


'''simple docstring'''


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

						lowerCamelCase__     						=  True
except ImportError:
						lowerCamelCase__     						=  False


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






def 			__lowerCAmelCase			(__lowerCAmelCase ):
		# Check we're not importing a "pkg_name" directory somewhere but the actual library by trying to grab the version
		_UpperCAmelCase		:							str             =   importlib.util.find_spec(__lowerCAmelCase ) is not None
		if package_exists:
				try:
						_UpperCAmelCase		:							str             =   importlib.metadata.metadata(__lowerCAmelCase )
						return True
				except importlib.metadata.PackageNotFoundError:
						return False






def 			__lowerCAmelCase			():
		return _torch_distributed_available






def 			__lowerCAmelCase			():
		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			():
		return importlib.metadata.version("oneccl_bind_pt" )






def 			__lowerCAmelCase			():
		return _is_package_available("transformer_engine" )






@lru_cache
def 			__lowerCAmelCase			(__lowerCAmelCase=True ):
		# Due to bugs on the amp series GPUs, we disable torch-xla on them
		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		:							Tuple             =   xm.xla_device()
						return True
				except RuntimeError:
						return False
		return _tpu_available






def 			__lowerCAmelCase			():
		return _is_package_available("deepspeed" )






def 			__lowerCAmelCase			(__lowerCAmelCase=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 			__lowerCAmelCase			():
		_UpperCAmelCase		:							Optional[int]             =   _is_package_available("bitsandbytes" )
		if package_exists:
				_UpperCAmelCase		:							Any             =   version.parse(importlib.metadata.version("bitsandbytes" ) )
				return compare_versions(__lowerCAmelCase   ,			">="   ,			"0.39.0" )
		return False






def 			__lowerCAmelCase			():
		_UpperCAmelCase		:							List[str]             =   _is_package_available("bitsandbytes" )
		if package_exists:
				_UpperCAmelCase		:							Tuple             =   version.parse(importlib.metadata.version("bitsandbytes" ) )
				return compare_versions(__lowerCAmelCase   ,			">="   ,			"0.37.2" )
		return False






def 			__lowerCAmelCase			():
		return _is_package_available("bitsandbytes" )






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






def 			__lowerCAmelCase			():
		return _is_package_available("safetensors" )






def 			__lowerCAmelCase			():
		return _is_package_available("transformers" )






def 			__lowerCAmelCase			():
		return _is_package_available("datasets" )






def 			__lowerCAmelCase			():
		return _is_package_available("aim" )






def 			__lowerCAmelCase			():
		return _is_package_available("tensorboard" ) or _is_package_available("tensorboardX" )






def 			__lowerCAmelCase			():
		return _is_package_available("wandb" )






def 			__lowerCAmelCase			():
		return _is_package_available("comet_ml" )






def 			__lowerCAmelCase			():
		return _is_package_available("boto3" )






def 			__lowerCAmelCase			():
		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"   ,			__lowerCAmelCase )
				return parse_flag_from_env("ACCELERATE_ENABLE_RICH"   ,			__lowerCAmelCase )
		return False






def 			__lowerCAmelCase			():
		return _is_package_available("sagemaker" )






def 			__lowerCAmelCase			():
		return _is_package_available("tqdm" )






def 			__lowerCAmelCase			():
		return _is_package_available("mlflow" )






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






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

		_UpperCAmelCase		:							Tuple             =   importlib.metadata.version("torch" )
		if importlib.util.find_spec("intel_extension_for_pytorch" ) is None:
				return False
		_UpperCAmelCase		:							Optional[int]             =   '''N/A'''
		try:
				_UpperCAmelCase		:							List[Any]             =   importlib.metadata.version("intel_extension_for_pytorch" )
		except importlib.metadata.PackageNotFoundError:
				return False
		_UpperCAmelCase		:							Tuple             =   get_major_and_minor_from_version(_torch_version )
		_UpperCAmelCase		:							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 			__lowerCAmelCase			(__lowerCAmelCase=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(__lowerCAmelCase   ,			"npu" ) and torch.npu.is_available()






@lru_cache
def 			__lowerCAmelCase			(__lowerCAmelCase=False ):
		if not parse_flag_from_env("ACCELERATE_USE_XPU"   ,			default=__lowerCAmelCase ):
				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		:							Tuple             =   torch.xpu.device_count()
						return torch.xpu.is_available()
				except RuntimeError:
						return False
		return hasattr(__lowerCAmelCase   ,			"xpu" ) and torch.xpu.is_available()




def       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a  ,   )		->	tuple[list[list[int]], list[list[str]]]:
       A_							:       Optional[Any]	      =				list(a							)
       A_							:       int	      =				list(a							)
       A_							:       Union[str, Any]	      =				len(a							)
       A_							:       Any	      =				len(a							)

       A_							:       Union[str, Any]	      =				[
           [0 for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]
       A_							:       Optional[int]	      =				[
           ['''0''' for _ in range(len_destination_seq + 1							)] for _ in range(len_source_seq + 1							)
       ]

       for i in range(1  ,   len_source_seq + 1							):
              A_							:       List[Any]	      =				i * delete_cost
              A_							:       List[Any]	      =				f"""D{source_seq[i - 1]:c}"""

       for i in range(1  ,   len_destination_seq + 1							):
              A_							:       Optional[Any]	      =				i * insert_cost
              A_							:       str	      =				f"""I{destination_seq[i - 1]:c}"""

       for i in range(1  ,   len_source_seq + 1							):
              for j in range(1  ,   len_destination_seq + 1							):
                     if source_seq[i - 1] == destination_seq[j - 1]:
                            A_							:       List[Any]	      =				costs[i - 1][j - 1] + copy_cost
                            A_							:       Tuple	      =				f"""C{source_seq[i - 1]:c}"""
                     else:
                            A_							:       Union[str, Any]	      =				costs[i - 1][j - 1] + replace_cost
                            A_							:       Dict	      =				f"""R{source_seq[i - 1]:c}""" + str(destination_seq[j - 1]							)

                     if costs[i - 1][j] + delete_cost < costs[i][j]:
                            A_							:       Dict	      =				costs[i - 1][j] + delete_cost
                            A_							:       Optional[int]	      =				f"""D{source_seq[i - 1]:c}"""

                     if costs[i][j - 1] + insert_cost < costs[i][j]:
                            A_							:       List[Any]	      =				costs[i][j - 1] + insert_cost
                            A_							:       Tuple	      =				f"""I{destination_seq[j - 1]:c}"""

       return costs, ops


def       a__      (					a  ,   a  ,   a							)		->	list[str]:
       if i == 0 and j == 0:
              return []
       else:
              if ops[i][j][0] == "C" or ops[i][j][0] == "R":
                     A_							:       Tuple	      =				assemble_transformation(a  ,   i - 1  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq
              elif ops[i][j][0] == "D":
                     A_							:       Any	      =				assemble_transformation(a  ,   i - 1  ,   a							)
                     seq.append(ops[i][j]							)
                     return seq
              else:
                     A_							:       Tuple	      =				assemble_transformation(a  ,   a  ,   j - 1							)
                     seq.append(ops[i][j]							)
                     return seq


if __name__ == "__main__":
    _lowerCAmelCase  ,			_lowerCAmelCase        =       compute_transform_tables('Python', 'Algorithms', -1, 1, 2, 2)

    _lowerCAmelCase        =       len(operations)
    _lowerCAmelCase        =       len(operations[0])
    _lowerCAmelCase        =       assemble_transformation(operations, m - 1, n - 1)

    _lowerCAmelCase        =       list('Python')
    _lowerCAmelCase        =       0
    _lowerCAmelCase        =       0

    with open('min_cost.txt', 'w') as file:
        for op in sequence:
            print(''.join(string))

            if op[0] == "C":
                file.write('%-16s' % 'Copy %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost -= 1
            elif op[0] == "R":
                _lowerCAmelCase        =       op[2]

                file.write('%-16s' % ('Replace %c' % op[1] + ' with ' + str(op[2])))
                file.write('\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 1
            elif op[0] == "D":
                string.pop(i)

                file.write('%-16s' % 'Delete %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2
            else:
                string.insert(i, op[1])

                file.write('%-16s' % 'Insert %c' % op[1])
                file.write('\t\t\t' + ''.join(string))
                file.write('\r\n')

                cost += 2

            i += 1

        print(''.join(string))
        print('Cost: ', cost)

        file.write('\r\nMinimum cost: ' + str(cost))


