						    = flax_key_tuple.split('''.'''					)

  if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
   a						    = flax_key_tuple_array[:-1] + ['''weight''']
   a						    = jnp.transpose(a      ,				(3, 2, 0, 1)					)
  elif flax_key_tuple_array[-1] == "kernel":
   a						    = flax_key_tuple_array[:-1] + ['''weight''']
   a						    = flax_tensor.T
  elif flax_key_tuple_array[-1] == "scale":
   a						    = flax_key_tuple_array[:-1] + ['''weight''']

  if "time_embedding" not in flax_key_tuple_array:
   for i, flax_key_tuple_string in enumerate(a					):
    a						    = (
        flax_key_tuple_string.replace('''_0'''      ,				'''.0'''					)
        .replace('''_1'''      ,				'''.1'''					)
        .replace('''_2'''      ,				'''.2'''					)
        .replace('''_3'''      ,				'''.3'''					)
        .replace('''_4'''      ,				'''.4'''					)
        .replace('''_5'''      ,				'''.5'''					)
        .replace('''_6'''      ,				'''.6'''					)
        .replace('''_7'''      ,				'''.7'''					)
        .replace('''_8'''      ,				'''.8'''					)
        .replace('''_9'''      ,				'''.9'''					)
    )

  a						    = '''.'''.join(a					)

  if flax_key in pt_model_dict:
   if flax_tensor.shape != pt_model_dict[flax_key].shape:
    raise ValueError(
        F"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
        F"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}."""					)
   else:
    # add weight to pytorch dict
    a						    = np.asarray(a					) if not isinstance(a      ,				np.ndarray					) else flax_tensor
    a						    = torch.from_numpy(a					)
    # remove from missing keys
    missing_keys.remove(a					)
  else:
   # weight is not expected by PyTorch model
   unexpected_keys.append(a					)

 pt_model.load_state_dict(a					)

 # re-transform missing_keys to list
 a						    = list(a					)

 if len(a					) > 0:
  logger.warning(
      '''Some weights of the Flax model were not used when initializing the PyTorch model'''
      F""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
      F""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
      ''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
      F""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
      ''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
      ''' FlaxBertForSequenceClassification model).'''					)
 if len(a					) > 0:
  logger.warning(
      F"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
      F""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
      ''' use it for predictions and inference.'''					)

 return pt_model










import argparse

import torch

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


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

UpperCAmelCase__							  =						[
    ["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					:Optional[Any]					)				->     List[Any]:
 if k == "embeddings.weight":
  return "shared.weight"

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

 if k.startswith('''encoder'''					):
  a						    = k.replace('''.attn'''      ,				'''.self_attn'''					)
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''final_layer_norm'''					)
 elif k.startswith('''decoder'''					):
  a						    = k.replace('''norm1'''      ,				'''self_attn_layer_norm'''					)
  a						    = k.replace('''norm2'''      ,				'''encoder_attn_layer_norm'''					)
  a						    = k.replace('''norm3'''      ,				'''final_layer_norm'''					)
 return k
def 		_a       ( a					:Dict					)				->     Tuple:
 a						    = [
     '''model.encoder.layernorm_embedding.weight''',
     '''model.encoder.layernorm_embedding.bias''',
     '''model.decoder.layernorm_embedding.weight''',
     '''model.decoder.layernorm_embedding.bias''',
 ]
 for k in keys:
  a						    = sd.pop(a					)
  a						    = k.replace('''layernorm_embedding'''      ,				'''layer_norm'''					)
  assert new_k not in sd
  a						    = v


UpperCAmelCase__							  =						["START"]
@torch.no_grad()
def 		_a       ( a					:Dict      ,				a					:str      ,				a					:int					)				->     int:
 a						    = torch.load(a      ,				map_location='''cpu'''					)
 a						    = model['''model''']
 a						    = BlenderbotConfig.from_json_file(a					)
 a						    = BlenderbotForConditionalGeneration(a					)
 a						    = m.model.state_dict().keys()
 a						    = []
 a						    = {}
 for k, v in sd.items():
  if k in IGNORE_KEYS:
   continue

  a						    = rename_state_dict_key(a					)
  if new_k not in valid_keys:
   failures.append([k, new_k]					)
  else:
   a						    = 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__							  =						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__							  =						parser.parse_args()
     convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)


