



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 						lowercase__      (__snake_case						,      unittest.TestCase						):

     """simple docstring"""






     __UpperCamelCase    :	str		=       KandinskyImgaImgPipeline
     __UpperCamelCase    :	Any		=       ['prompt', 'image_embeds', 'negative_image_embeds', 'image']
     __UpperCamelCase    :	int		=       [
         'prompt',
         'negative_prompt',
         'image_embeds',
         'negative_image_embeds',
         'image',
     ]
     __UpperCamelCase    :	Any		=       [
         'generator',
         'height',
         'width',
         'strength',
         'guidance_scale',
         'negative_prompt',
         'num_inference_steps',
         'return_dict',
         'guidance_scale',
         'num_images_per_prompt',
         'output_type',
         'return_dict',
     ]
     __UpperCamelCase    :	Optional[int]		=       False


     @property
     def 		lowercase   ( self :	Dict ):
      return 3_2


     @property
     def 		lowercase   ( self :	Optional[int] ):
      return 3_2


     @property
     def 		lowercase   ( self :	Optional[int] ):
      return self.time_input_dim


     @property
     def 		lowercase   ( self :	Dict ):
      return self.time_input_dim * 4


     @property
     def 		lowercase   ( self :	List[Any] ):
      return 1_0_0


     @property
     def 		lowercase   ( self :	List[Any] ):
      snake_case__				:							int      							= XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""" )
      return tokenizer


     @property
     def 		lowercase   ( self :	Optional[int] ):
      torch.manual_seed(0 )
      snake_case__				:							Optional[Any]      							= MCLIPConfig(
          numDims=self.cross_attention_dim   ,			transformerDimensions=self.text_embedder_hidden_size   ,			hidden_size=self.text_embedder_hidden_size   ,			intermediate_size=3_7   ,			num_attention_heads=4   ,			num_hidden_layers=5   ,			vocab_size=1_0_0_5   ,			)

      snake_case__				:							List[Any]      							= MultilingualCLIP(__a )
      snake_case__				:							List[Any]      							= text_encoder.eval()

      return text_encoder


     @property
     def 		lowercase   ( self :	List[str] ):
      torch.manual_seed(0 )

      snake_case__				:							Union[str, Any]      							= {
          '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,
      }

      snake_case__				:							Tuple      							= UNetaDConditionModel(**__a )
      return model


     @property
     def 		lowercase   ( self :	Optional[int] ):
      return {
          "block_out_channels": [3_2, 6_4],
          "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": 1_2,
          "out_channels": 3,
          "up_block_types": [
              "AttnUpDecoderBlock2D",
              "UpDecoderBlock2D",
          ],
          "vq_embed_dim": 4,
      }


     @property
     def 		lowercase   ( self :	Any ):
      torch.manual_seed(0 )
      snake_case__				:							List[Any]      							= VQModel(**self.dummy_movq_kwargs )
      return model


     def 		lowercase   ( self :	Optional[int] ):
      snake_case__				:							Tuple      							= self.dummy_text_encoder
      snake_case__				:							Any      							= self.dummy_tokenizer
      snake_case__				:							List[str]      							= self.dummy_unet
      snake_case__				:							Union[str, Any]      							= self.dummy_movq

      snake_case__				:							List[str]      							= {
          'num_train_timesteps': 1_0_0_0,
          '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,
      }

      snake_case__				:							Dict      							= DDIMScheduler(**__a )

      snake_case__				:							Optional[int]      							= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components


     def 		lowercase   ( self :	Any   ,			__a :	Any   ,			__a :	int=0 ):
      snake_case__				:							List[Any]      							= floats_tensor((1, self.cross_attention_dim)   ,			rng=random.Random(__a ) ).to(__a )
      snake_case__				:							Tuple      							= floats_tensor((1, self.cross_attention_dim)   ,			rng=random.Random(seed + 1 ) ).to(__a )
      # create init_image
      snake_case__				:							Dict      							= floats_tensor((1, 3, 6_4, 6_4)   ,			rng=random.Random(__a ) ).to(__a )
      snake_case__				:							Optional[int]      							= image.cpu().permute(0   ,			2   ,			3   ,			1 )[0]
      snake_case__				:							List[Any]      							= Image.fromarray(np.uinta(__a ) ).convert("""RGB""" ).resize((2_5_6, 2_5_6) )

      if str(__a ).startswith("""mps""" ):
       snake_case__				:							str      							= torch.manual_seed(__a )
      else:
       snake_case__				:							Optional[Any]      							= torch.Generator(device=__a ).manual_seed(__a )
      snake_case__				:							str      							= {
          'prompt': 'horse',
          'image': init_image,
          'image_embeds': image_embeds,
          'negative_image_embeds': negative_image_embeds,
          'generator': generator,
          'height': 6_4,
          'width': 6_4,
          'num_inference_steps': 1_0,
          'guidance_scale': 7.0,
          'strength': 0.2,
          'output_type': 'np',
      }
      return inputs


     def 		lowercase   ( self :	Optional[int] ):
      snake_case__				:							List[Any]      							= 'cpu'

      snake_case__				:							Dict      							= self.get_dummy_components()

      snake_case__				:							Optional[Any]      							= self.pipeline_class(**__a )
      snake_case__				:							List[str]      							= pipe.to(__a )

      pipe.set_progress_bar_config(disable=__a )

      snake_case__				:							List[Any]      							= pipe(**self.get_dummy_inputs(__a ) )
      snake_case__				:							List[str]      							= output.images

      snake_case__				:							Any      							= pipe(
          **self.get_dummy_inputs(__a )   ,			return_dict=__a   ,			)[0]

      snake_case__				:							Any      							= image[0, -3:, -3:, -1]
      snake_case__				:							Tuple      							= image_from_tuple[0, -3:, -3:, -1]

      assert image.shape == (1, 6_4, 6_4, 3)

      snake_case__				:							List[Any]      							= 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 						lowercase__      (unittest.TestCase						):

     """simple docstring"""






     def 		lowercase   ( self :	Any ):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()


     def 		lowercase   ( self :	int ):
      snake_case__				:							Tuple      							= load_numpy(
          """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
          """/kandinsky/kandinsky_img2img_frog.npy""" )

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

      snake_case__				:							Union[str, Any]      							= KandinskyPriorPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1-prior"""   ,			torch_dtype=torch.floataa )
      pipe_prior.to(__a )

      snake_case__				:							Optional[Any]      							= KandinskyImgaImgPipeline.from_pretrained(
          """kandinsky-community/kandinsky-2-1"""   ,			torch_dtype=torch.floataa )
      snake_case__				:							Tuple      							= pipeline.to(__a )

      pipeline.set_progress_bar_config(disable=__a )

      snake_case__				:							List[str]      							= torch.Generator(device="""cpu""" ).manual_seed(0 )
      snake_case__				:							List[Any]      							= pipe_prior(
          __a   ,			generator=__a   ,			num_inference_steps=5   ,			negative_prompt=""""""   ,			).to_tuple()

      snake_case__				:							str      							= pipeline(
          __a   ,			image=__a   ,			image_embeds=__a   ,			negative_image_embeds=__a   ,			generator=__a   ,			num_inference_steps=1_0_0   ,			height=7_6_8   ,			width=7_6_8   ,			strength=0.2   ,			output_type="""np"""   ,			)

      snake_case__				:							Any      							= output.images[0]

      assert image.shape == (7_6_8, 7_6_8, 3)

      assert_mean_pixel_difference(__a   ,			__a )








'''simple docstring'''






from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

if is_torch_available():
 from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowercase				=logging.get_logger(__name__)






@add_end_docstrings(lowerCAmelCase							)
class    __magic_name__				(						lowerCAmelCase							):






       def __init__(     self    ,				*snake_case    ,				**snake_case)		->    Dict:




          '''simple docstring'''
          super().__init__(*snake_case    ,				**snake_case)
          requires_backends(self    ,				'vision')
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == 'tf'
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)






       def      lowerCAmelCase						(     self    ,				snake_case=None)		->    str:




          '''simple docstring'''
          _UpperCAmelCase  :  Any				       ={}
          if top_k is not None:
             _UpperCAmelCase  :  Optional[int]				       =top_k
          return {}, {}, postprocess_params






       def __call__(     self    ,				snake_case    ,				**snake_case)		->    List[str]:




          '''simple docstring'''
          return super().__call__(snake_case    ,				**snake_case)






       def      lowerCAmelCase						(     self    ,				snake_case)		->    List[str]:




          '''simple docstring'''
          _UpperCAmelCase  :  int				       =load_image(snake_case)
          _UpperCAmelCase  :  Tuple				       =self.image_processor(images=snake_case    ,				return_tensors=self.framework)
          return model_inputs






       def      lowerCAmelCase						(     self    ,				snake_case)		->    Optional[Any]:




          '''simple docstring'''
          _UpperCAmelCase  :  List[str]				       =self.model(**snake_case)
          return model_outputs






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case=5)		->    Any:




          '''simple docstring'''
          if top_k > self.model.config.num_labels:
             _UpperCAmelCase  :  Optional[Any]				       =self.model.config.num_labels

          if self.framework == "pt":
             _UpperCAmelCase  :  List[Any]				       =model_outputs.logits.softmax(-1)[0]
             _UpperCAmelCase       ,			_UpperCAmelCase  :  Optional[Any]				       =probs.topk(snake_case)
          elif self.framework == "tf":
             _UpperCAmelCase  :  int				       =stable_softmax(model_outputs.logits    ,				axis=-1)[0]
             _UpperCAmelCase  :  List[Any]				       =tf.math.top_k(snake_case    ,				k=snake_case)
             _UpperCAmelCase       ,			_UpperCAmelCase  :  List[str]				       =topk.values.numpy(), topk.indices.numpy()
          else:
             raise ValueError(f"Unsupported framework: {self.framework}")

          _UpperCAmelCase  :  int				       =scores.tolist()
          _UpperCAmelCase  :  Tuple				       =ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case    ,				snake_case)]

