






"""simple docstring"""



import argparse
import collections

import jax
import jax.numpy as jnp
import torch
import torch.nn as nn
from clip.model import CLIP
from flax.training import checkpoints
from huggingface_hub import Repository

from transformers import (
    CLIPTokenizer,
    OwlViTConfig,
    OwlViTForObjectDetection,
    OwlViTImageProcessor,
    OwlViTModel,
    OwlViTProcessor,
)


_UpperCamelCase						:    Union[str, Any]		=						{
    'vit_b32': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 3_2,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_b16': {
        'embed_dim': 5_1_2,
        'image_resolution': 7_6_8,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 1_2,
        'vision_width': 7_6_8,
        'vision_patch_size': 1_6,
        'transformer_width': 5_1_2,
        'transformer_heads': 8,
        'transformer_layers': 1_2,
    },
    'vit_l14': {
        'embed_dim': 7_6_8,
        'image_resolution': 8_4_0,
        'context_length': 1_6,
        'vocab_size': 4_9_4_0_8,
        'vision_layers': 2_4,
        'vision_width': 1_0_2_4,
        'vision_patch_size': 1_4,
        'transformer_width': 7_6_8,
        'transformer_heads': 1_2,
        'transformer_layers': 1_2,
    },
}
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    List[str]=""						,   __snake_case     :    List[str]="/"       ):





  '''simple docstring'''





  lowercase				       =			[]

  for k, v in params.items():
    lowercase				       =			parent_key + sep + k if parent_key else k

    if isinstance(__snake_case						,   collections.MutableMapping       ):
      items.extend(flatten_nested_dict(__snake_case						,   __snake_case						,   sep=__snake_case       ).items()       )
    else:
      items.append((new_key, v)       )
  return dict(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  return jax.tree_util.tree_map(lambda __snake_case       : x.astype(jnp.floataa       ) if x.dtype == jnp.bfloataa else x						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    str						,   __snake_case     :    str       ):





  '''simple docstring'''





  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_weight.chunk(3						,   dim=0       )
  lowercase       ,	lowercase       ,	lowercase				       =			pt_attn_layer.in_proj_bias.chunk(3						,   dim=0       )

  lowercase				       =			pt_attn_layer.out_proj.weight
  lowercase				       =			pt_attn_layer.out_proj.bias

  lowercase				       =			q_proj
  lowercase				       =			q_proj_bias

  lowercase				       =			k_proj
  lowercase				       =			k_proj_bias

  lowercase				       =			v_proj
  lowercase				       =			v_proj_bias

  lowercase				       =			out_proj_weights
  lowercase				       =			out_proj_bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  copy_linear(hf_mlp.fca						,   pt_mlp.c_fc       )
  copy_linear(hf_mlp.fca						,   pt_mlp.c_proj       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			pt_linear.weight
  lowercase				       =			pt_linear.bias
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    str       ):





  '''simple docstring'''





  copy_linear(hf_layer.layer_norma						,   pt_layer.ln_a       )
  copy_linear(hf_layer.layer_norma						,   pt_layer.ln_a       )

  # copy MLP
  copy_mlp(hf_layer.mlp						,   pt_layer.mlp       )

  # copy attn
  copy_attn_layer(hf_layer.self_attn						,   pt_layer.attn       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    int						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  for hf_layer, pt_layer in zip(__snake_case						,   __snake_case       ):
    copy_layer(__snake_case						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.token_embedding.weight
  lowercase				       =			pt_model.positional_embedding

  # copy layer norm
  copy_linear(hf_encoder.final_layer_norm						,   pt_model.ln_final       )

  # copy hidden layers
  copy_layers(hf_encoder.encoder.layers						,   pt_model.transformer.resblocks       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    List[Any]       ):





  '''simple docstring'''





  lowercase				       =			pt_model.text_projection.data.T

  # copy text encoder
  copy_encoder(hf_model.text_model						,   __snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Dict						,   __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			pt_model.visual.proj.data.T

  # copy layer norms
  copy_linear(hf_model.vision_model.pre_layernorm						,   pt_model.visual.ln_pre       )
  copy_linear(hf_model.vision_model.post_layernorm						,   pt_model.visual.ln_post       )

  # copy embeds
  lowercase				       =			pt_model.visual.conva.weight.data
  lowercase				       =			pt_model.visual.class_embedding
  lowercase				       =			pt_model.visual.positional_embedding.data

  # copy encoder
  copy_layers(hf_model.vision_model.encoder.layers						,   pt_model.visual.transformer.resblocks       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple						,   __snake_case     :    Optional[int]       ):





  '''simple docstring'''





  lowercase				       =			flatten_nested_dict(flax_params['backbone']['merged_class_token']       )

  lowercase				       =			torch.from_numpy(flax_class_token_params['scale']       )
  lowercase				       =			torch.from_numpy(flax_class_token_params['bias']       )
  lowercase				       =			nn.Parameter(__snake_case       )
  lowercase				       =			nn.Parameter(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Union[str, Any]						,   __snake_case     :    Any       ):





  '''simple docstring'''





  lowercase				       =			hf_model.state_dict()
  lowercase				       =			{}

  # Rename class prediction head flax params to pytorch HF
  lowercase				       =			flatten_nested_dict(flax_params['class_head']       )

  for flax_key, v in flax_class_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )
    lowercase				       =			torch_key.replace('Dense_0'						,   'dense0'       )
    lowercase				       =			'class_head.' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      lowercase				       =			v.T

    lowercase				       =			nn.Parameter(torch.from_numpy(__snake_case       )       )

  # Rename box prediction box flax params to pytorch HF
  lowercase				       =			flatten_nested_dict(flax_params['obj_box_head']       )

  for flax_key, v in flax_box_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )
    lowercase				       =			torch_key.replace('_'						,   ''       ).lower()
    lowercase				       =			'box_head.' + torch_key

    if "weight" in torch_key and v.ndim == 2:
      lowercase				       =			v.T

    lowercase				       =			nn.Parameter(torch.from_numpy(__snake_case       )       )

  # Copy flax params to PyTorch params
  for name, param in new_params.items():
    if name in pt_params.keys():
      pt_params[name].copy_(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[Any]						,   __snake_case     :    Union[str, Any]       ):





  '''simple docstring'''





  for k, v in flax_attn_params.items():
    if k.startswith('transformer'       ):
      lowercase				       =			k.replace('transformer.resblocks'						,   'text_model.encoder.layers'       )
    else:
      lowercase				       =			k.replace('visual.transformer.resblocks'						,   'vision_model.encoder.layers'       )

    lowercase				       =			torch_key.replace('attn'						,   'self_attn'       )
    lowercase				       =			torch_key.replace('key'						,   'k_proj'       )
    lowercase				       =			torch_key.replace('value'						,   'v_proj'       )
    lowercase				       =			torch_key.replace('query'						,   'q_proj'       )
    lowercase				       =			torch_key.replace('out'						,   'out_proj'       )

    if "bias" in torch_key and v.ndim == 2:
      lowercase				       =			v.shape[0] * v.shape[1]
      lowercase				       =			v.reshape(__snake_case       )

    if "weight" in torch_key and "out" in torch_key:
      lowercase				       =			(v.shape[0] * v.shape[1], v.shape[2])
      lowercase				       =			v.reshape(__snake_case       ).T

    if "weight" in torch_key and "out" not in torch_key:
      lowercase				       =			(v.shape[0], v.shape[1] * v.shape[2])
      lowercase				       =			v.reshape(__snake_case       ).T

    # Copy flax CLIP attn params to HF PyTorch params
    lowercase				       =			torch.from_numpy(__snake_case       )
    hf_backbone.state_dict()[torch_key].copy_(__snake_case       )
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Tuple       ):





  '''simple docstring'''





  lowercase				       =			{}
  lowercase				       =			[]

  for k, v in params.items():
    if "attn." in k:
      lowercase				       =			k[: k.rindex('attn.'       ) + 5]
      if base in processed_attn_layers:
        continue

      processed_attn_layers.append(__snake_case       )
      lowercase				       =			params[base + 'out.weight'].shape[-1]
      lowercase				       =			params[base + 'out.weight'].reshape(__snake_case						,   __snake_case       ).T
      lowercase				       =			params[base + 'out.bias']
    else:
      lowercase				       =			v
  return new_params
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    Optional[int]						,   __snake_case     :    Optional[Any]       ):





  '''simple docstring'''





  lowercase				       =			CLIP(**__snake_case       )
  torch_model.eval()
  lowercase				       =			torch_model.state_dict()

  lowercase				       =			flatten_nested_dict(flax_params['backbone']['clip']       )
  lowercase				       =			{}

  for flax_key, v in flax_clip_params.items():
    lowercase				       =			flax_key.replace('/'						,   '.'       )
    lowercase				       =			torch_key.replace('text.token_embedding.embedding'						,   'token_embedding.kernel'       )

    if (
        torch_key.startswith('text.transformer'       )
        or torch_key.startswith('text.text_projection'       )
        or torch_key.startswith('text.ln_final'       )
        or torch_key.startswith('text.positional_embedding'       )
    ):
      lowercase				       =			torch_key[5:]

    lowercase				       =			torch_key.replace('text_projection.kernel'						,   'text_projection'       )
    lowercase				       =			torch_key.replace('visual.proj.kernel'						,   'visual.proj'       )
    lowercase				       =			torch_key.replace('.scale'						,   '.weight'       )
    lowercase				       =			torch_key.replace('.kernel'						,   '.weight'       )

    if "conv" in torch_key or "downsample.0.weight" in torch_key:
      lowercase				       =			v.transpose(3						,   2						,   0						,   1       )

    elif "weight" in torch_key and v.ndim == 2 and "embedding" not in torch_key:
      # Fully connected layers are transposed, embeddings are not
      lowercase				       =			v.T

    lowercase				       =			v

  lowercase				       =			_convert_attn_layers(__snake_case       )
  new_torch_params.update(__snake_case       )
  lowercase				       =			{}

  # Copy flax CLIP backbone params to PyTorch params
  for name, param in new_torch_params.items():
    if name in torch_clip_params.keys():
      lowercase				       =			torch.from_numpy(new_torch_params[name]       )
      torch_clip_params[name].copy_(__snake_case       )
    else:
      lowercase				       =			param

  return torch_clip_params, torch_model, attn_params
@torch.no_grad()
def 		_SCREAMING_SNAKE_CASE					(       __snake_case     :    List[str]						,   __snake_case     :    Tuple						,   __snake_case     :    Optional[int]						,   __snake_case     :    Tuple						,   __snake_case     :    Tuple=None       ):





  '''simple docstring'''





  lowercase				       =			Repository(__snake_case						,   clone_from=f'google/{pytorch_dump_folder_path}'       )
  repo.git_pull()

  if config_path is not None:
    lowercase				       =			OwlViTConfig.from_pretrained(__snake_case       )
  else:
    lowercase				       =			OwlViTConfig()

  lowercase				       =			OwlViTModel(__snake_case       ).eval()
  lowercase				       =			OwlViTForObjectDetection(__snake_case       ).eval()

  copy_text_model_and_projection(__snake_case						,   __snake_case       )
  copy_vision_model_and_projection(__snake_case						,   __snake_case       )
  lowercase				       =			pt_backbone.logit_scale
  copy_flax_attn_params(__snake_case						,   __snake_case       )

  lowercase				       =			hf_backbone
  copy_class_merge_token(__snake_case						,   __snake_case       )
  copy_class_box_heads(__snake_case						,   __snake_case       )

  # Save HF model
  hf_model.save_pretrained(repo.local_dir       )

  # Initialize image processor
  lowercase				       =			OwlViTImageProcessor(
      size=config.vision_config.image_size						,   crop_size=config.vision_config.image_size       )
  # Initialize tokenizer
  lowercase				       =			CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'						,   pad_token='!'						,   model_max_length=16       )

  # Initialize processor
  lowercase				       =			OwlViTProcessor(image_processor=__snake_case						,   tokenizer=__snake_case       )
  image_processor.save_pretrained(repo.local_dir       )
  processor.save_pretrained(repo.local_dir       )

  repo.git_add()
  repo.git_commit('Upload model and processor'       )
  repo.git_push()


if __name__ == "__main__":
    _UpperCamelCase						:    Any		=						argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--owlvit_version',
        default=None,
        type=str,
        required=True,
        help='OWL-ViT model name [clip_b16, clip_b32, clip_l14].',
    )
    parser.add_argument(
        '--owlvit_checkpoint', default=None, type=str, required=True, help='Path to flax model checkpoint.'
    )
    parser.add_argument('--hf_config', default=None, type=str, required=True, help='Path to HF model config.')
    parser.add_argument(
        '--pytorch_dump_folder_path', default='hf_model', type=str, help='Path to the output PyTorch model.'
    )
    _UpperCamelCase						:    Tuple		=						parser.parse_args()

    # Initialize PyToch clip model
    _UpperCamelCase						:    Union[str, Any]		=						args.owlvit_version
    if model_name == "clip_b16":
        _UpperCamelCase						:    List[Any]		=						CONFIGS['vit_b16']
    elif model_name == "clip_b32":
        _UpperCamelCase						:    Optional[Any]		=						CONFIGS['vit_b32']
    elif model_name == "clip_l14":
        _UpperCamelCase						:    Any		=						CONFIGS['vit_l14']

    # Load from checkpoint and convert params to float-32
    _UpperCamelCase						:    Optional[int]		=						checkpoints.restore_checkpoint(args.owlvit_checkpoint, target=None)['optimizer']['target']
    _UpperCamelCase						:    Optional[int]		=						jax.tree_util.tree_map(lambda x: x.astype(jnp.floataa) if x.dtype == jnp.bfloataa else x, variables)
    del variables

    # Convert CLIP backbone
    _UpperCamelCase,						_UpperCamelCase,						_UpperCamelCase						:    Optional[Any]		=						convert_clip_backbone(flax_params, torch_config)

    convert_owlvit_checkpoint(clip_pt, flax_params, attn_params, args.pytorch_dump_folder_path, args.hf_config)



'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, 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			):
   lowerCamelCase_            =KandinskyPipeline
   lowerCamelCase_            =[
       'prompt',
       'image_embeds',
       'negative_image_embeds',
   ]
   lowerCamelCase_            =['prompt', 'negative_prompt', 'image_embeds', 'negative_image_embeds']
   lowerCamelCase_            =[
       'generator',
       'height',
       'width',
       'latents',
       'guidance_scale',
       'negative_prompt',
       'num_inference_steps',
       'return_dict',
       'guidance_scale',
       'num_images_per_prompt',
       'output_type',
       'return_dict',
   ]
   lowerCamelCase_            =False


   @property
   def __UpperCAmelCase							( self					:						Optional[int])   ->       Union[str, Any]:
         return 32


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       int:
         return 32


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


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


   @property
   def __UpperCAmelCase							( self					:						List[str])   ->       str:
         return 100


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       List[str]:
         lowercase_     			=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base")
         return tokenizer


   @property
   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         torch.manual_seed(0)
         lowercase_     			=   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 ,		)

         lowercase_     			=   MultilingualCLIP(__lowerCAmelCase)
         lowercase_     			=   text_encoder.eval()

         return text_encoder


   @property
   def __UpperCAmelCase							( self					:						Tuple)   ->       Any:
         torch.manual_seed(0)

         lowercase_     			=   {
             "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,
         }

         lowercase_     			=   UNetaDConditionModel(**__lowerCAmelCase)
         return model


   @property
   def __UpperCAmelCase							( self					:						int)   ->       Optional[int]:
         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					:						Optional[Any])   ->       Tuple:
         torch.manual_seed(0)
         lowercase_     			=   VQModel(**self.dummy_movq_kwargs)
         return model


   def __UpperCAmelCase							( self					:						Tuple)   ->       Union[str, Any]:
         lowercase_     			=   self.dummy_text_encoder
         lowercase_     			=   self.dummy_tokenizer
         lowercase_     			=   self.dummy_unet
         lowercase_     			=   self.dummy_movq

         lowercase_     			=   DDIMScheduler(
             num_train_timesteps=1000 ,		beta_schedule="linear" ,		beta_start=0.0_0085 ,		beta_end=0.012 ,		clip_sample=__lowerCAmelCase ,		set_alpha_to_one=__lowerCAmelCase ,		steps_offset=1 ,		prediction_type="epsilon" ,		thresholding=__lowerCAmelCase ,		)

         lowercase_     			=   {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components


   def __UpperCAmelCase							( self					:						Union[str, Any] ,		__lowerCAmelCase					:						Optional[Any] ,		__lowerCAmelCase					:						List[str]=0)   ->       List[Any]:
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(__lowerCAmelCase)).to(__lowerCAmelCase)
         lowercase_     			=   floats_tensor((1, self.cross_attention_dim) ,		rng=random.Random(seed + 1)).to(__lowerCAmelCase)
         if str(__lowerCAmelCase).startswith("mps"):
               lowercase_     			=   torch.manual_seed(__lowerCAmelCase)
         else:
               lowercase_     			=   torch.Generator(device=__lowerCAmelCase).manual_seed(__lowerCAmelCase)
         lowercase_     			=   {
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 64,
             "width": 64,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs


   def __UpperCAmelCase							( self					:						Any)   ->       Optional[int]:
         lowercase_     			=   "cpu"

         lowercase_     			=   self.get_dummy_components()

         lowercase_     			=   self.pipeline_class(**__lowerCAmelCase)
         lowercase_     			=   pipe.to(__lowerCAmelCase)

         pipe.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   pipe(**self.get_dummy_inputs(__lowerCAmelCase))
         lowercase_     			=   output.images

         lowercase_     			=   pipe(
             **self.get_dummy_inputs(__lowerCAmelCase) ,		return_dict=__lowerCAmelCase ,		)[0]

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

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

         lowercase_     			=   np.array(
             [0.32_8663, 1.0, 0.2321_6873, 1.0, 0.9271_7564, 0.463_9046, 0.9689_4777, 0.3171_3378, 0.629_3953])

         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			):


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


   def __UpperCAmelCase							( self					:						List[str])   ->       Tuple:
         lowercase_     			=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy")

         lowercase_     			=   KandinskyPriorPipeline.from_pretrained(
             "kandinsky-community/kandinsky-2-1-prior" ,		torch_dtype=torch.floataa)
         pipe_prior.to(__lowerCAmelCase)

         lowercase_     			=   KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1" ,		torch_dtype=torch.floataa)
         lowercase_     			=   pipeline.to(__lowerCAmelCase)
         pipeline.set_progress_bar_config(disable=__lowerCAmelCase)

         lowercase_     			=   "red cat, 4k photo"

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_			,				lowercase_     			=   pipe_prior(
             __lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=5 ,		negative_prompt="" ,		).to_tuple()

         lowercase_     			=   torch.Generator(device="cuda").manual_seed(0)
         lowercase_     			=   pipeline(
             __lowerCAmelCase ,		image_embeds=__lowerCAmelCase ,		negative_image_embeds=__lowerCAmelCase ,		generator=__lowerCAmelCase ,		num_inference_steps=100 ,		output_type="np" ,		)

         lowercase_     			=   output.images[0]

         assert image.shape == (512, 512, 3)

         assert_mean_pixel_difference(__lowerCAmelCase ,		__lowerCAmelCase)






