


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

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


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

__lowerCamelCase        =     '''=======
>>>>>>>
'''

__lowerCamelCase        =     [
    '''TextEncoderConfig''',
    '''ByteTextEncoder''',
    '''SubwordTextEncoder''',
    '''encoder_config''',
    '''maybe_build_from_corpus''',
    '''manual_dir''',
]

__lowerCamelCase        =     [
    # (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  _a			(						__UpperCamelCase ):
     return ConvertCommand(args.tfds_path  ,  args.datasets_directory )


class   a__	(						lowerCAmelCase_    ):

       @staticmethod
       def 						UpperCAmelCase(   lowerCamelCase_      :     ArgumentParser    ):
            a_    :     Dict										=    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      :     Union[str, Any]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     str      ,							*lowerCamelCase_      :     Any    ):
            a_    :     int										=    get_logger("""datasets-cli/converting"""    )

            a_    :     Dict										=    tfds_path
            a_    :     List[str]										=    datasets_directory

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

            a_    :     List[str]										=    os.path.abspath(self._datasets_directory    )

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

            a_    :     Union[str, Any]										=    []
            a_    :     Union[str, Any]										=    []
            a_    :     Optional[Any]										=    {}

            if os.path.isdir(self._tfds_path    ):
                 a_    :     Any										=    os.listdir(lowerCamelCase_    )
            else:
                 a_    :     Optional[int]										=    [os.path.basename(self._tfds_path    )]

            for f_name in file_names:
                 self._logger.info(F'''Looking at file {f_name}'''    )
                 a_    :     Optional[int]										=    os.path.join(lowerCamelCase_      ,							lowerCamelCase_    )
                 a_    :     Optional[int]										=    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:
                      a_    :     Union[str, Any]										=    f.readlines()

                 a_    :     Optional[Any]										=    []
                 a_    :     Optional[int]										=    False
                 a_    :     Any										=    False
                 a_    :     Optional[int]										=    []
                 for line in lines:
                      a_    :     Dict										=    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_    :     Union[str, Any]										=    """import datasets\n"""
                      elif "import tensorflow" in out_line:
                           # order is important here
                           a_    :     Optional[Any]										=    """"""
                           continue
                      elif "from absl import logging" in out_line:
                           a_    :     List[Any]										=    """from datasets import logging\n"""
                      elif "getLogger" in out_line:
                           a_    :     Optional[int]										=    out_line.replace("""getLogger"""      ,							"""get_logger"""    )
                      elif any(expression in out_line for expression in TO_HIGHLIGHT    ):
                           a_    :     Any										=    True
                           a_    :     Dict										=    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:
                                a_    :     Dict										=    re.sub(lowerCamelCase_      ,							lowerCamelCase_      ,							lowerCamelCase_    )

                # Take care of saving utilities (to later move them together with main script)
                      if "tensorflow_datasets" in out_line:
                           a_    :     int										=    re.match(R"""from\stensorflow_datasets.*import\s([^\.\r\n]+)"""      ,							lowerCamelCase_    )
                           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_    :     List[Any]										=    True
                      out_lines.append(lowerCamelCase_    )

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







from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


__lowerCamelCase        =     logging.get_logger(__name__)  # pylint: disable=invalid-name


class   a__	(						lowerCAmelCase_    ):

       def __init__(   self      :     List[str]      ,							lowerCamelCase_      :     CLIPSegForImageSegmentation      ,							lowerCamelCase_      :     CLIPSegProcessor      ,							lowerCamelCase_      :     AutoencoderKL      ,							lowerCamelCase_      :     CLIPTextModel      ,							lowerCamelCase_      :     CLIPTokenizer      ,							lowerCamelCase_      :     UNetaDConditionModel      ,							lowerCamelCase_      :     Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]      ,							lowerCamelCase_      :     StableDiffusionSafetyChecker      ,							lowerCamelCase_      :     CLIPImageProcessor      ,							):
            super().__init__()

            if hasattr(scheduler.config      ,							"""steps_offset"""    ) and scheduler.config.steps_offset != 1:
                 a_    :     Dict										=    (
                     F'''The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'''
                     F''' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '''
                     """to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
                     """ in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
                     """ it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
                     """ file"""
                 )
                 deprecate("""steps_offset!=1"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     Union[str, Any]										=    dict(scheduler.config    )
                 a_    :     Optional[int]										=    1
                 a_    :     int										=    FrozenDict(lowerCamelCase_    )

            if hasattr(scheduler.config      ,							"""skip_prk_steps"""    ) and scheduler.config.skip_prk_steps is False:
                 a_    :     List[str]										=    (
                     F'''The configuration file of this scheduler: {scheduler} has not set the configuration'''
                     """ `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
                     """ sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
                     """ incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
                     """ Hub, it would be very nice if you could open a Pull request for the"""
                     """ `scheduler/scheduler_config.json` file"""
                 )
                 deprecate("""skip_prk_steps not set"""      ,							"""1.0.0"""      ,							lowerCamelCase_      ,							standard_warn=lowerCamelCase_    )
                 a_    :     List[str]										=    dict(scheduler.config    )
                 a_    :     List[str]										=    True
                 a_    :     str										=    FrozenDict(lowerCamelCase_    )

            if safety_checker is None:
                 logger.warning(
                     F'''You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'''
                     """ that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
                     """ results in services or applications open to the public. Both the diffusers team and Hugging Face"""
                     """ strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
                     """ it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
                     """ information, please have a look at https://github.com/huggingface/diffusers/pull/254 ."""    )

            self.register_modules(
                segmentation_model=lowerCamelCase_      ,							segmentation_processor=lowerCamelCase_      ,							vae=lowerCamelCase_      ,							text_encoder=lowerCamelCase_      ,							tokenizer=lowerCamelCase_      ,							unet=lowerCamelCase_      ,							scheduler=lowerCamelCase_      ,							safety_checker=lowerCamelCase_      ,							feature_extractor=lowerCamelCase_      ,							)

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Optional[Union[str, int]] = "auto"    ):
            if slice_size == "auto":
                 # half the attention head size is usually a good trade-off between
                 # speed and memory
                 a_    :     Any										=    self.unet.config.attention_head_dim // 2
            self.unet.set_attention_slice(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     str    ):
            self.enable_attention_slicing(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Any    ):
            if is_accelerate_available():
                 from accelerate import cpu_offload
            else:
                 raise ImportError("""Please install accelerate via `pip install accelerate`"""    )

            a_    :     Any										=    torch.device("""cuda"""    )

            for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
                 if cpu_offloaded_model is not None:
                      cpu_offload(lowerCamelCase_      ,							lowerCamelCase_    )

       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 						UpperCAmelCase(   self      :     int    ):
            if self.device != torch.device("""meta"""    ) or not hasattr(self.unet      ,							"""_hf_hook"""    ):
                 return self.device
            for module in self.unet.modules():
                 if (
                     hasattr(lowerCamelCase_      ,							"""_hf_hook"""    )
                     and hasattr(module._hf_hook      ,							"""execution_device"""    )
                     and module._hf_hook.execution_device is not None
                 ):
                      return torch.device(module._hf_hook.execution_device    )
            return self.device

       @torch.no_grad()
       def __call__(   self      :     Any      ,							lowerCamelCase_      :     Union[str, List[str]]      ,							lowerCamelCase_      :     Union[torch.FloatTensor, PIL.Image.Image]      ,							lowerCamelCase_      :     str      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_1_2      ,							lowerCamelCase_      :     int = 5_0      ,							lowerCamelCase_      :     float = 7.5      ,							lowerCamelCase_      :     Optional[Union[str, List[str]]] = None      ,							lowerCamelCase_      :     Optional[int] = 1      ,							lowerCamelCase_      :     float = 0.0      ,							lowerCamelCase_      :     Optional[torch.Generator] = None      ,							lowerCamelCase_      :     Optional[torch.FloatTensor] = None      ,							lowerCamelCase_      :     Optional[str] = "pil"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[Callable[[int, int, torch.FloatTensor], None]] = None      ,							lowerCamelCase_      :     int = 1      ,							**lowerCamelCase_      :     Any      ,							):
            a_    :     str										=    self.segmentation_processor(
                text=[text]      ,							images=[image]      ,							padding="""max_length"""      ,							return_tensors="""pt"""    ).to(self.device    )
            a_    :     List[str]										=    self.segmentation_model(**lowerCamelCase_    )
            a_    :     str										=    torch.sigmoid(outputs.logits    ).cpu().detach().unsqueeze(-1    ).numpy()
            a_    :     Optional[int]										=    self.numpy_to_pil(lowerCamelCase_    )[0].resize(image.size    )

            # Run inpainting pipeline with the generated mask
            a_    :     Any										=    StableDiffusionInpaintPipeline(
                vae=self.vae      ,							text_encoder=self.text_encoder      ,							tokenizer=self.tokenizer      ,							unet=self.unet      ,							scheduler=self.scheduler      ,							safety_checker=self.safety_checker      ,							feature_extractor=self.feature_extractor      ,							)
            return inpainting_pipeline(
                prompt=lowerCamelCase_      ,							image=lowerCamelCase_      ,							mask_image=lowerCamelCase_      ,							height=lowerCamelCase_      ,							width=lowerCamelCase_      ,							num_inference_steps=lowerCamelCase_      ,							guidance_scale=lowerCamelCase_      ,							negative_prompt=lowerCamelCase_      ,							num_images_per_prompt=lowerCamelCase_      ,							eta=lowerCamelCase_      ,							generator=lowerCamelCase_      ,							latents=lowerCamelCase_      ,							output_type=lowerCamelCase_      ,							return_dict=lowerCamelCase_      ,							callback=lowerCamelCase_      ,							callback_steps=lowerCamelCase_      ,							)



