

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

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


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

lowercase_									=   """=======
>>>>>>>
"""

lowercase_									=   [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

lowercase_									=   [
    # (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      a      (			A__	:   List[str]     )    ->      Any:





      """simple docstring"""



      return ConvertCommand(args.tfds_path      ,   args.datasets_directory     )







class  __lowerCAmelCase     (					lowercase_							):




  @staticmethod
  def   A__     (				lowerCAmelCase					)  -> int:
        '''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							,   lowerCAmelCase							,   lowerCAmelCase							,   *lowerCAmelCase					)  -> int:
        '''simple docstring'''






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

        _lowercase	       =tfds_path
        _lowercase	       =datasets_directory




  def   A__     (				self					)  -> Any:
        '''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 gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







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




				def     a							(					self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

											snake_case_							=    images[0, 253:256, 253:256, -1]

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




