




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

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


lowerCamelCase    :		List[Any]			   =       "<<<<<<< This should probably be modified because it mentions: "

lowerCamelCase    :		Optional[Any]			   =       "=======\n>>>>>>>\n"

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

lowerCamelCase    :		Tuple			   =       [
    # (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       _SCREAMING_SNAKE_CASE      (							lowercase     :	Namespace					):


      '''simple docstring'''



      return ConvertCommand(args.tfds_path     ,							args.datasets_directory					)







class   A(						UpperCamelCase       ):



 '''simple docstring'''


 @staticmethod
 def     a__			(    A_ :  ArgumentParser  )       ->     int:

       """simple docstring"""






       lowerCamelCase_							=   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 :  int						,							A_ :  str						,							A_ :  str						,							*A_ :  str  )       ->     Optional[Any]:

       """simple docstring"""






       lowerCamelCase_							=   get_logger('datasets-cli/converting'  )

       lowerCamelCase_							=   tfds_path
       lowerCamelCase_							=   datasets_directory





 def     a__			(    self :  Tuple  )       ->     Dict:

       """simple docstring"""






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

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

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

       lowerCamelCase_							=   []
       lowerCamelCase_							=   []
       lowerCamelCase_							=   {}

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

       for f_name in file_names:
             self._logger.info(f"""Looking at file {f_name}"""  )
             lowerCamelCase_							=   os.path.join(A_						,							A_  )
             lowerCamelCase_							=   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:
                   lowerCamelCase_							=   f.readlines()

             lowerCamelCase_							=   []
             lowerCamelCase_							=   False
             lowerCamelCase_							=   False
             lowerCamelCase_							=   []
             for line in lines:
                   lowerCamelCase_							=   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:
                         lowerCamelCase_							=   'import datasets\n'
                   elif "import tensorflow" in out_line:
                         # order is important here
                         lowerCamelCase_							=   ''
                         continue
                   elif "from absl import logging" in out_line:
                         lowerCamelCase_							=   'from datasets import logging\n'
                   elif "getLogger" in out_line:
                         lowerCamelCase_							=   out_line.replace('getLogger'						,							'get_logger'  )
                   elif any(expression in out_line for expression in TO_HIGHLIGHT  ):
                         lowerCamelCase_							=   True
                         lowerCamelCase_							=   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:
                               lowerCamelCase_							=   re.sub(A_						,							A_						,							A_  )

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

             if is_builder or "wmt" in f_name:
                   # We create a new directory for each dataset
                   lowerCamelCase_							=   f_name.replace('.py'						,							''  )
                   lowerCamelCase_							=   os.path.join(A_						,							A_  )
                   lowerCamelCase_							=   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:
                   lowerCamelCase_							=   os.path.basename(A_  )
                   lowerCamelCase_							=   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 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   A(						UpperCamelCase       ):



 '''simple docstring'''


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

 def __init__(    self :  Optional[Any]						,							A_ :  Tuple=None						,							A_ :  Tuple=None						,							**A_ :  List[str]  )       ->     Any:

       """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.'						,							A_						,							)
             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__(A_						,							A_  )

 def __call__(    self :  List[str]						,							A_ :  List[str]=None						,							A_ :  List[Any]=None						,							A_ :  Dict=None						,							A_ :  Tuple="max_length"						,							A_ :  int="np"						,							**A_ :  Union[str, Any]  )       ->     Union[str, Any]:

       """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(A_						,							A_  ) or (isinstance(A_						,							A_  ) and not isinstance(text[0]						,							A_  )):
                   lowerCamelCase_							=   [self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )]

             elif isinstance(A_						,							A_  ) and isinstance(text[0]						,							A_  ):
                   lowerCamelCase_							=   []

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

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

                         lowerCamelCase_							=   self.tokenizer(A_						,							padding=A_						,							return_tensors=A_						,							**A_  )
                         encodings.append(A_  )
             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(
                 A_						,							return_tensors=A_						,							**A_  ).pixel_values
             lowerCamelCase_							=   query_pixel_values

       if images is not None:
             lowerCamelCase_							=   self.image_processor(A_						,							return_tensors=A_						,							**A_  )

       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(**A_  )						,							tensor_type=A_  )

 def     a__			(    self :  Tuple						,							*A_ :  Dict						,							**A_ :  Dict  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process(*A_						,							**A_  )

 def     a__			(    self :  List[str]						,							*A_ :  Any						,							**A_ :  List[Any]  )       ->     Optional[int]:

       """simple docstring"""






       return self.image_processor.post_process_object_detection(*A_						,							**A_  )

 def     a__			(    self :  Any						,							*A_ :  str						,							**A_ :  List[Any]  )       ->     Any:

       """simple docstring"""






       return self.image_processor.post_process_image_guided_detection(*A_						,							**A_  )

 def     a__			(    self :  Union[str, Any]						,							*A_ :  Any						,							**A_ :  Union[str, Any]  )       ->     str:

       """simple docstring"""






       return self.tokenizer.batch_decode(*A_						,							**A_  )

 def     a__			(    self :  Optional[int]						,							*A_ :  List[Any]						,							**A_ :  int  )       ->     int:

       """simple docstring"""






       return self.tokenizer.decode(*A_						,							**A_  )

 @property
 def     a__			(    self :  List[Any]  )       ->     List[Any]:

       """simple docstring"""






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





 @property
 def     a__			(    self :  str  )       ->     List[str]:

       """simple docstring"""






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



