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       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			int					,	__UpperCamelCase       :			List[Any]					,	__UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			Any=None      )				->   Union[str, Any]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= 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.
    SCREAMING_SNAKE_CASE__     				= weight.view(torch.intaa      )
    SCREAMING_SNAKE_CASE__     				= """bfloat16"""
  SCREAMING_SNAKE_CASE__     				= weight.cpu().numpy()
  SCREAMING_SNAKE_CASE__     				= os.path.join(__UpperCamelCase					,	f"""{weight_name}.dat"""      )
  if index is not None:
    if dtype is None:
      SCREAMING_SNAKE_CASE__     				= str(array.dtype      )
    SCREAMING_SNAKE_CASE__     				= {"""dtype""": dtype, """shape""": list(array.shape      )}
  if array.ndim == 0:
    SCREAMING_SNAKE_CASE__     				= array[None]
  SCREAMING_SNAKE_CASE__     				= np.memmap(__UpperCamelCase					,	dtype=array.dtype					,	mode="""w+"""					,	shape=array.shape      )
  SCREAMING_SNAKE_CASE__     				= array[:]
  file_array.flush()
  return index



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, Any]					,	__UpperCamelCase       :			Any      )				->   Tuple:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= tuple(weight_info["""shape"""]      )
  if shape == ():
    # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
    SCREAMING_SNAKE_CASE__     				= (1,)

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

  SCREAMING_SNAKE_CASE__     				= np.memmap(__UpperCamelCase					,	dtype=__UpperCamelCase					,	shape=__UpperCamelCase					,	mode="""r"""      )

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

  return weight



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			List[str]					,	__UpperCamelCase       :			Union[str, Any]      )				->   Union[str, Any]:







  """simple docstring"""





  if index is None or len(__UpperCamelCase      ) == 0:
    # Nothing to save
    return

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

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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Union[str, os.PathLike]					,	__UpperCamelCase       :			Dict[str, torch.Tensor]      )				->   Tuple:







  """simple docstring"""





  os.makedirs(__UpperCamelCase					,	exist_ok=__UpperCamelCase      )
  SCREAMING_SNAKE_CASE__     				= {}
  for name, parameter in state_dict.items():
    SCREAMING_SNAKE_CASE__     				= offload_weight(__UpperCamelCase					,	__UpperCamelCase					,	__UpperCamelCase					,	index=__UpperCamelCase      )

  # Update index
  save_offload_index(__UpperCamelCase					,	__UpperCamelCase      )







class __snake_case							(		lowerCamelCase_						):




   def __init__(       self     :							List[str]			,      _lowercase     :							Mapping			,      _lowercase     :							str ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__     				= dataset
     SCREAMING_SNAKE_CASE__     				= prefix




   def __getitem__(       self     :							Tuple			,      _lowercase     :							str ):



     """simple docstring"""






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




   def __iter__(       self     :							Any ):



     """simple docstring"""






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




   def __len__(       self     :							Tuple ):



     """simple docstring"""






     return len(self.dataset )







class __snake_case							(		lowerCamelCase_						):




   def __init__(       self     :							Dict			,      _lowercase     :							Dict[str, torch.Tensor] = None			,      _lowercase     :							Optional[Union[str, os.PathLike]] = None			,      _lowercase     :							Mapping = None			,      _lowercase     :							Optional[int]=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.""" )

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




   def __getitem__(       self     :							Dict			,      _lowercase     :							str ):



     """simple docstring"""






     if key in self.state_dict:
       return self.state_dict[key]
     SCREAMING_SNAKE_CASE__     				= 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

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

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

     SCREAMING_SNAKE_CASE__     				= os.path.join(self.save_folder			,      f"""{key}.dat""" )
     return load_offloaded_weight(_lowercase			,      _lowercase )




   def __iter__(       self     :							Optional[Any] ):



     """simple docstring"""






     return iter(self.all_keys )




   def __len__(       self     :							Dict ):



     """simple docstring"""






     return len(self.all_keys )





def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			Dict[str, torch.Tensor]					,	__UpperCamelCase       :			List[str]      )				->   int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= {}
  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

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

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



def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   ModuleType:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= importlib.util.spec_from_file_location(file_path.name					,	str(__UpperCamelCase      )      )
  SCREAMING_SNAKE_CASE__     				= importlib.util.module_from_spec(__UpperCamelCase      )  # type: ignore
  spec.loader.exec_module(__UpperCamelCase      )  # type: ignore
  return module



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  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       __SCREAMING_SNAKE_CASE    (   )				->   str:







  """simple docstring"""





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



def       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__     				= []
  SCREAMING_SNAKE_CASE__     				= {
      """Accept""": """application/vnd.github.v3+json""",
      """Authorization""": """token """ + os.environ["""GITHUB_TOKEN"""],
  }
  SCREAMING_SNAKE_CASE__     				= requests.get(get_files_url()					,	headers=__UpperCamelCase      ).json()
  for file in files:
    SCREAMING_SNAKE_CASE__     				= 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       __SCREAMING_SNAKE_CASE    (   )				->   list[pathlib.Path]:







  """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 __UpperCamelCase      : f"""{path.parent.name}/{path.name}"""					,	)
def       __SCREAMING_SNAKE_CASE    (   __UpperCamelCase       :			pathlib.Path      )				->   None:







  """simple docstring"""





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