






'''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 a_    (    UpperCamelCase_		,      unittest.TestCase							):
   _snake_case        =							KandinskyImgaImgPipeline
   _snake_case        =							["""prompt""", """image_embeds""", """negative_image_embeds""", """image"""]
   _snake_case        =							[
       """prompt""",
       """negative_prompt""",
       """image_embeds""",
       """negative_image_embeds""",
       """image""",
   ]
   _snake_case        =							[
       """generator""",
       """height""",
       """width""",
       """strength""",
       """guidance_scale""",
       """negative_prompt""",
       """num_inference_steps""",
       """return_dict""",
       """guidance_scale""",
       """num_images_per_prompt""",
       """output_type""",
       """return_dict""",
   ]
   _snake_case        =							False







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      return 3_2







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      return 3_2







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      return self.time_input_dim







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






      return self.time_input_dim * 4







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Dict:
      """simple docstring"""






      return 1_0_0







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






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







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      torch.manual_seed(0)
      __snake_case				:      Dict     							= 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				:      int     							= MultilingualCLIP(__a)
      __snake_case				:      Any     							= text_encoder.eval()

      return text_encoder







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Union[str, Any]:
      """simple docstring"""






      torch.manual_seed(0)

      __snake_case				:      Optional[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				:      List[str]     							= UNetaDConditionModel(**__a)
      return model







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






      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     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      Tuple     							= self.dummy_text_encoder
      __snake_case				:      Union[str, Any]     							= self.dummy_tokenizer
      __snake_case				:      List[str]     							= self.dummy_unet
      __snake_case				:      List[str]     							= self.dummy_movq

      __snake_case				:      str     							= {
          'num_train_timesteps': 1_0_0_0,
          'beta_schedule': 'linear',
          'beta_start': 0.00_085,
          'beta_end': 0.012,
          'clip_sample': False,
          'set_alpha_to_one': False,
          'steps_offset': 0,
          'prediction_type': 'epsilon',
          'thresholding': False,
      }

      __snake_case				:      List[str]     							= DDIMScheduler(**__a)

      __snake_case				:      Any     							= {
          'text_encoder': text_encoder,
          'tokenizer': tokenizer,
          'unet': unet,
          'scheduler': scheduler,
          'movq': movq,
      }

      return components







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a=0)			->      List[Any]:
      """simple docstring"""






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

      if str(__a).startswith('mps'):
         __snake_case				:      Optional[int]     							= torch.manual_seed(__a)
      else:
         __snake_case				:      Any     							= torch.Generator(device=__a).manual_seed(__a)
      __snake_case				:      Union[str, Any]     							= {
          '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     SCREAMING_SNAKE_CASE__					(self)			->      str:
      """simple docstring"""






      __snake_case				:      Tuple     							= 'cpu'

      __snake_case				:      Dict     							= self.get_dummy_components()

      __snake_case				:      Optional[int]     							= self.pipeline_class(**__a)
      __snake_case				:      Tuple     							= pipe.to(__a)

      pipe.set_progress_bar_config(disable=__a)

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

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

      __snake_case				:      int     							= image[0, -3:, -3:, -1]
      __snake_case				:      Any     							= image_from_tuple[0, -3:, -3:, -1]

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

      __snake_case				:      List[str]     							= np.array(
          [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233])
      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 a_    (    unittest.TestCase							):







   def     SCREAMING_SNAKE_CASE__					(self)			->      List[Any]:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self)			->      Optional[Any]:
      """simple docstring"""






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

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

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

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

      pipeline.set_progress_bar_config(disable=__a)

      __snake_case				:      Dict     							= torch.Generator(device='cpu').manual_seed(0)
      __snake_case							,__snake_case				:      Optional[int]     							= pipe_prior(
          __a					,   generator=__a					,   num_inference_steps=5					,   negative_prompt=''					,   ).to_tuple()

      __snake_case				:      List[Any]     							= 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				:      Tuple     							= output.images[0]

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

      assert_mean_pixel_difference(__a					,   __a)







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A    	=   {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''TimmBackbone''']


if TYPE_CHECKING:
      from .configuration_timm_backbone import TimmBackboneConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_timm_backbone import TimmBackbone

else:
      import sys

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)