

from __future__ import annotations

from collections.abc import Callable
from typing import Any, Generic, TypeVar

a		=TypeVar("""T""")






class        A_		(       Generic[T]				):
							def __init__(		self		:			List[str]   ,SCREAMING_SNAKE_CASE__		:			list[T]   ,SCREAMING_SNAKE_CASE__		:			Callable[[T, T], T]):
								__lowerCamelCase				:				Any | T					       = None

								__lowerCamelCase				:				int					       = len(SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				list[T]					       = [any_type for _ in range(self.N)] + arr
								__lowerCamelCase				:				Optional[int]					       = fnc
								self.build()
							def     lowerCAmelCase     (		self		:			Dict):
								for p in range(self.N - 1   ,0   ,-1):
									__lowerCamelCase				:				List[str]					       = self.fn(self.st[p * 2]   ,self.st[p * 2 + 1])
							def     lowerCAmelCase     (		self		:			Tuple   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			T):
								p += self.N
								__lowerCamelCase				:				Dict					       = v
								while p > 1:
									__lowerCamelCase				:				List[Any]					       = p // 2
									__lowerCamelCase				:				List[Any]					       = self.fn(self.st[p * 2]   ,self.st[p * 2 + 1])


							def     lowerCAmelCase     (		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			int   ,SCREAMING_SNAKE_CASE__		:			int):  # noqa: E741
								__lowerCamelCase       , __lowerCamelCase				:				Dict					       = l + self.N, r + self.N

								__lowerCamelCase				:				T | None					       = None
								while l <= r:
									if l % 2 == 1:
										__lowerCamelCase				:				Dict					       = self.st[l] if res is None else self.fn(SCREAMING_SNAKE_CASE__   ,self.st[l])
									if r % 2 == 0:
										__lowerCamelCase				:				str					       = self.st[r] if res is None else self.fn(SCREAMING_SNAKE_CASE__   ,self.st[r])
									__lowerCamelCase       , __lowerCamelCase				:				Any					       = (l + 1) // 2, (r - 1) // 2
								return res


if __name__ == "__main__":
					from functools import reduce

					a		=[1, 10, -2, 9, -3, 8, 4, -7, 5, 6, 11, -12]

					a		={
					    0: 7,
					    1: 2,
					    2: 6,
					    3: -14,
					    4: 5,
					    5: 4,
					    6: 7,
					    7: -10,
					    8: 9,
					    9: 10,
					    10: 12,
					    11: 1,
					}

					a		=SegmentTree(test_array, min)
					a		=SegmentTree(test_array, max)
					a		=SegmentTree(test_array, lambda a, b: a + b)






					def    SCREAMING_SNAKE_CASE__       (  )  ->     None:
						for i in range(len(lowerCamelCase__			)			):
							for j in range(lowerCamelCase__	, len(lowerCamelCase__			)			):
								__lowerCamelCase				:				Any					       = reduce(lowerCamelCase__	, test_array[i : j + 1]			)
								__lowerCamelCase				:				Union[str, Any]					       = reduce(lowerCamelCase__	, test_array[i : j + 1]			)
								__lowerCamelCase				:				Dict					       = reduce(lambda lowerCamelCase__	, lowerCamelCase__			: a + b	, test_array[i : j + 1]			)
								assert min_range == min_segment_tree.query(lowerCamelCase__	, lowerCamelCase__			)
								assert max_range == max_segment_tree.query(lowerCamelCase__	, lowerCamelCase__			)
								assert sum_range == sum_segment_tree.query(lowerCamelCase__	, lowerCamelCase__			)

					test_all_segments()

					for index, value in test_updates.items():
										a		=value
										min_segment_tree.update(index, value)
										max_segment_tree.update(index, value)
										sum_segment_tree.update(index, value)
										test_all_segments()





import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
   a_    					=       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')
   a_    					=       parser.parse_args()

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

   a_    					=       model.state_dict()
   a_    					=       {}

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

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



