


import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class        UpperCAmelCase__			(   A_  ,    unittest.TestCase						):



    """simple docstring"""






    UpperCAmelCase__		:						List[str]    =    PaintByExamplePipeline
    UpperCAmelCase__		:						Tuple    =    IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
    UpperCAmelCase__		:						List[str]    =    IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    UpperCAmelCase__		:						List[str]    =    frozenset([]						)  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess






    def _a   (     self       )    ->			List[str]:
         torch.manual_seed(0       )
         __UpperCamelCase       				=UNetaDConditionModel(
             block_out_channels=(32, 64)   ,  layers_per_block=2   ,  sample_size=32   ,  in_channels=9   ,  out_channels=4   ,  down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,  up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,  cross_attention_dim=32   ,  )
         __UpperCamelCase       				=PNDMScheduler(skip_prk_steps=A_       )
         torch.manual_seed(0       )
         __UpperCamelCase       				=AutoencoderKL(
             block_out_channels=[32, 64]   ,  in_channels=3   ,  out_channels=3   ,  down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,  up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,  latent_channels=4   ,  )
         torch.manual_seed(0       )
         __UpperCamelCase       				=CLIPVisionConfig(
             hidden_size=32   ,  projection_dim=32   ,  intermediate_size=37   ,  layer_norm_eps=1E-05   ,  num_attention_heads=4   ,  num_hidden_layers=5   ,  image_size=32   ,  patch_size=4   ,  )
         __UpperCamelCase       				=PaintByExampleImageEncoder(A_   ,  proj_size=32       )
         __UpperCamelCase       				=CLIPImageProcessor(crop_size=32   ,  size=32       )

         __UpperCamelCase       				={
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'image_encoder': image_encoder,
             'safety_checker': None,
             'feature_extractor': feature_extractor,
         }
         return components






    def _a   (     self   ,  A_       )    ->			List[Any]:
         __UpperCamelCase       				=np.array(image.convert('RGB'       )       )
         __UpperCamelCase       				=image[None].transpose(0   ,  3   ,  1   ,  2       )
         __UpperCamelCase       				=torch.from_numpy(A_       ).to(dtype=torch.floataa       ) / 127.5 - 1.0
         return image






    def _a   (     self   ,  A_="cpu"   ,  A_=0       )    ->			List[Any]:
         # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
         __UpperCamelCase       				=floats_tensor((1, 3, 32, 32)   ,  rng=random.Random(A_       )       ).to(A_       )
         __UpperCamelCase       				=image.cpu().permute(0   ,  2   ,  3   ,  1       )[0]
         __UpperCamelCase       				=Image.fromarray(np.uinta(A_       )       ).convert('RGB'       ).resize((64, 64)       )
         __UpperCamelCase       				=Image.fromarray(np.uinta(image + 4       )       ).convert('RGB'       ).resize((64, 64)       )
         __UpperCamelCase       				=Image.fromarray(np.uinta(A_       )       ).convert('RGB'       ).resize((32, 32)       )

         if str(A_       ).startswith('mps'       ):
              __UpperCamelCase       				=torch.manual_seed(A_       )
         else:
              __UpperCamelCase       				=torch.Generator(device=A_       ).manual_seed(A_       )
         __UpperCamelCase       				={
             'example_image': example_image,
             'image': init_image,
             'mask_image': mask_image,
             'generator': generator,
             'num_inference_steps': 2,
             'guidance_scale': 6.0,
             'output_type': 'numpy',
         }
         return inputs






    def _a   (     self       )    ->			int:
         __UpperCamelCase       				=self.get_dummy_components()

         # make sure here that pndm scheduler skips prk
         __UpperCamelCase       				=PaintByExamplePipeline(**A_       )
         __UpperCamelCase       				=pipe.to('cpu'       )
         pipe.set_progress_bar_config(disable=A_       )

         __UpperCamelCase       				=self.get_dummy_inputs()
         __UpperCamelCase       				=pipe(**A_       )
         __UpperCamelCase       				=output.images

         __UpperCamelCase       				=image[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)
         __UpperCamelCase       				=np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]       )

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






    def _a   (     self       )    ->			Tuple:
         __UpperCamelCase       				='cpu'
         __UpperCamelCase       				=self.get_dummy_inputs()
         inputs.pop('mask_image'       )
         __UpperCamelCase       				=self.convert_to_pt(inputs.pop('image'       )       )
         __UpperCamelCase       				=image.clamp(0   ,  1       ) / 2

         # make sure here that pndm scheduler skips prk
         __UpperCamelCase       				=PaintByExamplePipeline(**self.get_dummy_components()       )
         __UpperCamelCase       				=pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )

         __UpperCamelCase       				=pipe(image=A_   ,  mask_image=mask_image[:, 0]   ,  **A_       )
         __UpperCamelCase       				=output.images

         __UpperCamelCase       				=image.cpu().permute(0   ,  2   ,  3   ,  1       )[0]
         __UpperCamelCase       				=mask_image.cpu().permute(0   ,  2   ,  3   ,  1       )[0]

         __UpperCamelCase       				=Image.fromarray(np.uinta(A_       )       ).convert('RGB'       )
         __UpperCamelCase       				=Image.fromarray(np.uinta(A_       )       ).convert('RGB'       )

         __UpperCamelCase       				=pipe(**self.get_dummy_inputs()       )
         __UpperCamelCase       				=output.images

         assert out_a.shape == (1, 64, 64, 3)
         assert np.abs(out_a.flatten() - out_a.flatten()       ).max() < 5E-2






    def _a   (     self       )    ->			Dict:
         super().test_inference_batch_single_identical(expected_max_diff=3E-3       )






@slow
@require_torch_gpu
class        UpperCAmelCase__			(   unittest.TestCase						):



    """simple docstring"""






    def _a   (     self       )    ->			Optional[Any]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






    def _a   (     self       )    ->			Optional[Any]:
         # make sure here that pndm scheduler skips prk
         __UpperCamelCase       				=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/paint_by_example/dog_in_bucket.png'       )
         __UpperCamelCase       				=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/paint_by_example/mask.png'       )
         __UpperCamelCase       				=load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/paint_by_example/panda.jpg'       )

         __UpperCamelCase       				=PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'       )
         __UpperCamelCase       				=pipe.to(A_       )
         pipe.set_progress_bar_config(disable=A_       )

         __UpperCamelCase       				=torch.manual_seed(321       )
         __UpperCamelCase       				=pipe(
             image=A_   ,  mask_image=A_   ,  example_image=A_   ,  generator=A_   ,  guidance_scale=5.0   ,  num_inference_steps=50   ,  output_type='np'   ,  )

         __UpperCamelCase       				=output.images

         __UpperCamelCase       				=image[0, -3:, -3:, -1]

         assert image.shape == (1, 512, 512, 3)
         __UpperCamelCase       				=np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]       )

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









import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()

def 		_UpperCAmelCase							(     SCREAMING_SNAKE_CASE__     :   List[Any]     ,	SCREAMING_SNAKE_CASE__     :   Union[str, Any]     ,	SCREAMING_SNAKE_CASE__     :   Any							):
     # Initialise PyTorch model
     __UpperCamelCase       				=json.load(open(SCREAMING_SNAKE_CASE__     ,	'r'							)							)
     __UpperCamelCase       				=GPTNeoConfig(
         hidden_size=config_json['n_embd']     ,	num_layers=config_json['n_layer']     ,	num_heads=config_json['n_head']     ,	attention_types=config_json['attention_types']     ,	max_position_embeddings=config_json['n_positions']     ,	resid_dropout=config_json['res_dropout']     ,	embed_dropout=config_json['embed_dropout']     ,	attention_dropout=config_json['attn_dropout']     ,	)
     print(F'Building PyTorch model from configuration: {config}'							)
     __UpperCamelCase       				=GPTNeoForCausalLM(SCREAMING_SNAKE_CASE__							)

     # Load weights from tf checkpoint
     load_tf_weights_in_gpt_neo(SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__     ,	SCREAMING_SNAKE_CASE__							)

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


if __name__ == "__main__":
   _A									=		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 mesh-tf model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
   )
   _A									=		parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)





