
'''simple docstring'''


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

import pytest
import requests

__lowercase							:				Optional[int]					=       pathlib.Path.cwd().joinpath('''project_euler''')
__lowercase							:				Any					=       pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

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




def lowercase_		(				_lowercase			):
					'''simple docstring'''

					lowerCamelCase_   : int       =						importlib.util.spec_from_file_location(file_path.name     ,    str(snake_case__			)			)
					lowerCamelCase_   : Optional[Any]       =						importlib.util.module_from_spec(snake_case__			)  # type: ignore
					spec.loader.exec_module(snake_case__			)  # type: ignore
					return module




def lowercase_		(				):
					'''simple docstring'''

					lowerCamelCase_   : Any       =						[]
					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(snake_case__			)
					return solution_file_paths




def lowercase_		(				):
					'''simple docstring'''

					with open(os.environ['''GITHUB_EVENT_PATH''']			) as file:
										lowerCamelCase_   : int       =						json.load(snake_case__			)
					return event["pull_request"]["url"] + "/files"




def lowercase_		(				):
					'''simple docstring'''

					lowerCamelCase_   : Optional[int]       =						[]
					lowerCamelCase_   : Any       =						{
					    '''Accept''': '''application/vnd.github.v3+json''',
					    '''Authorization''': '''token ''' + os.environ['''GITHUB_TOKEN'''],
					}
					lowerCamelCase_   : Optional[Any]       =						requests.get(get_files_url()     ,    headers=snake_case__			).json()
					for file in files:
										lowerCamelCase_   : Tuple       =						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(snake_case__			)
					return solution_file_paths




def lowercase_		(				):
					'''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 _lowercase			: F"""{path.parent.name}/{path.name}"""     ,    )
def lowercase_		(				_lowercase			):
					'''simple docstring'''

					lowerCamelCase_   : List[str]       =						solution_path.parent.name[8:].zfill(3			)
					lowerCamelCase_   : List[Any]       =						PROBLEM_ANSWERS[problem_number]
					lowerCamelCase_   : Any       =						convert_path_to_module(snake_case__			)
					lowerCamelCase_   : List[Any]       =						str(solution_module.solution()			)  # type: ignore
					lowerCamelCase_   : Tuple       =						hashlib.shaaaa(answer.encode()			).hexdigest()
					assert (
					    answer == expected
					), F"""Expected solution to {problem_number} to have hash {expected}, got {answer}"""




'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowercase							:				Any					=       {
    '''configuration_camembert''': ['''CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CamembertConfig''', '''CamembertOnnxConfig'''],
}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       ['''CamembertTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				str					=       ['''CamembertTokenizerFast''']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				List[Any]					=       [
					    '''CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''CamembertForCausalLM''',
					    '''CamembertForMaskedLM''',
					    '''CamembertForMultipleChoice''',
					    '''CamembertForQuestionAnswering''',
					    '''CamembertForSequenceClassification''',
					    '''CamembertForTokenClassification''',
					    '''CamembertModel''',
					    '''CamembertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					__lowercase							:				Any					=       [
					    '''TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFCamembertForCausalLM''',
					    '''TFCamembertForMaskedLM''',
					    '''TFCamembertForMultipleChoice''',
					    '''TFCamembertForQuestionAnswering''',
					    '''TFCamembertForSequenceClassification''',
					    '''TFCamembertForTokenClassification''',
					    '''TFCamembertModel''',
					    '''TFCamembertPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert import CamembertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_camembert_fast import CamembertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_camembert import (
										    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    CamembertForCausalLM,
										    CamembertForMaskedLM,
										    CamembertForMultipleChoice,
										    CamembertForQuestionAnswering,
										    CamembertForSequenceClassification,
										    CamembertForTokenClassification,
										    CamembertModel,
										    CamembertPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_camembert import (
										    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFCamembertForCausalLM,
										    TFCamembertForMaskedLM,
										    TFCamembertForMultipleChoice,
										    TFCamembertForQuestionAnswering,
										    TFCamembertForSequenceClassification,
										    TFCamembertForTokenClassification,
										    TFCamembertModel,
										    TFCamembertPreTrainedModel,
										)

else:
					import sys

					__lowercase							:				Union[str, Any]					=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


