

import importlib.util
import os
import platform
from argparse import ArgumentParser

import huggingface_hub

from .. import __version__ as version
from ..utils import (
    is_accelerate_available,
    is_flax_available,
    is_safetensors_available,
    is_tf_available,
    is_torch_available,
)
from . import BaseTransformersCLICommand



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Optional[int]      )			->      int:




				'''simple docstring'''





				return EnvironmentCommand()



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Dict      )			->      Optional[Any]:




				'''simple docstring'''





				return EnvironmentCommand(args.accelerate_config_file      )





class 					a__						(					__UpperCAmelCase			):
			"""simple docstring"""







			@staticmethod
			def      UpperCamelCase		(    lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							A__        =			parser.add_parser("env"       )
							download_parser.set_defaults(func=UpperCAmelCase_       )
							download_parser.add_argument(
							    "--accelerate-config_file"      ,      default=UpperCAmelCase_      ,      help="The accelerate config file to use for the default values in the launching script."      ,      )
							download_parser.set_defaults(func=UpperCAmelCase_       )


			def __init__(    self      ,      lowercase      ,      *lowercase       )  ->						List[str]:
							'''simple docstring'''






							A__        =			accelerate_config_file


			def      UpperCamelCase		(    self       )  ->						Union[str, Any]:
							'''simple docstring'''






							A__        =			"not installed"
							if is_safetensors_available():
											import safetensors

											A__        =			safetensors.__version__
							elif importlib.util.find_spec("safetensors"       ) is not None:
											import safetensors

											A__        =			F'{safetensors.__version__} but is ignored because of PyTorch version too old.'

							A__        =			"not installed"
							A__        =			A__        =			"not found"
							if is_accelerate_available():
											import accelerate
											from accelerate.commands.config import default_config_file, load_config_from_file

											A__        =			accelerate.__version__
											# Get the default from the config file.
											if self._accelerate_config_file is not None or os.path.isfile(UpperCAmelCase_       ):
															A__        =			load_config_from_file(self._accelerate_config_file       ).to_dict()

											A__        =			(
											    "\n".join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()]       )
											    if isinstance(UpperCAmelCase_      ,      UpperCAmelCase_       )
											    else F'\t{accelerate_config}'
											)

							A__        =			"not installed"
							A__        =			"NA"
							if is_torch_available():
											import torch

											A__        =			torch.__version__
											A__        =			torch.cuda.is_available()

							A__        =			"not installed"
							A__        =			"NA"
							if is_tf_available():
											import tensorflow as tf

											A__        =			tf.__version__
											try:
															# deprecated in v2.1
															A__        =			tf.test.is_gpu_available()
											except AttributeError:
															# returns list of devices, convert to bool
															A__        =			bool(tf.config.list_physical_devices("GPU"       )       )

							A__        =			"not installed"
							A__        =			"not installed"
							A__        =			"not installed"
							A__        =			"NA"
							if is_flax_available():
											import flax
											import jax
											import jaxlib

											A__        =			flax.__version__
											A__        =			jax.__version__
											A__        =			jaxlib.__version__
											A__        =			jax.lib.xla_bridge.get_backend().platform

							A__        =			{
							    "`transformers` version": version,
							    "Platform": platform.platform(),
							    "Python version": platform.python_version(),
							    "Huggingface_hub version": huggingface_hub.__version__,
							    "Safetensors version": F'{safetensors_version}',
							    "Accelerate version": F'{accelerate_version}',
							    "Accelerate config": F'{accelerate_config_str}',
							    "PyTorch version (GPU?)": F'{pt_version} ({pt_cuda_available})',
							    "Tensorflow version (GPU?)": F'{tf_version} ({tf_cuda_available})',
							    "Flax version (CPU?/GPU?/TPU?)": F'{flax_version} ({jax_backend})',
							    "Jax version": F'{jax_version}',
							    "JaxLib version": F'{jaxlib_version}',
							    "Using GPU in script?": "<fill in>",
							    "Using distributed or parallel set-up in script?": "<fill in>",
							}

							print("\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n"       )
							print(self.format_dict(UpperCAmelCase_       )       )

							return info


			@staticmethod
			def      UpperCamelCase		(    lowercase       )  ->						Optional[int]:
							'''simple docstring'''






							return "\n".join([F'- {prop}: {val}' for prop, val in d.items()]       ) + "\n"




from __future__ import annotations



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							str	,       SCREAMING_SNAKE_CASE_:							list[str] | None = None      )			->      list[list[str]]:




				'''simple docstring'''





				A__        =			word_bank or []
				# create a table
				A__        =			len(SCREAMING_SNAKE_CASE_      ) + 1

				A__        =			[]
				for _ in range(SCREAMING_SNAKE_CASE_      ):
								table.append([]      )
				# seed value
				A__        =			[[]]  # because empty string has empty combination

				# iterate through the indices
				for i in range(SCREAMING_SNAKE_CASE_      ):
								# condition
								if table[i] != []:
												for word in word_bank:
																# slice condition
																if target[i : i + len(SCREAMING_SNAKE_CASE_      )] == word:
																				A__        =			[
																				    [word, *way] for way in table[i]
																				]
																				# adds the word to every combination the current position holds
																				# now,push that combination to the table[i+len(word)]
																				table[i + len(SCREAMING_SNAKE_CASE_      )] += new_combinations

    # combinations are in reverse order so reverse for better output
				for combination in table[len(SCREAMING_SNAKE_CASE_      )]:
								combination.reverse()

				return table[len(SCREAMING_SNAKE_CASE_      )]


if __name__ == "__main__":
	print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
	print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
	print(
	    all_construct(
	        """hexagonosaurus""",
	        ["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
	    )
	)

