






"""simple docstring"""



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


if __name__ == "__main__":
						__A		:       Any  				=		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		:       Union[str, Any]  				=		parser.parse_args()

						if args.model_type == "roberta":
												__A		:       Any  				=		RobertaForMaskedLM.from_pretrained(args.model_name)
												__A		:       Optional[Any]  				=		'''roberta'''
						elif args.model_type == "gpt2":
												__A		:       Tuple  				=		GPTaLMHeadModel.from_pretrained(args.model_name)
												__A		:       List[Any]  				=		'''transformer'''

						__A		:       Any  				=		model.state_dict()
						__A		:       Any  				=		{}

						# Embeddings #
						if args.model_type == "gpt2":
												for param_name in ["wte.weight", "wpe.weight"]:
																		__A		:       Optional[int]  				=		state_dict[F"""{prefix}.{param_name}"""]
						else:
												for w in ["word_embeddings", "position_embeddings", "token_type_embeddings"]:
																		__A		:       Tuple  				=		F"""{prefix}.embeddings.{w}.weight"""
																		__A		:       Union[str, Any]  				=		state_dict[param_name]
												for w in ["weight", "bias"]:
																		__A		:       Union[str, Any]  				=		F"""{prefix}.embeddings.LayerNorm.{w}"""
																		__A		:       List[str]  				=		state_dict[param_name]

    # Transformer Blocks #
						__A		:       Optional[int]  				=		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		:       Union[str, Any]  				=		state_dict[
																														    F"""{prefix}.h.{teacher_idx}.{layer}.{w}"""
																														]
																		__A		:       int  				=		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		:       Optional[Any]  				=		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		:       List[str]  				=		state_dict[F"""{layer}"""]
												if args.vocab_transform:
																		for w in ["weight", "bias"]:
																								__A		:       List[str]  				=		state_dict[F"""lm_head.dense.{w}"""]
																								__A		:       Optional[int]  				=		state_dict[F"""lm_head.layer_norm.{w}"""]
						elif args.model_type == "gpt2":
												for w in ["weight", "bias"]:
																		__A		:       Union[str, Any]  				=		state_dict[F"""{prefix}.ln_f.{w}"""]
												__A		:       Optional[int]  				=		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"""



from __future__ import annotations

__A		:       List[Any]  				=		[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__A		:       str  				=		[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		lowercase_ : List[Any]							       =   len(__snake_case	)

		for i in range(__snake_case	):
				lowercase_ : float							       =   -1
				for j in range(i + 1		,					__snake_case	):
						if arr[i] < arr[j]:
								lowercase_ : List[str]							       =   arr[j]
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   []
		for i, outer in enumerate(__snake_case	):
				lowercase_ : float							       =   -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								lowercase_ : List[Any]							       =   inner
								break
				result.append(__snake_case	)
		return result



def 						lowercase    (				__snake_case				: list[float]	):
		lowercase_ : List[str]							       =   len(__snake_case	)
		lowercase_ : list[float]							       =   []
		lowercase_ : list[float]							       =   [-1] * arr_size

		for index in reversed(range(__snake_case	)	):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						lowercase_ : Optional[Any]							       =   stack[-1]
				stack.append(arr[index]	)
		return result


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

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						__A		:       int  				=		(
						    '''from __main__ import arr, next_greatest_element_slow, '''
						    '''next_greatest_element_fast, next_greatest_element'''
						)
						print(
						    '''next_greatest_element_slow():''',
						    timeit('''next_greatest_element_slow(arr)''', setup=setup),
						)
						print(
						    '''next_greatest_element_fast():''',
						    timeit('''next_greatest_element_fast(arr)''', setup=setup),
						)
						print(
						    '''     next_greatest_element():''',
						    timeit('''next_greatest_element(arr)''', setup=setup),
						)



