



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

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


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

__UpperCAmelCase			  =			'''=======
>>>>>>>
'''

__UpperCAmelCase			  =			[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

__UpperCAmelCase			  =			[
    # (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      (  snake_case__			:       Namespace      )      ->	List[Any]:
   return ConvertCommand(args.tfds_path			,	args.datasets_directory      )



class 							lowerCAmelCase_						(    a__				):
       @staticmethod
       def    snake_case_			(     SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    int		      =      parser.add_parser(
              'convert',     help='Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.',     )
          train_parser.add_argument(
              '--tfds_path',     type=SCREAMING_SNAKE_CASE_,     required=SCREAMING_SNAKE_CASE_,     help='Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.',     )
          train_parser.add_argument(
              '--datasets_directory',     type=SCREAMING_SNAKE_CASE_,     required=SCREAMING_SNAKE_CASE_,     help='Path to the HuggingFace Datasets folder.'			)
          train_parser.set_defaults(func=SCREAMING_SNAKE_CASE_			)
       def __init__(     self,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     *SCREAMING_SNAKE_CASE_			)	->						Any:
          UpperCamelCase       :    Dict		      =      get_logger('datasets-cli/converting'			)

          UpperCamelCase       :    Optional[Any]		      =      tfds_path
          UpperCamelCase       :    Union[str, Any]		      =      datasets_directory






       def    snake_case_			(     self			)	->						int:
          if os.path.isdir(self._tfds_path			):
             UpperCamelCase       :    List[str]		      =      os.path.abspath(self._tfds_path			)
          elif os.path.isfile(self._tfds_path			):
             UpperCamelCase       :    Optional[int]		      =      os.path.dirname(self._tfds_path			)
          else:
             raise ValueError('--tfds_path is neither a directory nor a file. Please check path.'			)

          UpperCamelCase       :    Optional[Any]		      =      os.path.abspath(self._datasets_directory			)

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

          UpperCamelCase       :    str		      =      []
          UpperCamelCase       :    Any		      =      []
          UpperCamelCase       :    int		      =      {}

          if os.path.isdir(self._tfds_path			):
             UpperCamelCase       :    str		      =      os.listdir(SCREAMING_SNAKE_CASE_			)
          else:
             UpperCamelCase       :    Any		      =      [os.path.basename(self._tfds_path			)]

          for f_name in file_names:
             self._logger.info(F"""Looking at file {f_name}"""			)
             UpperCamelCase       :    Optional[int]		      =      os.path.join(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
             UpperCamelCase       :    List[Any]		      =      os.path.join(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)

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

             with open(SCREAMING_SNAKE_CASE_,     encoding='utf-8'			) as f:
                UpperCamelCase       :    Any		      =      f.readlines()

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

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

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

             if needs_manual_update:
                with_manual_update.append(SCREAMING_SNAKE_CASE_			)

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

          for utils_file in utils_files:
             try:
                UpperCamelCase       :    Union[str, Any]		      =      os.path.basename(SCREAMING_SNAKE_CASE_			)
                UpperCamelCase       :    Union[str, Any]		      =      imports_to_builder_map[f_name.replace('.py',     ''			)]
                self._logger.info(F"""Moving {dest_folder} to {utils_file}"""			)
                shutil.copy(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			)
             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'."""			)







'''simple docstring'''



import re
from typing import Callable, List, Optional, Union

import tensorflow as tf


try:
      from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
      from tensorflow.keras.optimizers import Adam





class   UpperCAmelCase_    (tf.keras.optimizers.schedules.LearningRateSchedule    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = 1.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	)							->	Any:
         super().__init__()
         __lowerCamelCase  :      Optional[Any]														=						initial_learning_rate
         __lowerCamelCase  :      Optional[Any]														=						warmup_steps
         __lowerCamelCase  :      Union[str, Any]														=						power
         __lowerCamelCase  :      Optional[int]														=						decay_schedule_fn
         __lowerCamelCase  :      Any														=						name







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         with tf.name_scope(self.name or 'WarmUp'      ) as name:
              # Implements polynomial warmup. i.e., if global_step < warmup_steps, the
              # learning rate will be `global_step/num_warmup_steps * init_lr`.
              __lowerCamelCase  :      str														=						tf.cast(SCREAMING_SNAKE_CASE_     ,	tf.floataa      )
              __lowerCamelCase  :      Optional[int]														=						tf.cast(self.warmup_steps     ,	tf.floataa      )
              __lowerCamelCase  :      List[Any]														=						global_step_float / warmup_steps_float
              __lowerCamelCase  :      Optional[Any]														=						self.initial_learning_rate * tf.math.pow(SCREAMING_SNAKE_CASE_     ,	self.power      )
              return tf.cond(
                  global_step_float < warmup_steps_float     ,	lambda: warmup_learning_rate     ,	lambda: self.decay_schedule_fn(step - self.warmup_steps      )     ,	name=SCREAMING_SNAKE_CASE_     ,	)







    def  lowercase_							(				self      )							->	Optional[Any]:
         return {
             "initial_learning_rate": self.initial_learning_rate,
             "decay_schedule_fn": self.decay_schedule_fn,
             "warmup_steps": self.warmup_steps,
             "power": self.power,
             "name": self.name,
         }







def      UpperCAmelCase__   (      UpperCAmelCase_       :							float ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							float = 0.9 ,       UpperCAmelCase_       :							float = 0.999 ,       UpperCAmelCase_       :							float = 1e-8 ,       UpperCAmelCase_       :							Optional[float] = None ,       UpperCAmelCase_       :							Optional[float] = None ,       UpperCAmelCase_       :							float = 0.0 ,       UpperCAmelCase_       :							float = 1.0 ,       UpperCAmelCase_       :							Optional[List[str]] = None ,       )	->						int:
     __lowerCamelCase  :      int														=						tf.keras.optimizers.schedules.PolynomialDecay(
         initial_learning_rate=UpperCAmelCase_ ,       decay_steps=num_train_steps - num_warmup_steps ,       end_learning_rate=init_lr * min_lr_ratio ,       power=UpperCAmelCase_ ,       )
     if num_warmup_steps:
          __lowerCamelCase  :      str														=						WarmUp(
              initial_learning_rate=UpperCAmelCase_ ,       decay_schedule_fn=UpperCAmelCase_ ,       warmup_steps=UpperCAmelCase_ ,       )
     if weight_decay_rate > 0.0:
          __lowerCamelCase  :      List[Any]														=						AdamWeightDecay(
              learning_rate=UpperCAmelCase_ ,       weight_decay_rate=UpperCAmelCase_ ,       beta_a=UpperCAmelCase_ ,       beta_a=UpperCAmelCase_ ,       epsilon=UpperCAmelCase_ ,       clipnorm=UpperCAmelCase_ ,       global_clipnorm=UpperCAmelCase_ ,       exclude_from_weight_decay=['LayerNorm', 'layer_norm', 'bias'] ,       include_in_weight_decay=UpperCAmelCase_ ,       )
     else:
          __lowerCamelCase  :      Tuple														=						tf.keras.optimizers.Adam(
              learning_rate=UpperCAmelCase_ ,       beta_a=UpperCAmelCase_ ,       beta_a=UpperCAmelCase_ ,       epsilon=UpperCAmelCase_ ,       clipnorm=UpperCAmelCase_ ,       global_clipnorm=UpperCAmelCase_ ,       )
     # We return the optimizer and the LR scheduler in order to better track the
     # evolution of the LR independently of the optimizer.
     return optimizer, lr_schedule





class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self     ,	SCREAMING_SNAKE_CASE_ = 0.0_0_1     ,	SCREAMING_SNAKE_CASE_ = 0.9     ,	SCREAMING_SNAKE_CASE_ = 0.9_9_9     ,	SCREAMING_SNAKE_CASE_ = 1E-7     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = 0.0     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = None     ,	SCREAMING_SNAKE_CASE_ = "AdamWeightDecay"     ,	**SCREAMING_SNAKE_CASE_     ,	)							->	int:
         super().__init__(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      int														=						weight_decay_rate
         __lowerCamelCase  :      str														=						include_in_weight_decay
         __lowerCamelCase  :      List[Any]														=						exclude_from_weight_decay







    @classmethod
    def  lowercase_							(				cls     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         __lowerCamelCase  :      Any														=						{'WarmUp': WarmUp}
         return super(SCREAMING_SNAKE_CASE_     ,	cls      ).from_config(SCREAMING_SNAKE_CASE_     ,	custom_objects=SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	Optional[int]:
         super(SCREAMING_SNAKE_CASE_     ,	self      )._prepare_local(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Union[str, Any]														=						tf.constant(
             self.weight_decay_rate     ,	name='adam_weight_decay_rate'      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	int:
         __lowerCamelCase  :      Tuple														=						self._do_use_weight_decay(var.name      )
         if do_decay:
              return var.assign_sub(
                  learning_rate * var * apply_state[(var.device, var.dtype.base_dtype)]['weight_decay_rate']     ,	use_locking=self._use_locking     ,	)
         return tf.no_op()







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None     ,	**SCREAMING_SNAKE_CASE_      )							->	List[str]:
         __lowerCamelCase							,			__lowerCamelCase  :      Optional[Any]														=						list(zip(*SCREAMING_SNAKE_CASE_      )      )
         return super(SCREAMING_SNAKE_CASE_     ,	self      ).apply_gradients(zip(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )     ,	name=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if apply_state is None:
              return self._decayed_lr_t[var_dtype], {}

         __lowerCamelCase  :      Optional[int]														=						apply_state or {}
         __lowerCamelCase  :      Dict														=						apply_state.get((var_device, var_dtype)      )
         if coefficients is None:
              __lowerCamelCase  :      List[Any]														=						self._fallback_apply_state(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
              __lowerCamelCase  :      Optional[Any]														=						coefficients

         return coefficients["lr_t"], {"apply_state": apply_state}







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	str:
         __lowerCamelCase							,			__lowerCamelCase  :      Dict														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      Optional[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_dense(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_=None      )							->	Union[str, Any]:
         __lowerCamelCase							,			__lowerCamelCase  :      Tuple														=						self._get_lr(var.device     ,	var.dtype.base_dtype     ,	SCREAMING_SNAKE_CASE_      )
         __lowerCamelCase  :      List[Any]														=						self._decay_weights_op(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      )
         with tf.control_dependencies([decay]      ):
              return super(SCREAMING_SNAKE_CASE_     ,	self      )._resource_apply_sparse(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_      )







    def  lowercase_							(				self      )							->	List[Any]:
         __lowerCamelCase  :      Any														=						super().get_config()
         config.update({'weight_decay_rate': self.weight_decay_rate}      )
         return config







    def  lowercase_							(				self     ,	SCREAMING_SNAKE_CASE_      )							->	Dict:
         if self.weight_decay_rate == 0:
              return False

         if self._include_in_weight_decay:
              for r in self._include_in_weight_decay:
                   if re.search(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) is not None:
                        return True

         if self._exclude_from_weight_decay:
              for r in self._exclude_from_weight_decay:
                   if re.search(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_      ) is not None:
                        return False
         return True







class   UpperCAmelCase_    (_UpperCAmelCase    ):





    """simple docstring"""






    def __init__(				self      )							->	Tuple:
         __lowerCamelCase  :      Tuple														=						[]
         __lowerCamelCase  :      Optional[Any]														=						None







    @property
    def  lowercase_							(				self      )							->	List[str]:
         if self._accum_steps is None:
              __lowerCamelCase  :      Tuple														=						tf.Variable(
                  tf.constant(0     ,	dtype=tf.intaa      )     ,	trainable=SCREAMING_SNAKE_CASE_     ,	synchronization=tf.VariableSynchronization.ON_READ     ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,	)

         return self._accum_steps.value()







    @property
    def  lowercase_							(				self      )							->	List[str]:
         if not self._gradients:
              raise ValueError('The accumulator should be called first to initialize the gradients'      )
         return [gradient.value() if gradient is not None else gradient for gradient in self._gradients]







    def __call__(				self     ,	SCREAMING_SNAKE_CASE_      )							->	str:
         if not self._gradients:
              __lowerCamelCase  :      List[str]														=						self.step  # Create the step variable.
              self._gradients.extend(
                  [
                      tf.Variable(
                          tf.zeros_like(SCREAMING_SNAKE_CASE_      )     ,	trainable=SCREAMING_SNAKE_CASE_     ,	synchronization=tf.VariableSynchronization.ON_READ     ,	aggregation=tf.VariableAggregation.ONLY_FIRST_REPLICA     ,	)
                      if gradient is not None
                      else gradient
                      for gradient in gradients
                  ]      )
         if len(SCREAMING_SNAKE_CASE_      ) != len(self._gradients      ):
              raise ValueError(f'Expected {len(self._gradients      )} gradients, but got {len(SCREAMING_SNAKE_CASE_      )}'      )

         for accum_gradient, gradient in zip(self._gradients     ,	SCREAMING_SNAKE_CASE_      ):
              if accum_gradient is not None and gradient is not None:
                   accum_gradient.assign_add(SCREAMING_SNAKE_CASE_      )

         self._accum_steps.assign_add(1      )







    def  lowercase_							(				self      )							->	int:
         if not self._gradients:
              return
         self._accum_steps.assign(0      )
         for gradient in self._gradients:
              if gradient is not None:
                   gradient.assign(tf.zeros_like(SCREAMING_SNAKE_CASE_      )      )






