'''simple docstring'''
import importlib
import os
import platform
from pathlib import Path

from packaging import version

from .utils.logging import get_logger


a=	get_logger(__name__)

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

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

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

a=	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
a=	{'''1''', '''ON''', '''YES''', '''TRUE'''}
a=	ENV_VARS_TRUE_VALUES.union({'''AUTO'''})


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

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

a=	'''N/A'''
a=	False

if USE_TORCH in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TF not in ENV_VARS_TRUE_VALUES:
  a=	importlib.util.find_spec('''torch''') is not None
  if TORCH_AVAILABLE:
    try:
      a=	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''')

a=	'''N/A'''
a=	False

if USE_TF in ENV_VARS_TRUE_AND_AUTO_VALUES and USE_TORCH not in ENV_VARS_TRUE_VALUES:
  a=	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:
        a=	version.parse(importlib_metadata.version(package))
      except importlib_metadata.PackageNotFoundError:
        continue
      else:
        break
    else:
      a=	False
  if TF_AVAILABLE:
    if TF_VERSION.major < 2:
      logger.info(F"""TensorFlow found but with version {TF_VERSION}. `datasets` requires version 2 minimum.""")
      a=	False
    else:
      logger.info(F"""TensorFlow version {TF_VERSION} available.""")
else:
  logger.info('''Disabling Tensorflow because USE_TORCH is set''')


a=	'''N/A'''
a=	False

if USE_JAX in ENV_VARS_TRUE_AND_AUTO_VALUES:
  a=	importlib.util.find_spec('''jax''') is not None and importlib.util.find_spec('''jaxlib''') is not None
  if JAX_AVAILABLE:
    try:
      a=	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''')


a=	os.environ.get('''USE_BEAM''', '''AUTO''').upper()
a=	'''N/A'''
a=	False
if USE_BEAM in ENV_VARS_TRUE_AND_AUTO_VALUES:
  try:
    a=	version.parse(importlib_metadata.version('''apache_beam'''))
    a=	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
a=	importlib.util.find_spec('''sqlalchemy''') is not None

# Optional tools for feature decoding
a=	importlib.util.find_spec('''PIL''') is not None
a=	importlib.util.find_spec('''soundfile''') is not None and version.parse(
    importlib.import_module('''soundfile''').__libsndfile_version__
) >= version.parse('''1.0.31''')
a=	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
a=	importlib.util.find_spec('''rarfile''') is not None
a=	importlib.util.find_spec('''zstandard''') is not None
a=	importlib.util.find_spec('''lz4''') is not None
a=	importlib.util.find_spec('''py7zr''') is not None


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

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

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

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

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

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

# Download count for the website
a=	(
    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)
a=	1_0_0_0

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

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

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

# Parquet configuration
a=	1_0_0
a=	1_0_0
a=	1_0_0

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

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

# File names
a=	'''dataset.arrow'''
a=	'''indices.arrow'''
a=	'''state.json'''
a=	'''dataset_info.json'''
a=	'''dataset_infos.json'''
a=	'''LICENSE'''
a=	'''metric_info.json'''
a=	'''dataset_dict.json'''

a=	'''datasets_modules'''

a=	2_5_5

# Streaming
a=	2_0
a=	5

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

# Progress bars
a=	0.05  # 20 progress updates per sec






# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
from typing import List


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__   ):

					"""simple docstring"""






					if name != "training_function" and name != "main":
										raise ValueError(F"""Incorrect function name passed: {name}, choose either 'main' or 'training_function'"""   )
					__lowerCAmelCase,						__lowerCAmelCase       =				[], False
					for line in lines:
										if not found_start and F"""def {name}""" in line:
															__lowerCAmelCase       =				True
															good_lines.append(UpperCAmelCase__   )
															continue
										if found_start:
															if name == "training_function" and "def main" in line:
																				return good_lines
															if name == "main" and "if __name__" in line:
																				return good_lines
															good_lines.append(UpperCAmelCase__   )


def 							__lowercase     (	UpperCAmelCase__   ):

					"""simple docstring"""






					return [line for line in lines if not line.lstrip().startswith('#'   ) and line != "\n"]


def 							__lowercase     (	UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__		,       UpperCAmelCase__ = None   ):

					"""simple docstring"""






					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(os.path.abspath(os.path.join('examples'		,       'nlp_example.py'   )   )		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					with open(UpperCAmelCase__		,       'r'   ) as f:
										__lowerCAmelCase       =				f.readlines()
					if secondary_filename is not None:
										with open(UpperCAmelCase__		,       'r'   ) as f:
															__lowerCAmelCase       =				f.readlines()

    # This is our base, we remove all the code from here in our `full_filename` and `feature_filename` to find the new content
					if parser_only:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'main'   )   )
					else:
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										__lowerCAmelCase       =				clean_lines(get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )
										if secondary_filename is not None:
															__lowerCAmelCase       =				clean_lines(
															    get_function_contents_by_name(UpperCAmelCase__		,       'training_function'   )   )

					__lowerCAmelCase       =				'train_dataloader, eval_dataloader = get_dataloaders(accelerator, batch_size)\n'

					# Specific code in our script that differs from the full version, aka what is new
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # We keep track of the idxs just in case it's a repeated statement
					__lowerCAmelCase       =				iter(UpperCAmelCase__   )
					for i in range(len(UpperCAmelCase__   ) - 1   ):
										if i not in passed_idxs:
															__lowerCAmelCase       =				next(UpperCAmelCase__   )
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_feature_code.append(UpperCAmelCase__   )
																									passed_idxs.append(UpperCAmelCase__   )
																				else:
																									# Skip over the `config['num_epochs'] = 2` statement
																									__lowerCAmelCase       =				next(UpperCAmelCase__   )

    # Extract out just the new parts from the full_file_training_func
					__lowerCAmelCase       =				[]
					__lowerCAmelCase       =				[]  # We keep track of the idxs just in case it's a repeated statement
					for i, line in enumerate(UpperCAmelCase__   ):
										if i not in passed_idxs:
															if (line not in full_file_func) and (line.lstrip() != _dl_line):
																				if "TESTING_MOCKED_DATALOADERS" not in line:
																									new_full_example_parts.append(UpperCAmelCase__   )
																									passed_idxs.append(UpperCAmelCase__   )

    # Finally, get the overall diff
					__lowerCAmelCase       =				[line for line in new_feature_code if line not in new_full_example_parts]
					if secondary_filename is not None:
										__lowerCAmelCase       =				[line for line in full_file_contents if line not in secondary_file_func]
										__lowerCAmelCase       =				[line for line in diff_from_example if line not in diff_from_two]

					return diff_from_example






