

"""simple docstring"""





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

import pytest
import requests

__UpperCamelCase						:   int							   =	pathlib.Path.cwd().joinpath('''project_euler''')
__UpperCamelCase						:   Tuple							   =	pathlib.Path.cwd().joinpath(
    '''scripts''', '''project_euler_answers.json'''
)

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

def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		List[Any]						       =			importlib.util.spec_from_file_location(file_path.name		,  str(A_				)				)
	lowerCAmelCase__			:		Any						       =			importlib.util.module_from_spec(A_				)  # type: ignore
	spec.loader.exec_module(A_				)  # type: ignore
	return module

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Dict						       =			[]
	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 						__SCREAMING_SNAKE_CASE						(   ):
	with open(os.environ['''GITHUB_EVENT_PATH''']				) as file:
		lowerCAmelCase__			:		List[str]						       =			json.load(A_				)
	return event["pull_request"]["url"] + "/files"

def 						__SCREAMING_SNAKE_CASE						(   ):
	lowerCAmelCase__			:		Optional[int]						       =			[]
	lowerCAmelCase__			:		List[Any]						       =			{
	    '''Accept''': '''application/vnd.github.v3+json''',
	    '''Authorization''': '''token ''' + os.environ['''GITHUB_TOKEN'''],
	}
	lowerCAmelCase__			:		Optional[Any]						       =			requests.get(get_files_url()		,  headers=A_				).json()
	for file in files:
		lowerCAmelCase__			:		Optional[Any]						       =			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 						__SCREAMING_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 A_				: f'{path.parent.name}/{path.name}'		,  )
def 						__SCREAMING_SNAKE_CASE						(   A_				):
	lowerCAmelCase__			:		str						       =			solution_path.parent.name[8:].zfill(3				)
	lowerCAmelCase__			:		str						       =			PROBLEM_ANSWERS[problem_number]
	lowerCAmelCase__			:		Optional[Any]						       =			convert_path_to_module(A_				)
	lowerCAmelCase__			:		Union[str, Any]						       =			str(solution_module.solution()				)  # type: ignore
	lowerCAmelCase__			:		int						       =			hashlib.shaaaa(answer.encode()				).hexdigest()
	assert (
	    answer == expected
	), f'Expected solution to {problem_number} to have hash {expected}, got {answer}'







"""simple docstring"""





from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__UpperCamelCase						:   str							   =	logging.get_logger(__name__)

__UpperCamelCase						:   Union[str, Any]							   =	{
    '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/config.json''',
    '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/config.json''',
    '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/config.json''',
    '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/config.json''',
    '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/config.json''',
}







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					lowercase__       				= "gpt2"
					lowercase__       				= ["past_key_values"]
					lowercase__       				= {
					    "hidden_size": "n_embd",
					    "max_position_embeddings": "n_positions",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}







					def __init__( self    :       List[str]     ,lowercase_    :       Dict=5_0_2_5_7     ,lowercase_    :       Dict=1_0_2_4     ,lowercase_    :       Any=7_6_8     ,lowercase_    :       str=1_2     ,lowercase_    :       int=1_2     ,lowercase_    :       List[Any]=None     ,lowercase_    :       Dict="gelu_new"     ,lowercase_    :       Any=0.1     ,lowercase_    :       Optional[Any]=0.1     ,lowercase_    :       Tuple=0.1     ,lowercase_    :       Dict=1E-5     ,lowercase_    :       List[Any]=0.02     ,lowercase_    :       Tuple="cls_index"     ,lowercase_    :       Optional[int]=True     ,lowercase_    :       Union[str, Any]=None     ,lowercase_    :       Optional[Any]=True     ,lowercase_    :       List[Any]=0.1     ,lowercase_    :       List[str]=True     ,lowercase_    :       int=True     ,lowercase_    :       Optional[Any]=5_0_2_5_6     ,lowercase_    :       Dict=5_0_2_5_6     ,lowercase_    :       Union[str, Any]=False     ,lowercase_    :       Optional[Any]=False     ,**lowercase_    :       Dict     ,):
						lowerCAmelCase__			:		Tuple						       =			vocab_size
						lowerCAmelCase__			:		List[Any]						       =			n_positions
						lowerCAmelCase__			:		Optional[int]						       =			n_embd
						lowerCAmelCase__			:		Tuple						       =			n_layer
						lowerCAmelCase__			:		Optional[Any]						       =			n_head
						lowerCAmelCase__			:		List[Any]						       =			n_inner
						lowerCAmelCase__			:		Any						       =			activation_function
						lowerCAmelCase__			:		Optional[int]						       =			resid_pdrop
						lowerCAmelCase__			:		str						       =			embd_pdrop
						lowerCAmelCase__			:		Tuple						       =			attn_pdrop
						lowerCAmelCase__			:		List[str]						       =			layer_norm_epsilon
						lowerCAmelCase__			:		Union[str, Any]						       =			initializer_range
						lowerCAmelCase__			:		Optional[int]						       =			summary_type
						lowerCAmelCase__			:		Optional[Any]						       =			summary_use_proj
						lowerCAmelCase__			:		List[str]						       =			summary_activation
						lowerCAmelCase__			:		Optional[int]						       =			summary_first_dropout
						lowerCAmelCase__			:		Dict						       =			summary_proj_to_labels
						lowerCAmelCase__			:		Dict						       =			scale_attn_weights
						lowerCAmelCase__			:		Dict						       =			use_cache
						lowerCAmelCase__			:		List[Any]						       =			scale_attn_by_inverse_layer_idx
						lowerCAmelCase__			:		int						       =			reorder_and_upcast_attn

						lowerCAmelCase__			:		List[str]						       =			bos_token_id
						lowerCAmelCase__			:		List[str]						       =			eos_token_id

						super().__init__(bos_token_id=lowercase_     ,eos_token_id=lowercase_     ,**lowercase_	)







class        SCREAMING_SNAKE_CASE      (	a_ ):






					"""simple docstring"""



					def __init__( self    :       Optional[int]     ,lowercase_    :       PretrainedConfig     ,lowercase_    :       str = "default"     ,lowercase_    :       List[PatchingSpec] = None     ,lowercase_    :       bool = False     ,):
						super().__init__(lowercase_     ,task=lowercase_     ,patching_specs=lowercase_     ,use_past=lowercase_	)
						if not getattr(self._config     ,'''pad_token_id'''     ,lowercase_	):
							# TODO: how to do that better?
							lowerCAmelCase__			:		int						       =			0







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
						if self.use_past:
							self.fill_with_past_key_values_(lowercase_     ,direction='''inputs'''	)
							lowerCAmelCase__			:		str						       =			{0: '''batch''', 1: '''past_sequence + sequence'''}
						else:
							lowerCAmelCase__			:		List[str]						       =			{0: '''batch''', 1: '''sequence'''}

						return common_inputs







					@property
					def  __lowerCAmelCase				( self    :       Any	):
						return self._config.n_layer







					@property
					def  __lowerCAmelCase				( self    :       List[str]	):
						return self._config.n_head







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       PreTrainedTokenizer     ,lowercase_    :       int = -1     ,lowercase_    :       int = -1     ,lowercase_    :       bool = False     ,lowercase_    :       Optional[TensorType] = None     ,):
						lowerCAmelCase__			:		Any						       =			super(lowercase_     ,self	).generate_dummy_inputs(
						    lowercase_     ,batch_size=lowercase_     ,seq_length=lowercase_     ,is_pair=lowercase_     ,framework=lowercase_	)

						# We need to order the input in the way they appears in the forward()
						lowerCAmelCase__			:		List[Any]						       =			OrderedDict({'''input_ids''': common_inputs['''input_ids''']}	)

						# Need to add the past_keys
						if self.use_past:
							if not is_torch_available():
								raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''	)
							else:
								import torch

								lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			common_inputs['''input_ids'''].shape
								# Not using the same length for past_key_values
								lowerCAmelCase__			:		Dict						       =			seqlen + 2
								lowerCAmelCase__			:		Optional[Any]						       =			(
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								lowerCAmelCase__			:		Tuple						       =			[
								    (torch.zeros(lowercase_	), torch.zeros(lowercase_	)) for _ in range(self.num_layers	)
								]

						lowerCAmelCase__			:		Any						       =			common_inputs['''attention_mask''']
						if self.use_past:
							lowerCAmelCase__			:		Optional[Any]						       =			ordered_inputs['''attention_mask'''].dtype
							lowerCAmelCase__			:		Union[str, Any]						       =			torch.cat(
							    [ordered_inputs['''attention_mask'''], torch.ones(lowercase_     ,lowercase_     ,dtype=lowercase_	)]     ,dim=1	)

						return ordered_inputs







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return 1_3




