



'''simple docstring'''


import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     a_       		=os.path.abspath(lowercase__       )
     logger.info(F"""Converting TensorFlow checkpoint from {tf_path}"""       )
     # Load weights from TF model
     a_       		=tf.train.list_variables(lowercase__       )
     a_       		=[]
     a_       		=[]
     a_       		=[]
     for full_name, shape in init_vars:
          # logger.info(f"Loading TF weight {name} with shape {shape}")
          a_       		=full_name.split("/"       )
          if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
               logger.info(F"""Skipping non-model layer {full_name}"""       )
               continue
          if "optimizer" in full_name:
               logger.info(F"""Skipping optimization layer {full_name}"""       )
               continue
          if name[0] == "model":
               # ignore initial 'model'
               a_       		=name[1:]
          # figure out how many levels deep the name is
          a_       		=0
          for _name in name:
               if _name.startswith("layer_with_weights"       ):
                    depth += 1
               else:
                    break
          layer_depth.append(lowercase__       )
          # read data
          a_       		=tf.train.load_variable(lowercase__       ,	lowercase__       )
          names.append("/".join(lowercase__       )       )
          arrays.append(lowercase__       )
     logger.info(F"""Read a total of {len(lowercase__       ):,} layers"""       )

     # Sanity check
     if len(set(lowercase__       )       ) != 1:
          raise ValueError(F"""Found layer names with different depths (layer depth {list(set(lowercase__       )       )})"""       )
     a_       		=list(set(lowercase__       )       )[0]
     if layer_depth != 1:
          raise ValueError(
              "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
              " heads."       )

     # convert layers
     logger.info("Converting weights..."       )
     for full_name, array in zip(lowercase__       ,	lowercase__       ):
          a_       		=full_name.split("/"       )
          a_       		=model
          a_       		=[]
          for i, m_name in enumerate(lowercase__       ):
               if m_name == ".ATTRIBUTES":
                    # variable names end with .ATTRIBUTES/VARIABLE_VALUE
                    break
               if m_name.startswith("layer_with_weights"       ):
                    a_       		=int(m_name.split("-"       )[-1]       )
                    if layer_num <= 2:
                         # embedding layers
                         # layer_num 0: word_embeddings
                         # layer_num 1: position_embeddings
                         # layer_num 2: token_type_embeddings
                         continue
                    elif layer_num == 3:
                         # embedding LayerNorm
                         trace.extend(["embeddings", "LayerNorm"]       )
                         a_       		=getattr(lowercase__       ,	"embeddings"       )
                         a_       		=getattr(lowercase__       ,	"LayerNorm"       )
                    elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
                         # encoder layers
                         trace.extend(["encoder", "layer", str(layer_num - 4       )]       )
                         a_       		=getattr(lowercase__       ,	"encoder"       )
                         a_       		=getattr(lowercase__       ,	"layer"       )
                         a_       		=pointer[layer_num - 4]
                    elif layer_num == config.num_hidden_layers + 4:
                         # pooler layer
                         trace.extend(["pooler", "dense"]       )
                         a_       		=getattr(lowercase__       ,	"pooler"       )
                         a_       		=getattr(lowercase__       ,	"dense"       )
               elif m_name == "embeddings":
                    trace.append("embeddings"       )
                    a_       		=getattr(lowercase__       ,	"embeddings"       )
                    if layer_num == 0:
                         trace.append("word_embeddings"       )
                         a_       		=getattr(lowercase__       ,	"word_embeddings"       )
                    elif layer_num == 1:
                         trace.append("position_embeddings"       )
                         a_       		=getattr(lowercase__       ,	"position_embeddings"       )
                    elif layer_num == 2:
                         trace.append("token_type_embeddings"       )
                         a_       		=getattr(lowercase__       ,	"token_type_embeddings"       )
                    else:
                         raise ValueError(F"""Unknown embedding layer with name {full_name}"""       )
                    trace.append("weight"       )
                    a_       		=getattr(lowercase__       ,	"weight"       )
               elif m_name == "_attention_layer":
                    # self-attention layer
                    trace.extend(["attention", "self"]       )
                    a_       		=getattr(lowercase__       ,	"attention"       )
                    a_       		=getattr(lowercase__       ,	"self"       )
               elif m_name == "_attention_layer_norm":
                    # output attention norm
                    trace.extend(["attention", "output", "LayerNorm"]       )
                    a_       		=getattr(lowercase__       ,	"attention"       )
                    a_       		=getattr(lowercase__       ,	"output"       )
                    a_       		=getattr(lowercase__       ,	"LayerNorm"       )
               elif m_name == "_attention_output_dense":
                    # output attention dense
                    trace.extend(["attention", "output", "dense"]       )
                    a_       		=getattr(lowercase__       ,	"attention"       )
                    a_       		=getattr(lowercase__       ,	"output"       )
                    a_       		=getattr(lowercase__       ,	"dense"       )
               elif m_name == "_output_dense":
                    # output dense
                    trace.extend(["output", "dense"]       )
                    a_       		=getattr(lowercase__       ,	"output"       )
                    a_       		=getattr(lowercase__       ,	"dense"       )
               elif m_name == "_output_layer_norm":
                    # output dense
                    trace.extend(["output", "LayerNorm"]       )
                    a_       		=getattr(lowercase__       ,	"output"       )
                    a_       		=getattr(lowercase__       ,	"LayerNorm"       )
               elif m_name == "_key_dense":
                    # attention key
                    trace.append("key"       )
                    a_       		=getattr(lowercase__       ,	"key"       )
               elif m_name == "_query_dense":
                    # attention query
                    trace.append("query"       )
                    a_       		=getattr(lowercase__       ,	"query"       )
               elif m_name == "_value_dense":
                    # attention value
                    trace.append("value"       )
                    a_       		=getattr(lowercase__       ,	"value"       )
               elif m_name == "_intermediate_dense":
                    # attention intermediate dense
                    trace.extend(["intermediate", "dense"]       )
                    a_       		=getattr(lowercase__       ,	"intermediate"       )
                    a_       		=getattr(lowercase__       ,	"dense"       )
               elif m_name == "_output_layer_norm":
                    # output layer norm
                    trace.append("output"       )
                    a_       		=getattr(lowercase__       ,	"output"       )
               # weights & biases
               elif m_name in ["bias", "beta"]:
                    trace.append("bias"       )
                    a_       		=getattr(lowercase__       ,	"bias"       )
               elif m_name in ["kernel", "gamma"]:
                    trace.append("weight"       )
                    a_       		=getattr(lowercase__       ,	"weight"       )
               else:
                    logger.warning(F"""Ignored {m_name}"""       )
        # for certain layers reshape is necessary
          a_       		=".".join(lowercase__       )
          if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)"       ,	lowercase__       ) or re.match(
              r"(\S+)\.attention\.output\.dense\.weight"       ,	lowercase__       ):
               a_       		=array.reshape(pointer.data.shape       )
          if "kernel" in full_name:
               a_       		=array.transpose()
          if pointer.shape == array.shape:
               a_       		=torch.from_numpy(lowercase__       )
          else:
               raise ValueError(
                   F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
                   F""" {array.shape}"""       )
          logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}"""       )
     return model






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ):


     '''simple docstring'''







     logger.info(F"""Loading model based on config from {config_path}..."""       )
     a_       		=BertConfig.from_json_file(lowercase__       )
     a_       		=BertModel(lowercase__       )

     # Load weights from checkpoint
     logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}..."""       )
     load_tfa_weights_in_bert(lowercase__       ,	lowercase__       ,	lowercase__       )

     # Save pytorch-model
     logger.info(F"""Saving PyTorch model to {pytorch_dump_path}..."""       )
     torch.save(model.state_dict()       ,	lowercase__       )


if __name__ == "__main__":
  lowercase							       =			argparse.ArgumentParser()
  parser.add_argument(
      '''--tf_checkpoint_path''', type=str, required=True, help='''Path to the TensorFlow 2.x checkpoint path.'''
  )
  parser.add_argument(
      '''--bert_config_file''',
      type=str,
      required=True,
      help='''The config json file corresponding to the BERT model. This specifies the model architecture.''',
  )
  parser.add_argument(
      '''--pytorch_dump_path''',
      type=str,
      required=True,
      help='''Path to the output PyTorch model (must include filename).''',
  )
  lowercase							       =			parser.parse_args()
  convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''simple docstring'''


import torch

from transformers import CamembertForMaskedLM, CamembertTokenizer






def  UpperCAmelCase_       (		lowercase__       ,	lowercase__       ,	lowercase__       ,	lowercase__=5       ):


     '''simple docstring'''







     assert masked_input.count("<mask>"       ) == 1
     a_       		=torch.tensor(tokenizer.encode(lowercase__       ,	add_special_tokens=lowercase__       )       ).unsqueeze(0       )  # Batch size 1
     a_       		=model(lowercase__       )[0]  # The last hidden-state is the first element of the output tuple
     a_       		=(input_ids.squeeze() == tokenizer.mask_token_id).nonzero().item()
     a_       		=logits[0, masked_index, :]
     a_       		=logits.softmax(dim=0       )
     a_		,						a_       		=prob.topk(k=lowercase__       ,	dim=0       )
     a_       		=" ".join(
         [tokenizer.convert_ids_to_tokens(indices[i].item()       ) for i in range(len(lowercase__       )       )]       )
     a_       		=tokenizer.mask_token
     a_       		=[]
     for index, predicted_token_bpe in enumerate(topk_predicted_token_bpe.split(" "       )       ):
          a_       		=predicted_token_bpe.replace("\u2581"       ,	" "       )
          if " {0}".format(lowercase__       ) in masked_input:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(" {0}".format(lowercase__       )       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
          else:
               topk_filled_outputs.append(
                   (
                       masked_input.replace(lowercase__       ,	lowercase__       ),
                       values[index].item(),
                       predicted_token,
                   )       )
     return topk_filled_outputs


lowercase							       =			CamembertTokenizer.from_pretrained('''camembert-base''')
lowercase							       =			CamembertForMaskedLM.from_pretrained('''camembert-base''')
model.eval()

lowercase							       =			'''Le camembert est <mask> :)'''

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


