


"""simple docstring"""






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

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


_lowerCAmelCase							:    Optional[int]					   =	'''<<<<<<< This should probably be modified because it mentions: '''

_lowerCAmelCase							:    str					   =	'''=======
>>>>>>>
'''

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

_lowerCAmelCase							:    Dict					   =	[
    # (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        lowerCamelCase_(		_lowerCamelCase       )    ->     Optional[int]:
   '''simple docstring'''





   return ConvertCommand(args.tfds_path			,		args.datasets_directory       )




class 							A_      (       _a ):


  @staticmethod
  def 							_lowercase					(			__lowerCAmelCase:						ArgumentParser    ):







     '''simple docstring'''







     _lowerCamelCase   :				List[str]							=     parser.add_parser(
         "convert"							,help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."							,)
     train_parser.add_argument(
         "--tfds_path"							,type=__lowerCAmelCase							,required=__lowerCAmelCase							,help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."							,)
     train_parser.add_argument(
         "--datasets_directory"							,type=__lowerCAmelCase							,required=__lowerCAmelCase							,help="Path to the HuggingFace Datasets folder."    )
     train_parser.set_defaults(func=__lowerCAmelCase    )


  def __init__(			self:						str							,__lowerCAmelCase:						str							,__lowerCAmelCase:						str							,*__lowerCAmelCase:						List[Any]    ):







     '''simple docstring'''







     _lowerCamelCase   :				str							=     get_logger("datasets-cli/converting"    )

     _lowerCamelCase   :				Any							=     tfds_path
     _lowerCamelCase   :				Dict							=     datasets_directory






  def 							_lowercase					(			self:						Tuple    ):







     '''simple docstring'''







     if os.path.isdir(self._tfds_path    ):
        _lowerCamelCase   :				Optional[Any]							=     os.path.abspath(self._tfds_path    )
     elif os.path.isfile(self._tfds_path    ):
        _lowerCamelCase   :				Tuple							=     os.path.dirname(self._tfds_path    )
     else:
        raise ValueError("--tfds_path is neither a directory nor a file. Please check path."    )

     _lowerCamelCase   :				int							=     os.path.abspath(self._datasets_directory    )

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

     _lowerCamelCase   :				Tuple							=     []
     _lowerCamelCase   :				List[Any]							=     []
     _lowerCamelCase   :				str							=     {}

     if os.path.isdir(self._tfds_path    ):
        _lowerCamelCase   :				Tuple							=     os.listdir(__lowerCAmelCase    )
     else:
        _lowerCamelCase   :				str							=     [os.path.basename(self._tfds_path    )]

     for f_name in file_names:
        self._logger.info(F"""Looking at file {f_name}"""    )
        _lowerCamelCase   :				Tuple							=     os.path.join(__lowerCAmelCase							,__lowerCAmelCase    )
        _lowerCamelCase   :				int							=     os.path.join(__lowerCAmelCase							,__lowerCAmelCase    )

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

        with open(__lowerCAmelCase							,encoding="utf-8"    ) as f:
           _lowerCamelCase   :				Optional[Any]							=     f.readlines()

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

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

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

        if needs_manual_update:
           with_manual_update.append(__lowerCAmelCase    )

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

     for utils_file in utils_files:
        try:
           _lowerCamelCase   :				Dict							=     os.path.basename(__lowerCAmelCase    )
           _lowerCamelCase   :				Any							=     imports_to_builder_map[f_name.replace(".py"							,""    )]
           self._logger.info(F"""Moving {dest_folder} to {utils_file}"""    )
           shutil.copy(__lowerCAmelCase							,__lowerCAmelCase    )
        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 torch

from diffusers import DDPMScheduler

from .test_schedulers import SchedulerCommonTest



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  int	   =      (DDPMScheduler,)


 def   lowercase__  (       self       ,	**SCREAMING_SNAKE_CASE_):


   '''simple docstring'''




   lowercase__	: Tuple     		=   {
       """num_train_timesteps""": 10_00,
       """beta_start""": 0.0_0_0_1,
       """beta_end""": 0.0_2,
       """beta_schedule""": """linear""",
       """variance_type""": """fixed_small""",
       """clip_sample""": True,
   }

   config.update(**SCREAMING_SNAKE_CASE_)
   return config


 def   lowercase__  (       self):


   '''simple docstring'''




   for timesteps in [1, 5, 1_00, 10_00]:
     self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for beta_start, beta_end in zip([0.0_0_0_1, 0.0_0_1, 0.0_1, 0.1]       ,	[0.0_0_2, 0.0_2, 0.2, 2]):
     self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_       ,	beta_end=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for schedule in ["linear", "squaredcos_cap_v2"]:
     self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for variance in ["fixed_small", "fixed_large", "other"]:
     self.check_over_configs(variance_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for clip_sample in [True, False]:
     self.check_over_configs(clip_sample=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   self.check_over_configs(thresholding=SCREAMING_SNAKE_CASE_)
   for threshold in [0.5, 1.0, 2.0]:
     for prediction_type in ["epsilon", "sample", "v_prediction"]:
       self.check_over_configs(
           thresholding=SCREAMING_SNAKE_CASE_       ,	prediction_type=SCREAMING_SNAKE_CASE_       ,	sample_max_value=SCREAMING_SNAKE_CASE_       ,	)


 def   lowercase__  (       self):


   '''simple docstring'''




   for prediction_type in ["epsilon", "sample", "v_prediction"]:
     self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   for t in [0, 5_00, 9_99]:
     self.check_over_forward(time_step=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: Union[str, Any]     		=   self.get_scheduler_config()
   lowercase__	: List[Any]     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   assert torch.sum(torch.abs(scheduler._get_variance(0) - 0.0)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(4_87) - 0.0_0_9_7_9)) < 1E-5
   assert torch.sum(torch.abs(scheduler._get_variance(9_99) - 0.0_2)) < 1E-5


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Dict     		=   self.scheduler_classes[0]
   lowercase__	: str     		=   self.get_scheduler_config()
   lowercase__	: Tuple     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Any     		=   self.dummy_model()
   lowercase__	: List[Any]     		=   self.dummy_sample_deter
   lowercase__	: str     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: Dict     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: List[str]     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: str     		=   pred_prev_sample

   lowercase__	: Optional[int]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: Optional[Any]     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_5_8.9_6_0_6) < 1E-2
   assert abs(result_mean.item() - 0.3_3_7_2) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: List[Any]     		=   self.scheduler_classes[0]
   lowercase__	: Tuple     		=   self.get_scheduler_config(prediction_type="""v_prediction""")
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: Dict     		=   len(SCREAMING_SNAKE_CASE_)

   lowercase__	: Tuple     		=   self.dummy_model()
   lowercase__	: Union[str, Any]     		=   self.dummy_sample_deter
   lowercase__	: int     		=   torch.manual_seed(0)

   for t in reversed(range(SCREAMING_SNAKE_CASE_)):
     # 1. predict noise residual
     lowercase__	: List[Any]     		=   model(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)

     # 2. predict previous mean of sample x_t-1
     lowercase__	: int     		=   scheduler.step(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_       ,	generator=SCREAMING_SNAKE_CASE_).prev_sample

     # if t > 0:
     #     noise = self.dummy_sample_deter
     #     variance = scheduler.get_variance(t) ** (0.5) * noise
     #
     # sample = pred_prev_sample + variance
     lowercase__	: Tuple     		=   pred_prev_sample

   lowercase__	: Union[str, Any]     		=   torch.sum(torch.abs(SCREAMING_SNAKE_CASE_))
   lowercase__	: int     		=   torch.mean(torch.abs(SCREAMING_SNAKE_CASE_))

   assert abs(result_sum.item() - 2_0_2.0_2_9_6) < 1E-2
   assert abs(result_mean.item() - 0.2_6_3_1) < 1E-3


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: str     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: str     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[str]     		=   [1_00, 87, 50, 1, 0]

   scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   scheduler.timesteps

   for i, timestep in enumerate(SCREAMING_SNAKE_CASE_):
     if i == len(SCREAMING_SNAKE_CASE_) - 1:
       lowercase__	: Optional[int]     		=   -1
     else:
       lowercase__	: Tuple     		=   timesteps[i + 1]

     lowercase__	: Any     		=   scheduler.previous_timestep(SCREAMING_SNAKE_CASE_)
     lowercase__	: int     		=   prev_t.item()

     self.assertEqual(SCREAMING_SNAKE_CASE_       ,	SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: List[Any]     		=   [1_00, 87, 50, 51, 0]

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""`custom_timesteps` must be in descending order."""):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)


 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Union[str, Any]     		=   self.scheduler_classes[0]
   lowercase__	: List[Any]     		=   self.get_scheduler_config()
   lowercase__	: int     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: int     		=   [1_00, 87, 50, 1, 0]
   lowercase__	: Union[str, Any]     		=   len(SCREAMING_SNAKE_CASE_)

   with self.assertRaises(SCREAMING_SNAKE_CASE_       ,	msg="""Can only pass one of `num_inference_steps` or `custom_timesteps`."""):
     scheduler.set_timesteps(num_inference_steps=SCREAMING_SNAKE_CASE_       ,	timesteps=SCREAMING_SNAKE_CASE_)



 def   lowercase__  (       self):


   '''simple docstring'''




   lowercase__	: Optional[int]     		=   self.scheduler_classes[0]
   lowercase__	: int     		=   self.get_scheduler_config()
   lowercase__	: Dict     		=   scheduler_class(**SCREAMING_SNAKE_CASE_)

   lowercase__	: str     		=   [scheduler.config.num_train_timesteps]

   with self.assertRaises(
       SCREAMING_SNAKE_CASE_       ,	msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}"""       ,	):
     scheduler.set_timesteps(timesteps=SCREAMING_SNAKE_CASE_)






