






'''simple docstring'''

import hashlib
import importlib.util
import json
import os
import pathlib
from types import ModuleType

import pytest
import requests

A:   List[str]						=					pathlib.Path.cwd().joinpath("project_euler")
A:   Dict						=					pathlib.Path.cwd().joinpath(
    "scripts", "project_euler_answers.json"
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
						A:   dict[str, str]						=					json.load(file_handle)






def 						_UpperCAmelCase     (	a		:						pathlib.Path				)      ->      ModuleType:





						"""simple docstring"""
						lowercase_   : Union[str, Any]   	=      importlib.util.spec_from_file_location(file_path.name    ,      str(a				)				)
						lowercase_   : List[str]   	=      importlib.util.module_from_spec(a				)  # type: ignore
						spec.loader.exec_module(a				)  # type: ignore
						return module






def 						_UpperCAmelCase     (	)      ->      list[pathlib.Path]:





						"""simple docstring"""
						lowercase_   : Optional[int]   	=      []
						for problem_dir_path in PROJECT_EULER_DIR_PATH.iterdir():
												if problem_dir_path.is_file() or problem_dir_path.name.startswith('_'				):
																		continue
												for file_path in problem_dir_path.iterdir():
																		if file_path.suffix != ".py" or file_path.name.startswith(('_', 'test')				):
																								continue
																		solution_file_paths.append(a				)
						return solution_file_paths






def 						_UpperCAmelCase     (	)      ->      str:





						"""simple docstring"""
						with open(os.environ['GITHUB_EVENT_PATH']				) as file:
												lowercase_   : Optional[Any]   	=      json.load(a				)
						return event["pull_request"]["url"] + "/files"






def 						_UpperCAmelCase     (	)      ->      list[pathlib.Path]:





						"""simple docstring"""
						lowercase_   : int   	=      []
						lowercase_   : str   	=      {
						    'Accept': 'application/vnd.github.v3+json',
						    'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
						}
						lowercase_   : List[str]   	=      requests.get(get_files_url()    ,      headers=a				).json()
						for file in files:
												lowercase_   : Dict   	=      pathlib.Path.cwd().joinpath(file['filename']				)
												if (
												    filepath.suffix != ".py"
												    or filepath.name.startswith(('_', 'test')				)
												    or not filepath.name.startswith('sol'				)
												):
																		continue
												solution_file_paths.append(a				)
						return solution_file_paths






def 						_UpperCAmelCase     (	)      ->      list[pathlib.Path]:





						"""simple docstring"""
						if os.environ.get('CI'				) and os.environ.get('GITHUB_EVENT_NAME'				) == "pull_request":
												# Return only if there are any, otherwise default to all solutions
												if filepaths := added_solution_file_path():
																		return filepaths
						return all_solution_file_paths()






@pytest.mark.parametrize(
    'solution_path'    ,      collect_solution_file_paths()    ,      ids=lambda a				: f"{path.parent.name}/{path.name}"    ,      )
def 						_UpperCAmelCase     (	a		:						pathlib.Path				)      ->      None:





						"""simple docstring"""
						lowercase_   : str   	=      solution_path.parent.name[8:].zfill(3				)
						lowercase_   : str   	=      PROBLEM_ANSWERS[problem_number]
						lowercase_   : Optional[int]   	=      convert_path_to_module(a				)
						lowercase_   : List[str]   	=      str(solution_module.solution()				)  # type: ignore
						lowercase_   : Tuple   	=      hashlib.shaaaa(answer.encode()				).hexdigest()
						assert (
						    answer == expected
						), f"Expected solution to {problem_number} to have hash {expected}, got {answer}"








'''simple docstring'''

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 				__magic_name__  (						unittest.TestCase					):







	"""simple docstring"""



	def 							lowerCamelCase__		(				self	,   _lowercase							)    ->	Optional[int]:
							lowercase_   : Optional[int]   	=      list(tokenizer_class.max_model_input_sizes.keys()							)
							for model_name in sa_models[:1]:
													lowercase_   : List[str]   	=      tokenizer_class.from_pretrained(_lowercase							)
													self.assertIsNotNone(_lowercase							)
													self.assertIsInstance(_lowercase	,   _lowercase							)
													self.assertIsInstance(_lowercase	,   _lowercase							)

													for special_tok in tokenizer.all_special_tokens:
																			self.assertIsInstance(_lowercase	,   _lowercase							)
																			lowercase_   : Optional[int]   	=      tokenizer.convert_tokens_to_ids(_lowercase							)
																			self.assertIsInstance(_lowercase	,   _lowercase							)
	def 							lowerCamelCase__		(				self	,   _lowercase	,   _lowercase = None							)    ->	str:
							lowercase_   : int   	=      pickle.dumps(_lowercase							)
							self.assertIsNotNone(_lowercase							)

							lowercase_   : Any   	=      pickle.loads(_lowercase							)

							# 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(_lowercase	,   _lowercase							)							)
													else:
																			self.assertEqual(_lowercase	,   _lowercase							)
	@slow
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							self.check_tokenizer_from_pretrained(_lowercase							)
	def 							lowerCamelCase__		(				self							)    ->	str:
							self.assertEqual(TensorType('tf'							)	,   TensorType.TENSORFLOW							)
							self.assertEqual(TensorType('pt'							)	,   TensorType.PYTORCH							)
							self.assertEqual(TensorType('np'							)	,   TensorType.NUMPY							)
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	Dict:
							import numpy as np

							lowercase_   : Any   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : 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 							lowerCamelCase__		(				self							)    ->	Optional[int]:
							import tensorflow as tf

							def tf_array_equals(_lowercase	,   _lowercase							):
													return tf.reduce_all(tf.equal(_lowercase	,   _lowercase							)							)

							lowercase_   : Optional[Any]   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : str   	=      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							)	,   _lowercase							)

							with self.subTest('BatchEncoding (Rust, return_tensors=TENSORFLOW)'							):
													self.assert_dump_and_restore(
													    tokenizer_r('Small example to encode'	,   return_tensors=TensorType.TENSORFLOW							)	,   _lowercase							)
	@require_torch
	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							import torch

							lowercase_   : Dict   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : 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 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Dict   	=      BertTokenizer.from_pretrained('bert-base-cased'							)
							lowercase_   : Tuple   	=      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 							lowerCamelCase__		(				self							)    ->	List[Any]:
							lowercase_   : Optional[Any]   	=      BertTokenizerFast.from_pretrained('bert-base-cased'							)
							lowercase_   : Dict   	=      tokenizer_r(['Test', '\xad', 'test']	,   is_split_into_words=_lowercase							)

							self.assertEqual(encoded.word_to_tokens(0							)	,   TokenSpan(start=1	,   end=2							)							)
							self.assertEqual(encoded.word_to_tokens(1							)	,   _lowercase							)
							self.assertEqual(encoded.word_to_tokens(2							)	,   TokenSpan(start=2	,   end=3							)							)
	def 							lowerCamelCase__		(				self							)    ->	str:
							lowercase_   : Dict   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : Any   	=      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:
													lowercase_   : Dict   	=      batch.convert_to_tensors(tensor_type='np'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : Union[str, Any]   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Optional[int]   	=      batch.convert_to_tensors(tensor_type='np'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	@require_torch
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							lowercase_   : Dict   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : Optional[Any]   	=      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:
													lowercase_   : Optional[Any]   	=      batch.convert_to_tensors(tensor_type='pt'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : int   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Union[str, Any]   	=      batch.convert_to_tensors(tensor_type='pt'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	@require_tf
	def 							lowerCamelCase__		(				self							)    ->	Tuple:
							lowercase_   : Dict   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : List[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:
													lowercase_   : Tuple   	=      batch.convert_to_tensors(tensor_type='tf'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : int   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Optional[Any]   	=      batch.convert_to_tensors(tensor_type='tf'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	@require_flax
	def 							lowerCamelCase__		(				self							)    ->	Union[str, Any]:
							lowercase_   : int   	=      BatchEncoding({'inputs': [[1, 2, 3], [4, 5, 6]], 'labels': [0, 1]}							)
							lowercase_   : Optional[Any]   	=      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:
													lowercase_   : Dict   	=      batch.convert_to_tensors(tensor_type='jax'							)
							self.assertFalse(len(cs.err							)	,   msg=f"should have no warning, but got {cs.err}"							)

							lowercase_   : List[Any]   	=      BatchEncoding({'inputs': [1, 2, 3], 'labels': 0}							)
							lowercase_   : Dict   	=      batch.convert_to_tensors(tensor_type='jax'	,   prepend_batch_axis=_lowercase							)
							self.assertEqual(tensor_batch['inputs'].shape	,   (1, 3)							)
							self.assertEqual(tensor_batch['labels'].shape	,   (1,)							)
	def 							lowerCamelCase__		(				self							)    ->	List[str]:
							lowercase_   : Any   	=      [{'input_ids': np.array([0, 1, 2]							)}, {'input_ids': np.array([0, 1, 2, 3]							)}]
							lowercase_   : str   	=      BertTokenizer.from_pretrained('bert-base-cased'							)

							lowercase_   : Any   	=      tokenizer.pad(_lowercase	,   padding=_lowercase							)
							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]]							)
							lowercase_   : List[str]   	=      tokenizer.pad(_lowercase	,   padding=_lowercase	,   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 							lowerCamelCase__		(				self							)    ->	List[Any]:
							import torch

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

							lowercase_   : str   	=      tokenizer.pad(_lowercase	,   padding=_lowercase							)
							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]]							)
							lowercase_   : Tuple   	=      tokenizer.pad(_lowercase	,   padding=_lowercase	,   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 							lowerCamelCase__		(				self							)    ->	Any:
							import tensorflow as tf

							lowercase_   : Any   	=      [{'input_ids': tf.constant([0, 1, 2]							)}, {'input_ids': tf.constant([0, 1, 2, 3]							)}]
							lowercase_   : Dict   	=      BertTokenizer.from_pretrained('bert-base-cased'							)

							lowercase_   : Any   	=      tokenizer.pad(_lowercase	,   padding=_lowercase							)
							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]]							)
							lowercase_   : Dict   	=      tokenizer.pad(_lowercase	,   padding=_lowercase	,   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 							lowerCamelCase__		(				self							)    ->	str:
							lowercase_   : Optional[int]   	=      Tokenizer(WordPiece(unk_token='[UNK]'							)							)
							PreTrainedTokenizerFast(tokenizer_object=_lowercase							)







	@require_tokenizers
	def 							lowerCamelCase__		(				self							)    ->	int:
							lowercase_   : Union[str, Any]   	=      Tokenizer(WordPiece(unk_token='[UNK]'							)							)
							with tempfile.TemporaryDirectory() as tmpdirname:
													bert_tokenizer.save(os.path.join(_lowercase	,   'tokenizer.json'							)							)
													PreTrainedTokenizerFast(tokenizer_file=os.path.join(_lowercase	,   'tokenizer.json'							)							)
