

import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


_SCREAMING_SNAKE_CASE							 =			get_logger(__name__)

# Datasets
_SCREAMING_SNAKE_CASE							 =			"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/datasets"""
_SCREAMING_SNAKE_CASE							 =			"""https://cdn-datasets.huggingface.co/datasets/datasets"""
_SCREAMING_SNAKE_CASE							 =			"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/datasets/{path}/{name}"""

# Metrics
_SCREAMING_SNAKE_CASE							 =			"""https://s3.amazonaws.com/datasets.huggingface.co/datasets/metrics"""
_SCREAMING_SNAKE_CASE							 =			"""https://cdn-datasets.huggingface.co/datasets/metric"""
_SCREAMING_SNAKE_CASE							 =			"""https://raw.githubusercontent.com/huggingface/datasets/{revision}/metrics/{path}/{name}"""

# Hub
_SCREAMING_SNAKE_CASE							 =			os.environ.get('''HF_ENDPOINT''', '''https://huggingface.co''')
_SCREAMING_SNAKE_CASE							 =			HF_ENDPOINT + """/datasets/{repo_id}/resolve/{revision}/{path}"""
_SCREAMING_SNAKE_CASE							 =			"""main"""

_SCREAMING_SNAKE_CASE							 =			version.parse(platform.python_version())

if PY_VERSION < version.parse('''3.8'''):
      import importlib_metadata
else:
      import importlib.metadata as importlib_metadata

# General environment variables accepted values for booleans
_SCREAMING_SNAKE_CASE							 =			{"""1""", """ON""", """YES""", """TRUE"""}
_SCREAMING_SNAKE_CASE							 =			ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


# Imports
_SCREAMING_SNAKE_CASE							 =			version.parse(importlib_metadata.version('''dill'''))
_SCREAMING_SNAKE_CASE							 =			version.parse(importlib_metadata.version('''pandas'''))
_SCREAMING_SNAKE_CASE							 =			version.parse(importlib_metadata.version('''pyarrow'''))

_SCREAMING_SNAKE_CASE							 =			os.environ.get('''USE_TF''', '''AUTO''').upper()
_SCREAMING_SNAKE_CASE							 =			os.environ.get('''USE_TORCH''', '''AUTO''').upper()
_SCREAMING_SNAKE_CASE							 =			os.environ.get('''USE_JAX''', '''AUTO''').upper()

_SCREAMING_SNAKE_CASE							 =			"""N/A"""
_SCREAMING_SNAKE_CASE							 =			False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
      _SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''torch''') is not None
      if TORCH_AVAILABLE:
            try:
                  _SCREAMING_SNAKE_CASE							 =			version.parse(importlib_metadata.version('''torch'''))
                  logger.info(F'''PyTorch version {TORCH_VERSION} available.''')
            except importlib_metadata.PackageNotFoundError:
                  pass
else:
      logger.info('''Disabling PyTorch because USE_TF is set''')

_SCREAMING_SNAKE_CASE							 =			"""N/A"""
_SCREAMING_SNAKE_CASE							 =			False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
      _SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''tensorflow''') is not None
      if TF_AVAILABLE:
            # For the metadata, we have to look for both tensorflow and tensorflow-cpu
            for package in [
                "tensorflow",
                "tensorflow-cpu",
                "tensorflow-gpu",
                "tf-nightly",
                "tf-nightly-cpu",
                "tf-nightly-gpu",
                "intel-tensorflow",
                "tensorflow-rocm",
                "tensorflow-macos",
            ]:
                  try:
                        _SCREAMING_SNAKE_CASE							 =			version.parse(importlib_metadata.version(package))
                  except importlib_metadata.PackageNotFoundError:
                        continue
                  else:
                        break
            else:
                  _SCREAMING_SNAKE_CASE							 =			False
      if TF_AVAILABLE:
            if TF_VERSION.major < 2:
                  logger.info(F'''TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.''')
                  _SCREAMING_SNAKE_CASE							 =			False
            else:
                  logger.info(F'''TensorFlow version {TF_VERSION} available.''')
else:
      logger.info('''Disabling Tensorflow because USE_TORCH is set''')


_SCREAMING_SNAKE_CASE							 =			"""N/A"""
_SCREAMING_SNAKE_CASE							 =			False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
      _SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
      if JAX_AVAILABLE:
            try:
                  _SCREAMING_SNAKE_CASE							 =			version.parse(importlib_metadata.version('''jax'''))
                  logger.info(F'''JAX version {JAX_VERSION} available.''')
            except importlib_metadata.PackageNotFoundError:
                  pass
else:
      logger.info('''Disabling JAX because USE_JAX is set to False''')


_SCREAMING_SNAKE_CASE							 =			os.environ.get('''USE_BEAM''', '''AUTO''').upper()
_SCREAMING_SNAKE_CASE							 =			"""N/A"""
_SCREAMING_SNAKE_CASE							 =			False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
      try:
            _SCREAMING_SNAKE_CASE							 =			version.parse(importlib_metadata.version('''apache_beam'''))
            _SCREAMING_SNAKE_CASE							 =			True
            logger.info(F'''Apache Beam version {BEAM_VERSION} available.''')
      except importlib_metadata.PackageNotFoundError:
            pass
else:
      logger.info('''Disabling Apache Beam because USE_BEAM is set to False''')


# Optional tools for data loading
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''PIL''') is not None
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.1.0''')

# Optional compression tools
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''rarfile''') is not None
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''zstandard''') is not None
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''lz4''') is not None
_SCREAMING_SNAKE_CASE							 =			importlib.util.find_spec('''py7zr''') is not None


# Cache location
_SCREAMING_SNAKE_CASE							 =			"""~/.cache"""
_SCREAMING_SNAKE_CASE							 =			os.getenv('''XDG_CACHE_HOME''', DEFAULT_XDG_CACHE_HOME)
_SCREAMING_SNAKE_CASE							 =			os.path.join(XDG_CACHE_HOME, '''huggingface''')
_SCREAMING_SNAKE_CASE							 =			os.path.expanduser(os.getenv('''HF_HOME''', DEFAULT_HF_CACHE_HOME))

_SCREAMING_SNAKE_CASE							 =			os.path.join(HF_CACHE_HOME, '''datasets''')
_SCREAMING_SNAKE_CASE							 =			Path(os.getenv('''HF_DATASETS_CACHE''', DEFAULT_HF_DATASETS_CACHE))

_SCREAMING_SNAKE_CASE							 =			os.path.join(HF_CACHE_HOME, '''metrics''')
_SCREAMING_SNAKE_CASE							 =			Path(os.getenv('''HF_METRICS_CACHE''', DEFAULT_HF_METRICS_CACHE))

_SCREAMING_SNAKE_CASE							 =			os.path.join(HF_CACHE_HOME, '''modules''')
_SCREAMING_SNAKE_CASE							 =			Path(os.getenv('''HF_MODULES_CACHE''', DEFAULT_HF_MODULES_CACHE))

_SCREAMING_SNAKE_CASE							 =			"""downloads"""
_SCREAMING_SNAKE_CASE							 =			os.path.join(HF_DATASETS_CACHE, DOWNLOADED_DATASETS_DIR)
_SCREAMING_SNAKE_CASE							 =			Path(os.getenv('''HF_DATASETS_DOWNLOADED_DATASETS_PATH''', DEFAULT_DOWNLOADED_DATASETS_PATH))

_SCREAMING_SNAKE_CASE							 =			"""extracted"""
_SCREAMING_SNAKE_CASE							 =			os.path.join(DEFAULT_DOWNLOADED_DATASETS_PATH, EXTRACTED_DATASETS_DIR)
_SCREAMING_SNAKE_CASE							 =			Path(os.getenv('''HF_DATASETS_EXTRACTED_DATASETS_PATH''', DEFAULT_EXTRACTED_DATASETS_PATH))

# Download count for the website
_SCREAMING_SNAKE_CASE							 =			(
    os.environ.get('''HF_UPDATE_DOWNLOAD_COUNTS''', '''AUTO''').upper() in ENV_VARS_TRUE_AND_AUTO_VALUES
)

# Batch size constants. For more info, see:
# https://github.com/apache/arrow/blob/master/docs/source/cpp/arrays.rst#size-limitations-and-recommendations)
_SCREAMING_SNAKE_CASE							 =			1_0_0_0

# Size of the preloaded record batch in `Dataset.__iter__`
_SCREAMING_SNAKE_CASE							 =			1_0

# Pickling tables works only for small tables (<4GiB)
# For big tables, we write them on disk instead
_SCREAMING_SNAKE_CASE							 =			4 << 3_0

# Max shard size in bytes (e.g. to shard parquet datasets in push_to_hub or download_and_prepare)
_SCREAMING_SNAKE_CASE							 =			"""500MB"""

# Parquet configuration
_SCREAMING_SNAKE_CASE							 =			1_0_0
_SCREAMING_SNAKE_CASE							 =			1_0_0
_SCREAMING_SNAKE_CASE							 =			1_0_0

# Offline mode
_SCREAMING_SNAKE_CASE							 =			os.environ.get('''HF_DATASETS_OFFLINE''', '''AUTO''').upper() in ENV_VARS_TRUE_VALUES

# In-memory
_SCREAMING_SNAKE_CASE							 =			0  # Disabled
_SCREAMING_SNAKE_CASE							 =			float(os.environ.get('''HF_DATASETS_IN_MEMORY_MAX_SIZE''', DEFAULT_IN_MEMORY_MAX_SIZE))

# File names
_SCREAMING_SNAKE_CASE							 =			"""dataset.arrow"""
_SCREAMING_SNAKE_CASE							 =			"""indices.arrow"""
_SCREAMING_SNAKE_CASE							 =			"""state.json"""
_SCREAMING_SNAKE_CASE							 =			"""dataset_info.json"""
_SCREAMING_SNAKE_CASE							 =			"""dataset_infos.json"""
_SCREAMING_SNAKE_CASE							 =			"""LICENSE"""
_SCREAMING_SNAKE_CASE							 =			"""metric_info.json"""
_SCREAMING_SNAKE_CASE							 =			"""dataset_dict.json"""

_SCREAMING_SNAKE_CASE							 =			"""datasets_modules"""

_SCREAMING_SNAKE_CASE							 =			2_5_5

# Streaming
_SCREAMING_SNAKE_CASE							 =			2_0
_SCREAMING_SNAKE_CASE							 =			5

# Datasets without script
_SCREAMING_SNAKE_CASE							 =			2_0_0
_SCREAMING_SNAKE_CASE							 =			1_0
_SCREAMING_SNAKE_CASE							 =			2_0_0

# Progress bars
_SCREAMING_SNAKE_CASE							 =			0.0_5  # 20 progress updates per sec












"""simple docstring"""


def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	int:

 '''simple docstring'''






 if len(_UpperCAmelCase						) < len(_UpperCAmelCase						):
  return levenshtein_distance(_UpperCAmelCase						,  _UpperCAmelCase						)

 if len(_UpperCAmelCase						) == 0:
  return len(_UpperCAmelCase						)

 snake_case__     				=					list(range(len(_UpperCAmelCase						) + 1						)						)

 for i, ca in enumerate(_UpperCAmelCase						):
  snake_case__     				=					[i + 1]

  for j, ca in enumerate(_UpperCAmelCase						):
   # Calculate insertions, deletions and substitutions
   snake_case__     				=					previous_row[j + 1] + 1
   snake_case__     				=					current_row[j] + 1
   snake_case__     				=					previous_row[j] + (ca != ca)

   # Get the minimum to append to the current row
   current_row.append(min(_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)						)

  # Store the previous row
  snake_case__     				=					current_row

 # Returns the last element (distance)
 return previous_row[-1]


if __name__ == "__main__":
   a__       :  Optional[int]       		=					input("""Enter the first word:\n""").strip()
   a__       :  int       		=					input("""Enter the second word:\n""").strip()

   a__       :  Union[str, Any]       		=					levenshtein_distance(first_word, second_word)
   print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')

