


'''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


A__		:					str												=  """<<<<<<< This should probably be modified because it mentions: """

A__		:					Optional[Any]												=  """=======
>>>>>>>
"""

A__		:					Any												=  [
    """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 			UpperCAmelCase__					(	UpperCAmelCase_    :				Namespace							)     ->		Any:
     return ConvertCommand(args.tfds_path      ,    args.datasets_directory							)





class    UpperCAmelCase_  (_UpperCAmelCase  ):





    """simple docstring"""






    @staticmethod
    def       lowercase_				(							SCREAMING_SNAKE_CASE_	)				->     Union[str, Any]:
         __lowerCamelCase   :	Optional[Any]								=		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_	)				->     Union[str, Any]:
         __lowerCamelCase   :	List[str]								=		get_logger('datasets-cli/converting'	)

         __lowerCamelCase   :	List[str]								=		tfds_path
         __lowerCamelCase   :	Union[str, Any]								=		datasets_directory







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

         __lowerCamelCase   :	Union[str, Any]								=		os.path.abspath(self._datasets_directory	)

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

         __lowerCamelCase   :	Optional[int]								=		[]
         __lowerCamelCase   :	List[Any]								=		[]
         __lowerCamelCase   :	str								=		{}

         if os.path.isdir(self._tfds_path	):
              __lowerCamelCase   :	Tuple								=		os.listdir(SCREAMING_SNAKE_CASE_	)
         else:
              __lowerCamelCase   :	Union[str, Any]								=		[os.path.basename(self._tfds_path	)]

         for f_name in file_names:
              self._logger.info(f'Looking at file {f_name}'	)
              __lowerCamelCase   :	str								=		os.path.join(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	str								=		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:
                   __lowerCamelCase   :	Union[str, Any]								=		f.readlines()

              __lowerCamelCase   :	Dict								=		[]
              __lowerCamelCase   :	Optional[Any]								=		False
              __lowerCamelCase   :	Dict								=		False
              __lowerCamelCase   :	List[str]								=		[]
              for line in lines:
                   __lowerCamelCase   :	Optional[int]								=		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   :	List[Any]								=		'import datasets\n'
                   elif "import tensorflow" in out_line:
                        # order is important here
                        __lowerCamelCase   :	int								=		''
                        continue
                   elif "from absl import logging" in out_line:
                        __lowerCamelCase   :	Optional[Any]								=		'from datasets import logging\n'
                   elif "getLogger" in out_line:
                        __lowerCamelCase   :	List[Any]								=		out_line.replace('getLogger'			,     'get_logger'	)
                   elif any(expression in out_line for expression in TO_HIGHLIGHT	):
                        __lowerCamelCase   :	int								=		True
                        __lowerCamelCase   :	List[Any]								=		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:
                             __lowerCamelCase   :	List[str]								=		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:
                        __lowerCamelCase   :	List[str]								=		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(','	)	)
                        __lowerCamelCase   :	List[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   :	int								=		True
                   out_lines.append(SCREAMING_SNAKE_CASE_	)

              if is_builder or "wmt" in f_name:
                   # We create a new directory for each dataset
                   __lowerCamelCase   :	str								=		f_name.replace('.py'			,     ''	)
                   __lowerCamelCase   :	List[Any]								=		os.path.join(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Dict								=		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:
                   __lowerCamelCase   :	List[Any]								=		os.path.basename(SCREAMING_SNAKE_CASE_	)
                   __lowerCamelCase   :	Tuple								=		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 gc
import unittest

from parameterized import parameterized

from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow


if is_flax_available():
      import jax
      import jax.numpy as jnp





@slow
@require_flax
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         return f'gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE_	) for s in shape]	)}.npy'







    def       lowercase_				(							self	)				->     Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 4, 64, 64)			,     SCREAMING_SNAKE_CASE_=False	)				->     str:
         __lowerCamelCase   :	List[str]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Tuple								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return image







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=False			,     SCREAMING_SNAKE_CASE_="CompVis/stable-diffusion-v1-4"	)				->     Dict:
         __lowerCamelCase   :	Union[str, Any]								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		'bf16' if fpaa else None

         __lowerCamelCase				,       __lowerCamelCase   :	str								=		FlaxUNetaDConditionModel.from_pretrained(
             SCREAMING_SNAKE_CASE_			,     subfolder='unet'			,     dtype=SCREAMING_SNAKE_CASE_			,     revision=SCREAMING_SNAKE_CASE_	)
         return model, params







    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_=0			,     SCREAMING_SNAKE_CASE_=(4, 77, 7_68)			,     SCREAMING_SNAKE_CASE_=False	)				->     Union[str, Any]:
         __lowerCamelCase   :	Any								=		jnp.bfloataa if fpaa else jnp.floataa
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)	)			,     dtype=SCREAMING_SNAKE_CASE_	)
         return hidden_states







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [-0.2_3_2_3, -0.1_3_0_4, 0.0_8_1_3, -0.3_0_9_3, -0.0_9_1_9, -0.1_5_7_1, -0.1_1_2_5, -0.5_8_0_6]],
            [17, 0.5_5, [-0.0_8_3_1, -0.2_4_4_3, 0.0_9_0_1, -0.0_9_1_9, 0.3_3_9_6, 0.0_1_0_3, -0.3_7_4_3, 0.0_7_0_1]],
            [8, 0.8_9, [-0.4_8_6_3, 0.0_8_5_9, 0.0_8_7_5, -0.1_6_5_8, 0.9_1_9_9, -0.0_1_1_4, 0.4_8_3_9, 0.4_6_3_9]],
            [3, 10_00, [-0.5_6_4_9, 0.2_4_0_2, -0.5_5_1_8, 0.1_2_4_8, 1.1_3_2_8, -0.2_4_4_3, -0.0_3_2_5, -1.0_0_7_8]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Any:
         __lowerCamelCase				,       __lowerCamelCase   :	Tuple								=		self.get_unet_model(model_id='CompVis/stable-diffusion-v1-4'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Any								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[Any]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Any								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Optional[Any]								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, in the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)







    @parameterized.expand(
        [
            # fmt: off
            [83, 4, [0.1_5_1_4, 0.0_8_0_7, 0.1_6_2_4, 0.1_0_1_6, -0.1_8_9_6, 0.0_2_6_3, 0.0_6_7_7, 0.2_3_1_0]],
            [17, 0.5_5, [0.1_1_6_4, -0.0_2_1_6, 0.0_1_7_0, 0.1_5_8_9, -0.3_1_2_0, 0.1_0_0_5, -0.0_5_8_1, -0.1_4_5_8]],
            [8, 0.8_9, [-0.1_7_5_8, -0.0_1_6_9, 0.1_0_0_4, -0.1_4_1_1, 0.1_3_1_2, 0.1_1_0_3, -0.1_9_9_6, 0.2_1_3_9]],
            [3, 10_00, [0.1_2_1_4, 0.0_3_5_2, -0.0_7_3_1, -0.1_5_6_2, -0.0_9_9_4, -0.0_9_0_6, -0.2_3_4_0, -0.0_5_3_9]],
            # fmt: on
        ]	)
    def       lowercase_				(							self			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_	)				->     Optional[int]:
         __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		self.get_unet_model(model_id='stabilityai/stable-diffusion-2'			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	int								=		self.get_latents(SCREAMING_SNAKE_CASE_			,     shape=(4, 4, 96, 96)			,     fpaa=SCREAMING_SNAKE_CASE_	)
         __lowerCamelCase   :	Optional[int]								=		self.get_encoder_hidden_states(SCREAMING_SNAKE_CASE_			,     shape=(4, 77, 10_24)			,     fpaa=SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	List[str]								=		model.apply(
             {'params': params}			,     SCREAMING_SNAKE_CASE_			,     jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.intaa	)			,     encoder_hidden_states=SCREAMING_SNAKE_CASE_			,     ).sample

         assert sample.shape == latents.shape

         __lowerCamelCase   :	Optional[int]								=		jnp.asarray(jax.device_get((sample[-1, -2:, -2:, :2].flatten())	)			,     dtype=jnp.floataa	)
         __lowerCamelCase   :	Tuple								=		jnp.array(SCREAMING_SNAKE_CASE_			,     dtype=jnp.floataa	)

         # Found torch (float16) and flax (bfloat16) outputs to be within this tolerance, on the same hardware
         assert jnp.allclose(SCREAMING_SNAKE_CASE_			,     SCREAMING_SNAKE_CASE_			,     atol=1E-2	)






