"""simple docstring"""



import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							[
         '''decoder.version''',
         '''decoder.output_projection.weight''',
         '''_float_tensor''',
         '''decoder.embed_positions._float_tensor''',
     ]
     for k in ignore_keys:
          state_dict.pop(snake_case,      snake_case)





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case		,	__snake_case				=							emb.weight.shape
     __snake_case				=							nn.Linear(snake_case,      snake_case,      bias=snake_case)
     __snake_case				=							emb.weight.data
     return lin_layer





def    SCREAMING_SNAKE_CASE       (     snake_case):
     __snake_case				=							torch.load(snake_case,      map_location='''cpu''')
     __snake_case				=							Namespace(**checkpoint['''cfg''']['''model'''])
     __snake_case				=							checkpoint['''model''']
     remove_ignore_keys_(snake_case)
     __snake_case				=							state_dict['''decoder.embed_tokens.weight'''].shape[0]

     __snake_case				=							{key.replace('''decoder''',      '''model'''): val for key, val in state_dict.items()}

     __snake_case				=							XGLMConfig(
         vocab_size=snake_case,      max_position_embeddings=args.max_target_positions,      num_layers=args.decoder_layers,      attention_heads=args.decoder_attention_heads,      ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.decoder_embed_dim,      layerdrop=args.decoder_layerdrop,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='''gelu''',      scale_embedding=not args.no_scale_embedding,      tie_word_embeddings=args.share_decoder_input_output_embed,      )

     __snake_case				=							XGLMForCausalLM(snake_case)
     __snake_case				=							model.load_state_dict(snake_case,      strict=snake_case)
     print(snake_case)
     __snake_case				=							make_linear_from_emb(model.model.embed_tokens)

     return model


if __name__ == "__main__":
       __lowercase		:		int														=							argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("fairseq_path", type=str, help="path to a model.pt on local filesystem.")
       parser.add_argument("pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       __lowercase		:		Optional[int]														=							parser.parse_args()
       __lowercase		:		Dict														=							convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
       model.save_pretrained(args.pytorch_dump_folder_path)
"""simple docstring"""



import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case = True,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = math.inf,      snake_case = -math.inf,      snake_case = False,      snake_case = 1_00,      snake_case = 0.01,      snake_case = 1,      ):
     __snake_case				=							False
     __snake_case				=							search_prob
     __snake_case				=							start_temperate
     __snake_case				=							[]
     __snake_case				=							0
     __snake_case				=							None

     while not search_end:
          __snake_case				=							current_state.score()
          if best_state is None or current_score > best_state.score():
               __snake_case				=							current_state
          scores.append(snake_case)
          iterations += 1
          __snake_case				=							None
          __snake_case				=							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				=							random.randint(0,      len(snake_case) - 1)  # picking a random neighbor
               __snake_case				=							neighbors.pop(snake_case)
               __snake_case				=							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				=							change * -1  # in case we are finding minimum
               if change > 0:  # improves the solution
                    __snake_case				=							picked_neighbor
               else:
                    __snake_case				=							(math.e) ** (
                        change / current_temp
                    )  # probability generation function
                    if random.random() < probability:  # random number within probability
                         __snake_case				=							picked_neighbor
          __snake_case				=							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				=							True
          else:
               __snake_case				=							next_state

     if visualization:
          from matplotlib import pyplot as plt

          plt.plot(range(snake_case),      snake_case)
          plt.xlabel('''Iterations''')
          plt.ylabel('''Function values''')
          plt.show()
     return best_state


if __name__ == "__main__":





       def    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (x**2) + (y**2)

       # starting the problem with initial coordinates (12, 47)
       __lowercase		:		Dict														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		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)
       __lowercase		:		Any														=							SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		int														=							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    SCREAMING_SNAKE_CASE       (     snake_case,      snake_case):
            return (3 * x**2) - (6 * y)

       __lowercase		:		List[str]														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Dict														=							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()}"""
       )

       __lowercase		:		Any														=							SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
       __lowercase		:		Tuple														=							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()}"""
       )