
import os
import re
import shutil
from argparse import ArgumentParser, Namespace

from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger


lowercase__		:Union[str, Any]			  =   "<<<<<<< This should probably be modified because it mentions: "

lowercase__		:List[str]			  =   "=======\n>>>>>>>\n"

lowercase__		:Union[str, Any]			  =   [
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

lowercase__		:str			  =   [
    # (pattern, replacement)
    # Order is important here for some replacements
    (R"tfds\.core", R"datasets"),
    (R"tf\.io\.gfile\.GFile", R"open"),
    (R"tf\.([\w\d]+)", R"datasets.Value('\1')"),
    (R"tfds\.features\.Text\(\)", R"datasets.Value('string')"),
    (R"tfds\.features\.Text\(", R"datasets.Value('string'),"),
    (R"features\s*=\s*tfds.features.FeaturesDict\(", R"features=datasets.Features("),
    (R"tfds\.features\.FeaturesDict\(", R"dict("),
    (R"The TensorFlow Datasets Authors", R"The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"),
    (R"tfds\.", R"datasets."),
    (R"dl_manager\.manual_dir", R"self.config.data_dir"),
    (R"self\.builder_config", R"self.config"),
]



def 	UpperCamelCase   (							lowerCAmelCase__		):
					'''simple docstring'''
					return ConvertCommand(args.tfds_path			,      args.datasets_directory		)







class     lowercase		( SCREAMING_SNAKE_CASE__		):




							@staticmethod
							def 			A__				(    A__):
												lowercase						     =						parser.add_parser(
												    '''convert'''       ,help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''       ,)
												train_parser.add_argument(
												    '''--tfds_path'''       ,type=_a       ,required=_a       ,help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''       ,)
												train_parser.add_argument(
												    '''--datasets_directory'''       ,type=_a       ,required=_a       ,help='''Path to the HuggingFace Datasets folder.''')
												train_parser.set_defaults(func=_a)




							def __init__(    self       ,A__       ,A__       ,*A__):
												lowercase						     =						get_logger('''datasets-cli/converting''')

												lowercase						     =						tfds_path
												lowercase						     =						datasets_directory





							def 			A__				(    self):
												if os.path.isdir(self._tfds_path):
																	lowercase						     =						os.path.abspath(self._tfds_path)
												elif os.path.isfile(self._tfds_path):
																	lowercase						     =						os.path.dirname(self._tfds_path)
												else:
																	raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.''')

												lowercase						     =						os.path.abspath(self._datasets_directory)

												self._logger.info(f'Converting datasets from {abs_tfds_path} to {abs_datasets_path}')

												lowercase						     =						[]
												lowercase						     =						[]
												lowercase						     =						{}

												if os.path.isdir(self._tfds_path):
																	lowercase						     =						os.listdir(_a)
												else:
																	lowercase						     =						[os.path.basename(self._tfds_path)]

												for f_name in file_names:
																	self._logger.info(f'Looking at file {f_name}')
																	lowercase						     =						os.path.join(_a       ,_a)
																	lowercase						     =						os.path.join(_a       ,_a)

																	if not os.path.isfile(_a) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																						self._logger.info('''Skipping file''')
																						continue

																	with open(_a       ,encoding='''utf-8''') as f:
																						lowercase						     =						f.readlines()

																	lowercase						     =						[]
																	lowercase						     =						False
																	lowercase						     =						False
																	lowercase						     =						[]
																	for line in lines:
																						lowercase						     =						line

																						# Convert imports
																						if "import tensorflow.compat.v2 as tf" in out_line:
																											continue
																						elif "@tfds.core" in out_line:
																											continue
																						elif "builder=self" in out_line:
																											continue
																						elif "import tensorflow_datasets.public_api as tfds" in out_line:
																											lowercase						     =						"import datasets\n"
																						elif "import tensorflow" in out_line:
																											# order is important here
																											lowercase						     =						""
																											continue
																						elif "from absl import logging" in out_line:
																											lowercase						     =						"from datasets import logging\n"
																						elif "getLogger" in out_line:
																											lowercase						     =						out_line.replace('''getLogger'''       ,'''get_logger''')
																						elif any(expression in out_line for expression in TO_HIGHLIGHT):
																											lowercase						     =						True
																											lowercase						     =						list(filter(lambda A__: e in out_line       ,_a))
																											out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_a) + '''\n''')
																											out_lines.append(_a)
																											out_lines.append(_a)
																											continue
																						else:
																											for pattern, replacement in TO_CONVERT:
																																lowercase						     =						re.sub(_a       ,_a       ,_a)

                # Take care of saving utilities (to later move them together with main script)
																						if "tensorflow_datasets" in out_line:
																											lowercase						     =						re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)'''       ,_a)
																											tfds_imports.extend(imp.strip() for imp in match.group(1).split(''','''))
																											lowercase						     =						"from . import " + match.group(1)

																						# Check we have not forget anything
																						if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
																											raise ValueError(f'Error converting {out_line.strip()}')

																						if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
																											lowercase						     =						True
																						out_lines.append(_a)

																	if is_builder or "wmt" in f_name:
																						# We create a new directory for each dataset
																						lowercase						     =						f_name.replace('''.py'''       ,'''''')
																						lowercase						     =						os.path.join(_a       ,_a)
																						lowercase						     =						os.path.join(_a       ,_a)
																						os.makedirs(_a       ,exist_ok=_a)
																						self._logger.info(f'Adding directory {output_dir}')
																						imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
																	else:
																						# Utilities will be moved at the end
																						utils_files.append(_a)

																	if needs_manual_update:
																						with_manual_update.append(_a)

																	with open(_a       ,'''w'''       ,encoding='''utf-8''') as f:
																						f.writelines(_a)
																	self._logger.info(f'Converted in {output_file}')

												for utils_file in utils_files:
																	try:
																						lowercase						     =						os.path.basename(_a)
																						lowercase						     =						imports_to_builder_map[f_name.replace('''.py'''       ,'''''')]
																						self._logger.info(f'Moving {dest_folder} to {utils_file}')
																						shutil.copy(_a       ,_a)
																	except KeyError:
																						self._logger.error(f'Cannot find destination folder for {utils_file}. Please copy manually.')

												if with_manual_update:
																	for file_path in with_manual_update:
																						self._logger.warning(
																						    f'You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.')






import numpy as np

import datasets


lowercase__		:Dict			  =   "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"

lowercase__		:List[Any]			  =   "\\n@article{de2000mahalanobis,\n  title={The mahalanobis distance},\n  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n  journal={Chemometrics and intelligent laboratory systems},\n  volume={50},\n  number={1},\n  pages={1--18},\n  year={2000},\n  publisher={Elsevier}\n}\n"

lowercase__		:Dict			  =   "\nArgs:\n    X: List of datapoints to be compared with the `reference_distribution`.\n    reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n    mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n    >>> print(results)\n    {'mahalanobis': array([0.5])}\n"







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION		)
class     lowercase		( datasets.Metric		):




							def 			A__				(    self):
												return datasets.MetricInfo(
												    description=_DESCRIPTION       ,citation=_CITATION       ,inputs_description=_KWARGS_DESCRIPTION       ,features=datasets.Features(
												        {
												            '''X''': datasets.Sequence(datasets.Value('''float'''       ,id='''sequence''')       ,id='''X'''),
												        })       ,)





							def 			A__				(    self       ,A__       ,A__):
												# convert to numpy arrays
												lowercase						     =						np.array(A__)
												lowercase						     =						np.array(A__)

												# Assert that arrays are 2D
												if len(X.shape) != 2:
																	raise ValueError('''Expected `X` to be a 2D vector''')
												if len(reference_distribution.shape) != 2:
																	raise ValueError('''Expected `reference_distribution` to be a 2D vector''')
												if reference_distribution.shape[0] < 2:
																	raise ValueError(
																	    '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''')

												# Get mahalanobis distance for each prediction
												lowercase						     =						X - np.mean(A__)
												lowercase						     =						np.cov(reference_distribution.T)
												try:
																	lowercase						     =						np.linalg.inv(A__)
												except np.linalg.LinAlgError:
																	lowercase						     =						np.linalg.pinv(A__)
												lowercase						     =						np.dot(A__       ,A__)
												lowercase						     =						np.dot(A__       ,X_minus_mu.T).diagonal()

												return {"mahalanobis": mahal_dist}




