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     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase_						,							unittest.TestCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Any						=						KandinskyPipeline
   __UpperCAmelCase  :       Tuple						=						[
       "prompt",
       "image_embeds",
       "negative_image_embeds",
   ]
   __UpperCAmelCase  :       str						=						["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
   __UpperCAmelCase  :       Optional[int]						=						[
       "generator",
       "height",
       "width",
       "latents",
       "guidance_scale",
       "negative_prompt",
       "num_inference_steps",
       "return_dict",
       "guidance_scale",
       "num_images_per_prompt",
       "output_type",
       "return_dict",
   ]
   __UpperCAmelCase  :       Tuple						=						False







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      return 32







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      return 32







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      return self.time_input_dim







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      return self.time_input_dim * 4







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      return 100







   @property
   def       _A							(  self  ):


      '''simple docstring'''

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







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      torch.manual_seed(0  )
      __lowerCamelCase								=				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   ,							)

      __lowerCamelCase								=				MultilingualCLIP(__UpperCamelCase  )
      __lowerCamelCase								=				text_encoder.eval()

      return text_encoder







   @property
   def       _A							(  self  ):


      '''simple docstring'''

      torch.manual_seed(0  )

      __lowerCamelCase								=				{
          '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,
      }

      __lowerCamelCase								=				UNetaDConditionModel(**__UpperCamelCase  )
      return model







   @property
   def       _A							(  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       _A							(  self  ):


      '''simple docstring'''

      torch.manual_seed(0  )
      __lowerCamelCase								=				VQModel(**self.dummy_movq_kwargs  )
      return model







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.dummy_text_encoder
      __lowerCamelCase								=				self.dummy_tokenizer
      __lowerCamelCase								=				self.dummy_unet
      __lowerCamelCase								=				self.dummy_movq

      __lowerCamelCase								=				DDIMScheduler(
          num_train_timesteps=1000   ,							beta_schedule='linear'   ,							beta_start=0.0_0085   ,							beta_end=0.012   ,							clip_sample=__UpperCamelCase   ,							set_alpha_to_one=__UpperCamelCase   ,							steps_offset=1   ,							prediction_type='epsilon'   ,							thresholding=__UpperCamelCase   ,							)

      __lowerCamelCase								=				{
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }
      return components







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


      '''simple docstring'''

      __lowerCamelCase								=				floats_tensor((1, self.cross_attention_dim)   ,							rng=random.Random(__UpperCamelCase  )  ).to(__UpperCamelCase  )
      __lowerCamelCase								=				floats_tensor((1, self.cross_attention_dim)   ,							rng=random.Random(seed + 1  )  ).to(__UpperCamelCase  )
      if str(__UpperCamelCase  ).startswith('mps'  ):
         __lowerCamelCase								=				torch.manual_seed(__UpperCamelCase  )
      else:
         __lowerCamelCase								=				torch.Generator(device=__UpperCamelCase  ).manual_seed(__UpperCamelCase  )
      __lowerCamelCase								=				{
          '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       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				'cpu'

      __lowerCamelCase								=				self.get_dummy_components()

      __lowerCamelCase								=				self.pipeline_class(**__UpperCamelCase  )
      __lowerCamelCase								=				pipe.to(__UpperCamelCase  )

      pipe.set_progress_bar_config(disable=__UpperCamelCase  )

      __lowerCamelCase								=				pipe(**self.get_dummy_inputs(__UpperCamelCase  )  )
      __lowerCamelCase								=				output.images

      __lowerCamelCase								=				pipe(
          **self.get_dummy_inputs(__UpperCamelCase  )   ,							return_dict=__UpperCamelCase   ,							)[0]

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

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

      __lowerCamelCase								=				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     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



   '''simple docstring'''



   def       _A							(  self  ):


      '''simple docstring'''

      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
          '/kandinsky/kandinsky_text2img_cat_fp16.npy'  )

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

      __lowerCamelCase								=				KandinskyPipeline.from_pretrained('kandinsky-community/kandinsky-2-1'   ,							torch_dtype=torch.floataa  )
      __lowerCamelCase								=				pipeline.to(__UpperCamelCase  )
      pipeline.set_progress_bar_config(disable=__UpperCamelCase  )

      __lowerCamelCase								=				'red cat, 4k photo'

      __lowerCamelCase								=				torch.Generator(device='cuda'  ).manual_seed(0  )
      __lowerCamelCase		,__lowerCamelCase								=				pipe_prior(
          __UpperCamelCase   ,							generator=__UpperCamelCase   ,							num_inference_steps=5   ,							negative_prompt=''   ,							).to_tuple()

      __lowerCamelCase								=				torch.Generator(device='cuda'  ).manual_seed(0  )
      __lowerCamelCase								=				pipeline(
          __UpperCamelCase   ,							image_embeds=__UpperCamelCase   ,							negative_image_embeds=__UpperCamelCase   ,							generator=__UpperCamelCase   ,							num_inference_steps=100   ,							output_type='np'   ,							)

      __lowerCamelCase								=				output.images[0]

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

      assert_mean_pixel_difference(__UpperCamelCase   ,							__UpperCamelCase  )







"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE  :		List[Any]        =      {
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Optional[Any]        =      ['''DeformableDetrFeatureExtractor''']
     _SCREAMING_SNAKE_CASE  :		List[str]        =      ['''DeformableDetrImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      [
         '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DeformableDetrForObjectDetection''',
         '''DeformableDetrModel''',
         '''DeformableDetrPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
          from .image_processing_deformable_detr import DeformableDetrImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deformable_detr import (
              DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DeformableDetrForObjectDetection,
              DeformableDetrModel,
              DeformableDetrPreTrainedModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE  :		Tuple        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






