
"""simple docstring"""




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 	_lowercase    (							lowerCAmelCase_						, unittest.TestCase					):


     '''simple docstring'''





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



     def 			lowerCAmelCase__							(   self	)->       Optional[int]:
          torch.manual_seed(0	)
          UpperCAmelCase__				:			Dict							=    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__				:			Optional[int]							=    PNDMScheduler(skip_prk_steps=__UpperCamelCase	)
          torch.manual_seed(0	)
          UpperCAmelCase__				:			Dict							=    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__				:			Optional[Any]							=    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__				:			Union[str, Any]							=    PaintByExampleImageEncoder(__UpperCamelCase			,      proj_size=32	)
          UpperCAmelCase__				:			str							=    CLIPImageProcessor(crop_size=32			,      size=32	)

          UpperCAmelCase__				:			Any							=    {
              "unet": unet,
              "scheduler": scheduler,
              "vae": vae,
              "image_encoder": image_encoder,
              "safety_checker": None,
              "feature_extractor": feature_extractor,
          }
          return components



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



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

          if str(__UpperCamelCase	).startswith("mps"	):
               UpperCAmelCase__				:			Union[str, Any]							=    torch.manual_seed(__UpperCamelCase	)
          else:
               UpperCAmelCase__				:			Any							=    torch.Generator(device=__UpperCamelCase	).manual_seed(__UpperCamelCase	)
          UpperCAmelCase__				:			Dict							=    {
              "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 			lowerCAmelCase__							(   self	)->       List[str]:
          UpperCAmelCase__				:			Union[str, Any]							=    self.get_dummy_components()

          # make sure here that pndm scheduler skips prk
          UpperCAmelCase__				:			Any							=    PaintByExamplePipeline(**__UpperCamelCase	)
          UpperCAmelCase__				:			List[Any]							=    pipe.to("cpu"	)
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCAmelCase__				:			Tuple							=    self.get_dummy_inputs()
          UpperCAmelCase__				:			Dict							=    pipe(**__UpperCamelCase	)
          UpperCAmelCase__				:			Tuple							=    output.images

          UpperCAmelCase__				:			List[Any]							=    image[0, -3:, -3:, -1]

          assert image.shape == (1, 64, 64, 3)
          UpperCAmelCase__				:			int							=    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 			lowerCAmelCase__							(   self	)->       int:
          UpperCAmelCase__				:			str							=    "cpu"
          UpperCAmelCase__				:			Union[str, Any]							=    self.get_dummy_inputs()
          inputs.pop("mask_image"	)
          UpperCAmelCase__				:			Dict							=    self.convert_to_pt(inputs.pop("image"	)	)
          UpperCAmelCase__				:			str							=    image.clamp(0			,      1	) / 2

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

          UpperCAmelCase__				:			Tuple							=    pipe(image=__UpperCamelCase			,      mask_image=mask_image[:, 0]			,      **__UpperCamelCase	)
          UpperCAmelCase__				:			List[str]							=    output.images

          UpperCAmelCase__				:			Dict							=    image.cpu().permute(0			,      2			,      3			,      1	)[0]
          UpperCAmelCase__				:			Optional[Any]							=    mask_image.cpu().permute(0			,      2			,      3			,      1	)[0]

          UpperCAmelCase__				:			int							=    Image.fromarray(np.uinta(__UpperCamelCase	)	).convert("RGB"	)
          UpperCAmelCase__				:			int							=    Image.fromarray(np.uinta(__UpperCamelCase	)	).convert("RGB"	)

          UpperCAmelCase__				:			Optional[int]							=    pipe(**self.get_dummy_inputs()	)
          UpperCAmelCase__				:			Tuple							=    output.images

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



     def 			lowerCAmelCase__							(   self	)->       Union[str, Any]:
          super().test_inference_batch_single_identical(expected_max_diff=3E-3	)







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


     '''simple docstring'''





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



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

          UpperCAmelCase__				:			Any							=    PaintByExamplePipeline.from_pretrained("Fantasy-Studio/Paint-by-Example"	)
          UpperCAmelCase__				:			int							=    pipe.to(__UpperCamelCase	)
          pipe.set_progress_bar_config(disable=__UpperCamelCase	)

          UpperCAmelCase__				:			Dict							=    torch.manual_seed(3_21	)
          UpperCAmelCase__				:			Union[str, Any]							=    pipe(
              image=__UpperCamelCase			,      mask_image=__UpperCamelCase			,      example_image=__UpperCamelCase			,      generator=__UpperCamelCase			,      guidance_scale=5.0			,      num_inference_steps=50			,      output_type="np"			,      )

          UpperCAmelCase__				:			List[str]							=    output.images

          UpperCAmelCase__				:			Optional[Any]							=    image[0, -3:, -3:, -1]

          assert image.shape == (1, 5_12, 5_12, 3)
          UpperCAmelCase__				:			Tuple							=    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





"""simple docstring"""




import argparse

import torch
from flax.training.checkpoints import restore_checkpoint

from transformers import FNetConfig, FNetForPreTraining
from transformers.utils import logging


logging.set_verbosity_info()



def 				a__			(					lowerCAmelCase						:							Optional[Any]							,     lowerCAmelCase						:							Dict							,     lowerCAmelCase						:							Tuple		):






     '''simple docstring'''
     # Initialise PyTorch model
     UpperCAmelCase__				:			int							=    FNetConfig.from_json_file(lowerCAmelCase		)
     print(F"Building PyTorch model from configuration: {config}"		)
     UpperCAmelCase__				:			Union[str, Any]							=    FNetForPreTraining(lowerCAmelCase		)

     UpperCAmelCase__				:			int							=    restore_checkpoint(lowerCAmelCase							,     lowerCAmelCase		)
     UpperCAmelCase__				:			List[str]							=    checkpoint_dict["target"]

     # Embeddings
     # Position IDs
     UpperCAmelCase__				:			Union[str, Any]							=    fnet_pretraining_model.state_dict()

     UpperCAmelCase__				:			Optional[Any]							=    state_dict["fnet.embeddings.position_ids"]
     UpperCAmelCase__				:			Dict							=    {"fnet.embeddings.position_ids": position_ids}
     # Embedding Layers
     UpperCAmelCase__				:			Dict							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["position"]["embedding"][0]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["type"]["embedding"]		)
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["kernel"]		).T
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["hidden_mapping_in"]["bias"]		)
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["scale"]		)
     UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["layer_norm"]["bias"]		)

     # Encoder Layers
     for layer in range(config.num_hidden_layers		):
          UpperCAmelCase__				:			Any							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["mixing_layer_norm"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["kernel"]		).T
          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["intermediate"]["bias"]		)

          UpperCAmelCase__				:			Dict							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["kernel"]		).T
          UpperCAmelCase__				:			List[str]							=    torch.tensor(
              pretrained_model_params["encoder"][F"feed_forward_{layer}"]["output"]["bias"]		)

          UpperCAmelCase__				:			List[Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["scale"]		)
          UpperCAmelCase__				:			Union[str, Any]							=    torch.tensor(
              pretrained_model_params["encoder"][F"encoder_{layer}"]["output_layer_norm"]["bias"]		)

     # Pooler Layers
     UpperCAmelCase__				:			Optional[Any]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["kernel"]		).T
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(pretrained_model_params["encoder"]["pooler"]["bias"]		)

     # Masked LM Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["kernel"]		).T
     UpperCAmelCase__				:			int							=    torch.tensor(
         pretrained_model_params["predictions_dense"]["bias"]		)
     UpperCAmelCase__				:			Tuple							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["scale"]		)
     UpperCAmelCase__				:			List[str]							=    torch.tensor(
         pretrained_model_params["predictions_layer_norm"]["bias"]		)
     UpperCAmelCase__				:			List[Any]							=    torch.tensor(
         pretrained_model_params["encoder"]["embedder"]["word"]["embedding"]		)
     UpperCAmelCase__				:			Optional[int]							=    torch.tensor(
         pretrained_model_params["predictions_output"]["output_bias"]		)
     UpperCAmelCase__				:			Any							=    torch.tensor(pretrained_model_params["predictions_output"]["output_bias"]		)

     # Seq Relationship Layers
     UpperCAmelCase__				:			Any							=    torch.tensor(
         pretrained_model_params["classification"]["output_kernel"]		)
     UpperCAmelCase__				:			str							=    torch.tensor(
         pretrained_model_params["classification"]["output_bias"]		)

     # Load State Dict
     fnet_pretraining_model.load_state_dict(lowerCAmelCase		)

     # Save PreTrained
     print(F"Saving pretrained model to {save_path}"		)
     fnet_pretraining_model.save_pretrained(lowerCAmelCase		)


if __name__ == "__main__":
    A__   :						Union[str, Any] 							=     argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--flax_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--fnet_config_file""",
        default=None,
        type=str,
        required=True,
        help=(
            """The config json file corresponding to the pre-trained FNet model. \n"""
            """This specifies the model architecture."""
        ),
    )
    parser.add_argument("""--save_path""", default=None, type=str, required=True, help="""Path to the output model.""")
    A__   :						Dict 							=     parser.parse_args()
    convert_flax_checkpoint_to_pytorch(args.flax_checkpoint_path, args.fnet_config_file, args.save_path)



