




import gc
import tempfile
import unittest

import numpy as np
import torch

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


a_      						=   False

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

      """simple docstring"""

      def 					snake_case		(   self    :       str ):
          # clean up the VRAM after each test
          super().tearDown()
          gc.collect()
          torch.cuda.empty_cache()







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
          # remove text_unet
          pipe.remove_unused_weights()
          pipe.to(_lowerCamelCase )
          pipe.set_progress_bar_config(disable=_lowerCamelCase )

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

          __UpperCamelCase     						=  torch.manual_seed(0 )
          __UpperCamelCase     						=  pipe(
              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 )
              __UpperCamelCase     						=  VersatileDiffusionDualGuidedPipeline.from_pretrained(_lowerCamelCase )

          pipe.to(_lowerCamelCase )
          pipe.set_progress_bar_config(disable=_lowerCamelCase )

          __UpperCamelCase     						=  generator.manual_seed(0 )
          __UpperCamelCase     						=  pipe(
              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 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  VersatileDiffusionDualGuidedPipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
          pipe.remove_unused_weights()
          pipe.to(_lowerCamelCase )
          pipe.set_progress_bar_config(disable=_lowerCamelCase )

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

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

          assert image.shape == (1, 512, 512, 3)
          __UpperCamelCase     						=  np.array([0.07_87, 0.08_49, 0.08_26, 0.08_12, 0.08_07, 0.07_95, 0.08_18, 0.07_98, 0.07_79] )

          assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2













"""simple docstring"""





import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()




def 		lowerCAmelCase_	(  lowercase_   :							List[str] ,      lowercase_   :							List[str] ,      lowercase_   :							Dict		):

 '''simple docstring'''






 __SCREAMING_SNAKE_CASE :			Tuple												=       TaConfig.from_json_file(lowercase_		)
 print(F'''Building PyTorch model from configuration: {config}'''		)
 __SCREAMING_SNAKE_CASE :			List[str]												=       TaForConditionalGeneration(lowercase_		)

 # Load weights from tf checkpoint
 load_tf_weights_in_ta(lowercase_ ,      lowercase_ ,      lowercase_		)

 # Save pytorch-model
 print(F'''Save PyTorch model to {pytorch_dump_path}'''		)
 model.save_pretrained(lowercase_		)


if __name__ == "__main__":
    _lowerCamelCase       =	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help=(
            '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
        ),
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    _lowerCamelCase       =	parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)

