
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_   	=	"=======\n>>>>>>>\n"

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   (	UpperCamelCase:     Namespace  ):






	"""simple docstring"""


	return ConvertCommand(args.tfds_path			,							args.datasets_directory  )







class       a		(   __UpperCAmelCase					):







							@staticmethod
							def       UpperCAmelCase__					(						snake_case__  :  ArgumentParser ):
								"""simple docstring"""







								__lowerCAmelCase         =							parser.add_parser(
								    "convert"      ,				help="Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset."      ,				)
								train_parser.add_argument(
								    "--tfds_path"      ,				type=snake_case__      ,				required=snake_case__      ,				help="Path to a TensorFlow Datasets folder to convert or a single tfds file to convert."      ,				)
								train_parser.add_argument(
								    "--datasets_directory"      ,				type=snake_case__      ,				required=snake_case__      ,				help="Path to the HuggingFace Datasets folder." )
								train_parser.set_defaults(func=snake_case__ )







							def __init__(						self  :  List[Any]      ,				snake_case__  :  str      ,				snake_case__  :  str      ,				*snake_case__  :  str ):
								"""simple docstring"""







								__lowerCAmelCase         =							get_logger("datasets-cli/converting" )

								__lowerCAmelCase         =							tfds_path
								__lowerCAmelCase         =							datasets_directory







							def       UpperCAmelCase__					(						self  :  int ):
								"""simple docstring"""







								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(snake_case__ )
								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(snake_case__      ,				snake_case__ )
									__lowerCAmelCase         =							os.path.join(snake_case__      ,				snake_case__ )

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

									with open(snake_case__      ,				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 snake_case__ : e in out_line      ,				snake_case__ ) )
											out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(snake_case__ ) + "\n" )
											out_lines.append(snake_case__ )
											out_lines.append(snake_case__ )
											continue
										else:
											for pattern, replacement in TO_CONVERT:
												__lowerCAmelCase         =							re.sub(snake_case__      ,				snake_case__      ,				snake_case__ )

                # 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]+)"      ,				snake_case__ )
											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(snake_case__ )

									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(snake_case__      ,				snake_case__ )
										__lowerCAmelCase         =							os.path.join(snake_case__      ,				snake_case__ )
										os.makedirs(snake_case__      ,				exist_ok=snake_case__ )
										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(snake_case__ )

									if needs_manual_update:
										with_manual_update.append(snake_case__ )

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

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

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


UpperCamelCase_   	=	False

class       a		(   unittest.TestCase					):
							pass

@slow
@require_torch_gpu
class       a		(   unittest.TestCase					):







							def       UpperCAmelCase__					(						self  :  Dict ):
								"""simple docstring"""







								__lowerCAmelCase         =							VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion" )
								pipe.to(snake_case__ )
								pipe.set_progress_bar_config(disable=snake_case__ )

								__lowerCAmelCase         =							load_image(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
								__lowerCAmelCase         =							torch.manual_seed(0 )
								__lowerCAmelCase         =							pipe(
								    image=snake_case__      ,				generator=snake_case__      ,				guidance_scale=7.5      ,				num_inference_steps=50      ,				output_type="numpy"      ,				).images

								__lowerCAmelCase         =							image[0, 253:256, 253:256, -1]

								assert image.shape == (1, 512, 512, 3)
								__lowerCAmelCase         =							np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )

								assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2






