






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

 __snake_case							 =  True
except ImportError:
 __snake_case							 =  False


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


def   _A       (    SCREAMING_SNAKE_CASE__  :     str	):
      UpperCamelCase	:Tuple     							=       importlib.util.find_spec(SCREAMING_SNAKE_CASE_	) is not None
      if package_exists:
            try:
                  UpperCamelCase	:Union[str, Any]     							=       importlib.metadata.metadata(SCREAMING_SNAKE_CASE_	)
                  return True
            except importlib.metadata.PackageNotFoundError:
                  return False


def   _A       (    ):
      return _torch_distributed_available


def   _A       (    ):

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


def   _A       (    ):
      return _is_package_available('''transformer_engine'''	)


@lru_cache
def   _A       (    SCREAMING_SNAKE_CASE__  :     int=True	):

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


def   _A       (    ):
      return _is_package_available('''deepspeed'''	)


def   _A       (    SCREAMING_SNAKE_CASE__  :     Tuple=False	):

      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   _A       (    ):
      UpperCamelCase	:Union[str, Any]     							=       _is_package_available('''bitsandbytes'''	)
      if package_exists:
            UpperCamelCase	:Optional[int]     							=       version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
            return compare_versions(SCREAMING_SNAKE_CASE_					,  '''>='''					,  '''0.39.0'''	)
      return False


def   _A       (    ):
      UpperCamelCase	:Any     							=       _is_package_available('''bitsandbytes'''	)
      if package_exists:
            UpperCamelCase	:int     							=       version.parse(importlib.metadata.version('''bitsandbytes'''	)	)
            return compare_versions(SCREAMING_SNAKE_CASE_					,  '''>='''					,  '''0.37.2'''	)
      return False


def   _A       (    ):
      return _is_package_available('''bitsandbytes'''	)


def   _A       (    ):

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


def   _A       (    ):
      return _is_package_available('''safetensors'''	)


def   _A       (    ):
      return _is_package_available('''transformers'''	)


def   _A       (    ):
      return _is_package_available('''datasets'''	)


def   _A       (    ):
      return _is_package_available('''aim'''	)


def   _A       (    ):
      return _is_package_available('''tensorboard'''	) or _is_package_available('''tensorboardX'''	)


def   _A       (    ):
      return _is_package_available('''wandb'''	)


def   _A       (    ):
      return _is_package_available('''comet_ml'''	)


def   _A       (    ):
      return _is_package_available('''boto3'''	)


def   _A       (    ):

      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'''					,  SCREAMING_SNAKE_CASE_	)
            return parse_flag_from_env('''ACCELERATE_ENABLE_RICH'''					,  SCREAMING_SNAKE_CASE_	)
      return False


def   _A       (    ):
      return _is_package_available('''sagemaker'''	)


def   _A       (    ):
      return _is_package_available('''tqdm'''	)


def   _A       (    ):
      return _is_package_available('''mlflow'''	)


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


def   _A       (    ):

      def get_major_and_minor_from_version(SCREAMING_SNAKE_CASE__  :     Dict	):
            return str(version.parse(SCREAMING_SNAKE_CASE_	).major	) + "." + str(version.parse(SCREAMING_SNAKE_CASE_	).minor	)

      UpperCamelCase	:Union[str, Any]     							=       importlib.metadata.version('''torch'''	)
      if importlib.util.find_spec('''intel_extension_for_pytorch'''	) is None:
            return False
      UpperCamelCase	:Optional[Any]     							=       "N/A"
      try:
            UpperCamelCase	:str     							=       importlib.metadata.version('''intel_extension_for_pytorch'''	)
      except importlib.metadata.PackageNotFoundError:
            return False
      UpperCamelCase	:Dict     							=       get_major_and_minor_from_version(_torch_version	)
      UpperCamelCase	:Optional[int]     							=       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   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[int]=False	):

      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
                  UpperCamelCase	:Dict     							=       torch.npu.device_count()
                  return torch.npu.is_available()
            except RuntimeError:
                  return False
      return hasattr(SCREAMING_SNAKE_CASE_					,  '''npu'''	) and torch.npu.is_available()



@lru_cache
def   _A       (    SCREAMING_SNAKE_CASE__  :     Optional[int]=False	):

      if not parse_flag_from_env('''ACCELERATE_USE_XPU'''					,  default=SCREAMING_SNAKE_CASE_	):
            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
                  UpperCamelCase	:List[str]     							=       torch.xpu.device_count()
                  return torch.xpu.is_available()
            except RuntimeError:
                  return False
      return hasattr(SCREAMING_SNAKE_CASE_					,  '''xpu'''	) and torch.xpu.is_available()













import warnings
from collections.abc import Callable
from typing import Any

import numpy
from matplotlib import pyplot

__snake_case							 =  0.2_5 + 0.0j
__snake_case							 =  -0.4 + 0.6j
__snake_case							 =  -0.1 + 0.651j
__snake_case							 =  -2.0
__snake_case							 =  56
__snake_case							 =  2.0
__snake_case							 =  6_66


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return numpy.exp(SCREAMING_SNAKE_CASE__	) + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     complex					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray	):
    return z_values * z_values + c_parameter


def   _A       (    SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     int	):
    UpperCamelCase	:List[Any]     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:Any     							=       x.reshape((nb_pixels, 1)	)
    UpperCamelCase	:Tuple     							=       numpy.linspace(-window_size					,  SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
    UpperCamelCase	:int     							=       y.reshape((1, nb_pixels)	)
    return x + 1.0J * y


def   _A       (    SCREAMING_SNAKE_CASE__  :     Callable[[Any, numpy.ndarray], numpy.ndarray]					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     int					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  SCREAMING_SNAKE_CASE__  :     float | None = None					,  ):
    UpperCamelCase	:Dict     							=       z_a.astype('''complex64'''	)
    for _ in range(SCREAMING_SNAKE_CASE__	):
        UpperCamelCase	:int     							=       eval_function(SCREAMING_SNAKE_CASE__					,  SCREAMING_SNAKE_CASE__	)
        if infinity is not None:
            numpy.nan_to_num(SCREAMING_SNAKE_CASE__					,  copy=SCREAMING_SNAKE_CASE__					,  nan=SCREAMING_SNAKE_CASE__	)
            UpperCamelCase	:Tuple     							=       infinity
    return z_n


def   _A       (    SCREAMING_SNAKE_CASE__  :     str					,  SCREAMING_SNAKE_CASE__  :     Any					,  SCREAMING_SNAKE_CASE__  :     float					,  SCREAMING_SNAKE_CASE__  :     numpy.ndarray					,  ):
    UpperCamelCase	:Any     							=       (abs(SCREAMING_SNAKE_CASE__	)).transpose()
    UpperCamelCase	:Any     							=       abs_z_final[::-1, :]
    pyplot.matshow(abs_z_final < escape_radius	)
    pyplot.title(F'''Julia set of ${function_label}$, $c={function_params}$'''	)
    pyplot.show()


def   _A       (    ):
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in multiply'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''invalid value encountered in multiply'''					,  )
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in absolute'''	)
    warnings.filterwarnings(
        '''ignore'''					,  category=SCREAMING_SNAKE_CASE__					,  message='''overflow encountered in exp'''	)


if __name__ == "__main__":
 __snake_case							 =  prepare_grid(window_size, nb_pixels)

 ignore_overflow_warnings()  # See file header for explanations

 __snake_case							 =  24
 __snake_case							 =  2 * abs(c_cauliflower) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_cauliflower,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_cauliflower, escape_radius, z_final)

 __snake_case							 =  64
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  1_61
 __snake_case							 =  2 * abs(c_polynomial_a) + 1
 __snake_case							 =  iterate_function(
     eval_quadratic_polynomial,
     c_polynomial_a,
     nb_iterations,
     z_a,
     infinity=1.1 * escape_radius,
 )
 show_results("""z^2+c""", c_polynomial_a, escape_radius, z_final)

 __snake_case							 =  12
 __snake_case							 =  1_0_0_0_0.0
 __snake_case							 =  iterate_function(
     eval_exponential,
     c_exponential,
     nb_iterations,
     z_a + 2,
     infinity=1.0E1_0,
 )
 show_results("""e^z+c""", c_exponential, escape_radius, z_final)





