




"""simple docstring"""



import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


SCREAMING_SNAKE_CASE    =	False




class      UpperCAmelCase_  (						unittest.TestCase							):
				pass






@nightly
@require_torch_gpu
class      UpperCAmelCase_  (						unittest.TestCase							):




				def 							__magic_name__							(   self						:	List[Any]				)				->							str:




					'''simple docstring'''

					super().tearDown()
					gc.collect()
					torch.cuda.empty_cache()




				def 							__magic_name__							(   self						:	Optional[int]				)				->							Tuple:




					'''simple docstring'''

					A__	=    VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"     ,			torch_dtype=torch.floataa				)
					pipe.to(lowerCAmelCase_				)
					pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

					A__	=    load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"				)

					A__	=    torch.manual_seed(0				)
					A__	=    pipe.dual_guided(
					    prompt="first prompt"     ,			image=lowerCAmelCase_     ,			text_to_image_strength=0.75     ,			generator=lowerCAmelCase_     ,			guidance_scale=7.5     ,			num_inference_steps=2     ,			output_type="numpy"     ,			).images

					with tempfile.TemporaryDirectory() as tmpdirname:
						pipe.save_pretrained(lowerCAmelCase_				)
						A__	=    VersatileDiffusionPipeline.from_pretrained(lowerCAmelCase_     ,			torch_dtype=torch.floataa				)
					pipe.to(lowerCAmelCase_				)
					pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

					A__	=    generator.manual_seed(0				)
					A__	=    pipe.dual_guided(
					    prompt="first prompt"     ,			image=lowerCAmelCase_     ,			text_to_image_strength=0.75     ,			generator=lowerCAmelCase_     ,			guidance_scale=7.5     ,			num_inference_steps=2     ,			output_type="numpy"     ,			).images

					assert np.abs(image - new_image				).sum() < 1e-5, "Models don't have the same forward pass"




				def 							__magic_name__							(   self						:	List[Any]				)				->							Optional[int]:




					'''simple docstring'''

					A__	=    VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion"     ,			torch_dtype=torch.floataa				)
					pipe.to(lowerCAmelCase_				)
					pipe.set_progress_bar_config(disable=lowerCAmelCase_				)

					A__	=    "cyberpunk 2077"
					A__	=    load_image(
					    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"				)
					A__	=    torch.manual_seed(0				)
					A__	=    pipe.dual_guided(
					    prompt=lowerCAmelCase_     ,			image=lowerCAmelCase_     ,			text_to_image_strength=0.75     ,			generator=lowerCAmelCase_     ,			guidance_scale=7.5     ,			num_inference_steps=50     ,			output_type="numpy"     ,			).images

					A__	=    image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					A__	=    np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001]				)

					assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1

					A__	=    "A painting of a squirrel eating a burger "
					A__	=    torch.manual_seed(0				)
					A__	=    pipe.text_to_image(
					    prompt=lowerCAmelCase_     ,			generator=lowerCAmelCase_     ,			guidance_scale=7.5     ,			num_inference_steps=50     ,			output_type="numpy"				).images

					A__	=    image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					A__	=    np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778]				)

					assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1

					A__	=    pipe.image_variation(lowerCAmelCase_     ,			generator=lowerCAmelCase_     ,			output_type="numpy"				).images

					A__	=    image[0, 253:256, 253:256, -1]

					assert image.shape == (1, 512, 512, 3)
					A__	=    np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456]				)

					assert np.abs(image_slice.flatten() - expected_slice				).max() < 1e-1











"""simple docstring"""



import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

SCREAMING_SNAKE_CASE    =	logging.get_logger(__name__)


SCREAMING_SNAKE_CASE    =	{name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}







def   _SCREAMING_SNAKE_CASE       (							lowercase_      ,			lowercase_      ,			lowercase_      ,			lowercase_						)						->		Any:
 if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
  raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()						)}."""						)

 if tokenizer_name is None:
  A__	=    TOKENIZER_CLASSES
 else:
  A__	=    {tokenizer_name: getattr(lowercase_      ,			tokenizer_name + "Fast"						)}

 logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""						)

 for tokenizer_name in tokenizer_names:
  A__	=    TOKENIZER_CLASSES[tokenizer_name]

  A__	=    True
  if checkpoint_name is None:
   A__	=    list(tokenizer_class.max_model_input_sizes.keys()						)
  else:
   A__	=    [checkpoint_name]

  logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""						)

  for checkpoint in checkpoint_names:
   logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""						)

   # Load tokenizer
   A__	=    tokenizer_class.from_pretrained(lowercase_      ,			force_download=lowercase_						)

   # Save fast tokenizer
   logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""						)

   # For organization names we create sub-directories
   if "/" in checkpoint:
    A__,				A__	=    checkpoint.split("/"						)
    A__	=    os.path.join(lowercase_      ,			lowercase_						)
   elif add_prefix:
    A__	=    checkpoint
    A__	=    dump_path
   else:
    A__	=    None
    A__	=    dump_path

   logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()						)[0]:
    A__	=    list(tokenizer.pretrained_vocab_files_map.values()						)[0][checkpoint]
    A__	=    file_path.split(lowercase_						)[-1][0]
    if next_char == "/":
     A__	=    os.path.join(lowercase_      ,			lowercase_						)
     A__	=    None

    logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""						)

   A__	=    tokenizer.save_pretrained(
       lowercase_      ,			legacy_format=lowercase_      ,			filename_prefix=lowercase_						)
   logger.info(f"""=> File names {file_names}"""						)

   for file_name in file_names:
    if not file_name.endswith("tokenizer.json"						):
     os.remove(lowercase_						)
     logger.info(f"""=> removing {file_name}"""						)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE    =	argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
      )
      parser.add_argument(
          "--tokenizer_name",
          default=None,
          type=str,
          help=(
              f'Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will '
              "download and convert all the checkpoints from AWS."
          ),
      )
      parser.add_argument(
          "--checkpoint_name",
          default=None,
          type=str,
          help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
      )
      parser.add_argument(
          "--force_download",
          action="store_true",
          help="Re-download checkpoints.",
      )
      SCREAMING_SNAKE_CASE    =	parser.parse_args()

      convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)





