
"""simple docstring"""




from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

A__   :					List[str]							=   '\n    Examples:\n        ```py\n        >>> import torch\n        >>> import numpy as np\n\n        >>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline\n        >>> from transformers import pipeline\n        >>> from diffusers.utils import load_image\n\n\n        >>> def make_hint(image, depth_estimator):\n        ...     image = depth_estimator(image)["depth"]\n        ...     image = np.array(image)\n        ...     image = image[:, :, None]\n        ...     image = np.concatenate([image, image, image], axis=2)\n        ...     detected_map = torch.from_numpy(image).float() / 255.0\n        ...     hint = detected_map.permute(2, 0, 1)\n        ...     return hint\n\n\n        >>> depth_estimator = pipeline("depth-estimation")\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior = pipe_prior.to("cuda")\n\n        >>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16\n        ... )\n        >>> pipe = pipe.to("cuda")\n\n\n        >>> img = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/cat.png"\n        ... ).resize((768, 768))\n\n        >>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")\n\n        >>> prompt = "A robot, 4k photo"\n        >>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"\n\n        >>> generator = torch.Generator(device="cuda").manual_seed(43)\n\n        >>> image_emb, zero_image_emb = pipe_prior(\n        ...     prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator\n        ... ).to_tuple()\n\n        >>> images = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     hint=hint,\n        ...     num_inference_steps=50,\n        ...     generator=generator,\n        ...     height=768,\n        ...     width=768,\n        ... ).images\n\n        >>> images[0].save("robot_cat.png")\n        ```\n'




def  _lowerCAmelCase		(		_UpperCamelCase ,				_UpperCamelCase ,				_UpperCamelCase=8						):






  """simple docstring"""



  _lowercase:			Any       =     height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowercase:			int       =     width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class 			__magic_name__       (       SCREAMING_SNAKE_CASE__ ):
       def __init__(     self ,				A_ ,				A_ ,				A_ ,				)     ->	Union[str, Any]:





         """simple docstring"""




         super().__init__()

         self.register_modules(
             unet=A_ ,				scheduler=A_ ,				movq=A_ ,				)
         _lowercase:			Dict       =     2 ** (len(self.movq.config.block_out_channels       ) - 1)
       def 							lowercase_							(     self ,				A_ ,				A_ ,				A_ ,				A_ ,				A_ ,				A_       )     ->	str:





         """simple docstring"""




         if latents is None:
           _lowercase:			Optional[int]       =     randn_tensor(A_ ,				generator=A_ ,				device=A_ ,				dtype=A_       )
         else:
           if latents.shape != shape:
             raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}'''       )
           _lowercase:			List[str]       =     latents.to(A_       )

         _lowercase:			Union[str, Any]       =     latents * scheduler.init_noise_sigma
         return latents
       def 							lowercase_							(     self ,				A_=0       )     ->	Union[str, Any]:





         """simple docstring"""




         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError('''Please install accelerate via `pip install accelerate`'''       )

         _lowercase:			str       =     torch.device(f'''cuda:{gpu_id}'''       )

         _lowercase:			List[str]       =     [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(A_ ,				A_       )
       def 							lowercase_							(     self ,				A_=0       )     ->	Dict:





         """simple docstring"""




         if is_accelerate_available() and is_accelerate_version('''>=''' ,				'''0.17.0.dev0'''       ):
           from accelerate import cpu_offload_with_hook
         else:
           raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''       )

         _lowercase:			Tuple       =     torch.device(f'''cuda:{gpu_id}'''       )

         if self.device.type != "cpu":
           self.to('''cpu''' ,				silence_dtype_warnings=A_       )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         _lowercase:			Dict       =     None
         for cpu_offloaded_model in [self.unet, self.movq]:
           _lowercase:			Dict       =     cpu_offload_with_hook(A_ ,				A_ ,				prev_module_hook=A_       )

         # We'll offload the last model manually.
         _lowercase:			str       =     hook
       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def 							lowercase_							(     self       )     ->	Union[str, Any]:





         """simple docstring"""




         if not hasattr(self.unet ,				'''_hf_hook'''       ):
           return self.device
         for module in self.unet.modules():
           if (
               hasattr(A_ ,				'''_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()
       @replace_example_docstring(A_       )
       def __call__(     self ,				A_ ,				A_ ,				A_ ,				A_ = 512 ,				A_ = 512 ,				A_ = 100 ,				A_ = 4.0 ,				A_ = 1 ,				A_ = None ,				A_ = None ,				A_ = "pil" ,				A_ = True ,				)     ->	Dict:





         """simple docstring"""




         _lowercase:			str       =     self._execution_device

         _lowercase:			Optional[Any]       =     guidance_scale > 1.0

         if isinstance(A_ ,				A_       ):
           _lowercase:			Optional[Any]       =     torch.cat(A_ ,				dim=0       )
         if isinstance(A_ ,				A_       ):
           _lowercase:			str       =     torch.cat(A_ ,				dim=0       )
         if isinstance(A_ ,				A_       ):
           _lowercase:			Optional[int]       =     torch.cat(A_ ,				dim=0       )

         _lowercase:			Dict       =     image_embeds.shape[0] * num_images_per_prompt

         if do_classifier_free_guidance:
           _lowercase:			List[str]       =     image_embeds.repeat_interleave(A_ ,				dim=0       )
           _lowercase:			int       =     negative_image_embeds.repeat_interleave(A_ ,				dim=0       )
           _lowercase:			str       =     hint.repeat_interleave(A_ ,				dim=0       )

         _lowercase:			Dict       =     torch.cat([negative_image_embeds, image_embeds] ,				dim=0       ).to(dtype=self.unet.dtype ,				device=A_       )
         _lowercase:			Tuple       =     torch.cat([hint, hint] ,				dim=0       ).to(dtype=self.unet.dtype ,				device=A_       )
         self.scheduler.set_timesteps(A_ ,				device=A_       )
         _lowercase:			Optional[Any]       =     self.scheduler.timesteps

         _lowercase:			str       =     self.movq.config.latent_channels

         _lowercase:			Union[str, Any]       =     downscale_height_and_width(A_ ,				A_ ,				self.movq_scale_factor       )

         # create initial latent
         _lowercase:			Any       =     self.prepare_latents(
             (batch_size, num_channels_latents, height, width) ,				image_embeds.dtype ,				A_ ,				A_ ,				A_ ,				self.scheduler ,				)

         for i, t in enumerate(self.progress_bar(A_       )       ):
           # expand the latents if we are doing classifier free guidance
           _lowercase:			Optional[int]       =     torch.cat([latents] * 2       ) if do_classifier_free_guidance else latents

           _lowercase:			Tuple       =     {'''image_embeds''': image_embeds, '''hint''': hint}
           _lowercase:			Dict       =     self.unet(
               sample=A_ ,				timestep=A_ ,				encoder_hidden_states=A_ ,				added_cond_kwargs=A_ ,				return_dict=A_ ,				)[0]

           if do_classifier_free_guidance:
             _lowercase:			Any       =     noise_pred.split(latents.shape[1] ,				dim=1       )
             _lowercase:			int       =     noise_pred.chunk(2       )
             _lowercase:			Tuple       =     variance_pred.chunk(2       )
             _lowercase:			Optional[Any]       =     noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             _lowercase:			str       =     torch.cat([noise_pred, variance_pred_text] ,				dim=1       )

           if not (
               hasattr(self.scheduler.config ,				'''variance_type'''       )
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
             _lowercase:			Tuple       =     noise_pred.split(latents.shape[1] ,				dim=1       )

           # compute the previous noisy sample x_t -> x_t-1
           _lowercase:			Tuple       =     self.scheduler.step(
               A_ ,				A_ ,				A_ ,				generator=A_ ,				)[0]
         # post-processing
         _lowercase:			Dict       =     self.movq.decode(A_ ,				force_not_quantize=A_       )['''sample''']

         if output_type not in ["pt", "np", "pil"]:
           raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'''       )

         if output_type in ["np", "pil"]:
           _lowercase:			Optional[Any]       =     image * 0.5 + 0.5
           _lowercase:			Optional[int]       =     image.clamp(0 ,				1       )
           _lowercase:			Dict       =     image.cpu().permute(0 ,				2 ,				3 ,				1       ).float().numpy()

         if output_type == "pil":
           _lowercase:			Optional[Any]       =     self.numpy_to_pil(A_       )

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=A_       )


"""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\'.'''       )
