



from __future__ import annotations
class 	lowerCamelCase_							:







						def __init__(  self   :      int				,    lowerCAmelCase__   :      int				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			order

													# a_{0} ... a_{k}
													SCREAMING_SNAKE_CASE					:      Any     					=			[1.0] + [0.0] * order
													# b_{0} ... b_{k}
													SCREAMING_SNAKE_CASE					:      str     					=			[1.0] + [0.0] * order

													# x[n-1] ... x[n-k]
													SCREAMING_SNAKE_CASE					:      List[str]     					=			[0.0] * self.order
													# y[n-1] ... y[n-k]
													SCREAMING_SNAKE_CASE					:      Dict     					=			[0.0] * self.order







						def 					__lowercase    (  self   :      Dict				,    lowerCAmelCase__   :      list[float]				,    lowerCAmelCase__   :      list[float]				):

													"""simple docstring"""
													if len(lowerCAmelCase__				) < self.order:
																				SCREAMING_SNAKE_CASE					:      int     					=			[1.0, *a_coeffs]

													if len(lowerCAmelCase__				) != self.order + 1:
																				SCREAMING_SNAKE_CASE					:      str     					=			(
																				    F"""Expected a_coeffs to have {self.order + 1} elements """
																				    F"""for {self.order}-order filter, got {len(lowerCAmelCase__				)}"""
																				)
																				raise ValueError(lowerCAmelCase__				)

													if len(lowerCAmelCase__				) != self.order + 1:
																				SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			(
																				    F"""Expected b_coeffs to have {self.order + 1} elements """
																				    F"""for {self.order}-order filter, got {len(lowerCAmelCase__				)}"""
																				)
																				raise ValueError(lowerCAmelCase__				)

													SCREAMING_SNAKE_CASE					:      int     					=			a_coeffs
													SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			b_coeffs







						def 					__lowercase    (  self   :      Any				,    lowerCAmelCase__   :      float				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			0.0

													# Start at index 1 and do index 0 at the end.
													for i in range(1				,    self.order + 1				):
																				result += (
																				    self.b_coeffs[i] * self.input_history[i - 1]
																				    - self.a_coeffs[i] * self.output_history[i - 1]
																				)

													SCREAMING_SNAKE_CASE					:      List[Any]     					=			(result + self.b_coeffs[0] * sample) / self.a_coeffs[0]

													SCREAMING_SNAKE_CASE					:      Any     					=			self.input_history[:-1]
													SCREAMING_SNAKE_CASE					:      List[Any]     					=			self.output_history[:-1]

													SCREAMING_SNAKE_CASE					:      Any     					=			sample
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			result

													return result









'''simple docstring'''



import argparse

import torch

from transformers import GPTaLMHeadModel, RobertaForMaskedLM


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

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

							lowerCAmelCase_     :       List[Any]								=						model.state_dict()
							lowerCAmelCase_     :       List[Any]								=						{}

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

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




