





import argparse
import pathlib

import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse("""1.0.0a"""):
						raise Exception("""requires fairseq >= 1.0.0a""")

logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE      	=		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      	=		"""Hello world! cécé herlolip"""


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	FairseqRobertaModel.from_pretrained(__a   )
	roberta.eval()  # disable dropout
	snake_case_		:			Dict											=	roberta.model.encoder.sentence_encoder
	snake_case_		:			List[str]											=	XLMRobertaConfig(
	    vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings   , hidden_size=roberta.cfg.model.encoder_embed_dim   , num_hidden_layers=roberta.cfg.model.encoder_layers   , num_attention_heads=roberta.cfg.model.encoder_attention_heads   , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim   , max_position_embeddings=5_14   , type_vocab_size=1   , layer_norm_eps=1E-5   , )
	if classification_head:
		snake_case_		:			Optional[int]											=	roberta.model.classification_heads['mnli'].out_proj.weight.shape[0]

	print('Our RoBERTa config:'   , __a   )

	snake_case_		:			List[str]											=	XLMRobertaXLForSequenceClassification(__a   ) if classification_head else XLMRobertaXLForMaskedLM(__a   )
	model.eval()

	# Now let's copy all the weights.
	# Embeddings
	snake_case_		:			Dict											=	roberta_sent_encoder.embed_tokens.weight
	snake_case_		:			List[str]											=	roberta_sent_encoder.embed_positions.weight
	snake_case_		:			Optional[int]											=	torch.zeros_like(
	    model.roberta.embeddings.token_type_embeddings.weight   )  # just zero them out b/c RoBERTa doesn't use them.

	snake_case_		:			int											=	roberta_sent_encoder.layer_norm.weight
	snake_case_		:			List[str]											=	roberta_sent_encoder.layer_norm.bias

	for i in range(config.num_hidden_layers   ):
		# Encoder: start of layer
		snake_case_		:			BertLayer											=	model.roberta.encoder.layer[i]
		snake_case_		:			TransformerSentenceEncoderLayer											=	roberta_sent_encoder.layers[i]

		snake_case_		:			RobertaAttention											=	layer.attention
		snake_case_		:			Union[str, Any]											=	roberta_layer.self_attn_layer_norm.weight
		snake_case_		:			List[Any]											=	roberta_layer.self_attn_layer_norm.bias

		# self attention
		snake_case_		:			BertSelfAttention											=	layer.attention.self
		assert (
		    roberta_layer.self_attn.k_proj.weight.data.shape
		    == roberta_layer.self_attn.q_proj.weight.data.shape
		    == roberta_layer.self_attn.v_proj.weight.data.shape
		    == torch.Size((config.hidden_size, config.hidden_size)   )
		)

		snake_case_		:			Optional[Any]											=	roberta_layer.self_attn.q_proj.weight
		snake_case_		:			int											=	roberta_layer.self_attn.q_proj.bias
		snake_case_		:			int											=	roberta_layer.self_attn.k_proj.weight
		snake_case_		:			Any											=	roberta_layer.self_attn.k_proj.bias
		snake_case_		:			Any											=	roberta_layer.self_attn.v_proj.weight
		snake_case_		:			List[Any]											=	roberta_layer.self_attn.v_proj.bias

		# self-attention output
		snake_case_		:			BertSelfOutput											=	layer.attention.output
		assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
		snake_case_		:			List[Any]											=	roberta_layer.self_attn.out_proj.weight
		snake_case_		:			str											=	roberta_layer.self_attn.out_proj.bias

		# this one is final layer norm
		snake_case_		:			List[str]											=	roberta_layer.final_layer_norm.weight
		snake_case_		:			Tuple											=	roberta_layer.final_layer_norm.bias

		# intermediate
		snake_case_		:			BertIntermediate											=	layer.intermediate
		assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
		snake_case_		:			List[Any]											=	roberta_layer.fca.weight
		snake_case_		:			Optional[Any]											=	roberta_layer.fca.bias

		# output
		snake_case_		:			BertOutput											=	layer.output
		assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
		snake_case_		:			Tuple											=	roberta_layer.fca.weight
		snake_case_		:			Union[str, Any]											=	roberta_layer.fca.bias
		# end of layer

	if classification_head:
		snake_case_		:			Dict											=	roberta.model.classification_heads['mnli'].dense.weight
		snake_case_		:			int											=	roberta.model.classification_heads['mnli'].dense.bias
		snake_case_		:			int											=	roberta.model.classification_heads['mnli'].out_proj.weight
		snake_case_		:			Optional[int]											=	roberta.model.classification_heads['mnli'].out_proj.bias
	else:
		# LM Head
		snake_case_		:			List[str]											=	roberta.model.encoder.lm_head.dense.weight
		snake_case_		:			Optional[int]											=	roberta.model.encoder.lm_head.dense.bias
		snake_case_		:			Any											=	roberta.model.encoder.lm_head.layer_norm.weight
		snake_case_		:			Union[str, Any]											=	roberta.model.encoder.lm_head.layer_norm.bias
		snake_case_		:			Optional[int]											=	roberta.model.encoder.lm_head.weight
		snake_case_		:			int											=	roberta.model.encoder.lm_head.bias

	# Let's check that we get the same results.
	snake_case_		:			torch.Tensor											=	roberta.encode(__a   ).unsqueeze(0   )  # batch of size 1

	snake_case_		:			List[str]											=	model(__a   )[0]
	if classification_head:
		snake_case_		:			str											=	roberta.model.classification_heads['mnli'](roberta.extract_features(__a   )   )
	else:
		snake_case_		:			Tuple											=	roberta.model(__a   )[0]
	print(our_output.shape   , their_output.shape   )
	snake_case_		:			List[str]											=	torch.max(torch.abs(our_output - their_output   )   ).item()
	print(f"""max_absolute_diff = {max_absolute_diff}"""   )  # ~ 1e-7
	snake_case_		:			Tuple											=	torch.allclose(__a   , __a   , atol=1E-3   )
	print('Do both models output the same tensors?'   , '🔥' if success else '💩'   )
	if not success:
		raise Exception('Something went wRoNg'   )

	pathlib.Path(__a   ).mkdir(parents=__a   , exist_ok=__a   )
	print(f"""Saving model to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(__a   )


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    """--roberta_checkpoint_path""", default=None, type=str, required=True, help="""Path the official PyTorch dump."""
						)
						parser.add_argument(
						    """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
						)
						parser.add_argument(
						    """--classification_head""", action="""store_true""", help="""Whether to convert a final classification head."""
						)
						_SCREAMING_SNAKE_CASE      	=		parser.parse_args()
						convert_xlm_roberta_xl_checkpoint_to_pytorch(
						    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
						)








from statistics import mean

import numpy as np


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			Optional[Any]											=	0
	# Number of processes finished
	snake_case_		:			List[str]											=	0
	# Displays the finished process.
	# If it is 0, the performance is completed if it is 1, before the performance.
	snake_case_		:			str											=	[0] * no_of_process
	# List to include calculation results
	snake_case_		:			Optional[int]											=	[0] * no_of_process

	# Sort by arrival time.
	snake_case_		:			str											=	[burst_time[i] for i in np.argsort(__a   )]
	snake_case_		:			str											=	[process_name[i] for i in np.argsort(__a   )]
	arrival_time.sort()

	while no_of_process > finished_process_count:
		snake_case_		:			int											=	0
		while finished_process[i] == 1:
			i += 1
		if current_time < arrival_time[i]:
			snake_case_		:			str											=	arrival_time[i]

		snake_case_		:			Optional[Any]											=	0
		# Index showing the location of the process being performed
		snake_case_		:			Tuple											=	0
		# Saves the current response ratio.
		snake_case_		:			List[Any]											=	0
		for i in range(0   , __a   ):
			if finished_process[i] == 0 and arrival_time[i] <= current_time:
				snake_case_		:			Optional[Any]											=	(burst_time[i] + (current_time - arrival_time[i])) / burst_time[
				    i
				]
			if response_ratio < temp:
				snake_case_		:			Optional[int]											=	temp
				snake_case_		:			Optional[Any]											=	i

        # Calculate the turn around time
		snake_case_		:			Any											=	current_time + burst_time[loc] - arrival_time[loc]
		current_time += burst_time[loc]
		# Indicates that the process has been performed.
		snake_case_		:			Optional[Any]											=	1
		# Increase finished_process_count by 1
		finished_process_count += 1

	return turn_around_time


def 							SCREAMING_SNAKE_CASE__						(    __a   , __a   , __a   , __a   ):
	snake_case_		:			List[Any]											=	[0] * no_of_process
	for i in range(0   , __a   ):
		snake_case_		:			Optional[int]											=	turn_around_time[i] - burst_time[i]
	return waiting_time


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE      	=		5
						_SCREAMING_SNAKE_CASE      	=		["""A""", """B""", """C""", """D""", """E"""]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]
						_SCREAMING_SNAKE_CASE      	=		[1, 2, 3, 4, 5]

						_SCREAMING_SNAKE_CASE      	=		calculate_turn_around_time(
						    process_name, arrival_time, burst_time, no_of_process
						)
						_SCREAMING_SNAKE_CASE      	=		calculate_waiting_time(
						    process_name, turn_around_time, burst_time, no_of_process
						)

						print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
						for i in range(0, no_of_process):
												print(
												    F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
												    F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
												)

						print(F'''average waiting time : {mean(waiting_time):.5f}''')
						print(F'''average turn around time : {mean(turn_around_time):.5f}''')

