





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

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


a		:				Tuple  			=    '''<<<<<<< This should probably be modified because it mentions: '''

a		:				List[Any]  			=    '''=======
>>>>>>>
'''

a		:				List[str]  			=    [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

a		:				int  			=    [
    # (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	(			_lowercase		:					List[Any]      )				->Optional[Any]:






	'''simple docstring'''






	return ConvertCommand(args.tfds_path					, args.datasets_directory      )







class  __UpperCamelCase       (		UpperCAmelCase_		):






							@staticmethod
							def 					__a     (     lowerCAmelCase__   )			->				Optional[int]:
								a   :	Any			=       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   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      *lowerCAmelCase__   )			->				int:
								a   :	str			=       get_logger("datasets-cli/converting"   )

								a   :	int			=       tfds_path
								a   :	Any			=       datasets_directory






							def 					__a     (     self   )			->				int:


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

								a   :	Any			=       os.path.abspath(self._datasets_directory   )

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

								a   :	Optional[int]			=       []
								a   :	Dict			=       []
								a   :	List[str]			=       {}

								if os.path.isdir(self._tfds_path   ):
									a   :	Any			=       os.listdir(_snake_case   )
								else:
									a   :	List[Any]			=       [os.path.basename(self._tfds_path   )]

								for f_name in file_names:
									self._logger.info(f"""Looking at file {f_name}"""   )
									a   :	int			=       os.path.join(_snake_case   ,      _snake_case   )
									a   :	Union[str, Any]			=       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:
										a   :	List[Any]			=       f.readlines()

									a   :	List[str]			=       []
									a   :	Tuple			=       False
									a   :	List[str]			=       False
									a   :	int			=       []
									for line in lines:
										a   :	Optional[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:
											a   :	Optional[int]			=       "import datasets\n"
										elif "import tensorflow" in out_line:
											# order is important here
											a   :	str			=       ""
											continue
										elif "from absl import logging" in out_line:
											a   :	Optional[int]			=       "from datasets import logging\n"
										elif "getLogger" in out_line:
											a   :	Tuple			=       out_line.replace("getLogger"   ,      "get_logger"   )
										elif any(expression in out_line for expression in TO_HIGHLIGHT   ):
											a   :	Optional[Any]			=       True
											a   :	Tuple			=       list(filter(lambda lowerCAmelCase__   : 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:
												a   :	List[str]			=       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:
											a   :	Dict			=       re.match(R"from\stensorflow_datasets.*import\s([^\.\r\n]+)"   ,      _snake_case   )
											tfds_imports.extend(imp.strip() for imp in match.group(1   ).split(","   )   )
											a   :	str			=       "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:
											a   :	Optional[int]			=       True
										out_lines.append(_snake_case   )

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









"""simple docstring"""




import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin


@skip_mps
class  __UpperCamelCase       (		a__	, a__	, unittest.TestCase		):
							lowerCamelCase     :      Dict         =IFPipeline
							lowerCamelCase     :      int         =TEXT_TO_IMAGE_PARAMS - {"""width""", """height""", """latents"""}
							lowerCamelCase     :      int         =TEXT_TO_IMAGE_BATCH_PARAMS
							lowerCamelCase     :      int         =PipelineTesterMixin.required_optional_params - {"""latents"""}






							def 					__a     (     self   )			->				List[str]:
								return self._get_dummy_components()






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__=0   )			->				Dict:
								if str(lowerCAmelCase__   ).startswith("mps"   ):
									a   :	Tuple			=       torch.manual_seed(lowerCAmelCase__   )
								else:
									a   :	int			=       torch.Generator(device=lowerCAmelCase__   ).manual_seed(lowerCAmelCase__   )

								a   :	Optional[Any]			=       {
								    "prompt": "A painting of a squirrel eating a burger",
								    "generator": generator,
								    "num_inference_steps": 2,
								    "output_type": "numpy",
								}

								return inputs






							def 					__a     (     self   )			->				Union[str, Any]:
								self._test_save_load_optional_components()






							@unittest.skipIf(torch_device != "cuda"   ,      reason="float16 requires CUDA"   )
							def 					__a     (     self   )			->				Any:
								# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
								super().test_save_load_floataa(expected_max_diff=1E-1   )






							def 					__a     (     self   )			->				Union[str, Any]:
								self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )






							def 					__a     (     self   )			->				Optional[int]:
								self._test_save_load_local()






							def 					__a     (     self   )			->				Tuple:
								self._test_inference_batch_single_identical(
								    expected_max_diff=1E-2   ,      )






							@unittest.skipIf(
							    torch_device != "cuda" or not is_xformers_available()   ,      reason="XFormers attention is only available with CUDA and `xformers` installed"   ,      )
							def 					__a     (     self   )			->				str:
								self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )


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






							def 					__a     (     self   )			->				Optional[Any]:
								# clean up the VRAM after each test
								super().tearDown()
								gc.collect()
								torch.cuda.empty_cache()






							def 					__a     (     self   )			->				Tuple:
								# if

								a   :	Tuple			=       IFPipeline.from_pretrained("DeepFloyd/IF-I-XL-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   )

								a   :	str			=       IFSuperResolutionPipeline.from_pretrained(
								    "DeepFloyd/IF-II-L-v1.0"   ,      variant="fp16"   ,      torch_dtype=torch.floataa   ,      text_encoder=lowerCAmelCase__   ,      tokenizer=lowerCAmelCase__   )

								# pre compute text embeddings and remove T5 to save memory

								pipe_a.text_encoder.to("cuda"   )

								a, a   :	List[str]			=       pipe_a.encode_prompt("anime turtle"   ,      device="cuda"   )

								del pipe_a.tokenizer
								del pipe_a.text_encoder
								gc.collect()

								a   :	Optional[int]			=       None
								a   :	Optional[int]			=       None

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

								self._test_if(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# img2img

								a   :	Union[str, Any]			=       IFImgaImgPipeline(**pipe_a.components   )
								a   :	List[Any]			=       IFImgaImgSuperResolutionPipeline(**pipe_a.components   )

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

								self._test_if_imgaimg(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )

								pipe_a.remove_all_hooks()
								pipe_a.remove_all_hooks()

								# inpainting

								a   :	Union[str, Any]			=       IFInpaintingPipeline(**pipe_a.components   )
								a   :	List[str]			=       IFInpaintingSuperResolutionPipeline(**pipe_a.components   )

								pipe_a.enable_model_cpu_offload()
								pipe_a.enable_model_cpu_offload()

								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
								pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

								self._test_if_inpainting(lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Dict:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
								a   :	Dict			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	List[str]			=       output.images[0]

								assert image.shape == (64, 64, 3)

								a   :	Dict			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 13 * 10**9

								a   :	Optional[Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	List[str]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Union[str, Any]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	List[str]			=       output.images[0]

								assert image.shape == (256, 256, 3)

								a   :	int			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	Union[str, Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				int:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	int			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Tuple			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	List[Any]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	Tuple			=       output.images[0]

								assert image.shape == (64, 64, 3)

								a   :	int			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								a   :	Optional[int]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	List[Any]			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	str			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )

								a   :	Dict			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	int			=       output.images[0]

								assert image.shape == (256, 256, 3)

								a   :	str			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	Any			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






							def 					__a     (     self   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   ,      lowerCAmelCase__   )			->				Optional[int]:
								# pipeline 1

								_start_torch_memory_measurement()

								a   :	Union[str, Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	List[Any]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

								a   :	Optional[int]			=       torch.Generator(device="cpu"   ).manual_seed(0   )
								a   :	List[str]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      num_inference_steps=2   ,      generator=lowerCAmelCase__   ,      output_type="np"   ,      )

								a   :	List[Any]			=       output.images[0]

								assert image.shape == (64, 64, 3)

								a   :	Tuple			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 10 * 10**9

								a   :	Union[str, Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )

								# pipeline 2

								_start_torch_memory_measurement()

								a   :	str			=       torch.Generator(device="cpu"   ).manual_seed(0   )

								a   :	Optional[int]			=       floats_tensor((1, 3, 64, 64)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	int			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(0   )   ).to(lowerCAmelCase__   )
								a   :	Dict			=       floats_tensor((1, 3, 256, 256)   ,      rng=random.Random(1   )   ).to(lowerCAmelCase__   )

								a   :	Optional[int]			=       pipe_a(
								    prompt_embeds=lowerCAmelCase__   ,      negative_prompt_embeds=lowerCAmelCase__   ,      image=lowerCAmelCase__   ,      mask_image=lowerCAmelCase__   ,      original_image=lowerCAmelCase__   ,      generator=lowerCAmelCase__   ,      num_inference_steps=2   ,      output_type="np"   ,      )

								a   :	List[str]			=       output.images[0]

								assert image.shape == (256, 256, 3)

								a   :	Tuple			=       torch.cuda.max_memory_allocated()
								assert mem_bytes < 4 * 10**9

								a   :	Optional[Any]			=       load_numpy(
								    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy"   )
								assert_mean_pixel_difference(lowerCAmelCase__   ,      lowerCAmelCase__   )






def    _SCREAMING_SNAKE_CASE	(			)				->List[str]:






	'''simple docstring'''






	torch.cuda.empty_cache()
	torch.cuda.reset_max_memory_allocated()
	torch.cuda.reset_peak_memory_stats()


