
"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 			__magic_name__       (       SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					unittest.TestCase ):
       UpperCamelCase_   =		StableDiffusionInstructPixaPixPipeline
       UpperCamelCase_   =		TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
       UpperCamelCase_   =		TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
       UpperCamelCase_   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
       UpperCamelCase_   =		IMAGE_TO_IMAGE_IMAGE_PARAMS
       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         torch.manual_seed(0       )
         _lowercase:			List[Any]       =     UNetaDConditionModel(
             block_out_channels=(32, 64) ,				layers_per_block=2 ,				sample_size=32 ,				in_channels=8 ,				out_channels=4 ,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') ,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') ,				cross_attention_dim=32 ,				)
         _lowercase:			Union[str, Any]       =     PNDMScheduler(skip_prk_steps=A_       )
         torch.manual_seed(0       )
         _lowercase:			List[str]       =     AutoencoderKL(
             block_out_channels=[32, 64] ,				in_channels=3 ,				out_channels=3 ,				down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] ,				up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] ,				latent_channels=4 ,				)
         torch.manual_seed(0       )
         _lowercase:			Optional[Any]       =     CLIPTextConfig(
             bos_token_id=0 ,				eos_token_id=2 ,				hidden_size=32 ,				intermediate_size=37 ,				layer_norm_eps=1E-05 ,				num_attention_heads=4 ,				num_hidden_layers=5 ,				pad_token_id=1 ,				vocab_size=1000 ,				)
         _lowercase:			Optional[Any]       =     CLIPTextModel(A_       )
         _lowercase:			Optional[Any]       =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''       )

         _lowercase:			Dict       =     {
             '''unet''': unet,
             '''scheduler''': scheduler,
             '''vae''': vae,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''safety_checker''': None,
             '''feature_extractor''': None,
         }
         return components
       def 							lowercase_							(     self ,				A_ ,				A_=0       )     ->	str:





         """simple docstring"""




         _lowercase:			List[str]       =     floats_tensor((1, 3, 32, 32) ,				rng=random.Random(A_       )       ).to(A_       )
         _lowercase:			str       =     image.cpu().permute(0 ,				2 ,				3 ,				1       )[0]
         _lowercase:			List[Any]       =     Image.fromarray(np.uinta(A_       )       ).convert('''RGB'''       )
         if str(A_       ).startswith('''mps'''       ):
           _lowercase:			List[str]       =     torch.manual_seed(A_       )
         else:
           _lowercase:			Tuple       =     torch.Generator(device=A_       ).manual_seed(A_       )
         _lowercase:			int       =     {
             '''prompt''': '''A painting of a squirrel eating a burger''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 2,
             '''guidance_scale''': 6.0,
             '''image_guidance_scale''': 1,
             '''output_type''': '''numpy''',
         }
         return inputs
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowercase:			str       =     self.get_dummy_components()
         _lowercase:			str       =     StableDiffusionInstructPixaPixPipeline(**A_       )
         _lowercase:			List[Any]       =     sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			Optional[int]       =     self.get_dummy_inputs(A_       )
         _lowercase:			str       =     sd_pipe(**A_       ).images
         _lowercase:			str       =     image[0, -3:, -3:, -1]
         assert image.shape == (1, 32, 32, 3)
         _lowercase:			List[Any]       =     np.array([0.75_26, 0.37_50, 0.45_47, 0.61_17, 0.58_66, 0.50_16, 0.43_27, 0.56_42, 0.48_15]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Union[str, Any]       =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowercase:			List[str]       =     self.get_dummy_components()
         _lowercase:			List[str]       =     StableDiffusionInstructPixaPixPipeline(**A_       )
         _lowercase:			Union[str, Any]       =     sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			Union[str, Any]       =     self.get_dummy_inputs(A_       )
         _lowercase:			List[str]       =     '''french fries'''
         _lowercase:			Optional[Any]       =     sd_pipe(**A_ ,				negative_prompt=A_       )
         _lowercase:			Dict       =     output.images
         _lowercase:			List[str]       =     image[0, -3:, -3:, -1]

         assert image.shape == (1, 32, 32, 3)
         _lowercase:			Tuple       =     np.array([0.75_11, 0.36_42, 0.45_53, 0.62_36, 0.57_97, 0.50_13, 0.43_43, 0.56_11, 0.48_31]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




         _lowercase:			List[str]       =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowercase:			List[Any]       =     self.get_dummy_components()
         _lowercase:			Union[str, Any]       =     StableDiffusionInstructPixaPixPipeline(**A_       )
         _lowercase:			Any       =     sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			List[Any]       =     self.get_dummy_inputs(A_       )
         _lowercase:			Optional[int]       =     [inputs['''prompt''']] * 2

         _lowercase:			Dict       =     np.array(inputs['''image''']       ).astype(np.floataa       ) / 2_55.0
         _lowercase:			Tuple       =     torch.from_numpy(A_       ).unsqueeze(0       ).to(A_       )
         _lowercase:			Optional[int]       =     image / 2 + 0.5
         _lowercase:			Optional[int]       =     image.permute(0 ,				3 ,				1 ,				2       )
         _lowercase:			str       =     image.repeat(2 ,				1 ,				1 ,				1       )

         _lowercase:			Union[str, Any]       =     sd_pipe(**A_       ).images
         _lowercase:			Any       =     image[-1, -3:, -3:, -1]

         assert image.shape == (2, 32, 32, 3)
         _lowercase:			Dict       =     np.array([0.58_12, 0.57_48, 0.52_22, 0.59_08, 0.56_95, 0.71_74, 0.68_04, 0.55_23, 0.55_79]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			int       =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
         _lowercase:			str       =     self.get_dummy_components()
         _lowercase:			Optional[int]       =     EulerAncestralDiscreteScheduler(
             beta_start=0.0_00_85 ,				beta_end=0.0_12 ,				beta_schedule='''scaled_linear'''       )
         _lowercase:			Optional[int]       =     StableDiffusionInstructPixaPixPipeline(**A_       )
         _lowercase:			Optional[int]       =     sd_pipe.to(A_       )
         sd_pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			List[Any]       =     self.get_dummy_inputs(A_       )
         _lowercase:			Optional[Any]       =     sd_pipe(**A_       ).images
         _lowercase:			List[Any]       =     image[0, -3:, -3:, -1]

         _lowercase:			List[str]       =     [round(A_ ,				4       ) for x in image_slice.flatten().tolist()]
         print(''','''.join([str(A_       ) for x in slice]       )       )

         assert image.shape == (1, 32, 32, 3)
         _lowercase:			str       =     np.array([0.74_17, 0.38_42, 0.47_32, 0.57_76, 0.58_91, 0.51_39, 0.40_52, 0.56_73, 0.49_86]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 1E-3
       def 							lowercase_							(     self       )     ->	int:





         """simple docstring"""




         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )



       def 							lowercase_							(     self       )     ->	Optional[int]:





         """simple docstring"""




         _lowercase:			List[str]       =     self.get_dummy_components()
         _lowercase:			Optional[int]       =     StableDiffusionInstructPixaPixPipeline(**A_       )
         _lowercase:			Optional[int]       =     VaeImageProcessor(do_resize=A_ ,				do_normalize=A_       )
         _lowercase:			Optional[Any]       =     pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )

         _lowercase:			int       =     pipe(**self.get_dummy_inputs_by_type(A_ ,				input_image_type='''pt'''       )       )[0]

         _lowercase:			Dict       =     components['''vae''']
         _lowercase:			Optional[Any]       =     self.get_dummy_inputs_by_type(A_ ,				input_image_type='''pt'''       )

         for image_param in self.image_latents_params:
           if image_param in inputs.keys():
             _lowercase:			Optional[Any]       =     vae.encode(inputs[image_param]       ).latent_dist.mode()

         _lowercase:			int       =     pipe(**A_       )[0]

         _lowercase:			Union[str, Any]       =     np.abs(out - out_latents_inputs       ).max()
         self.assertLess(A_ ,				1E-4 ,				'''passing latents as image input generate different result from passing image'''       )


@slow
@require_torch_gpu
class 			__magic_name__       (       unittest.TestCase ):
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
       def 							lowercase_							(     self ,				A_=0       )     ->	List[str]:





         """simple docstring"""




         _lowercase:			List[str]       =     torch.manual_seed(A_       )
         _lowercase:			Optional[int]       =     load_image(
             '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''       )
         _lowercase:			Dict       =     {
             '''prompt''': '''turn him into a cyborg''',
             '''image''': image,
             '''generator''': generator,
             '''num_inference_steps''': 3,
             '''guidance_scale''': 7.5,
             '''image_guidance_scale''': 1.0,
             '''output_type''': '''numpy''',
         }
         return inputs
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Any       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			str       =     self.get_inputs()
         _lowercase:			List[Any]       =     pipe(**A_       ).images
         _lowercase:			Tuple       =     image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         _lowercase:			Any       =     np.array([0.59_02, 0.60_15, 0.60_27, 0.59_83, 0.60_92, 0.60_61, 0.57_65, 0.57_85, 0.55_55]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
       def 							lowercase_							(     self       )     ->	List[Any]:





         """simple docstring"""




         _lowercase:			Any       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_       )
         _lowercase:			Optional[int]       =     LMSDiscreteScheduler.from_config(pipe.scheduler.config       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			Union[str, Any]       =     self.get_inputs()
         _lowercase:			Dict       =     pipe(**A_       ).images
         _lowercase:			Any       =     image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         _lowercase:			int       =     np.array([0.65_78, 0.68_17, 0.69_72, 0.67_61, 0.68_56, 0.69_16, 0.64_28, 0.65_16, 0.63_01]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
       def 							lowercase_							(     self       )     ->	Dict:





         """simple docstring"""




         _lowercase:			Dict       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_       )
         _lowercase:			Optional[int]       =     DDIMScheduler.from_config(pipe.scheduler.config       )
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			Optional[Any]       =     self.get_inputs()
         _lowercase:			Any       =     pipe(**A_       ).images
         _lowercase:			List[str]       =     image[0, -3:, -3:, -1].flatten()

         assert image.shape == (1, 512, 512, 3)
         _lowercase:			str       =     np.array([0.38_28, 0.38_34, 0.38_18, 0.37_92, 0.38_65, 0.37_52, 0.37_92, 0.38_47, 0.37_53]       )

         assert np.abs(expected_slice - image_slice       ).max() < 1E-3
       def 							lowercase_							(     self       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			List[Any]       =     0

         def callback_fn(A_ ,				A_ ,				A_       ) -> None:
           _lowercase:			Dict       =     True
           nonlocal number_of_steps
           number_of_steps += 1
           if step == 1:
             _lowercase:			List[Any]       =     latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             _lowercase:			Any       =     latents[0, -3:, -3:, -1]
             _lowercase:			Optional[int]       =     np.array([-0.24_63, -0.46_44, -0.97_56, 1.51_76, 1.44_14, 0.78_66, 0.98_97, 0.85_21, 0.79_83]       )

             assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2
           elif step == 2:
             _lowercase:			Dict       =     latents.detach().cpu().numpy()
             assert latents.shape == (1, 4, 64, 64)
             _lowercase:			Any       =     latents[0, -3:, -3:, -1]
             _lowercase:			List[str]       =     np.array([-0.26_44, -0.46_26, -0.96_53, 1.51_76, 1.45_51, 0.76_86, 0.98_05, 0.84_52, 0.81_15]       )

             assert np.abs(latents_slice.flatten() - expected_slice       ).max() < 5E-2

         _lowercase:			Dict       =     False

         _lowercase:			Union[str, Any]       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_ ,				torch_dtype=torch.floataa       )
         _lowercase:			List[str]       =     pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			List[Any]       =     self.get_inputs()
         pipe(**A_ ,				callback=A_ ,				callback_steps=1       )
         assert callback_fn.has_been_called
         assert number_of_steps == 3
       def 							lowercase_							(     self       )     ->	List[str]:





         """simple docstring"""




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

         _lowercase:			List[str]       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             '''timbrooks/instruct-pix2pix''' ,				safety_checker=A_ ,				torch_dtype=torch.floataa       )
         _lowercase:			Optional[Any]       =     pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing(1       )
         pipe.enable_sequential_cpu_offload()

         _lowercase:			Optional[int]       =     self.get_inputs()
         _lowercase:			Dict       =     pipe(**A_       )

         _lowercase:			Optional[int]       =     torch.cuda.max_memory_allocated()
         # make sure that less than 2.2 GB is allocated
         assert mem_bytes < 2.2 * 10**9



       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     self.get_inputs()
         # resize to resolution that is divisible by 8 but not 16 or 32
         _lowercase:			str       =     inputs['''image'''].resize((504, 504)       )

         _lowercase:			Tuple       =     '''timbrooks/instruct-pix2pix'''
         _lowercase:			int       =     StableDiffusionInstructPixaPixPipeline.from_pretrained(
             A_ ,				safety_checker=A_ ,				)
         pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )
         pipe.enable_attention_slicing()

         _lowercase:			Union[str, Any]       =     pipe(**A_       )
         _lowercase:			Dict       =     output.images[0]

         _lowercase:			Any       =     image[255:258, 383:386, -1]

         assert image.shape == (504, 504, 3)
         _lowercase:			Tuple       =     np.array([0.27_26, 0.25_29, 0.26_64, 0.26_55, 0.26_41, 0.26_42, 0.25_91, 0.26_49, 0.25_90]       )

         assert np.abs(image_slice.flatten() - expected_slice       ).max() < 5E-3


"""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[Any]							=   '<<<<<<< This should probably be modified because it mentions: '

A__   :					Any							=   '=======\n>>>>>>>\n'

A__   :					int							=   [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A__   :					Union[str, 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  _lowerCAmelCase		(		_UpperCamelCase						):






  """simple docstring"""



  return ConvertCommand(args.tfds_path ,				args.datasets_directory						)







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       @staticmethod
       def 							lowercase_							(     A_       )     ->	Union[str, Any]:





         """simple docstring"""




         _lowercase:			int       =     parser.add_parser(
             '''convert''' ,				help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.''' ,				)
         train_parser.add_argument(
             '''--tfds_path''' ,				type=A_ ,				required=A_ ,				help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.''' ,				)
         train_parser.add_argument(
             '''--datasets_directory''' ,				type=A_ ,				required=A_ ,				help='''Path to the HuggingFace Datasets folder.'''       )
         train_parser.set_defaults(func=A_       )
       def __init__(     self ,				A_ ,				A_ ,				*A_       )     ->	Tuple:





         """simple docstring"""




         _lowercase:			Optional[Any]       =     get_logger('''datasets-cli/converting'''       )

         _lowercase:			Optional[Any]       =     tfds_path
         _lowercase:			Dict       =     datasets_directory



       def 							lowercase_							(     self       )     ->	Any:





         """simple docstring"""




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

         _lowercase:			Dict       =     os.path.abspath(self._datasets_directory       )

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

         _lowercase:			Tuple       =     []
         _lowercase:			List[str]       =     []
         _lowercase:			int       =     {}

         if os.path.isdir(self._tfds_path       ):
           _lowercase:			str       =     os.listdir(A_       )
         else:
           _lowercase:			Tuple       =     [os.path.basename(self._tfds_path       )]

         for f_name in file_names:
           self._logger.info(f'''Looking at file {f_name}'''       )
           _lowercase:			Optional[int]       =     os.path.join(A_ ,				A_       )
           _lowercase:			Optional[Any]       =     os.path.join(A_ ,				A_       )

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

           with open(A_ ,				encoding='''utf-8'''       ) as f:
             _lowercase:			Any       =     f.readlines()

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

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

           if is_builder or "wmt" in f_name:
             # We create a new directory for each dataset
             _lowercase:			List[str]       =     f_name.replace('''.py''' ,				''''''       )
             _lowercase:			Dict       =     os.path.join(A_ ,				A_       )
             _lowercase:			Dict       =     os.path.join(A_ ,				A_       )
             os.makedirs(A_ ,				exist_ok=A_       )
             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(A_       )

           if needs_manual_update:
             with_manual_update.append(A_       )

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

         for utils_file in utils_files:
           try:
             _lowercase:			Optional[int]       =     os.path.basename(A_       )
             _lowercase:			List[Any]       =     imports_to_builder_map[f_name.replace('''.py''' ,				''''''       )]
             self._logger.info(f'''Moving {dest_folder} to {utils_file}'''       )
             shutil.copy(A_ ,				A_       )
           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\'.'''       )
