"""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


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

SCREAMING_SNAKE_CASE__      						=  "=======\n>>>>>>>\n"

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

SCREAMING_SNAKE_CASE__      						=  [
    # (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__    (			_UpperCamelCase       :				Namespace					)					->				Union[str, Any]:





						"""simple docstring"""






						return ConvertCommand(args.tfds_path ,			args.datasets_directory					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				@staticmethod
				def 		snake_case (				lowerCAmelCase    ):






										"""simple docstring"""

										snake_case								=			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    ):






										"""simple docstring"""

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

										snake_case								=			tfds_path
										snake_case								=			datasets_directory

				def 		snake_case (				self    ):






										"""simple docstring"""

										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(lowerCAmelCase    )
										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(lowerCAmelCase		,    lowerCAmelCase    )
																snake_case								=			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:
																						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 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:
																																		snake_case								=			re.sub(lowerCAmelCase		,    lowerCAmelCase		,    lowerCAmelCase    )

                # 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]+)'		,    lowerCAmelCase    )
																												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(lowerCAmelCase    )

																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(lowerCAmelCase		,    lowerCAmelCase    )
																						snake_case								=			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:
																						snake_case								=			os.path.basename(lowerCAmelCase    )
																						snake_case								=			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'."""    )







"""simple docstring"""
SCREAMING_SNAKE_CASE__      						=  "0.18.2"

from .configuration_utils import ConfigMixin
from .utils import (
    OptionalDependencyNotAvailable,
    is_flax_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_onnx_available,
    is_scipy_available,
    is_torch_available,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    logging,
)


try:
					if not is_onnx_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_onnx_objects import *  # noqa F403
else:
					from .pipelines import OnnxRuntimeModel

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_pt_objects import *  # noqa F403
else:
					from .models import (
					    AutoencoderKL,
					    ControlNetModel,
					    ModelMixin,
					    PriorTransformer,
					    TaFilmDecoder,
					    TransformeraDModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    UNetaDModel,
					    UNetaDConditionModel,
					    VQModel,
					)
					from .optimization import (
					    get_constant_schedule,
					    get_constant_schedule_with_warmup,
					    get_cosine_schedule_with_warmup,
					    get_cosine_with_hard_restarts_schedule_with_warmup,
					    get_linear_schedule_with_warmup,
					    get_polynomial_decay_schedule_with_warmup,
					    get_scheduler,
					)
					from .pipelines import (
					    AudioPipelineOutput,
					    ConsistencyModelPipeline,
					    DanceDiffusionPipeline,
					    DDIMPipeline,
					    DDPMPipeline,
					    DiffusionPipeline,
					    DiTPipeline,
					    ImagePipelineOutput,
					    KarrasVePipeline,
					    LDMPipeline,
					    LDMSuperResolutionPipeline,
					    PNDMPipeline,
					    RePaintPipeline,
					    ScoreSdeVePipeline,
					)
					from .schedulers import (
					    CMStochasticIterativeScheduler,
					    DDIMInverseScheduler,
					    DDIMParallelScheduler,
					    DDIMScheduler,
					    DDPMParallelScheduler,
					    DDPMScheduler,
					    DEISMultistepScheduler,
					    DPMSolverMultistepInverseScheduler,
					    DPMSolverMultistepScheduler,
					    DPMSolverSinglestepScheduler,
					    EulerAncestralDiscreteScheduler,
					    EulerDiscreteScheduler,
					    HeunDiscreteScheduler,
					    IPNDMScheduler,
					    KarrasVeScheduler,
					    KDPMaAncestralDiscreteScheduler,
					    KDPMaDiscreteScheduler,
					    PNDMScheduler,
					    RePaintScheduler,
					    SchedulerMixin,
					    ScoreSdeVeScheduler,
					    UnCLIPScheduler,
					    UniPCMultistepScheduler,
					    VQDiffusionScheduler,
					)
					from .training_utils import EMAModel

try:
					if not (is_torch_available() and is_scipy_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_scipy_objects import *  # noqa F403
else:
					from .schedulers import LMSDiscreteScheduler

try:
					if not (is_torch_available() and is_torchsde_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_torchsde_objects import *  # noqa F403
else:
					from .schedulers import DPMSolverSDEScheduler

try:
					if not (is_torch_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    AltDiffusionImgaImgPipeline,
					    AltDiffusionPipeline,
					    AudioLDMPipeline,
					    CycleDiffusionPipeline,
					    IFImgaImgPipeline,
					    IFImgaImgSuperResolutionPipeline,
					    IFInpaintingPipeline,
					    IFInpaintingSuperResolutionPipeline,
					    IFPipeline,
					    IFSuperResolutionPipeline,
					    ImageTextPipelineOutput,
					    KandinskyImgaImgPipeline,
					    KandinskyInpaintPipeline,
					    KandinskyPipeline,
					    KandinskyPriorPipeline,
					    KandinskyVaaControlnetImgaImgPipeline,
					    KandinskyVaaControlnetPipeline,
					    KandinskyVaaImgaImgPipeline,
					    KandinskyVaaInpaintPipeline,
					    KandinskyVaaPipeline,
					    KandinskyVaaPriorEmbaEmbPipeline,
					    KandinskyVaaPriorPipeline,
					    LDMTextToImagePipeline,
					    PaintByExamplePipeline,
					    SemanticStableDiffusionPipeline,
					    ShapEImgaImgPipeline,
					    ShapEPipeline,
					    StableDiffusionAttendAndExcitePipeline,
					    StableDiffusionControlNetImgaImgPipeline,
					    StableDiffusionControlNetInpaintPipeline,
					    StableDiffusionControlNetPipeline,
					    StableDiffusionDepthaImgPipeline,
					    StableDiffusionDiffEditPipeline,
					    StableDiffusionImageVariationPipeline,
					    StableDiffusionImgaImgPipeline,
					    StableDiffusionInpaintPipeline,
					    StableDiffusionInpaintPipelineLegacy,
					    StableDiffusionInstructPixaPixPipeline,
					    StableDiffusionLatentUpscalePipeline,
					    StableDiffusionLDMaDPipeline,
					    StableDiffusionModelEditingPipeline,
					    StableDiffusionPanoramaPipeline,
					    StableDiffusionParadigmsPipeline,
					    StableDiffusionPipeline,
					    StableDiffusionPipelineSafe,
					    StableDiffusionPixaPixZeroPipeline,
					    StableDiffusionSAGPipeline,
					    StableDiffusionUpscalePipeline,
					    StableUnCLIPImgaImgPipeline,
					    StableUnCLIPPipeline,
					    TextToVideoSDPipeline,
					    TextToVideoZeroPipeline,
					    UnCLIPImageVariationPipeline,
					    UnCLIPPipeline,
					    UniDiffuserModel,
					    UniDiffuserPipeline,
					    UniDiffuserTextDecoder,
					    VersatileDiffusionDualGuidedPipeline,
					    VersatileDiffusionImageVariationPipeline,
					    VersatileDiffusionPipeline,
					    VersatileDiffusionTextToImagePipeline,
					    VideoToVideoSDPipeline,
					    VQDiffusionPipeline,
					)

try:
					if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import *  # noqa F403
else:
					from .pipelines import StableDiffusionKDiffusionPipeline

try:
					if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_transformers_and_onnx_objects import *  # noqa F403
else:
					from .pipelines import (
					    OnnxStableDiffusionImgaImgPipeline,
					    OnnxStableDiffusionInpaintPipeline,
					    OnnxStableDiffusionInpaintPipelineLegacy,
					    OnnxStableDiffusionPipeline,
					    OnnxStableDiffusionUpscalePipeline,
					    StableDiffusionOnnxPipeline,
					)

try:
					if not (is_torch_available() and is_librosa_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_torch_and_librosa_objects import *  # noqa F403
else:
					from .pipelines import AudioDiffusionPipeline, Mel

try:
					if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_transformers_and_torch_and_note_seq_objects import *  # noqa F403
else:
					from .pipelines import SpectrogramDiffusionPipeline

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_objects import *  # noqa F403
else:
					from .models.controlnet_flax import FlaxControlNetModel
					from .models.modeling_flax_utils import FlaxModelMixin
					from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
					from .models.vae_flax import FlaxAutoencoderKL
					from .pipelines import FlaxDiffusionPipeline
					from .schedulers import (
					    FlaxDDIMScheduler,
					    FlaxDDPMScheduler,
					    FlaxDPMSolverMultistepScheduler,
					    FlaxKarrasVeScheduler,
					    FlaxLMSDiscreteScheduler,
					    FlaxPNDMScheduler,
					    FlaxSchedulerMixin,
					    FlaxScoreSdeVeScheduler,
					)


try:
					if not (is_flax_available() and is_transformers_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_flax_and_transformers_objects import *  # noqa F403
else:
					from .pipelines import (
					    FlaxStableDiffusionControlNetPipeline,
					    FlaxStableDiffusionImgaImgPipeline,
					    FlaxStableDiffusionInpaintPipeline,
					    FlaxStableDiffusionPipeline,
					)

try:
					if not (is_note_seq_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from .utils.dummy_note_seq_objects import *  # noqa F403
else:
					from .pipelines import MidiProcessor


