



from __future__ import annotations

from typing import Any
class 	lowerCamelCase_							:







						def __init__(  self   :      Union[str, Any]				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      int				,    lowerCAmelCase__   :      float = 0				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			row, column
													SCREAMING_SNAKE_CASE					:      str     					=			[[default_value for c in range(_lowerCamelCase				)] for r in range(_lowerCamelCase				)]







						def __str__(  self   :      List[Any]				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      List[str]     					=			F"""Matrix consist of {self.row} rows and {self.column} columns\n"""

													# Make string identifier
													SCREAMING_SNAKE_CASE					:      Any     					=			0
													for row_vector in self.array:
																				for obj in row_vector:
																											SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			max(_lowerCamelCase				,    len(str(_lowerCamelCase				)				)				)
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			F"""%{max_element_length}s"""

													# Make string and return
													def single_line(lowerCAmelCase__   :      list[float]				) -> str:
																				nonlocal string_format_identifier
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			'''['''
																				line += ", ".join(string_format_identifier % (obj,) for obj in row_vector				)
																				line += "]"
																				return line

													s += "\n".join(single_line(_lowerCamelCase				) for row_vector in self.array				)
													return s







						def __repr__(  self   :      List[str]				):

													"""simple docstring"""
													return str(self				)







						def 					__lowercase    (  self   :      Optional[int]				,    lowerCAmelCase__   :      tuple[int, int]				):

													"""simple docstring"""
													if not (isinstance(_lowerCamelCase				,    (list, tuple)				) and len(_lowerCamelCase				) == 2):
																				return False
													elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
																				return False
													else:
																				return True







						def __getitem__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      tuple[int, int]				):

													"""simple docstring"""
													assert self.validate_indicies(_lowerCamelCase				)
													return self.array[loc[0]][loc[1]]







						def __setitem__(  self   :      int				,    lowerCAmelCase__   :      tuple[int, int]				,    lowerCAmelCase__   :      float				):

													"""simple docstring"""
													assert self.validate_indicies(_lowerCamelCase				)
													SCREAMING_SNAKE_CASE					:      str     					=			value







						def __add__(  self   :      Optional[Any]				,    lowerCAmelCase__   :      Matrix				):

													"""simple docstring"""
													assert isinstance(_lowerCamelCase				,    _lowerCamelCase				)
													assert self.row == another.row and self.column == another.column

													# Add
													SCREAMING_SNAKE_CASE					:      Dict     					=			Matrix(self.row				,    self.column				)
													for r in range(self.row				):
																				for c in range(self.column				):
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			self[r, c] + another[r, c]
													return result







						def __neg__(  self   :      Any				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      int     					=			Matrix(self.row				,    self.column				)
													for r in range(self.row				):
																				for c in range(self.column				):
																											SCREAMING_SNAKE_CASE					:      Tuple     					=			-self[r, c]
													return result







						def __sub__(  self   :      List[str]				,    lowerCAmelCase__   :      Matrix				):

													"""simple docstring"""
													return self + (-another)







						def __mul__(  self   :      str				,    lowerCAmelCase__   :      int | float | Matrix				):

													"""simple docstring"""

													if isinstance(_lowerCamelCase				,    (int, float)				):  # Scalar multiplication
																				SCREAMING_SNAKE_CASE					:      int     					=			Matrix(self.row				,    self.column				)
																				for r in range(self.row				):
																											for c in range(self.column				):
																																		SCREAMING_SNAKE_CASE					:      str     					=			self[r, c] * another
																				return result
													elif isinstance(_lowerCamelCase				,    _lowerCamelCase				):  # Matrix multiplication
																				assert self.column == another.row
																				SCREAMING_SNAKE_CASE					:      Optional[Any]     					=			Matrix(self.row				,    another.column				)
																				for r in range(self.row				):
																											for c in range(another.column				):
																																		for i in range(self.column				):
																																									result[r, c] += self[r, i] * another[i, c]
																				return result
													else:
																				SCREAMING_SNAKE_CASE					:      Dict     					=			F"""Unsupported type given for another ({type(_lowerCamelCase				)})"""
																				raise TypeError(_lowerCamelCase				)







						def 					__lowercase    (  self   :      Tuple				):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE					:      Tuple     					=			Matrix(self.column				,    self.row				)
													for r in range(self.row				):
																				for c in range(self.column				):
																											SCREAMING_SNAKE_CASE					:      List[str]     					=			self[r, c]
													return result







						def 					__lowercase    (  self   :      List[Any]				,    lowerCAmelCase__   :      Matrix				,    lowerCAmelCase__   :      Matrix				):

													"""simple docstring"""
													assert isinstance(_lowerCamelCase				,    _lowerCamelCase				) and isinstance(_lowerCamelCase				,    _lowerCamelCase				)
													assert self.row == self.column == u.row == v.row  # u, v should be column vector
													assert u.column == v.column == 1  # u, v should be column vector

													# Calculate
													SCREAMING_SNAKE_CASE					:      Optional[int]     					=			v.transpose()
													SCREAMING_SNAKE_CASE					:      Dict     					=			(v_t * self * u)[0, 0] + 1
													if numerator_factor == 0:
																				return None  # It's not invertable
													return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":
							def 					UpperCAmelCase   (						):
														# a^(-1)
														SCREAMING_SNAKE_CASE					:      List[str]     					=			Matrix(3					,		3					,		0				)
														for i in range(3				):
																					SCREAMING_SNAKE_CASE					:      List[Any]     					=			1
														print(F"""a^(-1) is {ainv}"""				)
														# u, v
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Matrix(3					,		1					,		0				)
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      List[str]     					=			1, 2, -3
														SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			Matrix(3					,		1					,		0				)
														SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					,SCREAMING_SNAKE_CASE					:      Union[str, Any]     					=			4, -2, 5
														print(F"""u is {u}"""				)
														print(F"""v is {v}"""				)
														print(F"""uv^T is {u * v.transpose()}"""				)
														# Sherman Morrison
														print(F"""(a + uv^T)^(-1) is {ainv.sherman_morrison(_lowerCAmelCase					,		_lowerCAmelCase				)}"""				)
							def 					UpperCAmelCase   (						):
														import doctest

														doctest.testmod()






							testa()









'''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)




