
"""simple docstring"""


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

import pytest
import requests

_lowercase											=			pathlib.Path.cwd().joinpath('''project_euler''')
_lowercase											=			pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

with open(PROJECT_EULER_ANSWERS_PATH) as file_handle:
		_lowercase											=			json.load(file_handle)






def  _snake_case			(						snake_case__				:       pathlib.Path       ):
	A  				=				importlib.util.spec_from_file_location(file_path.name		, str(snake_case__       )       )
	A  				=				importlib.util.module_from_spec(snake_case__       )  # type: ignore
	spec.loader.exec_module(snake_case__       )  # type: ignore
	return module






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






def  _snake_case			(						):
	A  				=				[]
	A  				=				{
	    'Accept': 'application/vnd.github.v3+json',
	    'Authorization': 'token ' + os.environ['GITHUB_TOKEN'],
	}
	A  				=				requests.get(get_files_url()		, headers=snake_case__       ).json()
	for file in files:
		A  				=				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  _snake_case			(						):
	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 snake_case__       : F'{path.parent.name}/{path.name}'		, )
def  _snake_case			(						snake_case__				:       pathlib.Path       ):
	A  				=				solution_path.parent.name[8:].zfill(3       )
	A  				=				PROBLEM_ANSWERS[problem_number]
	A  				=				convert_path_to_module(snake_case__       )
	A  				=				str(solution_module.solution()       )  # type: ignore
	A  				=				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 Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowercase											=			logging.get_logger(__name__)

_lowercase											=			{
    '''microsoft/prophetnet-large-uncased''': (
        '''https://huggingface.co/microsoft/prophetnet-large-uncased/resolve/main/config.json'''
    ),
}







class 			lowerCAmelCase_			( _lowercase      ):



						'''simple docstring'''


						_lowerCamelCase:      int =      '''prophetnet'''
						_lowerCamelCase:      Optional[Any] =      ['''past_key_values''']
						_lowerCamelCase:      Dict =      {
						    '''num_attention_heads''': '''num_encoder_attention_heads''',
						}


						def __init__(	self       : Tuple					,A_       : Optional[float] = 0.1					,A_       : Optional[Union[str, Callable]] = "gelu"					,A_       : Optional[int] = 3_0522					,A_       : Optional[int] = 1024					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[int] = 4096					,A_       : Optional[int] = 12					,A_       : Optional[int] = 16					,A_       : Optional[float] = 0.1					,A_       : Optional[float] = 0.1					,A_       : Optional[int] = 512					,A_       : Optional[float] = 0.02					,A_       : Optional[bool] = True					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 2					,A_       : Optional[int] = 32					,A_       : Optional[int] = 128					,A_       : Optional[bool] = False					,A_       : Optional[float] = 0.0					,A_       : Optional[bool] = True					,A_       : Optional[int] = 0					,A_       : Optional[int] = 1					,A_       : Optional[int] = 2					,**A_       : Tuple					,)       ->				Any:
							A  				=				vocab_size
							A  				=				hidden_size
							A  				=				encoder_ffn_dim
							A  				=				num_encoder_layers
							A  				=				num_encoder_attention_heads
							A  				=				decoder_ffn_dim
							A  				=				num_decoder_layers
							A  				=				num_decoder_attention_heads
							A  				=				max_position_embeddings
							A  				=				init_std  # Normal(0, this parameter)
							A  				=				activation_function

							# parameters for prophetnet
							A  				=				ngram
							A  				=				num_buckets
							A  				=				relative_max_distance
							A  				=				disable_ngram_loss
							A  				=				eps

							# 3 Types of Dropout
							A  				=				attention_dropout
							A  				=				activation_dropout
							A  				=				dropout

							A  				=				use_cache

							super().__init__(
							    pad_token_id=A_					,bos_token_id=A_					,eos_token_id=A_					,is_encoder_decoder=A_					,add_cross_attention=A_					,decoder_start_token_id=A_					,**A_					,)


						@property
						def   _SCREAMING_SNAKE_CASE (	self       : List[Any] )       ->				int:
							return self.num_encoder_layers + self.num_decoder_layers




						@num_hidden_layers.setter
						def   _SCREAMING_SNAKE_CASE (	self       : int					,A_       : Any )       ->				int:
							raise NotImplementedError(
							    'This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and'
							    ' `num_decoder_layers`.' )