



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

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


a__      :				int   	=			"<<<<<<< This should probably be modified because it mentions: "

a__      :				Optional[int]   	=			"=======\n>>>>>>>\n"

a__      :				int   	=			[
    "TextEncoderConfig",
    "ByteTextEncoder",
    "SubwordTextEncoder",
    "encoder_config",
    "maybe_build_from_corpus",
    "manual_dir",
]

a__      :				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   _lowerCAmelCase   (						A__	):
      return ConvertCommand(args.tfds_path						,  args.datasets_directory	)



class       UpperCAmelCase__(							__a	):


   @staticmethod
   def 			UpperCAmelCase		(						lowerCAmelCase  :			Tuple)	->				Dict:




         """simple docstring"""

         lowercase__     =  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  :			Tuple			,				lowerCAmelCase  :			Tuple			,				lowerCAmelCase  :			Union[str, Any]			,				*lowerCAmelCase  :			Dict)	->				Union[str, Any]:




         """simple docstring"""

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

         lowercase__     =  tfds_path
         lowercase__     =  datasets_directory



   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         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(lowerCAmelCase_)
         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(lowerCAmelCase_			,				lowerCAmelCase_)
               lowercase__     =  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:
                     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 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:
                                 lowercase__     =  re.sub(lowerCAmelCase_			,				lowerCAmelCase_			,				lowerCAmelCase_)

                # 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]+)'			,				lowerCAmelCase_)
                           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(lowerCAmelCase_)

               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(lowerCAmelCase_			,				lowerCAmelCase_)
                     lowercase__     =  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:
                     lowercase__     =  os.path.basename(lowerCAmelCase_)
                     lowercase__     =  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 DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class       UpperCAmelCase__(							lowerCamelCase	):

   '''simple docstring'''




   A    :  str       				=    (DDIMParallelScheduler,)
   A    :  Any       				=    (("eta", 0.0), ("num_inference_steps", 50))


   def 			UpperCAmelCase		(						self  :			Union[str, Any]			,				**lowerCAmelCase  :			Optional[int])	->				Dict:




         """simple docstring"""

         lowercase__     =  {
             'num_train_timesteps': 10_00,
             'beta_start': 0.00_01,
             'beta_end': 0.02,
             'beta_schedule': 'linear',
             'clip_sample': True,
         }

         config.update(**lowerCAmelCase)
         return config


   def 			UpperCAmelCase		(						self  :			int			,				**lowerCAmelCase  :			str)	->				Union[str, Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(**lowerCAmelCase)
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter

         scheduler.set_timesteps(lowerCAmelCase)

         for t in scheduler.timesteps:
               lowercase__     =  model(lowerCAmelCase			,				lowerCAmelCase)
               lowercase__     =  scheduler.step(lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase			,				lowerCAmelCase).prev_sample

         return sample


   def 			UpperCAmelCase		(						self  :			Tuple)	->				int:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Tuple)	->				Any:




         """simple docstring"""

         for steps_offset in [0, 1]:
               self.check_over_configs(steps_offset=lowerCAmelCase)

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config(steps_offset=1)
         lowercase__     =  scheduler_class(**lowerCAmelCase)
         scheduler.set_timesteps(5)
         assert torch.equal(scheduler.timesteps			,				torch.LongTensor([8_01, 6_01, 4_01, 2_01, 1]))


   def 			UpperCAmelCase		(						self  :			str)	->				Tuple:




         """simple docstring"""

         for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1]			,				[0.0_02, 0.02, 0.2, 2]):
               self.check_over_configs(beta_start=lowerCAmelCase			,				beta_end=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[str])	->				List[str]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			List[Any])	->				str:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			Optional[int])	->				int:




         """simple docstring"""

         for timestep_spacing in ["trailing", "leading"]:
               self.check_over_configs(timestep_spacing=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Any)	->				List[str]:




         """simple docstring"""

         for rescale_betas_zero_snr in [True, False]:
               self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			List[str])	->				Optional[int]:




         """simple docstring"""

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


   def 			UpperCAmelCase		(						self  :			int)	->				Optional[Any]:




         """simple docstring"""

         for t in [1, 10, 49]:
               self.check_over_forward(time_step=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				int:




         """simple docstring"""

         for t, num_inference_steps in zip([1, 10, 50]			,				[10, 50, 5_00]):
               self.check_over_forward(time_step=lowerCAmelCase			,				num_inference_steps=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         for t, eta in zip([1, 10, 49]			,				[0.0, 0.5, 1.0]):
               self.check_over_forward(time_step=lowerCAmelCase			,				eta=lowerCAmelCase)


   def 			UpperCAmelCase		(						self  :			Union[str, Any])	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_20			,				4_00) - 0.1_47_71)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_80			,				9_60) - 0.3_24_60)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(0			,				0) - 0.0)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(4_87			,				4_86) - 0.0_09_79)) < 1E-5
         assert torch.sum(torch.abs(scheduler._get_variance(9_99			,				9_98) - 0.02)) < 1E-5


   def 			UpperCAmelCase		(						self  :			Dict)	->				Tuple:




         """simple docstring"""

         lowercase__     =  self.scheduler_classes[0]
         lowercase__     =  self.get_scheduler_config()
         lowercase__     =  scheduler_class(**lowerCAmelCase)

         lowercase__,	lowercase__     =  10, 0.0
         scheduler.set_timesteps(lowerCAmelCase)

         lowercase__     =  self.dummy_model()
         lowercase__     =  self.dummy_sample_deter
         lowercase__     =  self.dummy_sample_deter + 0.1
         lowercase__     =  self.dummy_sample_deter - 0.1

         lowercase__     =  samplea.shape[0]
         lowercase__     =  torch.stack([samplea, samplea, samplea]			,				dim=0)
         lowercase__     =  torch.arange(lowerCAmelCase)[0:3, None].repeat(1			,				lowerCAmelCase)

         lowercase__     =  model(samples.flatten(0			,				1)			,				timesteps.flatten(0			,				1))
         lowercase__     =  scheduler.batch_step_no_noise(lowerCAmelCase			,				timesteps.flatten(0			,				1)			,				samples.flatten(0			,				1)			,				lowerCAmelCase)

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 11_47.79_04) < 1E-2
         assert abs(result_mean.item() - 0.49_82) < 1E-3


   def 			UpperCAmelCase		(						self  :			Any)	->				int:




         """simple docstring"""

         lowercase__     =  self.full_loop()

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_72.00_67) < 1E-2
         assert abs(result_mean.item() - 0.22_39_67) < 1E-3


   def 			UpperCAmelCase		(						self  :			int)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(prediction_type='v_prediction')

         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 52.53_02) < 1E-2
         assert abs(result_mean.item() - 0.06_84) < 1E-3


   def 			UpperCAmelCase		(						self  :			str)	->				Dict:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.82_95) < 1E-2
         assert abs(result_mean.item() - 0.19_51) < 1E-3



   def 			UpperCAmelCase		(						self  :			str)	->				List[Any]:




         """simple docstring"""

         lowercase__     =  self.full_loop(set_alpha_to_one=lowerCAmelCase			,				beta_start=0.01)
         lowercase__     =  torch.sum(torch.abs(lowerCAmelCase))
         lowercase__     =  torch.mean(torch.abs(lowerCAmelCase))

         assert abs(result_sum.item() - 1_49.07_84) < 1E-2
         assert abs(result_mean.item() - 0.19_41) < 1E-3
