
"""simple docstring"""


import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE      =     argparse.ArgumentParser(
          description=(
              """Extraction some layers of the full RobertaForMaskedLM or GPT2LMHeadModel for Transfer Learned"""
              """ Distillation"""
          )
      )
      parser.add_argument("""--model_type""", default="""roberta""", choices=["""roberta""", """gpt2"""])
      parser.add_argument("""--model_name""", default="""roberta-large""", type=str)
      parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_roberta_048131723.pth""", type=str)
      parser.add_argument("""--vocab_transform""", action="""store_true""")
      SCREAMING_SNAKE_CASE      =     parser.parse_args()

      if args.model_type == "roberta":
            SCREAMING_SNAKE_CASE      =     RobertaForMaskedLM.from_pretrained(args.model_name)
            SCREAMING_SNAKE_CASE      =     """roberta"""
      elif args.model_type == "gpt2":
            SCREAMING_SNAKE_CASE      =     GPTaLMHeadModel.from_pretrained(args.model_name)
            SCREAMING_SNAKE_CASE      =     """transformer"""

      SCREAMING_SNAKE_CASE      =     model.state_dict()
      SCREAMING_SNAKE_CASE      =     {}

      # Embeddings #
      if args.model_type == "gpt2":
            for param_name in ["wte.weight", "wpe.weight"]:
                  SCREAMING_SNAKE_CASE      =     state_dict[F'''{prefix}.{param_name}''']
      else:
            for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
                  SCREAMING_SNAKE_CASE      =     F'''{prefix}.embeddings.{w}.weight'''
                  SCREAMING_SNAKE_CASE      =     state_dict[param_name]
            for w in ["weight", "bias"]:
                  SCREAMING_SNAKE_CASE      =     F'''{prefix}.embeddings.LayerNorm.{w}'''
                  SCREAMING_SNAKE_CASE      =     state_dict[param_name]

    # Transformer Blocks #
      SCREAMING_SNAKE_CASE      =     0
      for teacher_idx in [0, 2, 4, 7, 9, 11]:
            if args.model_type == "gpt2":
                  for layer in ["ln_1", "attn.c_attn", "attn.c_proj", "ln_2", "mlp.c_fc", "mlp.c_proj"]:
                        for w in ["weight", "bias"]:
                              SCREAMING_SNAKE_CASE      =     state_dict[
                                  F'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                              ]
                  SCREAMING_SNAKE_CASE      =     state_dict[F'''{prefix}.h.{teacher_idx}.attn.bias''']
            else:
                  for layer in [
                      "attention.self.query",
                      "attention.self.key",
                      "attention.self.value",
                      "attention.output.dense",
                      "attention.output.LayerNorm",
                      "intermediate.dense",
                      "output.dense",
                      "output.LayerNorm",
                  ]:
                        for w in ["weight", "bias"]:
                              SCREAMING_SNAKE_CASE      =     state_dict[
                                  F'''{prefix}.encoder.layer.{teacher_idx}.{layer}.{w}'''
                              ]
            std_idx += 1

      # Language Modeling Head ###s
      if args.model_type == "roberta":
            for layer in ["lm_head.decoder.weight", "lm_head.bias"]:
                  SCREAMING_SNAKE_CASE      =     state_dict[F'''{layer}''']
            if args.vocab_transform:
                  for w in ["weight", "bias"]:
                        SCREAMING_SNAKE_CASE      =     state_dict[F'''lm_head.dense.{w}''']
                        SCREAMING_SNAKE_CASE      =     state_dict[F'''lm_head.layer_norm.{w}''']
      elif args.model_type == "gpt2":
            for w in ["weight", "bias"]:
                  SCREAMING_SNAKE_CASE      =     state_dict[F'''{prefix}.ln_f.{w}''']
            SCREAMING_SNAKE_CASE      =     state_dict["""lm_head.weight"""]

      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)







"""simple docstring"""


SCREAMING_SNAKE_CASE      =     {
    """meter""": """m""",
    """kilometer""": """km""",
    """megametre""": """Mm""",
    """gigametre""": """Gm""",
    """terametre""": """Tm""",
    """petametre""": """Pm""",
    """exametre""": """Em""",
    """zettametre""": """Zm""",
    """yottametre""": """Ym""",
}
# Exponent of the factor(meter)
SCREAMING_SNAKE_CASE      =     {
    """m""": 0,
    """km""": 3,
    """Mm""": 6,
    """Gm""": 9,
    """Tm""": 12,
    """Pm""": 15,
    """Em""": 18,
    """Zm""": 21,
    """Ym""": 24,
}



def 	lowerCamelCase__			(							UpperCAmelCase_		,      UpperCAmelCase_		,      UpperCAmelCase_				)->							float:







       """simple docstring"""
       UpperCamelCase					 =		from_type.lower().strip("s"				)
       UpperCamelCase					 =		to_type.lower().strip("s"				)

       UpperCamelCase					 =		UNIT_SYMBOL.get(UpperCAmelCase_		,      UpperCAmelCase_				)
       UpperCamelCase					 =		UNIT_SYMBOL.get(UpperCAmelCase_		,      UpperCAmelCase_				)

       if from_sanitized not in METRIC_CONVERSION:
              UpperCamelCase					 =		(
                  F"Invalid 'from_type' value: {from_type!r}.\n"
                  F"Conversion abbreviations are: {', '.join(UpperCAmelCase_				)}"
              )
              raise ValueError(UpperCAmelCase_				)
       if to_sanitized not in METRIC_CONVERSION:
              UpperCamelCase					 =		(
                  F"Invalid 'to_type' value: {to_type!r}.\n"
                  F"Conversion abbreviations are: {', '.join(UpperCAmelCase_				)}"
              )
              raise ValueError(UpperCAmelCase_				)
       UpperCamelCase					 =		METRIC_CONVERSION[from_sanitized]
       UpperCamelCase					 =		METRIC_CONVERSION[to_sanitized]
       UpperCamelCase					 =		1

       if from_exponent > to_exponent:
              UpperCamelCase					 =		from_exponent - to_exponent
       else:
              UpperCamelCase					 =		-(to_exponent - from_exponent)

       return value * pow(10		,      UpperCAmelCase_				)


if __name__ == "__main__":
      from doctest import testmod

      testmod()





