






import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

			UpperCAmelCase__      			=						model.state_dict()
			UpperCAmelCase__      			=						{}

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

    # Transformer Blocks #
			UpperCAmelCase__      			=						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"]:
															UpperCAmelCase__      			=						state_dict[
															    F"{prefix}.h.{teacher_idx}.{layer}.{w}"
															]
									UpperCAmelCase__      			=						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"]:
															UpperCAmelCase__      			=						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"]:
									UpperCAmelCase__      			=						state_dict[F"{layer}"]
						if args.vocab_transform:
									for w in ["weight", "bias"]:
												UpperCAmelCase__      			=						state_dict[F"lm_head.dense.{w}"]
												UpperCAmelCase__      			=						state_dict[F"lm_head.layer_norm.{w}"]
			elif args.model_type == "gpt2":
						for w in ["weight", "bias"]:
									UpperCAmelCase__      			=						state_dict[F"{prefix}.ln_f.{w}"]
						UpperCAmelCase__      			=						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 mpmath  # for roots of unity
import numpy as np







class       __A							:




      """simple docstring"""







      def __init__(		self   ,				a__=None   ,				a__=None):



             """simple docstring"""





             _lowerCamelCase				:  List[str]						=		list(poly_a or [0])[:]
             _lowerCamelCase				:  Union[str, Any]						=		list(poly_b or [0])[:]

             # Remove leading zero coefficients
             while self.polyA[-1] == 0:
                    self.polyA.pop()
             _lowerCamelCase				:  Optional[int]						=		len(self.polyA)

             while self.polyB[-1] == 0:
                    self.polyB.pop()
             _lowerCamelCase				:  Any						=		len(self.polyB)

             # Add 0 to make lengths equal a power of 2
             _lowerCamelCase				:  Dict						=		int(
                 2 ** np.ceil(np.loga(len(self.polyA) + len(self.polyB) - 1)))

             while len(self.polyA) < self.c_max_length:
                    self.polyA.append(0)
             while len(self.polyB) < self.c_max_length:
                    self.polyB.append(0)
             # A complex root used for the fourier transform
             _lowerCamelCase				:  int						=		complex(mpmath.root(x=1   ,				n=self.c_max_length   ,				k=1))

             # The product
             _lowerCamelCase				:  Optional[int]						=		self.__multiply()


      def 					__snake_case      (		self   ,				a__):



             """simple docstring"""





             _lowerCamelCase				:  List[Any]						=		[[x] for x in self.polyA] if which == '''A''' else [[x] for x in self.polyB]
             # Corner case
             if len(a__) <= 1:
                    return dft[0]
             #
             _lowerCamelCase				:  Optional[Any]						=		self.c_max_length // 2
             while next_ncol > 0:
                    _lowerCamelCase				:  Optional[Any]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  List[str]						=		self.root**next_ncol

                    # First half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] + current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Second half of next step
                    _lowerCamelCase				:  Tuple						=		1
                    for j in range(self.c_max_length // (next_ncol * 2)):
                           for i in range(a__):
                                  new_dft[i].append(dft[i][j] - current_root * dft[i + next_ncol][j])
                           current_root *= root
                    # Update
                    _lowerCamelCase				:  Tuple						=		new_dft
                    _lowerCamelCase				:  Union[str, Any]						=		next_ncol // 2
             return dft[0]


      def 					__snake_case      (		self):



             """simple docstring"""





             _lowerCamelCase				:  Optional[int]						=		self.__dft('''A''')
             _lowerCamelCase				:  Optional[Any]						=		self.__dft('''B''')
             _lowerCamelCase				:  List[str]						=		[[dft_a[i] * dft_b[i] for i in range(self.c_max_length)]]
             del dft_a
             del dft_b

             # Corner Case
             if len(inverce_c[0]) <= 1:
                    return inverce_c[0]
             # Inverse DFT
             _lowerCamelCase				:  str						=		2
             while next_ncol <= self.c_max_length:
                    _lowerCamelCase				:  Optional[int]						=		[[] for i in range(a__)]
                    _lowerCamelCase				:  Dict						=		self.root ** (next_ncol // 2)
                    _lowerCamelCase				:  List[str]						=		1
                    # First half of next step
                    for j in range(self.c_max_length // next_ncol):
                           for i in range(next_ncol // 2):
                                  # Even positions
                                  new_inverse_c[i].append(
                                      (
                                          inverce_c[i][j]
                                          + inverce_c[i][j + self.c_max_length // next_ncol]
                                      )
                                      / 2)
                                  # Odd positions
                                  new_inverse_c[i + next_ncol // 2].append(
                                      (
                                          inverce_c[i][j]
                                          - inverce_c[i][j + self.c_max_length // next_ncol]
                                      )
                                      / (2 * current_root))
                           current_root *= root
                    # Update
                    _lowerCamelCase				:  Any						=		new_inverse_c
                    next_ncol *= 2
             # Unpack
             _lowerCamelCase				:  Optional[int]						=		[round(x[0].real   ,				8) + round(x[0].imag   ,				8) * 1j for x in inverce_c]

             # Remove leading 0's
             while inverce_c[-1] == 0:
                    inverce_c.pop()
             return inverce_c



      def __str__(		self):



             """simple docstring"""





             _lowerCamelCase				:  Union[str, Any]						=		'''A = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyA[: self.len_A]))
             _lowerCamelCase				:  List[str]						=		'''B = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.polyB[: self.len_B]))
             _lowerCamelCase				:  Tuple						=		'''A*B = ''' + ''' + '''.join(
                 F"""{coef}*x^{i}""" for coef, i in enumerate(self.product))

             return F"""{a}\n{b}\n{c}"""


# Unit tests
if __name__ == "__main__":
       import doctest

       doctest.testmod()

