

"""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



"""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)