

import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
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  _A		(  __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Dict       =  KandinskyInpaintPipeline
  SCREAMING_SNAKE_CASE    :      Dict       =  ['''prompt''', '''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  [
      '''prompt''',
      '''negative_prompt''',
      '''image_embeds''',
      '''negative_image_embeds''',
      '''image''',
      '''mask_image''',
  ]
  SCREAMING_SNAKE_CASE    :      Union[str, Any]       =  [
      '''generator''',
      '''height''',
      '''width''',
      '''latents''',
      '''guidance_scale''',
      '''negative_prompt''',
      '''num_inference_steps''',
      '''return_dict''',
      '''guidance_scale''',
      '''num_images_per_prompt''',
      '''output_type''',
      '''return_dict''',
  ]
  SCREAMING_SNAKE_CASE    :      int       =  False

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 32

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 32

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.time_input_dim

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.time_input_dim * 4

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return 100

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'					)
        return tokenizer

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  str           =     MCLIPConfig(
            numDims=self.cross_attention_dim ,					transformerDimensions=self.text_embedder_hidden_size ,					hidden_size=self.text_embedder_hidden_size ,					intermediate_size=37 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					vocab_size=1005 ,					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     MultilingualCLIP(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     text_encoder.eval()

        return text_encoder

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)

        SCREAMING_SNAKE_CASE_				:  Dict           =     {
            'in_channels': 9,
            # Out channels is double in channels because predicts mean and variance
            'out_channels': 8,
            'addition_embed_type': 'text_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': 'text_image_proj',
            'cross_attention_dim': self.cross_attention_dim,
            'attention_head_dim': 4,
            'resnet_time_scale_shift': 'scale_shift',
            'class_embed_type': None,
        }

        SCREAMING_SNAKE_CASE_				:  str           =     UNetaDConditionModel(**_SCREAMING_SNAKE_CASE					)
        return model

  @property
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        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  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     VQModel(**self.dummy_movq_kwargs					)
        return model

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     self.dummy_text_encoder
        SCREAMING_SNAKE_CASE_				:  Any           =     self.dummy_tokenizer
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_unet
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.dummy_movq

        SCREAMING_SNAKE_CASE_				:  str           =     DDIMScheduler(
            num_train_timesteps=1000 ,					beta_schedule='linear' ,					beta_start=0.00085 ,					beta_end=0.012 ,					clip_sample=_SCREAMING_SNAKE_CASE ,					set_alpha_to_one=_SCREAMING_SNAKE_CASE ,					steps_offset=1 ,					prediction_type='epsilon' ,					thresholding=_SCREAMING_SNAKE_CASE ,					)

        SCREAMING_SNAKE_CASE_				:  List[str]           =     {
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'unet': unet,
            'scheduler': scheduler,
            'movq': movq,
        }

        return components

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     floats_tensor((1, self.cross_attention_dim) ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     floats_tensor((1, self.cross_attention_dim) ,					rng=random.Random(seed + 1					)					).to(_SCREAMING_SNAKE_CASE					)
        # create init_image
        SCREAMING_SNAKE_CASE_				:  Tuple           =     floats_tensor((1, 3, 64, 64) ,					rng=random.Random(_SCREAMING_SNAKE_CASE					)					).to(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     image.cpu().permute(0 ,					2 ,					3 ,					1					)[0]
        SCREAMING_SNAKE_CASE_				:  str           =     Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE					)					).convert('RGB'					).resize((256, 256)					)
        # create mask
        SCREAMING_SNAKE_CASE_				:  int           =     np.ones((64, 64) ,					dtype=np.floataa					)
        SCREAMING_SNAKE_CASE_				:  int           =     0

        if str(_SCREAMING_SNAKE_CASE					).startswith('mps'					):
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     torch.manual_seed(_SCREAMING_SNAKE_CASE					)
        else:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     torch.Generator(device=_SCREAMING_SNAKE_CASE					).manual_seed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     {
            'prompt': 'horse',
            'image': init_image,
            'mask_image': mask,
            'image_embeds': image_embeds,
            'negative_image_embeds': negative_image_embeds,
            'generator': generator,
            'height': 64,
            'width': 64,
            'num_inference_steps': 2,
            'guidance_scale': 4.0,
            'output_type': 'np',
        }
        return inputs

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Tuple           =     'cpu'

        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_dummy_components()

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.pipeline_class(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     pipe.to(_SCREAMING_SNAKE_CASE					)

        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     output.images

        SCREAMING_SNAKE_CASE_				:  str           =     pipe(
            **self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					) ,					return_dict=_SCREAMING_SNAKE_CASE ,					)[0]

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     image[0, -3:, -3:, -1]
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     image_from_tuple[0, -3:, -3:, -1]

        print(f"image.shape {image.shape}"					)

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

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     np.array(
            [0.8326919, 0.73790467, 0.20918581, 0.9309612, 0.5511791, 0.43713328, 0.5513321, 0.49922934, 0.59497786]					)

        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()}"



  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"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     load_numpy(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
            '/kandinsky/kandinsky_inpaint_cat_with_hat_fp16.npy'					)

        SCREAMING_SNAKE_CASE_				:  str           =     load_image(
            'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'					)
        SCREAMING_SNAKE_CASE_				:  int           =     np.ones((768, 768) ,					dtype=np.floataa					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     0

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     'a hat'

        SCREAMING_SNAKE_CASE_				:  List[str]           =     KandinskyPriorPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-prior' ,					torch_dtype=torch.floataa					)
        pipe_prior.to(_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     KandinskyInpaintPipeline.from_pretrained(
            'kandinsky-community/kandinsky-2-1-inpaint' ,					torch_dtype=torch.floataa					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipeline.to(_SCREAMING_SNAKE_CASE					)
        pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     torch.Generator(device='cpu'					).manual_seed(0					)
        SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     pipe_prior(
            _SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					num_inference_steps=5 ,					negative_prompt='' ,					).to_tuple()

        SCREAMING_SNAKE_CASE_				:  Any           =     pipeline(
            _SCREAMING_SNAKE_CASE ,					image=_SCREAMING_SNAKE_CASE ,					mask_image=_SCREAMING_SNAKE_CASE ,					image_embeds=_SCREAMING_SNAKE_CASE ,					negative_image_embeds=_SCREAMING_SNAKE_CASE ,					generator=_SCREAMING_SNAKE_CASE ,					num_inference_steps=100 ,					height=768 ,					width=768 ,					output_type='np' ,					)

        SCREAMING_SNAKE_CASE_				:  str           =     output.images[0]

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

        assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)









import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()

def    A_  (							a					,   a					,   a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  int           =     TaConfig.from_json_file(a  )
      print(f"Building PyTorch model from configuration: {config}"  )
      SCREAMING_SNAKE_CASE_				:  Tuple           =     TaForConditionalGeneration(a  )

      # Load weights from tf checkpoint
      load_tf_weights_in_ta(a					,   a					,   a  )

      # Save pytorch-model
      print(f"Save PyTorch model to {pytorch_dump_path}"  )
      model.save_pretrained(a  )


if __name__ == "__main__":
    lowerCAmelCase     :   int						     =  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(
        '--config_file',
        default=None,
        type=str,
        required=True,
        help=(
            'The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'
        ),
    )
    parser.add_argument(
        '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
    )
    lowerCAmelCase     :   List[str]						     =  parser.parse_args()
    convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)






