"""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"""
import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
					import torch

if is_vision_available():
					from PIL import Image

					from transformers import DonutImageProcessor



class   lowerCAmelCase_    (			unittest.TestCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase		,    lowerCAmelCase=7		,    lowerCAmelCase=3		,    lowerCAmelCase=18		,    lowerCAmelCase=30		,    lowerCAmelCase=4_00		,    lowerCAmelCase=True		,    lowerCAmelCase=None		,    lowerCAmelCase=True		,    lowerCAmelCase=False		,    lowerCAmelCase=True		,    lowerCAmelCase=True		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    lowerCAmelCase=[0.5, 0.5, 0.5]		,    ):






										"""simple docstring"""

										snake_case								=			parent
										snake_case								=			batch_size
										snake_case								=			num_channels
										snake_case								=			image_size
										snake_case								=			min_resolution
										snake_case								=			max_resolution
										snake_case								=			do_resize
										snake_case								=			size if size is not None else {'height': 18, 'width': 20}
										snake_case								=			do_thumbnail
										snake_case								=			do_align_axis
										snake_case								=			do_pad
										snake_case								=			do_normalize
										snake_case								=			image_mean
										snake_case								=			image_std

				def 		snake_case (				self    ):






										"""simple docstring"""

										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_thumbnail": self.do_thumbnail,
										    "do_align_long_axis": self.do_align_axis,
										    "do_pad": self.do_pad,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										}





@require_torch
@require_vision
class   lowerCAmelCase_    (			lowerCAmelCase     ,    unittest.TestCase					):



				"""simple docstring"""
				_lowerCAmelCase     :    str			  =						DonutImageProcessor if is_vision_available() else None

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			DonutImageProcessingTester(self    )

				@property
				def 		snake_case (				self    ):






										"""simple docstring"""

										return self.image_processor_tester.prepare_image_processor_dict()

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_resize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'size'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_thumbnail'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_align_long_axis'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_pad'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'do_normalize'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_mean'    )    )
										self.assertTrue(hasattr(lowerCAmelCase		,    'image_std'    )    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict    )
										self.assertEqual(image_processor.size		,    {'height': 18, 'width': 20}    )

										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=42    )
										self.assertEqual(image_processor.size		,    {'height': 42, 'width': 42}    )

										# Previous config had dimensions in (width, height) order
										snake_case								=			self.image_processing_class.from_dict(self.image_processor_dict		,    size=(42, 84)    )
										self.assertEqual(image_processor.size		,    {'height': 84, 'width': 42}    )

				def 		snake_case (				self    ):






										"""simple docstring"""

										pass

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PIL images
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    Image.Image    )

										# Test not batched input
										snake_case								=			image_processing(image_inputs[0]		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			image_processing(lowerCAmelCase		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random numpy tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase		,    numpify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    np.ndarray    )

										# Test not batched input
										snake_case								=			image_processing(image_inputs[0]		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			image_processing(lowerCAmelCase		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

				@is_flaky()
				def 		snake_case (				self    ):






										"""simple docstring"""

										snake_case								=			self.image_processing_class(**self.image_processor_dict    )
										# create random PyTorch tensors
										snake_case								=			prepare_image_inputs(self.image_processor_tester		,    equal_resolution=lowerCAmelCase		,    torchify=lowerCAmelCase    )
										for image in image_inputs:
																self.assertIsInstance(lowerCAmelCase		,    torch.Tensor    )

										# Test not batched input
										snake_case								=			image_processing(image_inputs[0]		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )

										# Test batched
										snake_case								=			image_processing(lowerCAmelCase		,    return_tensors='pt'    ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['height'],
										        self.image_processor_tester.size['width'],
										    )		,    )


