




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

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


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

UpperCAmelCase_						="""=======
>>>>>>>
"""

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

UpperCAmelCase_						=[
    # (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       (     _snake_case							):
							return ConvertCommand(args.tfds_path						, args.datasets_directory							)


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				@staticmethod
				def       __snake_case		( UpperCAmelCase_						):
											lowerCAmelCase   =		parser.add_parser(
											    '''convert'''       , help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.'''       , )
											train_parser.add_argument(
											    '''--tfds_path'''       , type=UpperCAmelCase_       , required=UpperCAmelCase_       , help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.'''       , )
											train_parser.add_argument(
											    '''--datasets_directory'''       , type=UpperCAmelCase_       , required=UpperCAmelCase_       , help='''Path to the HuggingFace Datasets folder.'''						)
											train_parser.set_defaults(func=UpperCAmelCase_						)



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_       , *UpperCAmelCase_						):
											lowerCAmelCase   =		get_logger('''datasets-cli/converting'''						)

											lowerCAmelCase   =		tfds_path
											lowerCAmelCase   =		datasets_directory






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

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

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

											lowerCAmelCase   =		[]
											lowerCAmelCase   =		[]
											lowerCAmelCase   =		{}

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

											for f_name in file_names:
																		self._logger.info(F"""Looking at file {f_name}"""						)
																		lowerCAmelCase   =		os.path.join(UpperCAmelCase_       , UpperCAmelCase_						)
																		lowerCAmelCase   =		os.path.join(UpperCAmelCase_       , UpperCAmelCase_						)

																		if not os.path.isfile(UpperCAmelCase_						) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
																									self._logger.info('''Skipping file'''						)
																									continue

																		with open(UpperCAmelCase_       , encoding='''utf-8'''						) as f:
																									lowerCAmelCase   =		f.readlines()

																		lowerCAmelCase   =		[]
																		lowerCAmelCase   =		False
																		lowerCAmelCase   =		False
																		lowerCAmelCase   =		[]
																		for line in lines:
																									lowerCAmelCase   =		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   =		'''import datasets\n'''
																									elif "import tensorflow" in out_line:
																																# order is important here
																																lowerCAmelCase   =		''''''
																																continue
																									elif "from absl import logging" in out_line:
																																lowerCAmelCase   =		'''from datasets import logging\n'''
																									elif "getLogger" in out_line:
																																lowerCAmelCase   =		out_line.replace('''getLogger'''       , '''get_logger'''						)
																									elif any(expression in out_line for expression in TO_HIGHLIGHT						):
																																lowerCAmelCase   =		True
																																lowerCAmelCase   =		list(filter(lambda UpperCAmelCase_						: e in out_line       , UpperCAmelCase_						)						)
																																out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(UpperCAmelCase_						) + '''\n'''						)
																																out_lines.append(UpperCAmelCase_						)
																																out_lines.append(UpperCAmelCase_						)
																																continue
																									else:
																																for pattern, replacement in TO_CONVERT:
																																							lowerCAmelCase   =		re.sub(UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						)

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

																		if is_builder or "wmt" in f_name:
																									# We create a new directory for each dataset
																									lowerCAmelCase   =		f_name.replace('''.py'''       , ''''''						)
																									lowerCAmelCase   =		os.path.join(UpperCAmelCase_       , UpperCAmelCase_						)
																									lowerCAmelCase   =		os.path.join(UpperCAmelCase_       , UpperCAmelCase_						)
																									os.makedirs(UpperCAmelCase_       , exist_ok=UpperCAmelCase_						)
																									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(UpperCAmelCase_						)

																		if needs_manual_update:
																									with_manual_update.append(UpperCAmelCase_						)

																		with open(UpperCAmelCase_       , '''w'''       , encoding='''utf-8'''						) as f:
																									f.writelines(UpperCAmelCase_						)
																		self._logger.info(F"""Converted in {output_file}"""						)

											for utils_file in utils_files:
																		try:
																									lowerCAmelCase   =		os.path.basename(UpperCAmelCase_						)
																									lowerCAmelCase   =		imports_to_builder_map[f_name.replace('''.py'''       , ''''''						)]
																									self._logger.info(F"""Moving {dest_folder} to {utils_file}"""						)
																									shutil.copy(UpperCAmelCase_       , UpperCAmelCase_						)
																		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'."""						)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


UpperCAmelCase_						={
    """configuration_efficientformer""": [
        """EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """EfficientFormerConfig""",
    ]
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=["""EfficientFormerImageProcessor"""]

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """EfficientFormerForImageClassification""",
		    """EfficientFormerForImageClassificationWithTeacher""",
		    """EfficientFormerModel""",
		    """EfficientFormerPreTrainedModel""",
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		UpperCAmelCase_						=[
		    """TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
		    """TFEfficientFormerForImageClassification""",
		    """TFEfficientFormerForImageClassificationWithTeacher""",
		    """TFEfficientFormerModel""",
		    """TFEfficientFormerPreTrainedModel""",
		]

if TYPE_CHECKING:
		from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .image_processing_efficientformer import EfficientFormerImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_efficientformer import (
				    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    EfficientFormerForImageClassification,
				    EfficientFormerForImageClassificationWithTeacher,
				    EfficientFormerModel,
				    EfficientFormerPreTrainedModel,
				)
		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_efficientformer import (
				    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEfficientFormerForImageClassification,
				    TFEfficientFormerForImageClassificationWithTeacher,
				    TFEfficientFormerModel,
				    TFEfficientFormerPreTrainedModel,
				)

else:
		import sys

		UpperCAmelCase_						=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



