


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_  ,						lowerCAmelCase_=5   )						->Dict:
   # Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
   assert masked_input.count("""<mask>"""   ) == 1
   UpperCAmelCase							=     torch.tensor(tokenizer.encode(lowerCAmelCase_  ,						add_special_tokens=lowerCAmelCase_   )   ).unsqueeze(0   )  # Batch size 1
   UpperCAmelCase							=     model(lowerCAmelCase_   )[0]  # The last hidden-state is the first element of the output tuple
   UpperCAmelCase							=     (input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
   UpperCAmelCase							=     logits[0, masked_index, :]
   UpperCAmelCase							=     logits.softmax(dim=0   )
   UpperCAmelCase				,  UpperCAmelCase							=     prob.topk(k=lowerCAmelCase_  ,						dim=0   )
   UpperCAmelCase							=     """ """.join(
       [tokenizer.convert_ids_to_tokens(indices[i].item()   ) for i in range(len(lowerCAmelCase_   )   )]   )
   UpperCAmelCase							=     tokenizer.mask_token
   UpperCAmelCase							=     []
   for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(""" """   )   ):
      UpperCAmelCase							=     predicted_token_bpe.replace("""\u2581"""  ,						""" """   )
      if " {0}".format(lowerCAmelCase_   ) in masked_input:
         topk_filled_outputs.append(
             (
                 masked_input.replace(""" {0}""".format(lowerCAmelCase_   )  ,						lowerCAmelCase_   ),
                 values[index].item(),
                 predicted_token,
             )   )
      else:
         topk_filled_outputs.append(
             (
                 masked_input.replace(lowerCAmelCase_  ,						lowerCAmelCase_   ),
                 values[index].item(),
                 predicted_token,
             )   )
   return topk_filled_outputs


__a     =			CamembertTokenizer.from_pretrained("""camembert-base""")
__a     =			CamembertForMaskedLM.from_pretrained("""camembert-base""")
model.eval()

__a     =			"""Le camembert est <mask> :)"""







print(fill_mask(masked_input, model, tokenizer, topk=3))







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


__a     =			logging.get_logger(__name__)
def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Tuple:
   try:
      with open(lowerCAmelCase_  ,						"""rb"""   ) as flax_state_f:
         UpperCAmelCase							=     from_bytes(lowerCAmelCase_  ,						flax_state_f.read()   )
   except UnpicklingError as e:
      try:
         with open(lowerCAmelCase_   ) 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(lowerCAmelCase_  ,						lowerCAmelCase_   )







def   _UpperCamelCase				(						lowerCAmelCase_  ,						lowerCAmelCase_   )						->Dict:

   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
   UpperCAmelCase							=     flatten_dict(jax.tree_util.tree_map(lambda lowerCAmelCase_   : x.dtype == jnp.bfloataa  ,						lowerCAmelCase_   )   ).values()
   if any(lowerCAmelCase_   ):
      # 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."""   )
      UpperCAmelCase							=     jax.tree_util.tree_map(
          lambda lowerCAmelCase_   : params.astype(np.floataa   ) if params.dtype == jnp.bfloataa else params  ,						lowerCAmelCase_   )

   UpperCAmelCase							=     """"""

   UpperCAmelCase							=     flatten_dict(lowerCAmelCase_  ,						sep="""."""   )
   UpperCAmelCase							=     pt_model.state_dict()

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

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      UpperCAmelCase							=     flax_key_tuple.split("""."""   )

      if flax_key_tuple_array[-1] == "kernel" and flax_tensor.ndim == 4:
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     jnp.transpose(lowerCAmelCase_  ,						(3, 2, 0, 1)   )
      elif flax_key_tuple_array[-1] == "kernel":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]
         UpperCAmelCase							=     flax_tensor.T
      elif flax_key_tuple_array[-1] == "scale":
         UpperCAmelCase							=     flax_key_tuple_array[:-1] + ["""weight"""]

      if "time_embedding" not in flax_key_tuple_array:
         for i, flax_key_tuple_string in enumerate(lowerCAmelCase_   ):
            UpperCAmelCase							=     (
                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"""   )
            )

      UpperCAmelCase							=     """.""".join(lowerCAmelCase_   )

      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
            UpperCAmelCase							=     np.asarray(lowerCAmelCase_   ) if not isinstance(lowerCAmelCase_  ,						np.ndarray   ) else flax_tensor
            UpperCAmelCase							=     torch.from_numpy(lowerCAmelCase_   )
            # remove from missing keys
            missing_keys.remove(lowerCAmelCase_   )
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(lowerCAmelCase_   )

   pt_model.load_state_dict(lowerCAmelCase_   )

   # re-transform missing_keys to list
   UpperCAmelCase							=     list(lowerCAmelCase_   )

   if len(lowerCAmelCase_   ) > 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(lowerCAmelCase_   ) > 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



