






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
     lowerCamelCase__							=     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""")
     lowerCamelCase__							=     parser.parse_args()

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

     lowerCamelCase__							=     model.state_dict()
     lowerCamelCase__							=     {}

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

    # Transformer Blocks #
     lowerCamelCase__							=     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"]:
                         lowerCamelCase__							=     state_dict[
                             f'''{prefix}.h.{teacher_idx}.{layer}.{w}'''
                         ]
               lowerCamelCase__							=     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"]:
                         lowerCamelCase__							=     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"]:
               lowerCamelCase__							=     state_dict[f'''{layer}''']
          if args.vocab_transform:
               for w in ["weight", "bias"]:
                    lowerCamelCase__							=     state_dict[f'''lm_head.dense.{w}''']
                    lowerCamelCase__							=     state_dict[f'''lm_head.layer_norm.{w}''']
     elif args.model_type == "gpt2":
          for w in ["weight", "bias"]:
               lowerCamelCase__							=     state_dict[f'''{prefix}.ln_f.{w}''']
          lowerCamelCase__							=     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)










import math

lowerCamelCase__							=     10
lowerCamelCase__							=     7
lowerCamelCase__							=     BALLS_PER_COLOUR * NUM_COLOURS






def   UpperCamelCase  (       snake_case__     :   int = 20  ):




     '''simple docstring'''






     __snake_case			:Dict          =						math.comb(snake_case__	,snake_case__  )
     __snake_case			:int          =						math.comb(NUM_BALLS - BALLS_PER_COLOUR	,snake_case__  )

     __snake_case			:List[str]          =						NUM_COLOURS * (1 - missing_colour / total)

     return f'''{result:.9f}'''


if __name__ == "__main__":
     print(solution(20))


