
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class 							SCREAMING_SNAKE_CASE__     (				SCREAMING_SNAKE_CASE_ ,		unittest.TestCase     ):

							"""simple docstring"""






							_snake_case  		=	RePaintPipeline
							_snake_case  		=	IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
							_snake_case  		=	PipelineTesterMixin.required_optional_params - {
							    'latents',
							    'num_images_per_prompt',
							    'callback',
							    'callback_steps',
							}
							_snake_case  		=	IMAGE_INPAINTING_BATCH_PARAMS
							_snake_case  		=	False



							def      A__		(	self      )-> str:





													'''simple docstring'''






													torch.manual_seed(0      )
													torch.manual_seed(0      )
													__UpperCamelCase  	=      UNetaDModel(
													    block_out_channels=(32, 64)  ,						layers_per_block=2  ,						sample_size=32  ,						in_channels=3  ,						out_channels=3  ,						down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')  ,						up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')  ,						)
													__UpperCamelCase  	=      RePaintScheduler()
													__UpperCamelCase  	=      {'''unet''': unet, '''scheduler''': scheduler}
													return components



							def      A__		(	self  ,						SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_=0      )-> Dict:





													'''simple docstring'''






													if str(SCREAMING_SNAKE_CASE_      ).startswith('''mps'''      ):
																			__UpperCamelCase  	=      torch.manual_seed(SCREAMING_SNAKE_CASE_      )
													else:
																			__UpperCamelCase  	=      torch.Generator(device=SCREAMING_SNAKE_CASE_      ).manual_seed(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      np.random.RandomState(SCREAMING_SNAKE_CASE_      ).standard_normal((1, 3, 32, 32)      )
													__UpperCamelCase  	=      torch.from_numpy(SCREAMING_SNAKE_CASE_      ).to(device=SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													__UpperCamelCase  	=      (image > 0).to(device=SCREAMING_SNAKE_CASE_  ,						dtype=torch.floataa      )
													__UpperCamelCase  	=      {
													    '''image''': image,
													    '''mask_image''': mask,
													    '''generator''': generator,
													    '''num_inference_steps''': 5,
													    '''eta''': 0.0,
													    '''jump_length''': 2,
													    '''jump_n_sample''': 2,
													    '''output_type''': '''numpy''',
													}
													return inputs



							def      A__		(	self      )-> Union[str, Any]:





													'''simple docstring'''






													__UpperCamelCase  	=      '''cpu'''  # ensure determinism for the device-dependent torch.Generator
													__UpperCamelCase  	=      self.get_dummy_components()
													__UpperCamelCase  	=      RePaintPipeline(**SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      sd_pipe.to(SCREAMING_SNAKE_CASE_      )
													sd_pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      self.get_dummy_inputs(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      sd_pipe(**SCREAMING_SNAKE_CASE_      ).images
													__UpperCamelCase  	=      image[0, -3:, -3:, -1]

													assert image.shape == (1, 32, 32, 3)
													__UpperCamelCase  	=      np.array([1.0_0_0_0, 0.5_4_2_6, 0.5_4_9_7, 0.2_2_0_0, 1.0_0_0_0, 1.0_0_0_0, 0.5_6_2_3, 1.0_0_0_0, 0.6_2_7_4]      )

													assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-3



							@skip_mps
							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






													return super().test_save_load_local()



							@unittest.skip('''non-deterministic pipeline'''      )
							def      A__		(	self      )-> int:





													'''simple docstring'''






													return super().test_inference_batch_single_identical()



							@skip_mps
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													return super().test_dict_tuple_outputs_equivalent()



							@skip_mps
							def      A__		(	self      )-> List[str]:





													'''simple docstring'''






													return super().test_save_load_optional_components()



							@skip_mps
							def      A__		(	self      )-> Tuple:





													'''simple docstring'''






													return super().test_attention_slicing_forward_pass()






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

							"""simple docstring"""






							def      A__		(	self      )-> Optional[Any]:





													'''simple docstring'''






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



							def      A__		(	self      )-> Dict:





													'''simple docstring'''






													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
													    '''repaint/celeba_hq_256.png'''      )
													__UpperCamelCase  	=      load_image(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''      )
													__UpperCamelCase  	=      load_numpy(
													    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
													    '''repaint/celeba_hq_256_result.npy'''      )

													__UpperCamelCase  	=      '''google/ddpm-ema-celebahq-256'''
													__UpperCamelCase  	=      UNetaDModel.from_pretrained(SCREAMING_SNAKE_CASE_      )
													__UpperCamelCase  	=      RePaintScheduler.from_pretrained(SCREAMING_SNAKE_CASE_      )

													__UpperCamelCase  	=      RePaintPipeline(unet=SCREAMING_SNAKE_CASE_  ,						scheduler=SCREAMING_SNAKE_CASE_      ).to(SCREAMING_SNAKE_CASE_      )
													repaint.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_      )
													repaint.enable_attention_slicing()

													__UpperCamelCase  	=      torch.manual_seed(0      )
													__UpperCamelCase  	=      repaint(
													    SCREAMING_SNAKE_CASE_  ,						SCREAMING_SNAKE_CASE_  ,						num_inference_steps=250  ,						eta=0.0  ,						jump_length=10  ,						jump_n_sample=10  ,						generator=SCREAMING_SNAKE_CASE_  ,						output_type='''np'''  ,						)
													__UpperCamelCase  	=      output.images[0]

													assert image.shape == (256, 256, 3)
													assert np.abs(expected_image - image      ).mean() < 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)



