
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 						__magic_name__ (    __a			:       List[str]       ,		__a			:       Tuple       ,		__a			:       Tuple						):



     '''simple docstring'''




     def get_masked_lm_array(__a			:       List[str]						):
          UpperCamelCase__				=				f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=				tf.train.load_variable(A__       ,		A__						)

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

          return torch.from_numpy(A__						)

     def get_encoder_array(__a			:       Optional[int]						):
          UpperCamelCase__				=				f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=				tf.train.load_variable(A__       ,		A__						)

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

          return torch.from_numpy(A__						)

     def get_encoder_layer_array(__a			:       Any       ,		__a			:       Tuple						):
          UpperCamelCase__				=				f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=				tf.train.load_variable(A__       ,		A__						)

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

          return torch.from_numpy(A__						)

     def get_encoder_attention_layer_array(__a			:       str       ,		__a			:       List[Any]       ,		__a			:       str						):
          UpperCamelCase__				=				f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__				=				tf.train.load_variable(A__       ,		A__						)
          UpperCamelCase__				=				array.reshape(A__						)

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

          return torch.from_numpy(A__						)

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

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

          # Self-attention
          UpperCamelCase__				=				layer.attention.self

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

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

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

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

          # Intermediate
          UpperCamelCase__				=				layer.intermediate

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

          # Output
          UpperCamelCase__				=				layer.output

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

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

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

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

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

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

     UpperCamelCase__				=				get_masked_lm_array("""embedding_table"""						)

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

     # Export final model
     model.save_pretrained(A__						)

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

     print("""Model conversion was done sucessfully!"""						)


if __name__ == "__main__":
    lowerCamelCase_							=     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_							=     parser.parse_args()
    convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)












import argparse
from pathlib import Path

import torch

from transformers import OPTConfig, OPTModel
from transformers.utils import logging


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



def A_	(		A__				)  ->		List[str]:
       a__						:	Union[str, Any] =				torch.load(A__		,      map_location='cpu'				)
       if "model" in sd.keys():
              a__						:	Tuple =				torch.load(A__		,      map_location='cpu'				)['model']

       # pop unnecessary weights
       a__						:	Optional[Any] =				[
           'decoder.version',
           'decoder.output_projection.weight',
       ]
       for key in keys_to_delete:
              if key in sd:
                     sd.pop(A__				)

       a__						:	Union[str, Any] =				{
           'decoder.project_in_dim.weight': 'decoder.project_in.weight',
           'decoder.project_out_dim.weight': 'decoder.project_out.weight',
           'decoder.layer_norm.weight': 'decoder.final_layer_norm.weight',
           'decoder.layer_norm.bias': 'decoder.final_layer_norm.bias',
       }
       for old_key, new_key in keys_to_rename.items():
              if old_key in sd:
                     a__						:	Any =				sd.pop(A__				)

       a__						:	Optional[int] =				list(sd.keys()				)
       for key in keys:
              if ".qkv_proj." in key:
                     a__						:	str =				sd[key]
                     # We split QKV in separate Q,K,V

                     a__						:	Union[str, Any] =				key.replace('.qkv_proj.'		,      '.q_proj.'				)
                     a__						:	int =				key.replace('.qkv_proj.'		,      '.k_proj.'				)
                     a__						:	Optional[int] =				key.replace('.qkv_proj.'		,      '.v_proj.'				)

                     a__						:	List[Any] =				value.shape[0]
                     assert depth % 3 == 0
                     # `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
                     # https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
                     a__ ,							a__ ,							a__						:	Any =				torch.split(A__		,      depth // 3		,      dim=0				)

                     a__						:	int =				q
                     a__						:	List[Any] =				k
                     a__						:	int =				v
                     del sd[key]

       return sd



@torch.no_grad()
def A_	(		A__		,      A__		,      A__=None				)  ->		Union[str, Any]:
       a__						:	Union[str, Any] =				load_checkpoint(A__				)

       if config is not None:
              a__						:	List[Any] =				OPTConfig.from_pretrained(A__				)
       else:
              a__						:	int =				OPTConfig()

       a__						:	Optional[int] =				OPTModel(A__				).half().eval()
       model.load_state_dict(A__				)

       # Check results
       Path(A__				).mkdir(exist_ok=A__				)
       model.save_pretrained(A__				)


if __name__ == "__main__":
    lowercase      :       str									=  argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--fairseq_path""",
        type=str,
        help=(
            """path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
            """ https://huggingface.co/models?other=opt_metasq"""
        ),
    )
    parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
    parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
    lowercase      :       Any									=  parser.parse_args()
    convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config)


