
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


lowerCamelCase__     :		Any        = get_logger(__name__)



class    _snake_case (						enum.Enum				):
 __lowerCAmelCase	:  Optional[Any]	   =      'all_checks'
 __lowerCAmelCase	:  int	   =      'basic_checks'
 __lowerCAmelCase	:  str	   =      'no_checks'



class    _snake_case (						UpperCAmelCase_				):
 pass



class    _snake_case (						UpperCAmelCase_				):
 pass



class    _snake_case (						UpperCAmelCase_				):
 pass



class    _snake_case (						UpperCAmelCase_				):
 pass

def 		UpperCamelCase						(   lowercase_			,			lowercase_			,			lowercase_=None )		->						List[str]:


  '''simple docstring'''






  if expected_checksums is None:
    logger.info("""Unable to verify checksums.""" )
    return
  if len(set(lowercase_ ) - set(lowercase_ ) ) > 0:
    raise ExpectedMoreDownloadedFiles(str(set(lowercase_ ) - set(lowercase_ ) ) )
  if len(set(lowercase_ ) - set(lowercase_ ) ) > 0:
    raise UnexpectedDownloadedFile(str(set(lowercase_ ) - set(lowercase_ ) ) )
  lowercase__	: Union[str, Any]     		=   [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
  lowercase__	: Dict     		=   """ for """ + verification_name if verification_name is not None else """"""
  if len(lowercase_ ) > 0:
    raise NonMatchingChecksumError(
        F'Checksums didn\'t match{for_verification_name}:\n'
        F'{bad_urls}\n'
        """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error""" )
  logger.info("""All the checksums matched successfully""" + for_verification_name )



class    _snake_case (						UpperCAmelCase_				):
 pass



class    _snake_case (						UpperCAmelCase_				):
 pass



class    _snake_case (						UpperCAmelCase_				):
 pass



class    _snake_case (						UpperCAmelCase_				):
 pass

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Union[str, Any]:


  '''simple docstring'''






  if expected_splits is None:
    logger.info("""Unable to verify splits sizes.""" )
    return
  if len(set(lowercase_ ) - set(lowercase_ ) ) > 0:
    raise ExpectedMoreSplits(str(set(lowercase_ ) - set(lowercase_ ) ) )
  if len(set(lowercase_ ) - set(lowercase_ ) ) > 0:
    raise UnexpectedSplits(str(set(lowercase_ ) - set(lowercase_ ) ) )
  lowercase__	: List[str]     		=   [
      {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
      for name in expected_splits
      if expected_splits[name].num_examples != recorded_splits[name].num_examples
  ]
  if len(lowercase_ ) > 0:
    raise NonMatchingSplitsSizesError(str(lowercase_ ) )
  logger.info("""All the splits matched successfully.""" )

def 		UpperCamelCase						(   lowercase_			,			lowercase_ = True )		->						dict:


  '''simple docstring'''






  if record_checksum:
    lowercase__	: Dict     		=   shaaaa()
    with open(lowercase_			,			"""rb""" ) as f:
      for chunk in iter(lambda: f.read(1 << 20 )			,			B"""""" ):
        m.update(lowercase_ )
      lowercase__	: Dict     		=   m.hexdigest()
  else:
    lowercase__	: Optional[Any]     		=   None
  return {"num_bytes": os.path.getsize(lowercase_ ), "checksum": checksum}



def 		UpperCamelCase						(   lowercase_ )		->						int:


  '''simple docstring'''






  if dataset_size and config.IN_MEMORY_MAX_SIZE:
    return dataset_size < config.IN_MEMORY_MAX_SIZE
  else:
    return False








# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from pathlib import Path

import torch

from ...utils import is_npu_available, is_xpu_available
from .config_args import ClusterConfig, default_json_config_file
from .config_utils import SubcommandHelpFormatter


lowerCamelCase__     :		Any        = """Create a default config file for Accelerate with only a few flags set."""

def 		UpperCamelCase						(   lowercase_="no"			,			lowercase_ = default_json_config_file			,			lowercase_ = False )		->						Any:


  '''simple docstring'''






  lowercase__	: Any     		=   Path(lowercase_ )
  path.parent.mkdir(parents=lowercase_			,			exist_ok=lowercase_ )
  if path.exists():
    print(
        F'Configuration already exists at {save_location}, will not override. Run `accelerate config` manually or pass a different `save_location`.' )
    return False
  lowercase__	: int     		=   mixed_precision.lower()
  if mixed_precision not in ["no", "fp16", "bf16", "fp8"]:
    raise ValueError(
        F'`mixed_precision` should be one of \'no\', \'fp16\', \'bf16\', or \'fp8\'. Received {mixed_precision}' )
  lowercase__	: Dict     		=   {
      """compute_environment""": """LOCAL_MACHINE""",
      """mixed_precision""": mixed_precision,
  }
  if torch.cuda.is_available():
    lowercase__	: Any     		=   torch.cuda.device_count()
    lowercase__	: Any     		=   num_gpus
    lowercase__	: Optional[int]     		=   False
    if num_gpus > 1:
      lowercase__	: Tuple     		=   """MULTI_GPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_xpu_available() and use_xpu:
    lowercase__	: Union[str, Any]     		=   torch.xpu.device_count()
    lowercase__	: str     		=   num_xpus
    lowercase__	: List[Any]     		=   False
    if num_xpus > 1:
      lowercase__	: str     		=   """MULTI_XPU"""
    else:
      lowercase__	: Optional[Any]     		=   """NO"""
  elif is_npu_available():
    lowercase__	: Tuple     		=   torch.npu.device_count()
    lowercase__	: Union[str, Any]     		=   num_npus
    lowercase__	: Union[str, Any]     		=   False
    if num_npus > 1:
      lowercase__	: List[Any]     		=   """MULTI_NPU"""
    else:
      lowercase__	: int     		=   """NO"""
  else:
    lowercase__	: Union[str, Any]     		=   0
    lowercase__	: str     		=   True
    lowercase__	: Union[str, Any]     		=   1
    lowercase__	: int     		=   """NO"""
  lowercase__	: Tuple     		=   ClusterConfig(**lowercase_ )
  config.to_json_file(lowercase_ )
  return path

def 		UpperCamelCase						(   lowercase_			,			lowercase_ )		->						Optional[Any]:


  '''simple docstring'''






  lowercase__	: List[str]     		=   parser.add_parser("""default"""			,			parents=lowercase_			,			help=lowercase_			,			formatter_class=lowercase_ )
  parser.add_argument(
      """--config_file"""			,			default=lowercase_			,			help=(
          """The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
          """location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
          """such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
          """with 'huggingface'."""
      )			,			dest="""save_location"""			,			)

  parser.add_argument(
      """--mixed_precision"""			,			choices=["""no""", """fp16""", """bf16"""]			,			type=lowercase_			,			help="""Whether or not to use mixed precision training. """
      """Choose between FP16 and BF16 (bfloat16) training. """
      """BF16 training is only supported on Nvidia Ampere GPUs and PyTorch 1.10 or later."""			,			default="""no"""			,			)
  parser.set_defaults(func=lowercase_ )
  return parser



def 		UpperCamelCase						(   lowercase_ )		->						Any:


  '''simple docstring'''






  lowercase__	: Optional[Any]     		=   write_basic_config(args.mixed_precision			,			args.save_location )
  if config_file:
    print(F'accelerate configuration saved at {config_file}' )






