


"""simple docstring"""







import unittest

from transformers import load_tool

from .test_tools_common import ToolTesterMixin


__lowercase          = """\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.\n\nIn March 2021, Hugging Face raised $40 million in a Series B funding round.[3]\n\nOn April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]\n"""






class 		_A    (    unittest.TestCase       ,lowercase_    ):







    """simple docstring"""




    def       __snake_case				(   self							:       Union[str, Any]):
      a		:						Optional[int] 				=     load_tool("summarization")
      self.tool.setup()
      a		:						Any 				=     load_tool("summarization"							,					remote=a__)






    def       __snake_case				(   self							:       Tuple):
      a		:						Optional[Any] 				=     self.tool(a__)
      self.assertEqual(
          a__							,					"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."							,					)






    def       __snake_case				(   self							:       List[Any]):
      a		:						Optional[int] 				=     self.remote_tool(a__)
      self.assertEqual(
          a__							,					"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."							,					)






    def       __snake_case				(   self							:       Optional[int]):
      a		:						Tuple 				=     self.tool(text=a__)
      self.assertEqual(
          a__							,					"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."							,					)






    def       __snake_case				(   self							:       int):
      a		:						Optional[Any] 				=     self.remote_tool(text=a__)
      self.assertEqual(
          a__							,					"Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf. In March 2021, Hugging Face raised $40 million in a Series B funding round. On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model. In 2022, the workshop concluded with the announcement of BLOOM."							,					)








import importlib
import importlib.metadata
import os
import warnings
from distutils.util import strtobool
from functools import lru_cache

import torch
from packaging import version
from packaging.version import parse

from .environment import parse_flag_from_env
from .versions import compare_versions, is_torch_version


try:
  import torch_xla.core.xla_model as xm  # noqa: F401

  _UpperCAmelCase    			=					True
except ImportError:
  _UpperCAmelCase    			=					False


# Cache this result has it's a C FFI call which can be pretty time-consuming
_UpperCAmelCase    			=					torch.distributed.is_available()


def 		_lowerCamelCase       (   _a  ):
  """simple docstring"""




  _lowerCamelCase      =						importlib.util.find_spec(_a  ) is not None
  if package_exists:
    try:
      _lowerCamelCase      =						importlib.metadata.metadata(_a  )
      return True
    except importlib.metadata.PackageNotFoundError:
      return False


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _torch_distributed_available


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  try:
    pass
  except ImportError:
    print(
        '''Intel(R) oneCCL Bindings for PyTorch* is required to run DDP on Intel(R) GPUs, but it is not'''
        ''' detected. If you see \"ValueError: Invalid backend: \'ccl\'\" error, please install Intel(R) oneCCL'''
        ''' Bindings for PyTorch*.'''  )
  return (
      importlib.util.find_spec('''torch_ccl'''  ) is not None
      or importlib.util.find_spec('''oneccl_bindings_for_pytorch'''  ) is not None
  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return importlib.metadata.version('''oneccl_bind_pt'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''transformer_engine'''  )


@lru_cache
def 		_lowerCamelCase       (   _a=True  ):
  """simple docstring"""




  if torch.cuda.is_available():
    return False
  if _tpu_available and check_device:
    try:
      # Will raise a RuntimeError if no XLA configuration is found
      _lowerCamelCase      =						xm.xla_device()
      return True
    except RuntimeError:
      return False
  return _tpu_available


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''deepspeed'''  )


def 		_lowerCamelCase       (   _a=False  ):
  """simple docstring"""




  if is_tpu_available():
    return not ignore_tpu
  if torch.cuda.is_available():
    return torch.cuda.is_bfaa_supported()
  if is_npu_available():
    return False
  return True


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						_is_package_available('''bitsandbytes'''  )
  if package_exists:
    _lowerCamelCase      =						version.parse(importlib.metadata.version('''bitsandbytes'''  )  )
    return compare_versions(_a			,   '''>='''			,   '''0.39.0'''  )
  return False


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  _lowerCamelCase      =						_is_package_available('''bitsandbytes'''  )
  if package_exists:
    _lowerCamelCase      =						version.parse(importlib.metadata.version('''bitsandbytes'''  )  )
    return compare_versions(_a			,   '''>='''			,   '''0.37.2'''  )
  return False


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''bitsandbytes'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  if strtobool(os.environ.get('''ACCELERATE_USE_MEGATRON_LM'''			,   '''False'''  )  ) == 1:
    _lowerCamelCase      =						importlib.util.find_spec('''megatron'''  ) is not None
    if package_exists:
      try:
        _lowerCamelCase      =						parse(importlib.metadata.version('''megatron-lm'''  )  )
        return compare_versions(_a			,   '''>='''			,   '''2.2.0'''  )
      except Exception as e:
        warnings.warn(F'''Parse Megatron version failed. Exception:{e}'''  )
        return False


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''safetensors'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''transformers'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''datasets'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''aim'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''tensorboard'''  ) or _is_package_available('''tensorboardX'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''wandb'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''comet_ml'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''boto3'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  if _is_package_available('''rich'''  ):
    if "ACCELERATE_DISABLE_RICH" in os.environ:
      warnings.warn(
          '''`ACCELERATE_DISABLE_RICH` is deprecated and will be removed in v0.22.0 and deactivated by default. Please use `ACCELERATE_ENABLE_RICH` if you wish to use `rich`.'''  )
      return not parse_flag_from_env('''ACCELERATE_DISABLE_RICH'''			,   _a  )
    return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''			,   _a  )
  return False


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''sagemaker'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''tqdm'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return _is_package_available('''mlflow'''  )


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  return is_torch_version('''>='''			,   '''1.12'''  ) and torch.backends.mps.is_available() and torch.backends.mps.is_built()


def 		_lowerCamelCase       (   ):
  """simple docstring"""




  def get_major_and_minor_from_version(_a  ):
    return str(version.parse(_a  ).major  ) + "." + str(version.parse(_a  ).minor  )

  _lowerCamelCase      =						importlib.metadata.version('''torch'''  )
  if importlib.util.find_spec('''intel_extension_for_pytorch'''  ) is None:
    return False
  _lowerCamelCase      =						'''N/A'''
  try:
    _lowerCamelCase      =						importlib.metadata.version('''intel_extension_for_pytorch'''  )
  except importlib.metadata.PackageNotFoundError:
    return False
  _lowerCamelCase      =						get_major_and_minor_from_version(_torch_version  )
  _lowerCamelCase      =						get_major_and_minor_from_version(_ipex_version  )
  if torch_major_and_minor != ipex_major_and_minor:
    warnings.warn(
        F'''Intel Extension for PyTorch {ipex_major_and_minor} needs to work with PyTorch {ipex_major_and_minor}.*,'''
        F''' but PyTorch {_torch_version} is found. Please switch to the matching version and run again.'''  )
    return False
  return True


@lru_cache
def 		_lowerCamelCase       (   _a=False  ):
  """simple docstring"""




  if importlib.util.find_spec('''torch'''  ) is None or importlib.util.find_spec('''torch_npu'''  ) is None:
    return False

  import torch
  import torch_npu  # noqa: F401

  if check_device:
    try:
      # Will raise a RuntimeError if no NPU is found
      _lowerCamelCase      =						torch.npu.device_count()
      return torch.npu.is_available()
    except RuntimeError:
      return False
  return hasattr(_a			,   '''npu'''  ) and torch.npu.is_available()






@lru_cache
def 		_lowerCamelCase       (   _a=False  ):
  """simple docstring"""




  if not parse_flag_from_env('''ACCELERATE_USE_XPU'''			,   default=_a  ):
    return False
  if is_ipex_available():
    import torch

    if is_torch_version('''<='''			,   '''1.12'''  ):
      return False
  else:
    return False

  import intel_extension_for_pytorch  # noqa: F401

  if check_device:
    try:
      # Will raise a RuntimeError if no XPU  is found
      _lowerCamelCase      =						torch.xpu.device_count()
      return torch.xpu.is_available()
    except RuntimeError:
      return False
  return hasattr(_a			,   '''xpu'''  ) and torch.xpu.is_available()




