


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

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


a_ :Any  							=			'<<<<<<< This should probably be modified because it mentions: '

a_ :int  							=			'=======\n>>>>>>>\n'

a_ :Dict  							=			[
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

a_ :int  							=			[
    # (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    a   (							A__							)	-> List[Any]:




      '''simple docstring'''

      return ConvertCommand(args.tfds_path      ,    args.datasets_directory							)





class 							lowercase  (						_UpperCAmelCase			):







     @staticmethod
     def 							lowercase__       (						_lowercase :						ArgumentParser      ):
           SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		parser.add_parser(
               '''convert'''   ,   help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''   ,   )
           train_parser.add_argument(
               '''--tfds_path'''   ,   type=_lowercase   ,   required=_lowercase   ,   help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''   ,   )
           train_parser.add_argument(
               '''--datasets_directory'''   ,   type=_lowercase   ,   required=_lowercase   ,   help='''Path to the HuggingFace Datasets folder.'''      )
           train_parser.set_defaults(func=_lowercase      )







     def __init__(						self :						str   ,   _lowercase :						str   ,   _lowercase :						str   ,   *_lowercase :						List[Any]      ):
           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		get_logger('''datasets-cli/converting'''      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		tfds_path
           SCREAMING_SNAKE_CASE__       :  List[Any]						     =		datasets_directory







     def 							lowercase__       (						self :						Union[str, Any]      ):
           if os.path.isdir(self._tfds_path      ):
                 SCREAMING_SNAKE_CASE__       :  Any						     =		os.path.abspath(self._tfds_path      )
           elif os.path.isfile(self._tfds_path      ):
                 SCREAMING_SNAKE_CASE__       :  int						     =		os.path.dirname(self._tfds_path      )
           else:
                 raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.'''      )

           SCREAMING_SNAKE_CASE__       :  Tuple						     =		os.path.abspath(self._datasets_directory      )

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

           SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[]
           SCREAMING_SNAKE_CASE__       :  str						     =		[]
           SCREAMING_SNAKE_CASE__       :  str						     =		{}

           if os.path.isdir(self._tfds_path      ):
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		os.listdir(_lowercase      )
           else:
                 SCREAMING_SNAKE_CASE__       :  int						     =		[os.path.basename(self._tfds_path      )]

           for f_name in file_names:
                 self._logger.info(f"""Looking at file {f_name}"""      )
                 SCREAMING_SNAKE_CASE__       :  int						     =		os.path.join(_lowercase   ,   _lowercase      )
                 SCREAMING_SNAKE_CASE__       :  int						     =		os.path.join(_lowercase   ,   _lowercase      )

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

                 with open(_lowercase   ,   encoding='''utf-8'''      ) as f:
                       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		f.readlines()

                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[]
                 SCREAMING_SNAKE_CASE__       :  Optional[int]						     =		False
                 SCREAMING_SNAKE_CASE__       :  int						     =		False
                 SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		[]
                 for line in lines:
                       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		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:
                             SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		'''import datasets\n'''
                       elif "import tensorflow" in out_line:
                             # order is important here
                             SCREAMING_SNAKE_CASE__       :  Any						     =		''''''
                             continue
                       elif "from absl import logging" in out_line:
                             SCREAMING_SNAKE_CASE__       :  Dict						     =		'''from datasets import logging\n'''
                       elif "getLogger" in out_line:
                             SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		out_line.replace('''getLogger'''   ,   '''get_logger'''      )
                       elif any(expression in out_line for expression in TO_HIGHLIGHT      ):
                             SCREAMING_SNAKE_CASE__       :  List[Any]						     =		True
                             SCREAMING_SNAKE_CASE__       :  Union[str, Any]						     =		list(filter(lambda _lowercase      : e in out_line   ,   _lowercase      )      )
                             out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(_lowercase      ) + '''\n'''      )
                             out_lines.append(_lowercase      )
                             out_lines.append(_lowercase      )
                             continue
                       else:
                             for pattern, replacement in TO_CONVERT:
                                   SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		re.sub(_lowercase   ,   _lowercase   ,   _lowercase      )

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

                 if is_builder or "wmt" in f_name:
                       # We create a new directory for each dataset
                       SCREAMING_SNAKE_CASE__       :  List[Any]						     =		f_name.replace('''.py'''   ,   ''''''      )
                       SCREAMING_SNAKE_CASE__       :  Optional[Any]						     =		os.path.join(_lowercase   ,   _lowercase      )
                       SCREAMING_SNAKE_CASE__       :  List[str]						     =		os.path.join(_lowercase   ,   _lowercase      )
                       os.makedirs(_lowercase   ,   exist_ok=_lowercase      )
                       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(_lowercase      )

                 if needs_manual_update:
                       with_manual_update.append(_lowercase      )

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

           for utils_file in utils_files:
                 try:
                       SCREAMING_SNAKE_CASE__       :  Any						     =		os.path.basename(_lowercase      )
                       SCREAMING_SNAKE_CASE__       :  Any						     =		imports_to_builder_map[f_name.replace('''.py'''   ,   ''''''      )]
                       self._logger.info(f"""Moving {dest_folder} to {utils_file}"""      )
                       shutil.copy(_lowercase   ,   _lowercase      )
                 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 warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			['''image_processor''', '''tokenizer''']
			snake_case_  						=			'''OwlViTImageProcessor'''
			snake_case_  						=			('''CLIPTokenizer''', '''CLIPTokenizerFast''')


			def __init__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       None
							if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'     ,		lowerCamelCase__     ,		)
											__lowerCamelCase									=       kwargs.pop('feature_extractor'				)

							__lowerCamelCase									=       image_processor if image_processor is not None else feature_extractor
							if image_processor is None:
											raise ValueError('You need to specify an `image_processor`.'				)
							if tokenizer is None:
											raise ValueError('You need to specify a `tokenizer`.'				)

							super().__init__(lowerCamelCase__     ,		lowerCamelCase__				)


			def __call__( self     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__=None     ,		lowerCamelCase__="max_length"     ,		lowerCamelCase__="np"     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''

							if text is None and query_images is None and images is None:
											raise ValueError(
											    'You have to specify at least one text or query image or image. All three cannot be none.'				)

							if text is not None:
											if isinstance(lowerCamelCase__     ,		lowerCamelCase__				) or (isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and not isinstance(text[0]     ,		lowerCamelCase__				)):
															__lowerCamelCase									=       [self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)]

											elif isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and isinstance(text[0]     ,		lowerCamelCase__				):
															__lowerCamelCase									=       []

															# Maximum number of queries across batch
															__lowerCamelCase									=       max([len(lowerCamelCase__				) for t in text]				)

															# Pad all batch samples to max number of text queries
															for t in text:
																			if len(lowerCamelCase__				) != max_num_queries:
																							__lowerCamelCase									=       t + [' '] * (max_num_queries - len(lowerCamelCase__				))

																			__lowerCamelCase									=       self.tokenizer(lowerCamelCase__     ,		padding=lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)
																			encodings.append(lowerCamelCase__				)
											else:
															raise TypeError('Input text should be a string, a list of strings or a nested list of strings'				)

											if return_tensors == "np":
															__lowerCamelCase									=       np.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       np.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											elif return_tensors == "jax" and is_flax_available():
															import jax.numpy as jnp

															__lowerCamelCase									=       jnp.concatenate([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       jnp.concatenate([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											elif return_tensors == "pt" and is_torch_available():
															import torch

															__lowerCamelCase									=       torch.cat([encoding['input_ids'] for encoding in encodings]     ,		dim=0				)
															__lowerCamelCase									=       torch.cat([encoding['attention_mask'] for encoding in encodings]     ,		dim=0				)

											elif return_tensors == "tf" and is_tf_available():
															import tensorflow as tf

															__lowerCamelCase									=       tf.stack([encoding['input_ids'] for encoding in encodings]     ,		axis=0				)
															__lowerCamelCase									=       tf.stack([encoding['attention_mask'] for encoding in encodings]     ,		axis=0				)

											else:
															raise ValueError('Target return tensor type could not be returned'				)

											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       input_ids
											__lowerCamelCase									=       attention_mask

							if query_images is not None:
											__lowerCamelCase									=       BatchEncoding()
											__lowerCamelCase									=       self.image_processor(
											    lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				).pixel_values
											__lowerCamelCase									=       query_pixel_values

							if images is not None:
											__lowerCamelCase									=       self.image_processor(lowerCamelCase__     ,		return_tensors=lowerCamelCase__     ,		**lowerCamelCase__				)

							if text is not None and images is not None:
											__lowerCamelCase									=       image_features.pixel_values
											return encoding
							elif query_images is not None and images is not None:
											__lowerCamelCase									=       image_features.pixel_values
											return encoding
							elif text is not None or query_images is not None:
											return encoding
							else:
											return BatchEncoding(data=dict(**lowerCamelCase__				)     ,		tensor_type=lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							return self.image_processor.post_process(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							return self.image_processor.post_process_object_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			def  lowercase_       ( self     ,		*lowerCamelCase__     ,		**lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__     ,		**lowerCamelCase__				)


			@property
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor_class






			@property
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'     ,		lowerCamelCase__     ,		)
							return self.image_processor


