






import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


UpperCamelCase 				=	[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
		import run_clm_flax
		import run_flax_glue
		import run_flax_ner
		import run_mlm_flax
		import run_qa
		import run_summarization_flax
		import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

UpperCamelCase 				=	logging.getLogger()

def        __lowerCamelCase			(		)							->  Dict:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  argparse.ArgumentParser()
						parser.add_argument("""-f"""		)
						_SCREAMING_SNAKE_CASE	      =  parser.parse_args()
						return args.f

def        __lowerCamelCase			(		snake_case__       ,snake_case__="eval"		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE	      =  os.path.join(_A       ,F'{split}_results.json'		)
						if os.path.exists(_A		):
												with open(_A       ,"""r"""		) as f:
																		return json.load(_A		)
						raise ValueError(F'can\'t find {path}'		)


UpperCamelCase 				=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)




class       __UpperCAmelCase		(lowercase__    ):







			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()
									_SCREAMING_SNAKE_CASE	      =  F'\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            '.split()

									with patch.object(__lowerCamelCase      ,	"""argv"""      ,	__lowerCamelCase     ):
															run_flax_glue.main()
															_SCREAMING_SNAKE_CASE	      =  get_results(__lowerCamelCase     )
															self.assertGreaterEqual(result["""eval_accuracy"""]      ,	0.75     )







			@slow
			def 							UpperCamelCase      (	self:  Optional[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()
									_SCREAMING_SNAKE_CASE	      =  F'\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

									with patch.object(__lowerCamelCase      ,	"""argv"""      ,	__lowerCamelCase     ):
															run_clm_flax.main()
															_SCREAMING_SNAKE_CASE	      =  get_results(__lowerCamelCase     )
															self.assertLess(result["""eval_perplexity"""]      ,	100     )







			@slow
			def 							UpperCamelCase      (	self:  List[Any]     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()
									_SCREAMING_SNAKE_CASE	      =  F'\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        '.split()

									with patch.object(__lowerCamelCase      ,	"""argv"""      ,	__lowerCamelCase     ):
															run_summarization_flax.main()
															_SCREAMING_SNAKE_CASE	      =  get_results(__lowerCamelCase      ,	split="""test"""     )
															self.assertGreaterEqual(result["""test_rouge1"""]      ,	10     )
															self.assertGreaterEqual(result["""test_rouge2"""]      ,	2     )
															self.assertGreaterEqual(result["""test_rougeL"""]      ,	7     )
															self.assertGreaterEqual(result["""test_rougeLsum"""]      ,	7     )







			@slow
			def 							UpperCamelCase      (	self:  Any     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()
									_SCREAMING_SNAKE_CASE	      =  F'\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        '.split()

									with patch.object(__lowerCamelCase      ,	"""argv"""      ,	__lowerCamelCase     ):
															run_mlm_flax.main()
															_SCREAMING_SNAKE_CASE	      =  get_results(__lowerCamelCase     )
															self.assertLess(result["""eval_perplexity"""]      ,	42     )







			@slow
			def 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()
									_SCREAMING_SNAKE_CASE	      =  F'\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            '.split()

									with patch.object(__lowerCamelCase      ,	"""argv"""      ,	__lowerCamelCase     ):
															run_ta_mlm_flax.main()
															_SCREAMING_SNAKE_CASE	      =  get_results(__lowerCamelCase     )
															self.assertGreaterEqual(result["""eval_accuracy"""]      ,	0.42     )







			@slow
			def 							UpperCamelCase      (	self:  int     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  7 if get_gpu_count() > 1 else 2

									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()
									_SCREAMING_SNAKE_CASE	      =  F'\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        '.split()

									with patch.object(__lowerCamelCase      ,	"""argv"""      ,	__lowerCamelCase     ):
															run_flax_ner.main()
															_SCREAMING_SNAKE_CASE	      =  get_results(__lowerCamelCase     )
															self.assertGreaterEqual(result["""eval_accuracy"""]      ,	0.75     )
															self.assertGreaterEqual(result["""eval_f1"""]      ,	0.3     )







			@slow
			def 							UpperCamelCase      (	self:  Dict     ):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE	      =  self.get_auto_remove_tmp_dir()
									_SCREAMING_SNAKE_CASE	      =  F'\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        '.split()

									with patch.object(__lowerCamelCase      ,	"""argv"""      ,	__lowerCamelCase     ):
															run_qa.main()
															_SCREAMING_SNAKE_CASE	      =  get_results(__lowerCamelCase     )
															self.assertGreaterEqual(result["""eval_f1"""]      ,	30     )
															self.assertGreaterEqual(result["""eval_exact"""]      ,	30     )





import math
import random
from typing import Any

from .hill_climbing import SearchProblem

def       lowercase_		(		_A			:    Optional[int]    ,					_A			:    bool = True    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    float = math.inf    ,					_A			:    float = -math.inf    ,					_A			:    bool = False    ,					_A			:    float = 100    ,					_A			:    float = 0.01    ,					_A			:    float = 1    ,					):

      """simple docstring"""




      lowerCamelCase__		:				List[Any]    =  False
      lowerCamelCase__		:				Optional[Any]    =  search_prob
      lowerCamelCase__		:				List[str]    =  start_temperate
      lowerCamelCase__		:				Optional[int]    =  []
      lowerCamelCase__		:				Any    =  0
      lowerCamelCase__		:				Optional[Any]    =  None

      while not search_end:
            lowerCamelCase__		:				Optional[Any]    =  current_state.score()
            if best_state is None or current_score > best_state.score():
                  lowerCamelCase__		:				List[Any]    =  current_state
            scores.append(_A						)
            iterations += 1
            lowerCamelCase__		:				List[Any]    =  None
            lowerCamelCase__		:				Optional[Any]    =  current_state.get_neighbors()
            while (
                next_state is None and neighbors
            ):  # till we do not find a neighbor that we can move to
                  lowerCamelCase__		:				Any    =  random.randint(0    ,					len(_A						) - 1						)  # picking a random neighbor
                  lowerCamelCase__		:				List[Any]    =  neighbors.pop(_A						)
                  lowerCamelCase__		:				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:
                        lowerCamelCase__		:				Dict    =  change * -1  # in case we are finding minimum
                  if change > 0:  # improves the solution
                        lowerCamelCase__		:				Optional[Any]    =  picked_neighbor
                  else:
                        lowerCamelCase__		:				str    =  (math.e) ** (
                            change / current_temp
                        )  # probability generation function
                        if random.random() < probability:  # random number within probability
                              lowerCamelCase__		:				Optional[int]    =  picked_neighbor
            lowerCamelCase__		:				Any    =  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
                  lowerCamelCase__		:				int    =  True
            else:
                  lowerCamelCase__		:				Optional[int]    =  next_state

      if visualization:
            from matplotlib import pyplot as plt

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


if __name__ == "__main__":

    def       lowercase_		(		_A			:    List[str]    ,					_A			:    int						):

          """simple docstring"""




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

    # starting the problem with initial coordinates (12, 47)
    A      :    Any		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    Dict		=					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)
    A      :    str		=					SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
    A      :    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       lowercase_		(		_A			:    int    ,					_A			:    Any						):

          """simple docstring"""




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

    A      :    Optional[int]		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Union[str, Any]		=					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()}'
    )

    A      :    Any		=					SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
    A      :    Optional[int]		=					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()}'
    )


