



'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaImgaImgPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 							lowercase       (  _lowerCamelCase					,					unittest.TestCase				):


 """simple docstring"""


 UpperCAmelCase							=  KandinskyVaaImgaImgPipeline
 UpperCAmelCase							=  ["""image_embeds""", """negative_image_embeds""", """image"""]
 UpperCAmelCase							=  [
     """image_embeds""",
     """negative_image_embeds""",
     """image""",
 ]
 UpperCAmelCase							=  [
     """generator""",
     """height""",
     """width""",
     """strength""",
     """guidance_scale""",
     """num_inference_steps""",
     """return_dict""",
     """guidance_scale""",
     """num_images_per_prompt""",
     """output_type""",
     """return_dict""",
 ]
 UpperCAmelCase							=  False







 @property
 def _snake_case (							self							)		->   Tuple:
        return 32







 @property
 def _snake_case (							self							)		->   Tuple:
        return 32







 @property
 def _snake_case (							self							)		->   Optional[Any]:
        return self.time_input_dim







 @property
 def _snake_case (							self							)		->   List[Any]:
        return self.time_input_dim * 4







 @property
 def _snake_case (							self							)		->   Tuple:
        return 100







 @property
 def _snake_case (							self							)		->   Optional[Any]:
        torch.manual_seed(0							)

        _UpperCAmelCase  :						List[Any]			     = {
            """in_channels""": 4,
            # Out channels is double in channels because predicts mean and variance
            """out_channels""": 8,
            """addition_embed_type""": """image""",
            """down_block_types""": ("""ResnetDownsampleBlock2D""", """SimpleCrossAttnDownBlock2D"""),
            """up_block_types""": ("""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""),
            """mid_block_type""": """UNetMidBlock2DSimpleCrossAttn""",
            """block_out_channels""": (self.block_out_channels_a, self.block_out_channels_a * 2),
            """layers_per_block""": 1,
            """encoder_hid_dim""": self.text_embedder_hidden_size,
            """encoder_hid_dim_type""": """image_proj""",
            """cross_attention_dim""": self.cross_attention_dim,
            """attention_head_dim""": 4,
            """resnet_time_scale_shift""": """scale_shift""",
            """class_embed_type""": None,
        }

        _UpperCAmelCase  :						Tuple			     = UNetaDConditionModel(**a_							)
        return model







 @property
 def _snake_case (							self							)		->   Tuple:
        return {
            "block_out_channels": [32, 64],
            "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
            "in_channels": 3,
            "latent_channels": 4,
            "layers_per_block": 1,
            "norm_num_groups": 8,
            "norm_type": "spatial",
            "num_vq_embeddings": 12,
            "out_channels": 3,
            "up_block_types": [
                "AttnUpDecoderBlock2D",
                "UpDecoderBlock2D",
            ],
            "vq_embed_dim": 4,
        }







 @property
 def _snake_case (							self							)		->   Any:
        torch.manual_seed(0							)
        _UpperCAmelCase  :						Optional[Any]			     = VQModel(**self.dummy_movq_kwargs							)
        return model







 def _snake_case (							self							)		->   Optional[Any]:
        _UpperCAmelCase  :						Optional[int]			     = self.dummy_unet
        _UpperCAmelCase  :						List[Any]			     = self.dummy_movq

        _UpperCAmelCase  :						Optional[int]			     = {
            """num_train_timesteps""": 1_000,
            """beta_schedule""": """linear""",
            """beta_start""": 0.0_0085,
            """beta_end""": 0.012,
            """clip_sample""": False,
            """set_alpha_to_one""": False,
            """steps_offset""": 0,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
        }

        _UpperCAmelCase  :						Dict			     = DDIMScheduler(**a_							)

        _UpperCAmelCase  :						int			     = {
            """unet""": unet,
            """scheduler""": scheduler,
            """movq""": movq,
        }

        return components







 def _snake_case (							self  ,a_  ,a_=0							)		->   Dict:
        _UpperCAmelCase  :						Optional[int]			     = floats_tensor((1, self.text_embedder_hidden_size)  ,rng=random.Random(a_							)							).to(a_							)
        _UpperCAmelCase  :						Optional[int]			     = floats_tensor((1, self.text_embedder_hidden_size)  ,rng=random.Random(seed + 1							)							).to(
            a_							)
        # create init_image
        _UpperCAmelCase  :						Optional[Any]			     = floats_tensor((1, 3, 64, 64)  ,rng=random.Random(a_							)							).to(a_							)
        _UpperCAmelCase  :						str			     = image.cpu().permute(0  ,2  ,3  ,1							)[0]
        _UpperCAmelCase  :						Tuple			     = Image.fromarray(np.uinta(a_							)							).convert("""RGB"""							).resize((256, 256)							)

        if str(a_							).startswith("""mps"""							):
               _UpperCAmelCase  :						Optional[int]			     = torch.manual_seed(a_							)
        else:
               _UpperCAmelCase  :						Tuple			     = torch.Generator(device=a_							).manual_seed(a_							)
        _UpperCAmelCase  :						Optional[int]			     = {
            """image""": init_image,
            """image_embeds""": image_embeds,
            """negative_image_embeds""": negative_image_embeds,
            """generator""": generator,
            """height""": 64,
            """width""": 64,
            """num_inference_steps""": 10,
            """guidance_scale""": 7.0,
            """strength""": 0.2,
            """output_type""": """np""",
        }
        return inputs







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Dict			     = """cpu"""

        _UpperCAmelCase  :						int			     = self.get_dummy_components()

        _UpperCAmelCase  :						Optional[Any]			     = self.pipeline_class(**a_							)
        _UpperCAmelCase  :						List[Any]			     = pipe.to(a_							)

        pipe.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						List[str]			     = pipe(**self.get_dummy_inputs(a_							)							)
        _UpperCAmelCase  :						Optional[int]			     = output.images

        _UpperCAmelCase  :						Tuple			     = pipe(
            **self.get_dummy_inputs(a_							)  ,return_dict=a_  ,)[0]

        _UpperCAmelCase  :						Tuple			     = image[0, -3:, -3:, -1]
        _UpperCAmelCase  :						Optional[int]			     = image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        _UpperCAmelCase  :						Optional[int]			     = np.array(
            [0.619_9778, 0.6398_4406, 0.4614_5785, 0.6294_4984, 0.562_2215, 0.4730_6132, 0.4744_1456, 0.460_7606, 0.4871_9263]							)
        assert (
            np.abs(image_slice.flatten() - expected_slice							).max() < 1E-2
        ), f''' expected_slice {expected_slice}, but got {image_slice.flatten()}'''
        assert (
            np.abs(image_from_tuple_slice.flatten() - expected_slice							).max() < 1E-2
        ), f''' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'''







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


 """simple docstring"""


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







 def _snake_case (							self							)		->   Dict:
        _UpperCAmelCase  :						Union[str, Any]			     = load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
            """/kandinskyv22/kandinskyv22_img2img_frog.npy"""							)

        _UpperCAmelCase  :						Optional[Any]			     = load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main""" """/kandinsky/cat.png"""							)
        _UpperCAmelCase  :						Optional[int]			     = """A red cartoon frog, 4k"""

        _UpperCAmelCase  :						Dict			     = KandinskyVaaPriorPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-prior"""  ,torch_dtype=torch.floataa							)
        pipe_prior.to(a_							)

        _UpperCAmelCase  :						Optional[Any]			     = KandinskyVaaImgaImgPipeline.from_pretrained(
            """kandinsky-community/kandinsky-2-2-decoder"""  ,torch_dtype=torch.floataa							)
        _UpperCAmelCase  :						Any			     = pipeline.to(a_							)

        pipeline.set_progress_bar_config(disable=a_							)

        _UpperCAmelCase  :						Dict			     = torch.Generator(device="""cpu"""							).manual_seed(0							)
        _UpperCAmelCase     ,_UpperCAmelCase  :						Any			     = pipe_prior(
            a_  ,generator=a_  ,num_inference_steps=5  ,negative_prompt=""""""  ,).to_tuple()

        _UpperCAmelCase  :						List[str]			     = pipeline(
            image=a_  ,image_embeds=a_  ,negative_image_embeds=a_  ,generator=a_  ,num_inference_steps=100  ,height=768  ,width=768  ,strength=0.2  ,output_type="""np"""  ,)

        _UpperCAmelCase  :						Optional[int]			     = output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(a_  ,a_							)








'''simple docstring'''






import argparse

import torch

from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging


logging.set_verbosity_info()




def     snake_case_					(     lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)->		Union[str, Any]:





       '''simple docstring'''


       _UpperCAmelCase  :						int			     = RemBertConfig.from_json_file(lowerCAmelCase_		)
       print("""Building PyTorch model from configuration: {}""".format(str(lowerCAmelCase_		)		)		)
       _UpperCAmelCase  :						Any			     = RemBertModel(lowerCAmelCase_		)

       # Load weights from tf checkpoint
       load_tf_weights_in_rembert(lowerCAmelCase_       ,   lowerCAmelCase_       ,   lowerCAmelCase_		)

       # Save pytorch-model
       print("""Save PyTorch model to {}""".format(lowerCAmelCase_		)		)
       torch.save(model.state_dict()       ,   lowerCAmelCase_		)


if __name__ == "__main__":
   A_							: Optional[Any]						 =	argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       """--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
   )
   parser.add_argument(
       """--rembert_config_file""",
       default=None,
       type=str,
       required=True,
       help=(
           """The config json file corresponding to the pre-trained RemBERT model. \n"""
           """This specifies the model architecture."""
       ),
   )
   parser.add_argument(
       """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
   )
   A_							: Any						 =	parser.parse_args()
   convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)



