


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

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


UpperCamelCase					:       Dict			    =     """<<<<<<< This should probably be modified because it mentions: """

UpperCamelCase					:       List[Any]			    =     """=======
>>>>>>>
"""

UpperCamelCase					:       Optional[Any]			    =     [
    """TextEncoderConfig""",
    """ByteTextEncoder""",
    """SubwordTextEncoder""",
    """encoder_config""",
    """maybe_build_from_corpus""",
    """manual_dir""",
]

UpperCamelCase					:       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 UpperCamelCase_				(				__a      )	->   Optional[Any]:
      return ConvertCommand(args.tfds_path			,				args.datasets_directory      )



class A__     ( A__  ):



  """simple docstring"""



  @staticmethod
  def    _UpperCamelCase( lowerCamelCase__			:  ArgumentParser       ):
        a__	:  List[str]     =   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			:  List[str]				,				lowerCamelCase__			:  str				,				lowerCamelCase__			:  str				,				*lowerCamelCase__			:  Tuple       ):
        a__	:  str     =   get_logger("datasets-cli/converting"       )

        a__	:  Optional[Any]     =   tfds_path
        a__	:  Optional[int]     =   datasets_directory

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

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

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

        a__	:  Tuple     =   []
        a__	:  str     =   []
        a__	:  List[Any]     =   {}

        if os.path.isdir(self._tfds_path       ):
              a__	:  List[str]     =   os.listdir(lowerCamelCase__       )
        else:
              a__	:  Union[str, Any]     =   [os.path.basename(self._tfds_path       )]

        for f_name in file_names:
              self._logger.info(f'''Looking at file {f_name}'''       )
              a__	:  Any     =   os.path.join(lowerCamelCase__				,				lowerCamelCase__       )
              a__	:  Dict     =   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__	:  List[Any]     =   f.readlines()

              a__	:  Union[str, Any]     =   []
              a__	:  Union[str, Any]     =   False
              a__	:  Union[str, Any]     =   False
              a__	:  Dict     =   []
              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__	:  List[Any]     =   "import datasets\n"
                    elif "import tensorflow" in out_line:
                          # order is important here
                          a__	:  List[str]     =   ""
                          continue
                    elif "from absl import logging" in out_line:
                          a__	:  Dict     =   "from datasets import logging\n"
                    elif "getLogger" in out_line:
                          a__	:  List[Any]     =   out_line.replace("getLogger"				,				"get_logger"       )
                    elif any(expression in out_line for expression in TO_HIGHLIGHT       ):
                          a__	:  List[str]     =   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__	:  Tuple     =   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__	:  Optional[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__	:  Optional[Any]     =   "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(lowerCamelCase__       )

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









"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class      UpperCamelCase_			(__A	):





	def __init__( self   :					List[Any]      ,      lowerCAmelCase_   :					TransformeraDModel      ,      lowerCAmelCase_   :					AutoencoderKL      ,      lowerCAmelCase_   :					KarrasDiffusionSchedulers      ,      lowerCAmelCase_   :					Optional[Dict[int, str]] = None      ,      )		->     List[Any]:
				super().__init__()
				self.register_modules(transformer=lowerCAmelCase_      ,      vae=lowerCAmelCase_      ,      scheduler=lowerCAmelCase_		)

				# create a imagenet -> id dictionary for easier use
				UpperCAmelCase_	:					str     	=   {}
				if idalabel is not None:
							for key, value in idalabel.items():
										for label in value.split(","		):
													UpperCAmelCase_	:					Union[str, Any]     	=   int(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   dict(sorted(self.labels.items()		)		)





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      lowerCAmelCase_   :					Union[str, List[str]]		)		->     List[int]:

				if not isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					str     	=   list(lowerCAmelCase_		)

				for l in label:
							if l not in self.labels:
										raise ValueError(
										    f"""{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}."""		)

				return [self.labels[l] for l in label]





	@torch.no_grad()
	def __call__( self   :					List[str]      ,      lowerCAmelCase_   :					List[int]      ,      lowerCAmelCase_   :					float = 4.0      ,      lowerCAmelCase_   :					Optional[Union[torch.Generator, List[torch.Generator]]] = None      ,      lowerCAmelCase_   :					int = 50      ,      lowerCAmelCase_   :					Optional[str] = "pil"      ,      lowerCAmelCase_   :					bool = True      ,      )		->     Union[ImagePipelineOutput, Tuple]:
				UpperCAmelCase_	:					Optional[int]     	=   len(lowerCAmelCase_		)
				UpperCAmelCase_	:					int     	=   self.transformer.config.sample_size
				UpperCAmelCase_	:					Optional[Any]     	=   self.transformer.config.in_channels

				UpperCAmelCase_	:					Tuple     	=   randn_tensor(
				    shape=(batch_size, latent_channels, latent_size, latent_size)      ,      generator=lowerCAmelCase_      ,      device=self.device      ,      dtype=self.transformer.dtype      ,      )
				UpperCAmelCase_	:					str     	=   torch.cat([latents] * 2		) if guidance_scale > 1 else latents

				UpperCAmelCase_	:					List[Any]     	=   torch.tensor(lowerCAmelCase_      ,      device=self.device		).reshape(-1		)
				UpperCAmelCase_	:					Optional[Any]     	=   torch.tensor([1_000] * batch_size      ,      device=self.device		)
				UpperCAmelCase_	:					int     	=   torch.cat([class_labels, class_null]      ,      0		) if guidance_scale > 1 else class_labels

				# set step values
				self.scheduler.set_timesteps(lowerCAmelCase_		)

				for t in self.progress_bar(self.scheduler.timesteps		):
							if guidance_scale > 1:
										UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input[: len(lowerCAmelCase_		) // 2]
										UpperCAmelCase_	:					int     	=   torch.cat([half, half]      ,      dim=0		)
							UpperCAmelCase_	:					Optional[int]     	=   self.scheduler.scale_model_input(lowerCAmelCase_      ,      lowerCAmelCase_		)

							UpperCAmelCase_	:					Union[str, Any]     	=   t
							if not torch.is_tensor(lowerCAmelCase_		):
										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
										# This would be a good case for the `match` statement (Python 3.10+)
										UpperCAmelCase_	:					List[Any]     	=   latent_model_input.device.type == "mps"
										if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
													UpperCAmelCase_	:					Optional[int]     	=   torch.floataa if is_mps else torch.floataa
										else:
													UpperCAmelCase_	:					int     	=   torch.intaa if is_mps else torch.intaa
										UpperCAmelCase_	:					Optional[int]     	=   torch.tensor([timesteps]      ,      dtype=lowerCAmelCase_      ,      device=latent_model_input.device		)
							elif len(timesteps.shape		) == 0:
										UpperCAmelCase_	:					List[Any]     	=   timesteps[None].to(latent_model_input.device		)
							# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
							UpperCAmelCase_	:					List[Any]     	=   timesteps.expand(latent_model_input.shape[0]		)
							# predict noise model_output
							UpperCAmelCase_	:					List[Any]     	=   self.transformer(
							    lowerCAmelCase_      ,      timestep=lowerCAmelCase_      ,      class_labels=lowerCAmelCase_		).sample

							# perform guidance
							if guidance_scale > 1:
										UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
										UpperCAmelCase_	,      UpperCAmelCase_	:					Union[str, Any]     	=   torch.split(lowerCAmelCase_      ,      len(lowerCAmelCase_		) // 2      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   uncond_eps + guidance_scale * (cond_eps - uncond_eps)
										UpperCAmelCase_	:					Tuple     	=   torch.cat([half_eps, half_eps]      ,      dim=0		)

										UpperCAmelCase_	:					Optional[Any]     	=   torch.cat([eps, rest]      ,      dim=1		)

							# learned sigma
							if self.transformer.config.out_channels // 2 == latent_channels:
										UpperCAmelCase_	,      UpperCAmelCase_	:					List[str]     	=   torch.split(lowerCAmelCase_      ,      lowerCAmelCase_      ,      dim=1		)
							else:
										UpperCAmelCase_	:					Optional[Any]     	=   noise_pred

							# compute previous image: x_t -> x_t-1
							UpperCAmelCase_	:					Union[str, Any]     	=   self.scheduler.step(lowerCAmelCase_      ,      lowerCAmelCase_      ,      lowerCAmelCase_		).prev_sample

				if guidance_scale > 1:
							UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[Any]     	=   latent_model_input.chunk(2      ,      dim=0		)
				else:
							UpperCAmelCase_	:					Optional[int]     	=   latent_model_input

				UpperCAmelCase_	:					Dict     	=   1 / self.vae.config.scaling_factor * latents
				UpperCAmelCase_	:					Dict     	=   self.vae.decode(lowerCAmelCase_		).sample

				UpperCAmelCase_	:					Tuple     	=   (samples / 2 + 0.5).clamp(0      ,      1		)

				# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
				UpperCAmelCase_	:					Optional[Any]     	=   samples.cpu().permute(0      ,      2      ,      3      ,      1		).float().numpy()

				if output_type == "pil":
							UpperCAmelCase_	:					Optional[int]     	=   self.numpy_to_pil(lowerCAmelCase_		)

				if not return_dict:
							return (samples,)

				return ImagePipelineOutput(images=lowerCAmelCase_		)



