






'''simple docstring'''



import gc
import random
import unittest

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

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, 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 				__SCREAMING_SNAKE_CASE							(				_lowerCAmelCase   ,   unittest.TestCase  ):
      __a	       =KandinskyImgaImgPipeline
      __a	       =["prompt", "image_embeds", "negative_image_embeds", "image"]
      __a	       =[
          "prompt",
          "negative_prompt",
          "image_embeds",
          "negative_image_embeds",
          "image",
      ]
      __a	       =[
          "generator",
          "height",
          "width",
          "strength",
          "guidance_scale",
          "negative_prompt",
          "num_inference_steps",
          "return_dict",
          "guidance_scale",
          "num_images_per_prompt",
          "output_type",
          "return_dict",
      ]
      __a	       =False




      @property
      def 			__UpperCamelCase				(	self   )				->str:



         '''simple docstring'''
         return 32




      @property
      def 			__UpperCamelCase				(	self   )				->List[Any]:



         '''simple docstring'''
         return 32




      @property
      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         return self.time_input_dim




      @property
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         return self.time_input_dim * 4




      @property
      def 			__UpperCamelCase				(	self   )				->Optional[int]:



         '''simple docstring'''
         return 100




      @property
      def 			__UpperCamelCase				(	self   )				->Dict:



         '''simple docstring'''
         __a					  =     XLMRobertaTokenizerFast.from_pretrained('YiYiXu/tiny-random-mclip-base'   )
         return tokenizer




      @property
      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         torch.manual_seed(0   )
         __a					  =     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			, )

         __a					  =     MultilingualCLIP(lowerCamelCase   )
         __a					  =     text_encoder.eval()

         return text_encoder




      @property
      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         torch.manual_seed(0   )

         __a					  =     {
             'in_channels': 4,
             # 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,
         }

         __a					  =     UNetaDConditionModel(**lowerCamelCase   )
         return model




      @property
      def 			__UpperCamelCase				(	self   )				->str:



         '''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   )				->Any:



         '''simple docstring'''
         torch.manual_seed(0   )
         __a					  =     VQModel(**self.dummy_movq_kwargs   )
         return model




      def 			__UpperCamelCase				(	self   )				->List[str]:



         '''simple docstring'''
         __a					  =     self.dummy_text_encoder
         __a					  =     self.dummy_tokenizer
         __a					  =     self.dummy_unet
         __a					  =     self.dummy_movq

         __a					  =     {
             'num_train_timesteps': 1000,
             'beta_schedule': 'linear',
             'beta_start': 0.00085,
             'beta_end': 0.012,
             'clip_sample': False,
             'set_alpha_to_one': False,
             'steps_offset': 0,
             'prediction_type': 'epsilon',
             'thresholding': False,
         }

         __a					  =     DDIMScheduler(**lowerCamelCase   )

         __a					  =     {
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'unet': unet,
             'scheduler': scheduler,
             'movq': movq,
         }

         return components




      def 			__UpperCamelCase				(	self			, lowerCamelCase			, lowerCamelCase=0   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     floats_tensor((1, self.cross_attention_dim)			, rng=random.Random(lowerCamelCase   )   ).to(lowerCamelCase   )
         __a					  =     floats_tensor((1, self.cross_attention_dim)			, rng=random.Random(seed + 1   )   ).to(lowerCamelCase   )
         # create init_image
         __a					  =     floats_tensor((1, 3, 64, 64)			, rng=random.Random(lowerCamelCase   )   ).to(lowerCamelCase   )
         __a					  =     image.cpu().permute(0			, 2			, 3			, 1   )[0]
         __a					  =     Image.fromarray(np.uinta(lowerCamelCase   )   ).convert('RGB'   ).resize((256, 256)   )

         if str(lowerCamelCase   ).startswith('mps'   ):
            __a					  =     torch.manual_seed(lowerCamelCase   )
         else:
            __a					  =     torch.Generator(device=lowerCamelCase   ).manual_seed(lowerCamelCase   )
         __a					  =     {
             'prompt': 'horse',
             '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 			__UpperCamelCase				(	self   )				->Optional[Any]:



         '''simple docstring'''
         __a					  =     'cpu'

         __a					  =     self.get_dummy_components()

         __a					  =     self.pipeline_class(**lowerCamelCase   )
         __a					  =     pipe.to(lowerCamelCase   )

         pipe.set_progress_bar_config(disable=lowerCamelCase   )

         __a					  =     pipe(**self.get_dummy_inputs(lowerCamelCase   )   )
         __a					  =     output.images

         __a					  =     pipe(
             **self.get_dummy_inputs(lowerCamelCase   )			, return_dict=lowerCamelCase			, )[0]

         __a					  =     image[0, -3:, -3:, -1]
         __a					  =     image_from_tuple[0, -3:, -3:, -1]

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

         __a					  =     np.array(
             [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]   )
         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 				__SCREAMING_SNAKE_CASE							(				unittest.TestCase  ):




      def 			__UpperCamelCase				(	self   )				->int:



         '''simple docstring'''
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()






      def 			__UpperCamelCase				(	self   )				->Tuple:



         '''simple docstring'''
         __a					  =     load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/kandinsky/kandinsky_img2img_frog.npy'   )

         __a					  =     load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png'   )
         __a					  =     'A red cartoon frog, 4k'

         __a					  =     KandinskyPriorPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1-prior'			, torch_dtype=torch.floataa   )
         pipe_prior.to(lowerCamelCase   )

         __a					  =     KandinskyImgaImgPipeline.from_pretrained(
             'kandinsky-community/kandinsky-2-1'			, torch_dtype=torch.floataa   )
         __a					  =     pipeline.to(lowerCamelCase   )

         pipeline.set_progress_bar_config(disable=lowerCamelCase   )

         __a					  =     torch.Generator(device='cpu'   ).manual_seed(0   )
         __a  ,      __a					  =     pipe_prior(
             lowerCamelCase			, generator=lowerCamelCase			, num_inference_steps=5			, negative_prompt=''			, ).to_tuple()

         __a					  =     pipeline(
             lowerCamelCase			, image=lowerCamelCase			, image_embeds=lowerCamelCase			, negative_image_embeds=lowerCamelCase			, generator=lowerCamelCase			, num_inference_steps=100			, height=768			, width=768			, strength=0.2			, output_type='np'			, )

         __a					  =     output.images[0]

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

         assert_mean_pixel_difference(lowerCamelCase			, lowerCamelCase   )







'''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  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Dict     )	->      List[Any]:

   """simple docstring"""
   # Initialise PyTorch model
   __a					  =     RemBertConfig.from_json_file(SCREAMING_SNAKE_CASE__     )
   print('Building PyTorch model from configuration: {}'.format(str(SCREAMING_SNAKE_CASE__     )     )     )
   __a					  =     RemBertModel(SCREAMING_SNAKE_CASE__     )

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

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


if __name__ == "__main__":
 __UpperCamelCase   :							List[str]         =   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."""
 )
 __UpperCamelCase   :							List[str]         =   parser.parse_args()
 convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)