

import math
from typing import Callable, List, Optional, Union

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

from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from diffusers.schedulers import DDIMScheduler, DDPMScheduler, LMSDiscreteScheduler, PNDMScheduler

def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			int   ,				lowercase_:			List[str]=[] )					->				Union[str, Any]:
 A__							:   Tuple					       =		size[0] - overlap_pixels * 2
 A__							:   Tuple					       =		size[1] - overlap_pixels * 2
 for letter in ["l", "r"]:
  if letter in remove_borders:
   size_x += overlap_pixels
 for letter in ["t", "b"]:
  if letter in remove_borders:
   size_y += overlap_pixels
 A__							:   Optional[Any]					       =		np.ones((size_y, size_x)   ,				dtype=np.uinta ) * 255
 A__							:   List[str]					       =		np.pad(lowercase_   ,				mode="""linear_ramp"""   ,				pad_width=lowercase_   ,				end_values=0 )

 if "l" in remove_borders:
  A__							:   str					       =		mask[:, overlap_pixels : mask.shape[1]]
 if "r" in remove_borders:
  A__							:   int					       =		mask[:, 0 : mask.shape[1] - overlap_pixels]
 if "t" in remove_borders:
  A__							:   Optional[Any]					       =		mask[overlap_pixels : mask.shape[0], :]
 if "b" in remove_borders:
  A__							:   str					       =		mask[0 : mask.shape[0] - overlap_pixels, :]
 return mask

def 						UpperCamelCase (lowercase_:			Dict   ,				lowercase_:			List[Any]   ,				lowercase_:			Any )					->				List[str]:
 return max(lowercase_   ,				min(lowercase_   ,				lowercase_ ) )

def 						UpperCamelCase (lowercase_:			[int]   ,				lowercase_:			[int]   ,				lowercase_:			[int] )					->				Any:
 return (
     clamp(rect[0]   ,				min[0]   ,				max[0] ),
     clamp(rect[1]   ,				min[1]   ,				max[1] ),
     clamp(rect[2]   ,				min[0]   ,				max[0] ),
     clamp(rect[3]   ,				min[1]   ,				max[1] ),
 )

def 						UpperCamelCase (lowercase_:			[int]   ,				lowercase_:			int   ,				lowercase_:			[int] )					->				int:
 A__							:   str					       =		list(lowercase_ )
 rect[0] -= overlap
 rect[1] -= overlap
 rect[2] += overlap
 rect[3] += overlap
 A__							:   Union[str, Any]					       =		clamp_rect(lowercase_   ,				[0, 0]   ,				[image_size[0], image_size[1]] )
 return rect

def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Tuple   ,				lowercase_:			Union[str, Any]   ,				lowercase_:			str )					->				Union[str, Any]:
 A__							:   Optional[Any]					       =		Image.new("""RGB"""   ,				(tile.size[0] + original_slice, tile.size[1]) )
 result.paste(
     original_image.resize((tile.size[0], tile.size[1])   ,				Image.BICUBIC ).crop(
         (slice_x, 0, slice_x + original_slice, tile.size[1]) )   ,				(0, 0)   ,				)
 result.paste(lowercase_   ,				(original_slice, 0) )
 return result

def 						UpperCamelCase (lowercase_:			int   ,				lowercase_:			Optional[Any] )					->				int:
 A__							:   Tuple					       =		(original_image_slice * 4, 0, tile.size[0], tile.size[1])
 A__							:   str					       =		tile.crop(lowercase_ )
 return tile

def 						UpperCamelCase (lowercase_:			Any   ,				lowercase_:			Tuple )					->				List[str]:
 A__							:   Tuple					       =		n % d
 return n - divisor



class    _a						(__magic_name__    ):


     '''simple docstring'''






     def __init__(			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__ = 350   ,	):
      super().__init__(
          vae=A__   ,	text_encoder=A__   ,	tokenizer=A__   ,	unet=A__   ,	low_res_scheduler=A__   ,	scheduler=A__   ,	max_noise_level=A__   ,	)






     def   __A      (			self   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	**A__      ):
      torch.manual_seed(0      )
      A__							:   Optional[Any]					       =		(
          min(image.size[0] - (tile_size + original_image_slice)   ,	x * tile_size      ),
          min(image.size[1] - (tile_size + original_image_slice)   ,	y * tile_size      ),
          min(image.size[0]   ,	(x + 1) * tile_size      ),
          min(image.size[1]   ,	(y + 1) * tile_size      ),
      )
      A__							:   List[Any]					       =		add_overlap_rect(A__   ,	A__   ,	image.size      )
      A__							:   Union[str, Any]					       =		image.crop(A__      )
      A__							:   Optional[int]					       =		((crop_rect[0] + ((crop_rect[2] - crop_rect[0]) / 2)) / image.size[0]) * tile.size[0]
      A__							:   int					       =		translated_slice_x - (original_image_slice / 2)
      A__							:   List[str]					       =		max(0   ,	A__      )
      A__							:   Tuple					       =		squeeze_tile(A__   ,	A__   ,	A__   ,	A__      )
      A__							:   Optional[int]					       =		to_input.size
      A__							:   Optional[int]					       =		to_input.resize((tile_size, tile_size)   ,	Image.BICUBIC      )
      A__							:   Tuple					       =		super(A__   ,	self      ).__call__(image=A__   ,	**A__      ).images[0]
      A__							:   Optional[Any]					       =		upscaled_tile.resize((orig_input_size[0] * 4, orig_input_size[1] * 4)   ,	Image.BICUBIC      )
      A__							:   Tuple					       =		unsqueeze_tile(A__   ,	A__      )
      A__							:   str					       =		upscaled_tile.resize((tile.size[0] * 4, tile.size[1] * 4)   ,	Image.BICUBIC      )
      A__							:   Any					       =		[]
      if x == 0:
       remove_borders.append("""l"""      )
      elif crop_rect[2] == image.size[0]:
       remove_borders.append("""r"""      )
      if y == 0:
       remove_borders.append("""t"""      )
      elif crop_rect[3] == image.size[1]:
       remove_borders.append("""b"""      )
      A__							:   List[Any]					       =		Image.fromarray(
          make_transparency_mask(
              (upscaled_tile.size[0], upscaled_tile.size[1])   ,	tile_border * 4   ,	remove_borders=A__      )   ,	mode="""L"""   ,	)
      final_image.paste(
          A__   ,	(crop_rect_with_overlap[0] * 4, crop_rect_with_overlap[1] * 4)   ,	A__      )






     @torch.no_grad()
     def __call__(			self   ,	A__   ,	A__   ,	A__ = 75   ,	A__ = 9.0   ,	A__ = 50   ,	A__ = None   ,	A__ = 1   ,	A__ = 0.0   ,	A__ = None   ,	A__ = None   ,	A__ = None   ,	A__ = 1   ,	A__ = 128   ,	A__ = 32   ,	A__ = 32   ,	):
      A__							:   List[str]					       =		Image.new("""RGB"""   ,	(image.size[0] * 4, image.size[1] * 4)      )
      A__							:   Union[str, Any]					       =		math.ceil(image.size[0] / tile_size      )
      A__							:   Tuple					       =		math.ceil(image.size[1] / tile_size      )
      A__							:   Union[str, Any]					       =		tcx * tcy
      A__							:   int					       =		0
      for y in range(A__      ):
       for x in range(A__      ):
        self._process_tile(
            A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	A__   ,	prompt=A__   ,	num_inference_steps=A__   ,	guidance_scale=A__   ,	noise_level=A__   ,	negative_prompt=A__   ,	num_images_per_prompt=A__   ,	eta=A__   ,	generator=A__   ,	latents=A__   ,	)
        current_count += 1
        if callback is not None:
         callback({"""progress""": current_count / total_tile_count, """image""": final_image}      )
      return final_image



def 						UpperCamelCase ()					->				Union[str, Any]:
 # Run a demo
 A__							:   Optional[int]					       =		"""stabilityai/stable-diffusion-x4-upscaler"""
 A__							:   List[Any]					       =		StableDiffusionTiledUpscalePipeline.from_pretrained(lowercase_   ,				revision="""fp16"""   ,				torch_dtype=torch.floataa )
 A__							:   Any					       =		pipe.to("""cuda""" )
 A__							:   str					       =		Image.open("""../../docs/source/imgs/diffusers_library.jpg""" )

 def callback(lowercase_:			str ):
  print(f"""progress: {obj['progress']:.4f}""" )
  obj["image"].save("""diffusers_library_progress.jpg""" )

 A__							:   Optional[Any]					       =		pipe(image=lowercase_   ,				prompt="""Black font, white background, vector"""   ,				noise_level=40   ,				callback=lowercase_ )
 final_image.save("""diffusers_library.jpg""" )


if __name__ == "__main__":
 main()






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

A_	:			int  	=      '=======\n>>>>>>>\n'

A_	:			Optional[Any]  	=      [
    'TextEncoderConfig',
    'ByteTextEncoder',
    'SubwordTextEncoder',
    'encoder_config',
    'maybe_build_from_corpus',
    'manual_dir',
]

A_	:			List[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 (lowercase_:			Namespace )					->				str:
	return ConvertCommand(args.tfds_path   ,				args.datasets_directory )



class    _a						(__magic_name__    ):


					'''simple docstring'''






					@staticmethod
					def   __A      (			A__      ):
						A__							:   Any					       =		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__      ):
						A__							:   Optional[Any]					       =		get_logger("""datasets-cli/converting"""      )

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






					def   __A      (			self      ):
						if os.path.isdir(self._tfds_path      ):
							A__							:   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__							:   Optional[int]					       =		os.path.abspath(self._datasets_directory      )

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

						A__							:   Optional[Any]					       =		[]
						A__							:   List[Any]					       =		[]
						A__							:   List[Any]					       =		{}

						if os.path.isdir(self._tfds_path      ):
							A__							:   List[str]					       =		os.listdir(A__      )
						else:
							A__							:   Optional[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(A__   ,	A__      )
							A__							:   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:
								A__							:   Dict					       =		f.readlines()

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

                # 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]+)"""   ,	A__      )
									tfds_imports.extend(imp.strip() for imp in match.group(1      ).split(""","""      )      )
									A__							:   Tuple					       =		"""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[str]					       =		True
								out_lines.append(A__      )

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



