





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 			__A(    lowerCAmelCase_    ,      unittest.TestCase			):
     __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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            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=__UpperCamelCase   )
            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-0_5,							num_attention_heads=4,							num_hidden_layers=5,							image_size=32,							patch_size=4,							)
            _UpperCamelCase														=    PaintByExampleImageEncoder(__UpperCamelCase,							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 						_UpperCamelCase  (   self,							A   ):





            """simple docstring"""

            _UpperCamelCase														=    np.array(image.convert('''RGB'''   )   )
            _UpperCamelCase														=    image[None].transpose(0,							3,							1,							2   )
            _UpperCamelCase														=    torch.from_numpy(__UpperCamelCase   ).to(dtype=torch.floataa   ) / 127.5 - 1.0
            return image

     def 						_UpperCamelCase  (   self,							A="cpu",							A=0   ):





            """simple docstring"""

            _UpperCamelCase														=    floats_tensor((1, 3, 32, 32),							rng=random.Random(__UpperCamelCase   )   ).to(__UpperCamelCase   )
            _UpperCamelCase														=    image.cpu().permute(0,							2,							3,							1   )[0]
            _UpperCamelCase														=    Image.fromarray(np.uinta(__UpperCamelCase   )   ).convert('''RGB'''   ).resize((64, 64)   )
            _UpperCamelCase														=    Image.fromarray(np.uinta(image + 4   )   ).convert('''RGB'''   ).resize((64, 64)   )
            _UpperCamelCase														=    Image.fromarray(np.uinta(__UpperCamelCase   )   ).convert('''RGB'''   ).resize((32, 32)   )

            if str(__UpperCamelCase   ).startswith('''mps'''   ):
                   _UpperCamelCase														=    torch.manual_seed(__UpperCamelCase   )
            else:
                   _UpperCamelCase														=    torch.Generator(device=__UpperCamelCase   ).manual_seed(__UpperCamelCase   )
            _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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _UpperCamelCase														=    self.get_dummy_components()

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

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

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

            assert image.shape == (1, 64, 64, 3)
            _UpperCamelCase														=    np.array([0.4_686, 0.5_687, 0.4_007, 0.5_218, 0.5_741, 0.4_482, 0.4_940, 0.4_629, 0.4_503]   )

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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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(__UpperCamelCase   )
            pipe.set_progress_bar_config(disable=__UpperCamelCase   )

            _UpperCamelCase														=    pipe(image=__UpperCamelCase,							mask_image=mask_image[:, 0],							**__UpperCamelCase   )
            _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(__UpperCamelCase   )   ).convert('''RGB'''   )
            _UpperCamelCase														=    Image.fromarray(np.uinta(__UpperCamelCase   )   ).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 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            super().test_inference_batch_single_identical(expected_max_diff=3E-3   )






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

     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

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



     def 						_UpperCamelCase  (   self   ):





            """simple docstring"""

            _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(__UpperCamelCase   )
            pipe.set_progress_bar_config(disable=__UpperCamelCase   )

            _UpperCamelCase														=    torch.manual_seed(321   )
            _UpperCamelCase														=    pipe(
                image=__UpperCamelCase,							mask_image=__UpperCamelCase,							example_image=__UpperCamelCase,							generator=__UpperCamelCase,							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([0.4_834, 0.4_811, 0.4_874, 0.5_122, 0.5_081, 0.5_144, 0.5_291, 0.5_290, 0.5_374]   )

            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)



