
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
    lowerCamelCase      =argparse.ArgumentParser(
        description=(
            "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"
            " Distillation"
        )
    )
    parser.add_argument("--model_type", default="bert", choices=["bert"])
    parser.add_argument("--model_name", default="bert-base-uncased", type=str)
    parser.add_argument("--dump_checkpoint", default="serialization_dir/tf_bert-base-uncased_0247911.pth", type=str)
    parser.add_argument("--vocab_transform", action="store_true")
    lowerCamelCase      =parser.parse_args()

    if args.model_type == "bert":
        lowerCamelCase      =BertForMaskedLM.from_pretrained(args.model_name)
        lowerCamelCase      ="bert"
    else:
        raise ValueError("args.model_type should be \"bert\".")

    lowerCamelCase      =model.state_dict()
    lowerCamelCase      ={}

    for w in ["word_embeddings", "position_embeddings"]:
        lowerCamelCase      =state_dict[F'''{prefix}.embeddings.{w}.weight''']
    for w in ["weight", "bias"]:
        lowerCamelCase      =state_dict[F'''{prefix}.embeddings.LayerNorm.{w}''']

    lowerCamelCase      =0
    for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
        for w in ["weight", "bias"]:
            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
            ]
            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
            ]
            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}'''
            ]

            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}'''
            ]
            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
            ]

            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
            ]
            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
            ]
            lowerCamelCase      =state_dict[
                F'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
            ]
        std_idx += 1

    lowerCamelCase      =state_dict["cls.predictions.decoder.weight"]
    lowerCamelCase      =state_dict["cls.predictions.bias"]
    if args.vocab_transform:
        for w in ["weight", "bias"]:
            lowerCamelCase      =state_dict[F'''cls.predictions.transform.dense.{w}''']
            lowerCamelCase      =state_dict[F'''cls.predictions.transform.LayerNorm.{w}''']

    print(F'''N layers selected for distillation: {std_idx}''')
    print(F'''Number of params transferred for distillation: {len(compressed_sd.keys())}''')

    print(F'''Save transferred checkpoint to {args.dump_checkpoint}.''')
    torch.save(compressed_sd, args.dump_checkpoint)




from argparse import ArgumentParser

from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand


lowerCamelCase      =logging.get_logger(__name__)  # pylint: disable=invalid-name


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      if not path:
            return "pipe"

      for ext in PipelineDataFormat.SUPPORTED_FORMATS:
            if path.endswith(UpperCamelCase__		):
                  return ext

      raise Exception(
          f'''Unable to determine file format from file extension {path}. '''
          f'''Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}'''		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				pipeline(
          task=args.task ,		model=args.model if args.model else None ,		config=args.config ,		tokenizer=args.tokenizer ,		device=args.device ,		)
      UpperCamelCase__    :   Any					=				try_infer_format_from_ext(args.input		) if args.format == '''infer''' else args.format
      UpperCamelCase__    :   Tuple					=				PipelineDataFormat.from_str(
          format=UpperCamelCase__ ,		output_path=args.output ,		input_path=args.input ,		column=args.column if args.column else nlp.default_input_names ,		overwrite=args.overwrite ,		)
      return RunCommand(UpperCamelCase__ ,		UpperCamelCase__		)




class 				_lowerCamelCase (      UpperCamelCase_ ):





     """simple docstring"""







     def __init__(							self						,						__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  )  ->   List[Any]:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				nlp
           UpperCamelCase__    :   Dict					=				reader






     @staticmethod
     def    __SCREAMING_SNAKE_CASE			(							__SCREAMING_SNAKE_CASE  )  ->   str:




           """simple docstring"""







           UpperCamelCase__    :   Optional[Any]					=				parser.add_parser('''run'''						,						help='''Run a pipeline through the CLI'''  )
           run_parser.add_argument('''--task'''						,						choices=get_supported_tasks()						,						help='''Task to run'''  )
           run_parser.add_argument('''--input'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file to use for inference'''  )
           run_parser.add_argument('''--output'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Path to the file that will be used post to write results.'''  )
           run_parser.add_argument('''--model'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model to instantiate.'''  )
           run_parser.add_argument('''--config'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name or path to the model\'s config to instantiate.'''  )
           run_parser.add_argument(
               '''--tokenizer'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the tokenizer to use. (default: same as the model name)'''  )
           run_parser.add_argument(
               '''--column'''						,						type=__SCREAMING_SNAKE_CASE						,						help='''Name of the column to use as input. (For multi columns input as QA use column1,columns2)'''						,						)
           run_parser.add_argument(
               '''--format'''						,						type=__SCREAMING_SNAKE_CASE						,						default='''infer'''						,						choices=PipelineDataFormat.SUPPORTED_FORMATS						,						help='''Input format to read from'''						,						)
           run_parser.add_argument(
               '''--device'''						,						type=__SCREAMING_SNAKE_CASE						,						default=-1						,						help='''Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)'''						,						)
           run_parser.add_argument('''--overwrite'''						,						action='''store_true'''						,						help='''Allow overwriting the output file.'''  )
           run_parser.set_defaults(func=__SCREAMING_SNAKE_CASE  )






     def    __SCREAMING_SNAKE_CASE			(							self  )  ->   Any:




           """simple docstring"""







           UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				self._nlp, []

           for entry in self._reader:
                 UpperCamelCase__    :   List[str]					=				nlp(**__SCREAMING_SNAKE_CASE  ) if self._reader.is_multi_columns else nlp(__SCREAMING_SNAKE_CASE  )
                 if isinstance(__SCREAMING_SNAKE_CASE						,						__SCREAMING_SNAKE_CASE  ):
                       outputs.append(__SCREAMING_SNAKE_CASE  )
                 else:
                       outputs += output

        # Saving data
           if self._nlp.binary_output:
                 UpperCamelCase__    :   Union[str, Any]					=				self._reader.save_binary(__SCREAMING_SNAKE_CASE  )
                 logger.warning(F'''Current pipeline requires output to be in binary format, saving at {binary_path}'''  )
           else:
                 self._reader.save(__SCREAMING_SNAKE_CASE  )


