






'''simple docstring'''




import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__ = True     ,__magic_name__ = math.inf     ,__magic_name__ = -math.inf     ,__magic_name__ = math.inf     ,__magic_name__ = -math.inf     ,__magic_name__ = False     ,__magic_name__ = 100     ,__magic_name__ = 0.01     ,__magic_name__ = 1     ,)->    List[Any]:



			"""simple docstring"""



			snake_case_				:					Dict   =       False
			snake_case_				:					List[str]   =       search_prob
			snake_case_				:					List[Any]   =       start_temperate
			snake_case_				:					Union[str, Any]   =       []
			snake_case_				:					List[str]   =       0
			snake_case_				:					List[Any]   =       None

			while not search_end:
						snake_case_				:					List[str]   =       current_state.score()
						if best_state is None or current_score > best_state.score():
									snake_case_				:					str   =       current_state
						scores.append(_UpperCamelCase						)
						iterations += 1
						snake_case_				:					Tuple   =       None
						snake_case_				:					int   =       current_state.get_neighbors()
						while (
						    next_state is None and neighbors
						):  # till we do not find a neighbor that we can move to
									snake_case_				:					Any   =       random.randint(0     ,len(_UpperCamelCase						) - 1						)  # picking a random neighbor
									snake_case_				:					Optional[int]   =       neighbors.pop(_UpperCamelCase						)
									snake_case_				:					Dict   =       picked_neighbor.score() - current_score

									if (
									    picked_neighbor.x > max_x
									    or picked_neighbor.x < min_x
									    or picked_neighbor.y > max_y
									    or picked_neighbor.y < min_y
									):
												continue  # neighbor outside our bounds

									if not find_max:
												snake_case_				:					int   =       change * -1  # in case we are finding minimum
									if change > 0:  # improves the solution
												snake_case_				:					Dict   =       picked_neighbor
									else:
												snake_case_				:					str   =       (math.e) ** (
												    change / current_temp
												)  # probability generation function
												if random.random() < probability:  # random number within probability
															snake_case_				:					Union[str, Any]   =       picked_neighbor
						snake_case_				:					str   =       current_temp - (current_temp * rate_of_decrease)

						if current_temp < threshold_temp or next_state is None:
									# temperature below threshold, or could not find a suitable neighbor
									snake_case_				:					str   =       True
						else:
									snake_case_				:					int   =       next_state

			if visualization:
						from matplotlib import pyplot as plt

						plt.plot(range(_UpperCamelCase						)     ,_UpperCamelCase						)
						plt.xlabel("Iterations"						)
						plt.ylabel("Function values"						)
						plt.show()
			return best_state


if __name__ == "__main__":

		def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Tuple:



					"""simple docstring"""



					return (x**2) + (y**2)

		# starting the problem with initial coordinates (12, 47)
		__lowerCamelCase   :					Tuple   							=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase   :					Union[str, Any]   							=					simulated_annealing(
		    prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
		)
		print(
		    '''The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
		    f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
		)

		# starting the problem with initial coordinates (12, 47)
		__lowerCamelCase   :					Optional[Any]   							=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase   :					str   							=					simulated_annealing(
		    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
		)
		print(
		    '''The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '''
		    f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
		)

		def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__						)->    Union[str, Any]:



					"""simple docstring"""



					return (3 * x**2) - (6 * y)

		__lowerCamelCase   :					Any   							=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase   :					Tuple   							=					simulated_annealing(prob, find_max=False, visualization=True)
		print(
		    '''The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
		    f'''{local_min.score()}'''
		)

		__lowerCamelCase   :					Tuple   							=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
		__lowerCamelCase   :					Optional[Any]   							=					simulated_annealing(prob, find_max=True, visualization=True)






		print(
		    '''The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '''
		    f'''{local_min.score()}'''
		)












'''simple docstring'''




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()
__lowerCamelCase   :					Union[str, Any]   							=					logging.get_logger(__name__)

__lowerCamelCase   :					Union[str, Any]   							=					'''Hello world! cécé herlolip'''

def 	__UpperCAmelCase       ( __magic_name__     ,__magic_name__     ,__magic_name__						)->    Optional[Any]:



			"""simple docstring"""



			snake_case_				:					str   =       FairseqRobertaModel.from_pretrained(__magic_name__						)
			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=514     ,type_vocab_size=1     ,layer_norm_eps=1E-5     ,)
			if classification_head:
						snake_case_				:					List[str]   =       roberta.model.classification_heads["mnli"].out_proj.weight.shape[0]

			print("Our RoBERTa config:"     ,__magic_name__						)

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

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

			snake_case_				:					Union[str, Any]   =       roberta_sent_encoder.layer_norm.weight
			snake_case_				:					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_				:					Dict   =       roberta_layer.self_attn_layer_norm.weight
						snake_case_				:					Dict   =       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_				:					Dict   =       roberta_layer.self_attn.q_proj.weight
						snake_case_				:					Any   =       roberta_layer.self_attn.q_proj.bias
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.weight
						snake_case_				:					Optional[Any]   =       roberta_layer.self_attn.k_proj.bias
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.v_proj.weight
						snake_case_				:					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[str]   =       roberta_layer.self_attn.out_proj.weight
						snake_case_				:					Optional[int]   =       roberta_layer.self_attn.out_proj.bias

						# this one is final layer norm
						snake_case_				:					int   =       roberta_layer.final_layer_norm.weight
						snake_case_				:					Union[str, Any]   =       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[str]   =       roberta_layer.fca.weight
						snake_case_				:					List[Any]   =       roberta_layer.fca.bias

						# output
						snake_case_				:					BertOutput   =       layer.output
						assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
						snake_case_				:					Any   =       roberta_layer.fca.weight
						snake_case_				:					Any   =       roberta_layer.fca.bias
						# end of layer

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

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

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

			pathlib.Path(__magic_name__						).mkdir(parents=__magic_name__     ,exist_ok=__magic_name__						)
			print(F'''Saving model to {pytorch_dump_folder_path}'''						)
			model.save_pretrained(__magic_name__						)


if __name__ == "__main__":
		__lowerCamelCase   :					Any   							=					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.'''
		)
		__lowerCamelCase   :					Tuple   							=					parser.parse_args()
		convert_xlm_roberta_xl_checkpoint_to_pytorch(
		    args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)




