




import os
import pickle
import tempfile
import unittest
from typing import Callable, Optional

import numpy as np

from transformers import (
    BatchEncoding,
    BertTokenizer,
    BertTokenizerFast,
    PreTrainedTokenizer,
    PreTrainedTokenizerFast,
    TensorType,
    TokenSpan,
    is_tokenizers_available,
)
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import CaptureStderr, require_flax, require_tf, require_tokenizers, require_torch, slow


if is_tokenizers_available():
	from tokenizers import Tokenizer
	from tokenizers.models import WordPiece


class  lowerCamelCase     (		unittest.TestCase    ):






			'''simple docstring'''





			def 				UpperCAmelCase__							(			self      : List[Any]							,    lowerCamelCase_      : str	)    -> Dict:
								__magic_name__				:						Dict     				=				list(tokenizer_class.max_model_input_sizes.keys()	)
								for model_name in sa_models[:1]:
													__magic_name__				:						int     				=				tokenizer_class.from_pretrained(lowerCamelCase_	)
													self.assertIsNotNone(lowerCamelCase_	)
													self.assertIsInstance(lowerCamelCase_							,    lowerCamelCase_	)
													self.assertIsInstance(lowerCamelCase_							,    lowerCamelCase_	)

													for special_tok in tokenizer.all_special_tokens:
																		self.assertIsInstance(lowerCamelCase_							,    lowerCamelCase_	)
																		__magic_name__				:						Union[str, Any]     				=				tokenizer.convert_tokens_to_ids(lowerCamelCase_	)
																		self.assertIsInstance(lowerCamelCase_							,    lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : List[str]							,    lowerCamelCase_      : BatchEncoding							,    lowerCamelCase_      : Optional[Callable] = None	)    -> Tuple:
								__magic_name__				:						Any     				=				pickle.dumps(lowerCamelCase_	)
								self.assertIsNotNone(lowerCamelCase_	)

								__magic_name__				:						Union[str, Any]     				=				pickle.loads(lowerCamelCase_	)

								# Ensure is_fast is correctly restored
								self.assertEqual(be_restored.is_fast							,    be_original.is_fast	)

								# Ensure encodings are potentially correctly restored
								if be_original.is_fast:
													self.assertIsNotNone(be_restored.encodings	)
								else:
													self.assertIsNone(be_restored.encodings	)

								# Ensure the keys are the same
								for original_v, restored_v in zip(be_original.values()							,    be_restored.values()	):
													if equal_op:
																		self.assertTrue(equal_op(lowerCamelCase_							,    lowerCamelCase_	)	)
													else:
																		self.assertEqual(lowerCamelCase_							,    lowerCamelCase_	)

			@slow
			def 				UpperCAmelCase__							(			self      : Dict	)    -> Any:
								self.check_tokenizer_from_pretrained(lowerCamelCase_	)

			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> List[Any]:
								self.assertEqual(TensorType('''tf'''	)							,    TensorType.TENSORFLOW	)
								self.assertEqual(TensorType('''pt'''	)							,    TensorType.PYTORCH	)
								self.assertEqual(TensorType('''np'''	)							,    TensorType.NUMPY	)

			@require_tokenizers
			def 				UpperCAmelCase__							(			self      : Tuple	)    -> List[Any]:
								import numpy as np

								__magic_name__				:						List[str]     				=				BertTokenizer.from_pretrained('''bert-base-cased'''	)
								__magic_name__				:						str     				=				BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								# Python no tensor
								with self.subTest('''BatchEncoding (Python, return_tensors=None)'''	):
													self.assert_dump_and_restore(tokenizer_p('''Small example to encode'''	)	)

								with self.subTest('''BatchEncoding (Python, return_tensors=NUMPY)'''	):
													self.assert_dump_and_restore(
													    tokenizer_p('''Small example to encode'''							,    return_tensors=TensorType.NUMPY	)							,    np.array_equal	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=None)'''	):
													self.assert_dump_and_restore(tokenizer_r('''Small example to encode'''	)	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=NUMPY)'''	):
													self.assert_dump_and_restore(
													    tokenizer_r('''Small example to encode'''							,    return_tensors=TensorType.NUMPY	)							,    np.array_equal	)

			@require_tf
			@require_tokenizers
			def 				UpperCAmelCase__							(			self      : str	)    -> Optional[Any]:
								import tensorflow as tf

								def tf_array_equals(lowerCamelCase_      : str							,    lowerCamelCase_      : List[str]	):
													return tf.reduce_all(tf.equal(lowerCamelCase_							,    lowerCamelCase_	)	)

								__magic_name__				:						Optional[int]     				=				BertTokenizer.from_pretrained('''bert-base-cased'''	)
								__magic_name__				:						List[Any]     				=				BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								with self.subTest('''BatchEncoding (Python, return_tensors=TENSORFLOW)'''	):
													self.assert_dump_and_restore(
													    tokenizer_p('''Small example to encode'''							,    return_tensors=TensorType.TENSORFLOW	)							,    lowerCamelCase_	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=TENSORFLOW)'''	):
													self.assert_dump_and_restore(
													    tokenizer_r('''Small example to encode'''							,    return_tensors=TensorType.TENSORFLOW	)							,    lowerCamelCase_	)

			@require_torch
			@require_tokenizers
			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> Optional[Any]:
								import torch

								__magic_name__				:						Dict     				=				BertTokenizer.from_pretrained('''bert-base-cased'''	)
								__magic_name__				:						Optional[int]     				=				BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								with self.subTest('''BatchEncoding (Python, return_tensors=PYTORCH)'''	):
													self.assert_dump_and_restore(
													    tokenizer_p('''Small example to encode'''							,    return_tensors=TensorType.PYTORCH	)							,    torch.equal	)

								with self.subTest('''BatchEncoding (Rust, return_tensors=PYTORCH)'''	):
													self.assert_dump_and_restore(
													    tokenizer_r('''Small example to encode'''							,    return_tensors=TensorType.PYTORCH	)							,    torch.equal	)

			@require_tokenizers
			def 				UpperCAmelCase__							(			self      : List[Any]	)    -> Any:
								__magic_name__				:						str     				=				BertTokenizer.from_pretrained('''bert-base-cased'''	)
								__magic_name__				:						str     				=				BertTokenizerFast.from_pretrained('''bert-base-cased'''	)

								with self.subTest('''Python Tokenizer'''	):
													self.assertFalse(tokenizer_p('''Small example to_encode'''	).is_fast	)

								with self.subTest('''Rust Tokenizer'''	):
													self.assertTrue(tokenizer_r('''Small example to_encode'''	).is_fast	)

			@require_tokenizers
			def 				UpperCAmelCase__							(			self      : Any	)    -> int:
								__magic_name__				:						int     				=				BertTokenizerFast.from_pretrained('''bert-base-cased'''	)
								__magic_name__				:						Tuple     				=				tokenizer_r(['''Test''', '''\xad''', '''test''']							,    is_split_into_words=lowerCamelCase_	)

								self.assertEqual(encoded.word_to_tokens(0	)							,    TokenSpan(start=1							,    end=2	)	)
								self.assertEqual(encoded.word_to_tokens(1	)							,    lowerCamelCase_	)
								self.assertEqual(encoded.word_to_tokens(2	)							,    TokenSpan(start=2							,    end=3	)	)

			def 				UpperCAmelCase__							(			self      : int	)    -> List[str]:
								__magic_name__				:						Dict     				=				BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								__magic_name__				:						Dict     				=				batch.convert_to_tensors(tensor_type='''np'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
													__magic_name__				:						Tuple     				=				batch.convert_to_tensors(tensor_type='''np'''	)
								self.assertFalse(len(cs.err	)							,    msg=F'''should have no warning, but got {cs.err}'''	)

								__magic_name__				:						Any     				=				BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								__magic_name__				:						Optional[Any]     				=				batch.convert_to_tensors(tensor_type='''np'''							,    prepend_batch_axis=lowerCamelCase_	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)	)

			@require_torch
			def 				UpperCAmelCase__							(			self      : str	)    -> Tuple:
								__magic_name__				:						List[Any]     				=				BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								__magic_name__				:						Tuple     				=				batch.convert_to_tensors(tensor_type='''pt'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
													__magic_name__				:						str     				=				batch.convert_to_tensors(tensor_type='''pt'''	)
								self.assertFalse(len(cs.err	)							,    msg=F'''should have no warning, but got {cs.err}'''	)

								__magic_name__				:						Optional[int]     				=				BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								__magic_name__				:						Tuple     				=				batch.convert_to_tensors(tensor_type='''pt'''							,    prepend_batch_axis=lowerCamelCase_	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)	)

			@require_tf
			def 				UpperCAmelCase__							(			self      : Any	)    -> int:
								__magic_name__				:						Any     				=				BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								__magic_name__				:						Union[str, Any]     				=				batch.convert_to_tensors(tensor_type='''tf'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
													__magic_name__				:						Any     				=				batch.convert_to_tensors(tensor_type='''tf'''	)
								self.assertFalse(len(cs.err	)							,    msg=F'''should have no warning, but got {cs.err}'''	)

								__magic_name__				:						Tuple     				=				BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								__magic_name__				:						List[Any]     				=				batch.convert_to_tensors(tensor_type='''tf'''							,    prepend_batch_axis=lowerCamelCase_	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)	)

			@require_flax
			def 				UpperCAmelCase__							(			self      : Union[str, Any]	)    -> int:
								__magic_name__				:						Dict     				=				BatchEncoding({'''inputs''': [[1, 2, 3], [4, 5, 6]], '''labels''': [0, 1]}	)
								__magic_name__				:						List[str]     				=				batch.convert_to_tensors(tensor_type='''jax'''	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (2, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (2,)	)
								# test converting the converted
								with CaptureStderr() as cs:
													__magic_name__				:						str     				=				batch.convert_to_tensors(tensor_type='''jax'''	)
								self.assertFalse(len(cs.err	)							,    msg=F'''should have no warning, but got {cs.err}'''	)

								__magic_name__				:						Dict     				=				BatchEncoding({'''inputs''': [1, 2, 3], '''labels''': 0}	)
								__magic_name__				:						Any     				=				batch.convert_to_tensors(tensor_type='''jax'''							,    prepend_batch_axis=lowerCamelCase_	)
								self.assertEqual(tensor_batch['''inputs'''].shape							,    (1, 3)	)
								self.assertEqual(tensor_batch['''labels'''].shape							,    (1,)	)

			def 				UpperCAmelCase__							(			self      : Tuple	)    -> int:
								__magic_name__				:						List[Any]     				=				[{'''input_ids''': np.array([0, 1, 2]	)}, {'''input_ids''': np.array([0, 1, 2, 3]	)}]
								__magic_name__				:						List[Any]     				=				BertTokenizer.from_pretrained('''bert-base-cased'''	)

								__magic_name__				:						Dict     				=				tokenizer.pad(lowerCamelCase_							,    padding=lowerCamelCase_	)
								self.assertTrue(isinstance(batch['''input_ids''']							,    np.ndarray	)	)
								self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
								__magic_name__				:						str     				=				tokenizer.pad(lowerCamelCase_							,    padding=lowerCamelCase_							,    return_tensors='''np'''	)
								self.assertTrue(isinstance(batch['''input_ids''']							,    np.ndarray	)	)
								self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)

			@require_torch
			def 				UpperCAmelCase__							(			self      : Any	)    -> List[Any]:
								import torch

								__magic_name__				:						Optional[Any]     				=				[{'''input_ids''': torch.tensor([0, 1, 2]	)}, {'''input_ids''': torch.tensor([0, 1, 2, 3]	)}]
								__magic_name__				:						int     				=				BertTokenizer.from_pretrained('''bert-base-cased'''	)

								__magic_name__				:						Any     				=				tokenizer.pad(lowerCamelCase_							,    padding=lowerCamelCase_	)
								self.assertTrue(isinstance(batch['''input_ids''']							,    torch.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
								__magic_name__				:						List[Any]     				=				tokenizer.pad(lowerCamelCase_							,    padding=lowerCamelCase_							,    return_tensors='''pt'''	)
								self.assertTrue(isinstance(batch['''input_ids''']							,    torch.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)

			@require_tf
			def 				UpperCAmelCase__							(			self      : Optional[Any]	)    -> Union[str, Any]:
								import tensorflow as tf

								__magic_name__				:						Tuple     				=				[{'''input_ids''': tf.constant([0, 1, 2]	)}, {'''input_ids''': tf.constant([0, 1, 2, 3]	)}]
								__magic_name__				:						Optional[int]     				=				BertTokenizer.from_pretrained('''bert-base-cased'''	)

								__magic_name__				:						Optional[int]     				=				tokenizer.pad(lowerCamelCase_							,    padding=lowerCamelCase_	)
								self.assertTrue(isinstance(batch['''input_ids''']							,    tf.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].numpy().tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)
								__magic_name__				:						Optional[int]     				=				tokenizer.pad(lowerCamelCase_							,    padding=lowerCamelCase_							,    return_tensors='''tf'''	)
								self.assertTrue(isinstance(batch['''input_ids''']							,    tf.Tensor	)	)
								self.assertEqual(batch['''input_ids'''].numpy().tolist()							,    [[0, 1, 2, tokenizer.pad_token_id], [0, 1, 2, 3]]	)

			@require_tokenizers
			def 				UpperCAmelCase__							(			self      : Optional[int]	)    -> int:
								__magic_name__				:						Dict     				=				Tokenizer(WordPiece(unk_token='''[UNK]'''	)	)
								PreTrainedTokenizerFast(tokenizer_object=lowerCamelCase_	)


			@require_tokenizers
			def 				UpperCAmelCase__							(			self      : List[str]	)    -> List[Any]:
								__magic_name__				:						List[str]     				=				Tokenizer(WordPiece(unk_token='''[UNK]'''	)	)
								with tempfile.TemporaryDirectory() as tmpdirname:
													bert_tokenizer.save(os.path.join(lowerCamelCase_							,    '''tokenizer.json'''	)	)
													PreTrainedTokenizerFast(tokenizer_file=os.path.join(lowerCamelCase_							,    '''tokenizer.json'''	)	)









import math


class  lowerCamelCase     :






			'''simple docstring'''





			def __init__(			self      : Dict							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : int							,    lowerCamelCase_      : Union[str, Any]	)    -> Optional[Any]:
								__magic_name__				:						Union[str, Any]     				=				x
								__magic_name__				:						Optional[int]     				=				y
								__magic_name__				:						List[str]     				=				step_size
								__magic_name__				:						Union[str, Any]     				=				function_to_optimize

			def 				UpperCAmelCase__							(			self      : int	)    -> int:
								return self.function(self.x							,    self.y	)

			def 				UpperCAmelCase__							(			self      : Dict	)    -> Any:
								__magic_name__				:						Optional[int]     				=				self.step_size
								return [
								    SearchProblem(lowerCamelCase_							,    lowerCamelCase_							,    lowerCamelCase_							,    self.function	)
								    for x, y in (
								        (self.x - step_size, self.y - step_size),
								        (self.x - step_size, self.y),
								        (self.x - step_size, self.y + step_size),
								        (self.x, self.y - step_size),
								        (self.x, self.y + step_size),
								        (self.x + step_size, self.y - step_size),
								        (self.x + step_size, self.y),
								        (self.x + step_size, self.y + step_size),
								    )
								]

			def __hash__(			self      : Dict	)    -> Tuple:
								return hash(str(self	)	)

			def __eq__(			self      : List[str]							,    lowerCamelCase_      : Dict	)    -> Union[str, Any]:
								if isinstance(lowerCamelCase_							,    lowerCamelCase_	):
													return hash(str(self	)	) == hash(str(lowerCamelCase_	)	)
								return False


			def __str__(			self      : Tuple	)    -> Tuple:
								return F'''x: {self.x} y: {self.y}'''







def 		lowercase__ (		__A:						int				,__A:						bool = True				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						float = math.inf				,__A:						float = -math.inf				,__A:						bool = False				,__A:						int = 1_0_0_0_0				,):





					'''simple docstring'''






					__magic_name__				:						Union[str, Any]     				=				search_prob
					__magic_name__				:						int     				=				[]  # list to store the current score at each iteration
					__magic_name__				:						Any     				=				0
					__magic_name__				:						Optional[Any]     				=				False
					__magic_name__				:						Dict     				=				set()
					while not solution_found and iterations < max_iter:
										visited.add(__A					)
										iterations += 1
										__magic_name__				:						int     				=				current_state.score()
										scores.append(__A					)
										__magic_name__				:						Union[str, Any]     				=				current_state.get_neighbors()
										__magic_name__				:						str     				=				-math.inf
										__magic_name__				:						Optional[Any]     				=				math.inf
										__magic_name__				:						Any     				=				None  # to hold the next best neighbor
										for neighbor in neighbors:
															if neighbor in visited:
																				continue  # do not want to visit the same state again
															if (
															    neighbor.x > max_x
															    or neighbor.x < min_x
															    or neighbor.y > max_y
															    or neighbor.y < min_y
															):
																				continue  # neighbor outside our bounds
															__magic_name__				:						Tuple     				=				neighbor.score() - current_score
															if find_max:  # finding max
																				# going to direction with greatest ascent
																				if change > max_change and change > 0:
																									__magic_name__				:						List[str]     				=				change
																									__magic_name__				:						List[str]     				=				neighbor
															else:  # finding min
																				# to direction with greatest descent
																				if change < min_change and change < 0:
																									__magic_name__				:						List[Any]     				=				change
																									__magic_name__				:						Optional[int]     				=				neighbor
										if next_state is not None:
															# we found at least one neighbor which improved the current state
															__magic_name__				:						Any     				=				next_state
										else:
															# since we have no neighbor that improves the solution we stop the search
															__magic_name__				:						List[str]     				=				True

					if visualization:
										from matplotlib import pyplot as plt

										plt.plot(range(__A					)				,__A					)
										plt.xlabel('''Iterations'''					)
										plt.ylabel('''Function values'''					)
										plt.show()

					return current_state


if __name__ == "__main__":
	import doctest

	doctest.testmod()







	def 		lowercase__ (		__A:						Optional[Any]				,__A:						str					):





						'''simple docstring'''






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

	# starting the problem with initial coordinates (3, 4)
	__lowerCamelCase :    Any      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Any      						=      hill_climbing(prob, find_max=False)
	print(
	    '''The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
	    F"""{local_min.score()}"""
	)

	# starting the problem with initial coordinates (12, 47)
	__lowerCamelCase :    Optional[int]      						=      SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Union[str, Any]      						=      hill_climbing(
	    prob, find_max=False, max_x=1_00, 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()}"""
	)







	def 		lowercase__ (		__A:						List[Any]				,__A:						int					):





						'''simple docstring'''






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

	__lowerCamelCase :    List[str]      						=      SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
	__lowerCamelCase :    Optional[int]      						=      hill_climbing(prob, find_max=True)
	print(
	    '''The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: '''
	    F"""{local_min.score()}"""
	)



