

import argparse
import json
from pathlib import Path

import requests
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    BeitConfig,
    BeitForImageClassification,
    BeitForMaskedImageModeling,
    BeitForSemanticSegmentation,
    BeitImageProcessor,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
_a									=  logging.get_logger(__name__)







def _a					(			SCREAMING_SNAKE_CASE						:     int       ,   SCREAMING_SNAKE_CASE						:     int=False       ,   SCREAMING_SNAKE_CASE						:     Any=False						)       ->       Any:

 """simple docstring"""






 __lowerCAmelCase:  Dict        = 'backbone.' if is_semantic else ''

 __lowerCAmelCase:  List[str]        = []
 for i in range(config.num_hidden_layers						):
  # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
  rename_keys.append((f'''{prefix}blocks.{i}.norm1.weight''', f'''beit.encoder.layer.{i}.layernorm_before.weight''')						)
  rename_keys.append((f'''{prefix}blocks.{i}.norm1.bias''', f'''beit.encoder.layer.{i}.layernorm_before.bias''')						)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.attn.proj.weight''', f'''beit.encoder.layer.{i}.attention.output.dense.weight''')						)
  rename_keys.append(
      (f'''{prefix}blocks.{i}.attn.proj.bias''', f'''beit.encoder.layer.{i}.attention.output.dense.bias''')						)
  rename_keys.append((f'''{prefix}blocks.{i}.norm2.weight''', f'''beit.encoder.layer.{i}.layernorm_after.weight''')						)
  rename_keys.append((f'''{prefix}blocks.{i}.norm2.bias''', f'''beit.encoder.layer.{i}.layernorm_after.bias''')						)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.weight''', f'''beit.encoder.layer.{i}.intermediate.dense.weight''')						)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc1.bias''', f'''beit.encoder.layer.{i}.intermediate.dense.bias''')						)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.weight''', f'''beit.encoder.layer.{i}.output.dense.weight''')						)
  rename_keys.append((f'''{prefix}blocks.{i}.mlp.fc2.bias''', f'''beit.encoder.layer.{i}.output.dense.bias''')						)

 # projection layer + position embeddings
 rename_keys.extend(
     [
         (f'''{prefix}cls_token''', 'beit.embeddings.cls_token'),
         (f'''{prefix}patch_embed.proj.weight''', 'beit.embeddings.patch_embeddings.projection.weight'),
         (f'''{prefix}patch_embed.proj.bias''', 'beit.embeddings.patch_embeddings.projection.bias'),
     ]						)

 if has_lm_head:
  # mask token + shared relative position bias + layernorm
  rename_keys.extend(
      [
          ('mask_token', 'beit.embeddings.mask_token'),
          (
              'rel_pos_bias.relative_position_bias_table',
              'beit.encoder.relative_position_bias.relative_position_bias_table',
          ),
          (
              'rel_pos_bias.relative_position_index',
              'beit.encoder.relative_position_bias.relative_position_index',
          ),
          ('norm.weight', 'layernorm.weight'),
          ('norm.bias', 'layernorm.bias'),
      ]						)
 elif is_semantic:
  # semantic segmentation classification heads
  rename_keys.extend(
      [
          ('decode_head.conv_seg.weight', 'decode_head.classifier.weight'),
          ('decode_head.conv_seg.bias', 'decode_head.classifier.bias'),
          ('auxiliary_head.conv_seg.weight', 'auxiliary_head.classifier.weight'),
          ('auxiliary_head.conv_seg.bias', 'auxiliary_head.classifier.bias'),
      ]						)
 else:
  # layernorm + classification head
  rename_keys.extend(
      [
          ('fc_norm.weight', 'beit.pooler.layernorm.weight'),
          ('fc_norm.bias', 'beit.pooler.layernorm.bias'),
          ('head.weight', 'classifier.weight'),
          ('head.bias', 'classifier.bias'),
      ]						)

 return rename_keys







def _a					(			SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[Any]=False       ,   SCREAMING_SNAKE_CASE						:     Optional[int]=False						)       ->       List[str]:

 """simple docstring"""






 for i in range(config.num_hidden_layers						):
  __lowerCAmelCase:  Union[str, Any]        = 'backbone.' if is_semantic else ''
  # queries, keys and values
  __lowerCAmelCase:  List[Any]        = state_dict.pop(f'''{prefix}blocks.{i}.attn.qkv.weight'''						)
  __lowerCAmelCase:  List[str]        = state_dict.pop(f'''{prefix}blocks.{i}.attn.q_bias'''						)
  __lowerCAmelCase:  Optional[Any]        = state_dict.pop(f'''{prefix}blocks.{i}.attn.v_bias'''						)

  __lowerCAmelCase:  List[Any]        = in_proj_weight[
      : config.hidden_size, :
  ]
  __lowerCAmelCase:  List[Any]        = q_bias
  __lowerCAmelCase:  int        = in_proj_weight[
      config.hidden_size : config.hidden_size * 2, :
  ]
  __lowerCAmelCase:  Tuple        = in_proj_weight[
      -config.hidden_size :, :
  ]
  __lowerCAmelCase:  Optional[int]        = v_bias

  # gamma_1 and gamma_2
  # we call them lambda because otherwise they are renamed when using .from_pretrained
  __lowerCAmelCase:  Tuple        = state_dict.pop(f'''{prefix}blocks.{i}.gamma_1'''						)
  __lowerCAmelCase:  Any        = state_dict.pop(f'''{prefix}blocks.{i}.gamma_2'''						)

  __lowerCAmelCase:  Union[str, Any]        = gamma_a
  __lowerCAmelCase:  List[str]        = gamma_a

  # relative_position bias table + index
  if not has_lm_head:
   # each layer has its own relative position bias
   __lowerCAmelCase:  Tuple        = state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_bias_table'''						)
   __lowerCAmelCase:  List[Any]        = state_dict.pop(f'''{prefix}blocks.{i}.attn.relative_position_index'''						)

   __lowerCAmelCase:  Dict        = table
   __lowerCAmelCase:  Union[str, Any]        = index







def _a					(			SCREAMING_SNAKE_CASE						:     Any       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     Any						)       ->       Tuple:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = dct.pop(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Optional[int]        = val







def _a					(			)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Any        = 'http://images.cocodataset.org/val2017/000000039769.jpg'
 __lowerCAmelCase:  List[str]        = Image.open(requests.get(SCREAMING_SNAKE_CASE       ,   stream=SCREAMING_SNAKE_CASE						).raw						)
 return im







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Dict						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Optional[int]        = BeitConfig()
 __lowerCAmelCase:  Dict        = False
 __lowerCAmelCase:  Optional[int]        = False
 __lowerCAmelCase:  str        = 'huggingface/label-files'
 # set config parameters based on URL
 if checkpoint_url[-9:-4] == "pt22k":
  # masked image modeling
  __lowerCAmelCase:  Dict        = True
  __lowerCAmelCase:  int        = True
  __lowerCAmelCase:  Optional[int]        = True
 elif checkpoint_url[-9:-4] == "ft22k":
  # intermediate fine-tuning on ImageNet-22k
  __lowerCAmelCase:  Any        = True
  __lowerCAmelCase:  int        = 2_18_41
  __lowerCAmelCase:  int        = 'imagenet-22k-id2label.json'
  __lowerCAmelCase:  str        = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   repo_type='dataset'						)       ,   'r'						)						)
  __lowerCAmelCase:  Tuple        = {int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
  # this dataset contains 21843 labels but the model only has 21841
  # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
  del idalabel[92_05]
  del idalabel[1_50_27]
  __lowerCAmelCase:  Optional[Any]        = idalabel
  __lowerCAmelCase:  Union[str, Any]        = {v: k for k, v in idalabel.items()}
 elif checkpoint_url[-8:-4] == "to1k":
  # fine-tuning on ImageNet-1k
  __lowerCAmelCase:  Union[str, Any]        = True
  __lowerCAmelCase:  Optional[int]        = 10_00
  __lowerCAmelCase:  List[str]        = 'imagenet-1k-id2label.json'
  __lowerCAmelCase:  str        = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   repo_type='dataset'						)       ,   'r'						)						)
  __lowerCAmelCase:  str        = {int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
  __lowerCAmelCase:  Dict        = idalabel
  __lowerCAmelCase:  Any        = {v: k for k, v in idalabel.items()}
  if "384" in checkpoint_url:
   __lowerCAmelCase:  Optional[Any]        = 3_84
  if "512" in checkpoint_url:
   __lowerCAmelCase:  Dict        = 5_12
 elif "ade20k" in checkpoint_url:
  # fine-tuning
  __lowerCAmelCase:  Optional[int]        = True
  __lowerCAmelCase:  str        = 1_50
  __lowerCAmelCase:  Optional[Any]        = 'ade20k-id2label.json'
  __lowerCAmelCase:  Tuple        = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   repo_type='dataset'						)       ,   'r'						)						)
  __lowerCAmelCase:  Dict        = {int(SCREAMING_SNAKE_CASE						): v for k, v in idalabel.items()}
  __lowerCAmelCase:  Optional[Any]        = idalabel
  __lowerCAmelCase:  Optional[int]        = {v: k for k, v in idalabel.items()}
  __lowerCAmelCase:  Optional[Any]        = 6_40
  __lowerCAmelCase:  Dict        = True
 else:
  raise ValueError('Checkpoint not supported, URL should either end with \'pt22k\', \'ft22k\', \'to1k\' or \'ade20k\''						)

 # size of the architecture
 if "base" in checkpoint_url:
  pass
 elif "large" in checkpoint_url:
  __lowerCAmelCase:  List[Any]        = 10_24
  __lowerCAmelCase:  Optional[Any]        = 40_96
  __lowerCAmelCase:  str        = 24
  __lowerCAmelCase:  Dict        = 16
  if "ade20k" in checkpoint_url:
   __lowerCAmelCase:  Optional[int]        = 6_40
   __lowerCAmelCase:  List[str]        = [7, 11, 15, 23]
 else:
  raise ValueError('Should either find \'base\' or \'large\' in checkpoint URL'						)

 # load state_dict of original model, remove and rename some keys
 __lowerCAmelCase:  Tuple        = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE       ,   map_location='cpu'       ,   check_hash=SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Tuple        = state_dict['model'] if 'ade20k' not in checkpoint_url else state_dict['state_dict']

 __lowerCAmelCase:  List[str]        = create_rename_keys(SCREAMING_SNAKE_CASE       ,   has_lm_head=SCREAMING_SNAKE_CASE       ,   is_semantic=SCREAMING_SNAKE_CASE						)
 for src, dest in rename_keys:
  rename_key(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
 read_in_q_k_v(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   has_lm_head=SCREAMING_SNAKE_CASE       ,   is_semantic=SCREAMING_SNAKE_CASE						)
 if is_semantic:
  # add prefix to decoder keys
  for key, val in state_dict.copy().items():
   __lowerCAmelCase:  List[Any]        = state_dict.pop(SCREAMING_SNAKE_CASE						)
   if key.startswith('backbone.fpn'						):
    __lowerCAmelCase:  List[str]        = key.replace('backbone.fpn'       ,   'fpn'						)
   __lowerCAmelCase:  int        = val

    # load HuggingFace model
 if checkpoint_url[-9:-4] == "pt22k":
  __lowerCAmelCase:  Tuple        = BeitForMaskedImageModeling(SCREAMING_SNAKE_CASE						)
 elif "ade20k" in checkpoint_url:
  __lowerCAmelCase:  Optional[Any]        = BeitForSemanticSegmentation(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  int        = BeitForImageClassification(SCREAMING_SNAKE_CASE						)
 model.eval()
 model.load_state_dict(SCREAMING_SNAKE_CASE						)

 # Check outputs on an image
 if is_semantic:
  __lowerCAmelCase:  Union[str, Any]        = BeitImageProcessor(size=config.image_size       ,   do_center_crop=SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  Optional[Any]        = load_dataset('hf-internal-testing/fixtures_ade20k'       ,   split='test'						)
  __lowerCAmelCase:  Optional[int]        = Image.open(ds[0]['file']						)
 else:
  __lowerCAmelCase:  Dict        = BeitImageProcessor(
      size=config.image_size       ,   resample=PILImageResampling.BILINEAR       ,   do_center_crop=SCREAMING_SNAKE_CASE						)
  __lowerCAmelCase:  int        = prepare_img()

 __lowerCAmelCase:  Tuple        = image_processor(images=SCREAMING_SNAKE_CASE       ,   return_tensors='pt'						)
 __lowerCAmelCase:  Optional[int]        = encoding['pixel_values']

 __lowerCAmelCase:  Tuple        = model(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Tuple        = outputs.logits

 # verify logits
 __lowerCAmelCase:  str        = torch.Size([1, 10_00]						)
 if checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k'						):
  __lowerCAmelCase:  List[str]        = torch.Size([1, 1_96, 81_92]						)
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k'						):
  __lowerCAmelCase:  Optional[Any]        = torch.Size([1, 1_96, 81_92]						)
 elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22k'						):
  __lowerCAmelCase:  List[str]        = torch.Size([1, 2_18_41]						)
  __lowerCAmelCase:  Optional[int]        = torch.tensor([2.2_2_8_8, 2.4_6_7_1, 0.7_3_9_5]						)
  __lowerCAmelCase:  Optional[int]        = 23_97
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22k'						):
  __lowerCAmelCase:  Tuple        = torch.Size([1, 2_18_41]						)
  __lowerCAmelCase:  List[str]        = torch.tensor([1.6_8_8_1, -0.2_7_8_7, 0.5_9_0_1]						)
  __lowerCAmelCase:  Optional[Any]        = 23_96
 elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft1k'						):
  __lowerCAmelCase:  List[str]        = torch.tensor([0.1_2_4_1, 0.0_7_9_8, -0.6_5_6_9]						)
  __lowerCAmelCase:  List[Any]        = 2_85
 elif checkpoint_url[:-4].endswith('beit_base_patch16_224_pt22k_ft22kto1k'						):
  __lowerCAmelCase:  str        = torch.tensor([-1.2_3_8_5, -1.0_9_8_7, -1.0_1_0_8]						)
  __lowerCAmelCase:  List[Any]        = 2_81
 elif checkpoint_url[:-4].endswith('beit_base_patch16_384_pt22k_ft22kto1k'						):
  __lowerCAmelCase:  str        = torch.tensor([-1.5_3_0_3, -0.9_4_8_4, -0.3_1_4_7]						)
  __lowerCAmelCase:  List[Any]        = 7_61
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft1k'						):
  __lowerCAmelCase:  Any        = torch.tensor([0.4_6_1_0, -0.0_9_2_8, 0.2_0_8_6]						)
  __lowerCAmelCase:  Tuple        = 7_61
 elif checkpoint_url[:-4].endswith('beit_large_patch16_224_pt22k_ft22kto1k'						):
  __lowerCAmelCase:  Optional[Any]        = torch.tensor([-0.4_8_0_4, 0.6_2_5_7, -0.1_8_3_7]						)
  __lowerCAmelCase:  Union[str, Any]        = 7_61
 elif checkpoint_url[:-4].endswith('beit_large_patch16_384_pt22k_ft22kto1k'						):
  __lowerCAmelCase:  Tuple        = torch.tensor([[-0.5_1_2_2, 0.5_1_1_7, -0.2_1_1_3]]						)
  __lowerCAmelCase:  Any        = 7_61
 elif checkpoint_url[:-4].endswith('beit_large_patch16_512_pt22k_ft22kto1k'						):
  __lowerCAmelCase:  Dict        = torch.tensor([-0.3_0_6_2, 0.7_2_6_1, 0.4_8_5_2]						)
  __lowerCAmelCase:  Union[str, Any]        = 7_61
 elif checkpoint_url[:-4].endswith('beit_base_patch16_640_pt22k_ft22ktoade20k'						):
  __lowerCAmelCase:  Any        = (1, 1_50, 1_60, 1_60)
  __lowerCAmelCase:  Optional[Any]        = torch.tensor(
      [
          [[-4.9_2_2_5, -2.3_9_5_4, -3.0_5_2_2], [-2.8_8_2_2, -1.0_0_4_6, -1.7_5_6_1], [-2.9_5_4_9, -1.3_2_2_8, -2.1_3_4_7]],
          [[-5.8_1_6_8, -3.4_1_2_9, -4.0_7_7_8], [-3.8_6_5_1, -2.2_2_1_4, -3.0_2_7_7], [-3.8_3_5_6, -2.4_6_4_3, -3.3_5_3_5]],
          [[-0.0_0_7_8, 3.9_9_5_2, 4.0_7_5_4], [2.9_8_5_6, 4.6_9_4_4, 5.0_0_3_5], [3.2_4_1_3, 4.7_8_1_3, 4.9_9_6_9]],
      ]						)
 elif checkpoint_url[:-4].endswith('beit_large_patch16_640_pt22k_ft22ktoade20k'						):
  __lowerCAmelCase:  int        = (1, 1_50, 1_60, 1_60)
  __lowerCAmelCase:  str        = torch.tensor(
      [
          [[-4.3_3_0_5, -2.3_0_4_9, -3.0_1_6_1], [-2.9_5_9_1, -1.5_3_0_5, -2.2_2_5_1], [-3.4_1_9_8, -1.8_0_0_4, -2.9_0_6_2]],
          [[-5.8_9_2_2, -3.7_4_3_5, -4.3_9_7_8], [-4.2_0_6_3, -2.7_8_7_2, -3.4_7_5_5], [-4.2_7_9_1, -3.1_8_7_4, -4.1_6_8_1]],
          [[0.9_8_9_5, 4.3_4_6_7, 4.7_6_6_3], [4.2_4_7_6, 5.6_8_3_0, 6.1_5_1_8], [4.5_5_5_0, 6.2_4_9_5, 6.5_1_5_4]],
      ]						)
 else:
  raise ValueError('Can\'t verify logits as model is not supported'						)

 assert logits.shape == expected_shape, "Shape of logits not as expected"
 if not has_lm_head:
  if is_semantic:
   assert torch.allclose(
       logits[0, :3, :3, :3]       ,   SCREAMING_SNAKE_CASE       ,   atol=1E-3						), "First elements of logits not as expected"
  else:
   print('Predicted class idx:'       ,   logits.argmax(-1						).item()						)
   assert torch.allclose(
       logits[0, :3]       ,   SCREAMING_SNAKE_CASE       ,   atol=1E-3						), "First elements of logits not as expected"
   assert logits.argmax(-1						).item() == expected_class_idx, "Predicted class index not as expected"

 Path(SCREAMING_SNAKE_CASE						).mkdir(exist_ok=SCREAMING_SNAKE_CASE						)
 print(f'''Saving model to {pytorch_dump_folder_path}'''						)
 model.save_pretrained(SCREAMING_SNAKE_CASE						)
 print(f'''Saving image processor to {pytorch_dump_folder_path}'''						)
 image_processor.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  _a									=  argparse.ArgumentParser()

  parser.add_argument(
      '''--checkpoint_url''',
      default='''https://conversationhub.blob.core.windows.net/beit-share-public/beit/beit_base_patch16_224_pt22k_ft22kto1k.pth''',
      type=str,
      help='''URL to the original PyTorch checkpoint (.pth file).''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
  )
  _a									=  parser.parse_args()
  convert_beit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)













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

import pytest
import requests

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

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







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







def   snake_case		(				):
				with open(os.environ['GITHUB_EVENT_PATH'] ) as file:
								A									=   json.load(UpperCAmelCase )
				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=UpperCAmelCase ).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(UpperCAmelCase )
				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 UpperCAmelCase : f'{path.parent.name}/{path.name}',    )
def   snake_case		(				UpperCAmelCase						:							pathlib.Path ):
				A									=   solution_path.parent.name[8:].zfill(3 )
				A									=   PROBLEM_ANSWERS[problem_number]
				A									=   convert_path_to_module(UpperCAmelCase )
				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}'





