






'''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	:			Optional[int]         ='''<<<<<<< This should probably be modified because it mentions: '''

_A	:			List[str]         ='''=======
>>>>>>>
'''

_A	:			Optional[Any]         =[
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

_A	:			List[Any]         =[
    # (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_       (UpperCamelCase      )      ->       Any:
							return ConvertCommand(args.tfds_path				,			args.datasets_directory      )



class _lowercase	(				_lowercase		):




							@staticmethod
							def 		lowerCamelCase_				(							UpperCamelCase__:				ArgumentParser     ):
														lowerCamelCase__					:	Any         =       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:				Any			,							UpperCamelCase__:				str			,							UpperCamelCase__:				str			,							*UpperCamelCase__:				Any     ):
														lowerCamelCase__					:	List[str]         =       get_logger("""datasets-cli/converting"""     )

														lowerCamelCase__					:	Union[str, Any]         =       tfds_path
														lowerCamelCase__					:	Union[str, Any]         =       datasets_directory




							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														if os.path.isdir(self._tfds_path     ):
																					lowerCamelCase__					:	int         =       os.path.abspath(self._tfds_path     )
														elif os.path.isfile(self._tfds_path     ):
																					lowerCamelCase__					:	Tuple         =       os.path.dirname(self._tfds_path     )
														else:
																					raise ValueError("""--tfds_path is neither a directory nor a file. Please check path."""     )

														lowerCamelCase__					:	Optional[Any]         =       os.path.abspath(self._datasets_directory     )

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

														lowerCamelCase__					:	Optional[Any]         =       []
														lowerCamelCase__					:	str         =       []
														lowerCamelCase__					:	str         =       {}

														if os.path.isdir(self._tfds_path     ):
																					lowerCamelCase__					:	int         =       os.listdir(UpperCamelCase__     )
														else:
																					lowerCamelCase__					:	Dict         =       [os.path.basename(self._tfds_path     )]

														for f_name in file_names:
																					self._logger.info(F'''Looking at file {f_name}'''     )
																					lowerCamelCase__					:	Union[str, Any]         =       os.path.join(UpperCamelCase__			,							UpperCamelCase__     )
																					lowerCamelCase__					:	Tuple         =       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__					:	Dict         =       f.readlines()

																					lowerCamelCase__					:	Union[str, Any]         =       []
																					lowerCamelCase__					:	List[str]         =       False
																					lowerCamelCase__					:	List[Any]         =       False
																					lowerCamelCase__					:	Optional[Any]         =       []
																					for line in lines:
																												lowerCamelCase__					:	Union[str, Any]         =       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__					:	str         =       """import datasets\n"""
																												elif "import tensorflow" in out_line:
																																			# order is important here
																																			lowerCamelCase__					:	Optional[int]         =       """"""
																																			continue
																												elif "from absl import logging" in out_line:
																																			lowerCamelCase__					:	Union[str, 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__					:	str         =       True
																																			lowerCamelCase__					:	Optional[int]         =       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__					:	Dict         =       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__					:	str         =       re.match(R"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""			,							UpperCamelCase__     )
																																			tfds_imports.extend(imp.strip() for imp in match.group(1     ).split(""","""     )     )
																																			lowerCamelCase__					:	Tuple         =       """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__					:	List[Any]         =       True
																												out_lines.append(UpperCamelCase__     )

																					if is_builder or "wmt" in f_name:
																												# We create a new directory for each dataset
																												lowerCamelCase__					:	Any         =       f_name.replace(""".py"""			,							""""""     )
																												lowerCamelCase__					:	Optional[Any]         =       os.path.join(UpperCamelCase__			,							UpperCamelCase__     )
																												lowerCamelCase__					:	Union[str, Any]         =       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__					:	int         =       os.path.basename(UpperCamelCase__     )
																												lowerCamelCase__					:	Optional[int]         =       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\'.'''     )









'''simple docstring'''







import inspect
import unittest

from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import MobileNetVaForImageClassification, MobileNetVaModel
			from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import MobileNetVaImageProcessor

class _lowercase	(				_lowercase		):




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__					:	str         =       self.config_class(**self.inputs_dict     )
														self.parent.assertTrue(hasattr(UpperCamelCase__			,							"""tf_padding"""     )     )
														self.parent.assertTrue(hasattr(UpperCamelCase__			,							"""depth_multiplier"""     )     )



class _lowercase	:




							def __init__(							self:				str			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				Tuple=13			,							UpperCamelCase__:				Optional[int]=3			,							UpperCamelCase__:				List[Any]=32			,							UpperCamelCase__:				Optional[Any]=0.25			,							UpperCamelCase__:				int=8			,							UpperCamelCase__:				Any=True			,							UpperCamelCase__:				Dict=1_024			,							UpperCamelCase__:				Optional[int]=32			,							UpperCamelCase__:				Tuple="relu6"			,							UpperCamelCase__:				int=0.1			,							UpperCamelCase__:				List[Any]=0.02			,							UpperCamelCase__:				Optional[Any]=True			,							UpperCamelCase__:				Union[str, Any]=True			,							UpperCamelCase__:				Union[str, Any]=10			,							UpperCamelCase__:				str=None			,							):
														lowerCamelCase__					:	Optional[Any]         =       parent
														lowerCamelCase__					:	List[str]         =       batch_size
														lowerCamelCase__					:	Optional[int]         =       num_channels
														lowerCamelCase__					:	Optional[int]         =       image_size
														lowerCamelCase__					:	Optional[Any]         =       depth_multiplier
														lowerCamelCase__					:	Union[str, Any]         =       min_depth
														lowerCamelCase__					:	Optional[Any]         =       tf_padding
														lowerCamelCase__					:	str         =       int(last_hidden_size * depth_multiplier     )
														lowerCamelCase__					:	Any         =       output_stride
														lowerCamelCase__					:	int         =       hidden_act
														lowerCamelCase__					:	Tuple         =       classifier_dropout_prob
														lowerCamelCase__					:	Dict         =       use_labels
														lowerCamelCase__					:	Tuple         =       is_training
														lowerCamelCase__					:	Optional[Any]         =       num_labels
														lowerCamelCase__					:	Union[str, Any]         =       initializer_range
														lowerCamelCase__					:	Optional[Any]         =       scope




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Optional[Any]         =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

														lowerCamelCase__					:	Optional[Any]         =       None
														lowerCamelCase__					:	Dict         =       None
														if self.use_labels:
																					lowerCamelCase__					:	Union[str, Any]         =       ids_tensor([self.batch_size]			,							self.num_labels     )
																					lowerCamelCase__					:	Dict         =       ids_tensor([self.batch_size, self.image_size, self.image_size]			,							self.num_labels     )

														lowerCamelCase__					:	Dict         =       self.get_config()

														return config, pixel_values, labels, pixel_labels




							def 		lowerCamelCase_				(							self:				str     ):
														return MobileNetVaConfig(
														    num_channels=self.num_channels			,							image_size=self.image_size			,							depth_multiplier=self.depth_multiplier			,							min_depth=self.min_depth			,							tf_padding=self.tf_padding			,							hidden_act=self.hidden_act			,							classifier_dropout_prob=self.classifier_dropout_prob			,							initializer_range=self.initializer_range			,							)




							def 		lowerCamelCase_				(							self:				Tuple			,							UpperCamelCase__:				int			,							UpperCamelCase__:				str			,							UpperCamelCase__:				Any			,							UpperCamelCase__:				Optional[int]     ):
														lowerCamelCase__					:	List[str]         =       MobileNetVaModel(config=UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	List[str]         =       model(UpperCamelCase__     )
														self.parent.assertEqual(
														    result.last_hidden_state.shape			,							(
														        self.batch_size,
														        self.last_hidden_size,
														        self.image_size // self.output_stride,
														        self.image_size // self.output_stride,
														    )			,							)




							def 		lowerCamelCase_				(							self:				str			,							UpperCamelCase__:				Tuple			,							UpperCamelCase__:				Optional[int]			,							UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Union[str, Any]     ):
														lowerCamelCase__					:	List[str]         =       self.num_labels
														lowerCamelCase__					:	Optional[Any]         =       MobileNetVaForImageClassification(UpperCamelCase__     )
														model.to(UpperCamelCase__     )
														model.eval()
														lowerCamelCase__					:	List[Any]         =       model(UpperCamelCase__			,							labels=UpperCamelCase__     )
														self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_labels)     )




							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														lowerCamelCase__					:	str         =       self.prepare_config_and_inputs()
														lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__  ,		lowerCamelCase__					:	int         =       config_and_inputs
														lowerCamelCase__					:	Optional[int]         =       {"""pixel_values""": pixel_values}
														return config, inputs_dict



@require_torch
class _lowercase	(				_lowercase				,      _lowercase				,      unittest.TestCase		):
							a				      =       (MobileNetVaModel, MobileNetVaForImageClassification) if is_torch_available() else ()
							a				      =       (
							    {"""feature-extraction""": MobileNetVaModel, """image-classification""": MobileNetVaForImageClassification}
							    if is_torch_available()
							    else {}
							)

							a				      =       False
							a				      =       False
							a				      =       False
							a				      =       False




							def 		lowerCamelCase_				(							self:				List[str]     ):
														lowerCamelCase__					:	Optional[int]         =       MobileNetVaModelTester(self     )
														lowerCamelCase__					:	List[str]         =       MobileNetVaConfigTester(self			,							config_class=UpperCamelCase__			,							has_text_modality=UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Union[str, Any]     ):
														self.config_tester.run_common_tests()




							@unittest.skip(reason="""MobileNetV1 does not use inputs_embeds"""     )
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														pass




							@unittest.skip(reason="""MobileNetV1 does not support input and output embeddings"""     )
							def 		lowerCamelCase_				(							self:				Optional[Any]     ):
														pass




							@unittest.skip(reason="""MobileNetV1 does not output attentions"""     )
							def 		lowerCamelCase_				(							self:				Any     ):
														pass




							def 		lowerCamelCase_				(							self:				Any     ):
														lowerCamelCase__  ,		lowerCamelCase__					:	Optional[Any]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	Dict         =       model_class(UpperCamelCase__     )
																					lowerCamelCase__					:	Dict         =       inspect.signature(model.forward     )
																					# signature.parameters is an OrderedDict => so arg_names order is deterministic
																					lowerCamelCase__					:	List[Any]         =       [*signature.parameters.keys()]

																					lowerCamelCase__					:	Dict         =       ["""pixel_values"""]
																					self.assertListEqual(arg_names[:1]			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														lowerCamelCase__					:	Tuple         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				str     ):
														def check_hidden_states_output(UpperCamelCase__:				List[Any]			,							UpperCamelCase__:				Dict			,							UpperCamelCase__:				List[Any]     ):
																					lowerCamelCase__					:	str         =       model_class(UpperCamelCase__     )
																					model.to(UpperCamelCase__     )
																					model.eval()

																					with torch.no_grad():
																												lowerCamelCase__					:	Union[str, Any]         =       model(**self._prepare_for_class(UpperCamelCase__			,							UpperCamelCase__     )     )

																					lowerCamelCase__					:	List[Any]         =       outputs.hidden_states

																					lowerCamelCase__					:	Tuple         =       26
																					self.assertEqual(len(UpperCamelCase__     )			,							UpperCamelCase__     )

														lowerCamelCase__  ,		lowerCamelCase__					:	List[str]         =       self.model_tester.prepare_config_and_inputs_for_common()

														for model_class in self.all_model_classes:
																					lowerCamelCase__					:	List[Any]         =       True
																					check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )

																					# check that output_hidden_states also work using config
																					del inputs_dict["output_hidden_states"]
																					lowerCamelCase__					:	Optional[Any]         =       True

																					check_hidden_states_output(UpperCamelCase__			,							UpperCamelCase__			,							UpperCamelCase__     )




							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	int         =       self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_for_image_classification(*UpperCamelCase__     )




							@slow
							def 		lowerCamelCase_				(							self:				List[str]     ):
														for model_name in MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																					lowerCamelCase__					:	Dict         =       MobileNetVaModel.from_pretrained(UpperCamelCase__     )
																					self.assertIsNotNone(UpperCamelCase__     )





def  SCREAMING_SNAKE_CASE_       ()      ->       Union[str, Any]:
							lowerCamelCase__					:	Optional[int]         =       Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
							return image



@require_torch
@require_vision
class _lowercase	(				unittest.TestCase		):




							@cached_property
							def 		lowerCamelCase_				(							self:				Optional[int]     ):
														return (
														    MobileNetVaImageProcessor.from_pretrained("""google/mobilenet_v1_1.0_224"""     ) if is_vision_available() else None
														)




							@slow
							def 		lowerCamelCase_				(							self:				Dict     ):
														lowerCamelCase__					:	List[Any]         =       MobileNetVaForImageClassification.from_pretrained("""google/mobilenet_v1_1.0_224"""     ).to(UpperCamelCase__     )

														lowerCamelCase__					:	Dict         =       self.default_image_processor
														lowerCamelCase__					:	int         =       prepare_img()
														lowerCamelCase__					:	List[Any]         =       image_processor(images=UpperCamelCase__			,							return_tensors="""pt"""     ).to(UpperCamelCase__     )

														# forward pass
														with torch.no_grad():
																					lowerCamelCase__					:	str         =       model(**UpperCamelCase__     )

														# verify the logits
														lowerCamelCase__					:	List[str]         =       torch.Size((1, 1_001)     )
														self.assertEqual(outputs.logits.shape			,							UpperCamelCase__     )

														lowerCamelCase__					:	List[str]         =       torch.tensor([-4.1_739, -1.1_233, 3.1_205]     ).to(UpperCamelCase__     )

														self.assertTrue(torch.allclose(outputs.logits[0, :3]			,							UpperCamelCase__			,							atol=1e-4     )     )

