code: 
  
  """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    __A       ( _SCREAMING_SNAKE_CASE,					unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   __lowerCAmelCase				      =						PaintByExamplePipeline
   __lowerCAmelCase				      =						IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
   __lowerCAmelCase				      =						IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
   __lowerCAmelCase				      =						frozenset([]    )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  str:
       torch.manual_seed(0		)
       a					=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				,					)
       a					=PNDMScheduler(skip_prk_steps=__A		)
       torch.manual_seed(0		)
       a					=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		)
       a					=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				,					)
       a					=PaintByExampleImageEncoder(__A				,					proj_size=32		)
       a					=CLIPImageProcessor(crop_size=32				,					size=32		)
  
       a					={
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''image_encoder''': image_encoder,
           '''safety_checker''': None,
           '''feature_extractor''': feature_extractor,
       }
       return components
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A		)      ->  Tuple:
       a					=np.array(image.convert('''RGB'''		)		)
       a					=image[None].transpose(0				,					3				,					1				,					2		)
       a					=torch.from_numpy(__A		).to(dtype=torch.floataa		) / 127.5 - 1.0
       return image
  
  
   def    SCREAMING_SNAKE_CASE							(	self				,					__A="cpu"				,					__A=0		)      ->  Any:
       # TODO: use tensor inputs instead of PIL, this is here just to leave the old expected_slices untouched
       a					=floats_tensor((1, 3, 32, 32)				,					rng=random.Random(__A		)		).to(__A		)
       a					=image.cpu().permute(0				,					2				,					3				,					1		)[0]
       a					=Image.fromarray(np.uinta(__A		)		).convert('''RGB'''		).resize((64, 64)		)
       a					=Image.fromarray(np.uinta(image + 4		)		).convert('''RGB'''		).resize((64, 64)		)
       a					=Image.fromarray(np.uinta(__A		)		).convert('''RGB'''		).resize((32, 32)		)
  
       if str(__A		).startswith('''mps'''		):
           a					=torch.manual_seed(__A		)
       else:
           a					=torch.Generator(device=__A		).manual_seed(__A		)
       a					={
           '''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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a					=self.get_dummy_components()
  
       # make sure here that pndm scheduler skips prk
       a					=PaintByExamplePipeline(**__A		)
       a					=pipe.to('''cpu'''		)
       pipe.set_progress_bar_config(disable=__A		)
  
       a					=self.get_dummy_inputs()
       a					=pipe(**__A		)
       a					=output.images
  
       a					=image[0, -3:, -3:, -1]
  
       assert image.shape == (1, 64, 64, 3)
       a					=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    SCREAMING_SNAKE_CASE							(	self		)      ->  List[str]:
       a					='''cpu'''
       a					=self.get_dummy_inputs()
       inputs.pop('''mask_image'''		)
       a					=self.convert_to_pt(inputs.pop('''image'''		)		)
       a					=image.clamp(0				,					1		) / 2
  
       # make sure here that pndm scheduler skips prk
       a					=PaintByExamplePipeline(**self.get_dummy_components()		)
       a					=pipe.to(__A		)
       pipe.set_progress_bar_config(disable=__A		)
  
       a					=pipe(image=__A				,					mask_image=mask_image[:, 0]				,					**__A		)
       a					=output.images
  
       a					=image.cpu().permute(0				,					2				,					3				,					1		)[0]
       a					=mask_image.cpu().permute(0				,					2				,					3				,					1		)[0]
  
       a					=Image.fromarray(np.uinta(__A		)		).convert('''RGB'''		)
       a					=Image.fromarray(np.uinta(__A		)		).convert('''RGB'''		)
  
       a					=pipe(**self.get_dummy_inputs()		)
       a					=output.images
  
       assert out_a.shape == (1, 64, 64, 3)
       assert np.abs(out_a.flatten() - out_a.flatten()		).max() < 5E-2
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Tuple:
       super().test_inference_batch_single_identical(expected_max_diff=3E-3		)
  
  
  
  
  
  
  @slow
  @require_torch_gpu
  class    __A       ( unittest.TestCase    ):
  
  
  
  
  
  
   """simple docstring"""
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  int:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()
  
  
  
  
  
   def    SCREAMING_SNAKE_CASE							(	self		)      ->  Optional[int]:
       # make sure here that pndm scheduler skips prk
       a					=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/dog_in_bucket.png'''		)
       a					=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/mask.png'''		)
       a					=load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/paint_by_example/panda.jpg'''		)
  
       a					=PaintByExamplePipeline.from_pretrained('''Fantasy-Studio/Paint-by-Example'''		)
       a					=pipe.to(__A		)
       pipe.set_progress_bar_config(disable=__A		)
  
       a					=torch.manual_seed(321		)
       a					=pipe(
           image=__A				,					mask_image=__A				,					example_image=__A				,					generator=__A				,					guidance_scale=5.0				,					num_inference_steps=50				,					output_type='''np'''				,					)
  
       a					=output.images
  
       a					=image[0, -3:, -3:, -1]
  
       assert image.shape == (1, 512, 512, 3)
       a					=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
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  
  
  
  import argparse
  
  from tax import checkpoints
  
  from transformers import FlaxTaForConditionalGeneration, TaConfig
  def _A		(		lowercase ,					lowercase ,					lowercase	):
  
  
  
  
  
  
  
      """simple docstring"""
      a					=TaConfig.from_pretrained(lowercase	)
      a					=FlaxTaForConditionalGeneration(config=lowercase	)
      a					=checkpoints.load_tax_checkpoint(lowercase	)
  
      a					='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']
  
      # Encoder
      for layer_index in range(config.num_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']
  
          if split_mlp_wi:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_attention_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
          a					=tax_mlp_layer_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_encoder_rel_embedding
  
      # Assigning
      a					=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      a					=tax_encoder_norm
  
      # Decoder
      for layer_index in range(config.num_decoder_layers	):
          a					=f'''layers_{str(lowercase	)}'''
  
          # Self-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
          a					=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
              '''scale'''
          ]
  
          # Encoder-Decoder-Attention
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
              '''kernel'''
          ]
          a					=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
              '''kernel'''
          ]
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']
  
          # MLP
          if split_mlp_wi:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
          else:
              a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']
  
          a					=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']
  
          # Layer Normalization
          a					=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']
  
          # Assigning
          a					=tax_attention_key
          a					=tax_attention_out
          a					=tax_attention_query
          a					=tax_attention_value
  
          a					=tax_pre_attention_layer_norm
  
          a					=tax_enc_dec_attention_key
          a					=tax_enc_dec_attention_out
          a					=tax_enc_dec_attention_query
          a					=tax_enc_dec_attention_value
  
          a					=tax_cross_layer_norm
  
          if split_mlp_wi:
              a					=tax_mlp_wi_a
              a					=tax_mlp_wi_a
          else:
              a					=tax_mlp_wi
  
          a					=tax_mlp_wo
  
          a					=txa_mlp_layer_norm
  
      # Decoder Normalization
      a					=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      a					=txa_decoder_norm
  
      # Only for layer 0:
      a					=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      a					=tax_decoder_rel_embedding
  
      # Token Embeddings
      a					=tax_model['''target''']['''token_embedder''']['''embedding''']
      a					=txa_token_embeddings
  
      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
          a					=tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']
  
      flax_model.save_pretrained(lowercase	)
      print('''T5X Model was sucessfully converted!'''	)
  
  
  if __name__ == "__main__":
         lowerCamelCase_     :     Dict    		=						argparse.ArgumentParser()
         # Required parameters
         parser.add_argument(
             """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
         )
         parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
         parser.add_argument(
             """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
         )
         lowerCamelCase_     :     Optional[int]    		=						parser.parse_args()
         convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)