


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

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


lowerCamelCase__							:						Tuple							     =   """<<<<<<< This should probably be modified because it mentions: """

lowerCamelCase__							:						str							     =   """=======
>>>>>>>
"""

lowerCamelCase__							:						List[Any]							     =   [
    """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 SCREAMING_SNAKE_CASE	(			__lowerCAmelCase				)    ->				Tuple:
  return ConvertCommand(args.tfds_path							,   args.datasets_directory				)


class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     @staticmethod
     def 	SCREAMING_SNAKE_CASE__						(					_a:ArgumentParser   ):
       snake_case__						= 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:Any     ,		_a:str     ,		_a:str     ,		*_a:Any   ):
       snake_case__						= get_logger('''datasets-cli/converting'''   )

       snake_case__						= tfds_path
       snake_case__						= datasets_directory







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

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

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

       snake_case__						= []
       snake_case__						= []
       snake_case__						= {}

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

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

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

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

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

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest
class 		__magic_name__						(snake_case_				):





     '''simple docstring'''



     __lowercase				:							str	   =       (CMStochasticIterativeScheduler,)
     __lowercase				:							List[str]	   =       10

     def 	SCREAMING_SNAKE_CASE__						(					self:int     ,		**_a:Optional[int]   ):
       snake_case__						= {
           '''num_train_timesteps''': 2_01,
           '''sigma_min''': 0.002,
           '''sigma_max''': 80.0,
       }

       config.update(**_a   )
       return config

     def 	SCREAMING_SNAKE_CASE__						(					self:Optional[int]   ):
       snake_case__						= 10

       snake_case__						= self.get_scheduler_config()
       snake_case__						= self.scheduler_classes[0](**_a   )

       scheduler.set_timesteps(_a   )

       snake_case__						= scheduler.timesteps[0]
       snake_case__						= scheduler.timesteps[1]

       snake_case__						= self.dummy_sample
       snake_case__						= 0.1 * sample

       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample
       snake_case__						= scheduler.step(_a     ,		_a     ,		_a   ).prev_sample

       self.assertEqual(output_a.shape     ,		sample.shape   )
       self.assertEqual(output_a.shape     ,		output_a.shape   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:List[str]   ):
       for clip_denoised in [True, False]:
         self.check_over_configs(clip_denoised=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:int   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= 1
       scheduler.set_timesteps(_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for i, t in enumerate(_a   ):
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 192.7614   ) < 1e-2
       assert abs(result_mean.item() - 0.2510   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [1_06, 0]
       scheduler.set_timesteps(timesteps=_a   )
       snake_case__						= scheduler.timesteps

       snake_case__						= torch.manual_seed(0   )

       snake_case__						= self.dummy_model()
       snake_case__						= self.dummy_sample_deter * scheduler.init_noise_sigma

       for t in timesteps:
         # 1. scale model input
         snake_case__						= scheduler.scale_model_input(_a     ,		_a   )

         # 2. predict noise residual
         snake_case__						= model(_a     ,		_a   )

         # 3. predict previous sample x_t-1
         snake_case__						= scheduler.step(_a     ,		_a     ,		_a     ,		generator=_a   ).prev_sample

         snake_case__						= pred_prev_sample

       snake_case__						= torch.sum(torch.abs(_a   )   )
       snake_case__						= torch.mean(torch.abs(_a   )   )

       assert abs(result_sum.item() - 347.6357   ) < 1e-2
       assert abs(result_mean.item() - 0.4527   ) < 1e-3

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 15, 0]

       with self.assertRaises(_a     ,		msg='''`timesteps` must be in descending order.'''   ):
         scheduler.set_timesteps(timesteps=_a   )

     def 	SCREAMING_SNAKE_CASE__						(					self:Any   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [39, 30, 12, 1, 0]
       snake_case__						= len(_a   )

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







     def 	SCREAMING_SNAKE_CASE__						(					self:Tuple   ):
       snake_case__						= self.scheduler_classes[0]
       snake_case__						= self.get_scheduler_config()
       snake_case__						= scheduler_class(**_a   )

       snake_case__						= [scheduler.config.num_train_timesteps]

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

