





'''simple docstring'''

import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()





def 						UpperCamelCase    (							_lowerCamelCase : str						,	_lowerCamelCase : str						,	_lowerCamelCase : str	):
    def get_masked_lm_array(_lowerCamelCase : str	):
        A__    =      F"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        A__    =      tf.train.load_variable(_lowerCamelCase						,	_lowerCamelCase	)

        if "kernel" in name:
            A__    =      array.transpose()

        return torch.from_numpy(_lowerCamelCase	)

    def get_encoder_array(_lowerCamelCase : str	):
        A__    =      F"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        A__    =      tf.train.load_variable(_lowerCamelCase						,	_lowerCamelCase	)

        if "kernel" in name:
            A__    =      array.transpose()

        return torch.from_numpy(_lowerCamelCase	)

    def get_encoder_layer_array(_lowerCamelCase : int						,	_lowerCamelCase : str	):
        A__    =      F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        A__    =      tf.train.load_variable(_lowerCamelCase						,	_lowerCamelCase	)

        if "kernel" in name:
            A__    =      array.transpose()

        return torch.from_numpy(_lowerCamelCase	)

    def get_encoder_attention_layer_array(_lowerCamelCase : int						,	_lowerCamelCase : str						,	_lowerCamelCase : int	):
        A__    =      F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
        A__    =      tf.train.load_variable(_lowerCamelCase						,	_lowerCamelCase	)
        A__    =      array.reshape(_lowerCamelCase	)

        if "kernel" in name:
            A__    =      array.transpose()

        return torch.from_numpy(_lowerCamelCase	)

    print(F"Loading model based on config from {config_path}..."	)
    A__    =      BertConfig.from_json_file(_lowerCamelCase	)
    A__    =      BertForMaskedLM(_lowerCamelCase	)

    # Layers
    for layer_index in range(0						,	config.num_hidden_layers	):
        A__    =      model.bert.encoder.layer[layer_index]

        # Self-attention
        A__    =      layer.attention.self

        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_query_dense/kernel"						,	self_attn.query.weight.data.shape	)
        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_query_dense/bias"						,	self_attn.query.bias.data.shape	)
        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_key_dense/kernel"						,	self_attn.key.weight.data.shape	)
        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_key_dense/bias"						,	self_attn.key.bias.data.shape	)
        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_value_dense/kernel"						,	self_attn.value.weight.data.shape	)
        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_value_dense/bias"						,	self_attn.value.bias.data.shape	)

        # Self-attention Output
        A__    =      layer.attention.output

        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_output_dense/kernel"						,	self_output.dense.weight.data.shape	)
        A__    =      get_encoder_attention_layer_array(
            _lowerCamelCase						,	"_output_dense/bias"						,	self_output.dense.bias.data.shape	)

        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_attention_layer_norm/gamma"	)
        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_attention_layer_norm/beta"	)

        # Intermediate
        A__    =      layer.intermediate

        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_intermediate_dense/kernel"	)
        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_intermediate_dense/bias"	)

        # Output
        A__    =      layer.output

        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_output_dense/kernel"	)
        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_output_dense/bias"	)

        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_output_layer_norm/gamma"	)
        A__    =      get_encoder_layer_array(_lowerCamelCase						,	"_output_layer_norm/beta"	)

    # Embeddings
    A__    =      get_encoder_array("_position_embedding_layer/embeddings"	)
    A__    =      get_encoder_array("_type_embedding_layer/embeddings"	)
    A__    =      get_encoder_array("_embedding_norm_layer/gamma"	)
    A__    =      get_encoder_array("_embedding_norm_layer/beta"	)

    # LM Head
    A__    =      model.cls.predictions.transform

    A__    =      get_masked_lm_array("dense/kernel"	)
    A__    =      get_masked_lm_array("dense/bias"	)

    A__    =      get_masked_lm_array("layer_norm/gamma"	)
    A__    =      get_masked_lm_array("layer_norm/beta"	)

    A__    =      get_masked_lm_array("embedding_table"	)

    # Pooling
    A__    =      BertPooler(config=_lowerCamelCase	)
    A__    =      get_encoder_array("_pooler_layer/kernel"	)
    A__    =      get_encoder_array("_pooler_layer/bias"	)

    # Export final model
    model.save_pretrained(_lowerCamelCase	)

    # Integration test - should load without any errors ;)
    A__    =      BertForMaskedLM.from_pretrained(_lowerCamelCase	)
    print(new_model.eval()	)

    print("Model conversion was done sucessfully!"	)


if __name__ == "__main__":
  __lowerCAmelCase							:     int		     =argparse.ArgumentParser()
  parser.add_argument(
      "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping 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.",
  )
  __lowerCAmelCase							:     Optional[int]		     =parser.parse_args()
  convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)










'''simple docstring'''

import argparse

import torch

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


logging.set_verbosity_info()
__lowerCAmelCase							:     Union[str, Any]		     =logging.get_logger(__name__)

__lowerCAmelCase							:     Optional[int]		     =[
    ["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 						UpperCamelCase    (							_lowerCamelCase : int	):
    if k == "embeddings.weight":
        return "shared.weight"

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

    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 						UpperCamelCase    (							_lowerCamelCase : int	):
    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(_lowerCamelCase	)
        A__    =      k.replace("layernorm_embedding"						,	"layer_norm"	)
        assert new_k not in sd
        A__    =      v


__lowerCAmelCase							:     Optional[int]		     =["START"]





@torch.no_grad()
def 						UpperCamelCase    (							_lowerCamelCase : Dict						,	_lowerCamelCase : Optional[Any]						,	_lowerCamelCase : Optional[Any]	):
    A__    =      torch.load(_lowerCamelCase						,	map_location="cpu"	)
    A__    =      model["model"]
    A__    =      BlenderbotConfig.from_json_file(_lowerCamelCase	)
    A__    =      BlenderbotForConditionalGeneration(_lowerCamelCase	)
    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(_lowerCamelCase	)
        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(_lowerCamelCase	)
    m.model.load_state_dict(_lowerCamelCase						,	strict=_lowerCamelCase	)
    m.half()
    m.save_pretrained(_lowerCamelCase	)


if __name__ == "__main__":
  __lowerCAmelCase							:     Union[str, Any]		     =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							:     Union[str, Any]		     =parser.parse_args()
  convert_parlai_checkpoint(args.src_path, args.save_dir, args.hf_config_json)



