
import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available




def 				lowerCamelCase__				(						a__  :  List[str]	,				a__  :  Optional[Any]	,				a__  :  Any	,				a__  :  List[Any]=None     )		->	List[Any]:
       UpperCamelCase_							       =       None
       # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
       if str(weight.dtype     ) == "torch.bfloat16":
              # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
              UpperCamelCase_							       =       weight.view(torch.intaa     )
              UpperCamelCase_							       =       """bfloat16"""
       UpperCamelCase_							       =       weight.cpu().numpy()
       UpperCamelCase_							       =       os.path.join(a__	,				f'''{weight_name}.dat'''     )
       if index is not None:
              if dtype is None:
                     UpperCamelCase_							       =       str(array.dtype     )
              UpperCamelCase_							       =       {"""dtype""": dtype, """shape""": list(array.shape     )}
       if array.ndim == 0:
              UpperCamelCase_							       =       array[None]
       UpperCamelCase_							       =       np.memmap(a__	,				dtype=array.dtype	,				mode="""w+"""	,				shape=array.shape     )
       UpperCamelCase_							       =       array[:]
       file_array.flush()
       return index




def 				lowerCamelCase__				(						a__  :  Union[str, Any]	,				a__  :  List[str]     )		->	Dict:
       UpperCamelCase_							       =       tuple(weight_info["""shape"""]     )
       if shape == ():
              # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
              UpperCamelCase_							       =       (1,)

       UpperCamelCase_							       =       weight_info["""dtype"""]
       if dtype == "bfloat16":
              # NumPy does not support bfloat16 so this was saved as a int16
              UpperCamelCase_							       =       """int16"""

       UpperCamelCase_							       =       np.memmap(a__	,				dtype=a__	,				shape=a__	,				mode="""r"""     )

       if len(weight_info["""shape"""]     ) == 0:
              UpperCamelCase_							       =       weight[0]
       UpperCamelCase_							       =       torch.tensor(a__     )
       if weight_info["dtype"] == "bfloat16":
              UpperCamelCase_							       =       weight.view(torch.bfloataa     )

       return weight




def 				lowerCamelCase__				(						a__  :  str	,				a__  :  int     )		->	int:
       if index is None or len(a__     ) == 0:
              # Nothing to save
              return

       UpperCamelCase_							       =       os.path.join(a__	,				"""index.json"""     )
       if os.path.isfile(a__     ):
              with open(a__	,				"""r"""	,				encoding="""utf-8"""     ) as f:
                     UpperCamelCase_							       =       json.load(a__     )
       else:
              UpperCamelCase_							       =       {}
       current_index.update(a__     )

       with open(a__	,				"""w"""	,				encoding="""utf-8"""     ) as f:
              json.dump(a__	,				a__	,				indent=2     )




def 				lowerCamelCase__				(						a__  :  Any	,				a__  :  List[Any]     )		->	List[Any]:
       os.makedirs(a__	,				exist_ok=a__     )
       UpperCamelCase_							       =       {}
       for name, parameter in state_dict.items():
              UpperCamelCase_							       =       offload_weight(a__	,				a__	,				a__	,				index=a__     )

       # Update index
       save_offload_index(a__	,				a__     )

class 		lowercase_       (       _SCREAMING_SNAKE_CASE				):

 def __init__(      self     ,	__UpperCamelCase     ,	__UpperCamelCase     ):




        """simple docstring"""


        UpperCamelCase_							       =       dataset
        UpperCamelCase_							       =       prefix

 def __getitem__(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        return self.dataset[f'''{self.prefix}{key}''']

 def __iter__(      self     ):




        """simple docstring"""


        return iter([key for key in self.dataset if key.startswith(self.prefix     )]     )

 def __len__(      self     ):




        """simple docstring"""


        return len(self.dataset     )



class 		lowercase_       (       _SCREAMING_SNAKE_CASE				):

 def __init__(      self     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	__UpperCamelCase = None     ,	__UpperCamelCase=None     ,	):




        """simple docstring"""


        if state_dict is None and save_folder is None:
               raise ValueError("""Need either a `state_dict` or a `save_folder` containing offloaded weights."""     )

        UpperCamelCase_							       =       {} if state_dict is None else state_dict
        UpperCamelCase_							       =       save_folder
        if index is None and save_folder is not None:
               with open(os.path.join(__A     ,	"""index.json"""     )     ) as f:
                      UpperCamelCase_							       =       json.load(__A     )
        UpperCamelCase_							       =       {} if index is None else index
        UpperCamelCase_							       =       list(self.state_dict.keys()     )
        self.all_keys.extend([key for key in self.index if key not in self.all_keys]     )
        UpperCamelCase_							       =       device

 def __getitem__(      self     ,	__UpperCamelCase     ):




        """simple docstring"""


        if key in self.state_dict:
               return self.state_dict[key]
        UpperCamelCase_							       =       self.index[key]
        if weight_info.get("""safetensors_file"""     ) is not None:
               if not is_safetensors_available():
                      raise ImportError("""These offloaded weights require the use of safetensors: `pip install safetensors`."""     )

               from safetensors import safe_open

               UpperCamelCase_							       =       """cpu""" if self.device is None else self.device
               with safe_open(weight_info["""safetensors_file"""]     ,	framework="""pt"""     ,	device=__A     ) as f:
                      UpperCamelCase_							       =       f.get_tensor(weight_info.get("""weight_name"""     ,	__A     )     )

               if "dtype" in weight_info:
                      return tensor.to(getattr(__A     ,	weight_info["""dtype"""]     )     )
               else:
                      return tensor

        UpperCamelCase_							       =       os.path.join(self.save_folder     ,	f'''{key}.dat'''     )
        return load_offloaded_weight(__A     ,	__A     )

 def __iter__(      self     ):




        """simple docstring"""


        return iter(self.all_keys     )

 def __len__(      self     ):




        """simple docstring"""


        return len(self.all_keys     )




def 				lowerCamelCase__				(						a__  :  int	,				a__  :  str     )		->	Any:
       UpperCamelCase_							       =       {}
       for module_name in submodule_names:
              # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
              # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
              result.update(
                  {
                      key: param
                      for key, param in state_dict.items()
                      if key == module_name or key.startswith(module_name + """."""     )
                  }     )
       return result





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

import pytest
import requests

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

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




def 				lowerCamelCase__				(						a__  :  pathlib.Path     )		->	ModuleType:
       UpperCamelCase_							       =       importlib.util.spec_from_file_location(file_path.name	,      str(a__     )     )
       UpperCamelCase_							       =       importlib.util.module_from_spec(a__     )  # type: ignore
       spec.loader.exec_module(a__     )  # type: ignore
       return module




def 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       UpperCamelCase_							       =       []
       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 				lowerCamelCase__				(						)		->	str:
       with open(os.environ["""GITHUB_EVENT_PATH"""]     ) as file:
              UpperCamelCase_							       =       json.load(a__     )
       return event["pull_request"]["url"] + "/files"




def 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       UpperCamelCase_							       =       []
       UpperCamelCase_							       =       {
           """Accept""": """application/vnd.github.v3+json""",
           """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
       }
       UpperCamelCase_							       =       requests.get(get_files_url()	,      headers=a__     ).json()
       for file in files:
              UpperCamelCase_							       =       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 				lowerCamelCase__				(						)		->	list[pathlib.Path]:
       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 				lowerCamelCase__				(						a__  :  pathlib.Path     )		->	None:
       UpperCamelCase_							       =       solution_path.parent.name[8:].zfill(3     )
       UpperCamelCase_							       =       PROBLEM_ANSWERS[problem_number]
       UpperCamelCase_							       =       convert_path_to_module(a__     )
       UpperCamelCase_							       =       str(solution_module.solution()     )  # type: ignore
       UpperCamelCase_							       =       hashlib.shaaaa(answer.encode()     ).hexdigest()
       assert (
           answer == expected
       ), f'''Expected solution to {problem_number} to have hash {expected}, got {answer}'''



