
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       _snake_case   (	UpperCAmelCase_  ,						unittest.TestCase							):
 __lowerCAmelCase  :     Dict        =			KandinskyImgaImgPipeline
 __lowerCAmelCase  :     Tuple        =			['prompt', 'image_embeds', 'negative_image_embeds', 'image']
 __lowerCAmelCase  :     Optional[Any]        =			[
     'prompt',
     'negative_prompt',
     'image_embeds',
     'negative_image_embeds',
     'image',
 ]
 __lowerCAmelCase  :     str        =			[
     'generator',
     'height',
     'width',
     'strength',
     'guidance_scale',
     'negative_prompt',
     'num_inference_steps',
     'return_dict',
     'guidance_scale',
     'num_images_per_prompt',
     'output_type',
     'return_dict',
 ]
 __lowerCAmelCase  :     int        =			False


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 32


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 32


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return self.time_input_dim


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return self.time_input_dim * 4


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   return 1_00


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[Any]    		=      XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base""")
   return tokenizer


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__  :   List[str]    		=      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=10_05					,  )

   lowercase__  :   Optional[int]    		=      MultilingualCLIP(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Tuple    		=      text_encoder.eval()

   return text_encoder


 @property
 def      lowercase__     (					self):


   '''simple docstring'''




   torch.manual_seed(0)

   lowercase__  :   int    		=      {
       """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__  :   List[Any]    		=      UNetaDConditionModel(**SCREAMING_SNAKE_CASE_)
   return model


 @property
 def      lowercase__     (					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      lowercase__     (					self):


   '''simple docstring'''




   torch.manual_seed(0)
   lowercase__  :   int    		=      VQModel(**self.dummy_movq_kwargs)
   return model


 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Tuple    		=      self.dummy_text_encoder
   lowercase__  :   str    		=      self.dummy_tokenizer
   lowercase__  :   Optional[Any]    		=      self.dummy_unet
   lowercase__  :   List[Any]    		=      self.dummy_movq

   lowercase__  :   Dict    		=      {
       """num_train_timesteps""": 10_00,
       """beta_schedule""": """linear""",
       """beta_start""": 0.0_0_0_8_5,
       """beta_end""": 0.0_1_2,
       """clip_sample""": False,
       """set_alpha_to_one""": False,
       """steps_offset""": 0,
       """prediction_type""": """epsilon""",
       """thresholding""": False,
   }

   lowercase__  :   Any    		=      DDIMScheduler(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   Any    		=      {
       """text_encoder""": text_encoder,
       """tokenizer""": tokenizer,
       """unet""": unet,
       """scheduler""": scheduler,
       """movq""": movq,
   }

   return components


 def      lowercase__     (					self					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_=0):


   '''simple docstring'''




   lowercase__  :   str    		=      floats_tensor((1, self.cross_attention_dim)					,  rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      floats_tensor((1, self.cross_attention_dim)					,  rng=random.Random(seed + 1)).to(SCREAMING_SNAKE_CASE_)
   # create init_image
   lowercase__  :   Union[str, Any]    		=      floats_tensor((1, 3, 64, 64)					,  rng=random.Random(SCREAMING_SNAKE_CASE_)).to(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[Any]    		=      image.cpu().permute(0					,  2					,  3					,  1)[0]
   lowercase__  :   Dict    		=      Image.fromarray(np.uinta(SCREAMING_SNAKE_CASE_)).convert("""RGB""").resize((2_56, 2_56))

   if str(SCREAMING_SNAKE_CASE_).startswith("""mps"""):
     lowercase__  :   Optional[Any]    		=      torch.manual_seed(SCREAMING_SNAKE_CASE_)
   else:
     lowercase__  :   List[Any]    		=      torch.Generator(device=SCREAMING_SNAKE_CASE_).manual_seed(SCREAMING_SNAKE_CASE_)
   lowercase__  :   Optional[Any]    		=      {
       """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      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Union[str, Any]    		=      """cpu"""

   lowercase__  :   Optional[int]    		=      self.get_dummy_components()

   lowercase__  :   int    		=      self.pipeline_class(**SCREAMING_SNAKE_CASE_)
   lowercase__  :   List[Any]    		=      pipe.to(SCREAMING_SNAKE_CASE_)

   pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Optional[Any]    		=      pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_))
   lowercase__  :   List[str]    		=      output.images

   lowercase__  :   Any    		=      pipe(
       **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_)					,  return_dict=SCREAMING_SNAKE_CASE_					,  )[0]

   lowercase__  :   Dict    		=      image[0, -3:, -3:, -1]
   lowercase__  :   Dict    		=      image_from_tuple[0, -3:, -3:, -1]

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

   lowercase__  :   Any    		=      np.array(
       [0.6_1_4_7_4_9_4_3, 0.6_0_7_3_5_3_9, 0.4_3_3_0_8_5_4_4, 0.5_9_2_8_2_6_9, 0.4_7_4_9_3_5_9_5, 0.4_6_7_5_5_9_7_3, 0.4_6_1_3_8_3_8, 0.4_5_3_6_8_7_9_7, 0.5_0_1_1_9_2_3_3])
   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       _snake_case   (	unittest.TestCase							):


 def      lowercase__     (					self):


   '''simple docstring'''




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



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   str    		=      load_numpy(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
       """/kandinsky/kandinsky_img2img_frog.npy""")

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

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

   lowercase__  :   List[str]    		=      KandinskyImgaImgPipeline.from_pretrained(
       """kandinsky-community/kandinsky-2-1"""					,  torch_dtype=torch.floataa)
   lowercase__  :   Optional[int]    		=      pipeline.to(SCREAMING_SNAKE_CASE_)

   pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_)

   lowercase__  :   Dict    		=      torch.Generator(device="""cpu""").manual_seed(0)
   lowercase__		,    lowercase__  :   List[Any]    		=      pipe_prior(
       SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  num_inference_steps=5					,  negative_prompt=""""""					,  ).to_tuple()

   lowercase__  :   List[str]    		=      pipeline(
       SCREAMING_SNAKE_CASE_					,  image=SCREAMING_SNAKE_CASE_					,  image_embeds=SCREAMING_SNAKE_CASE_					,  negative_image_embeds=SCREAMING_SNAKE_CASE_					,  generator=SCREAMING_SNAKE_CASE_					,  num_inference_steps=1_00					,  height=7_68					,  width=7_68					,  strength=0.2					,  output_type="""np"""					,  )

   lowercase__  :   Any    		=      output.images[0]

   assert image.shape == (7_68, 7_68, 3)

   assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_)








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__    :		List[str]          =   logging.get_logger(__name__)

lowerCamelCase__    :		str          =   {
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}



class       _snake_case   (	UpperCAmelCase_							):
 __lowerCAmelCase  :     Optional[int]        =			'dpt'


 def __init__(					self					,  SCREAMING_SNAKE_CASE_=7_68					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=12					,  SCREAMING_SNAKE_CASE_=30_72					,  SCREAMING_SNAKE_CASE_="gelu"					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0					,  SCREAMING_SNAKE_CASE_=0.0_2					,  SCREAMING_SNAKE_CASE_=1E-12					,  SCREAMING_SNAKE_CASE_=3_84					,  SCREAMING_SNAKE_CASE_=16					,  SCREAMING_SNAKE_CASE_=3					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=[2, 5, 8, 11]					,  SCREAMING_SNAKE_CASE_="project"					,  SCREAMING_SNAKE_CASE_=[4, 2, 1, 0.5]					,  SCREAMING_SNAKE_CASE_=[96, 1_92, 3_84, 7_68]					,  SCREAMING_SNAKE_CASE_=2_56					,  SCREAMING_SNAKE_CASE_=-1					,  SCREAMING_SNAKE_CASE_=False					,  SCREAMING_SNAKE_CASE_=True					,  SCREAMING_SNAKE_CASE_=0.4					,  SCREAMING_SNAKE_CASE_=2_55					,  SCREAMING_SNAKE_CASE_=0.1					,  SCREAMING_SNAKE_CASE_=[1, 10_24, 24, 24]					,  SCREAMING_SNAKE_CASE_=[0, 1]					,  SCREAMING_SNAKE_CASE_=None					,  **SCREAMING_SNAKE_CASE_					,  ):


   '''simple docstring'''




   super().__init__(**SCREAMING_SNAKE_CASE_)

   lowercase__  :   List[str]    		=      hidden_size
   lowercase__  :   str    		=      is_hybrid

   if self.is_hybrid:
     if backbone_config is None:
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   Any    		=      {
           """global_padding""": """same""",
           """layer_type""": """bottleneck""",
           """depths""": [3, 4, 9],
           """out_features""": ["""stage1""", """stage2""", """stage3"""],
           """embedding_dynamic_padding""": True,
       }
       lowercase__  :   Optional[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       logger.info("""Initializing the config with a `BiT` backbone.""")
       lowercase__  :   List[Any]    		=      BitConfig(**SCREAMING_SNAKE_CASE_)
     elif isinstance(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_):
       lowercase__  :   int    		=      backbone_config
     else:
       raise ValueError(
           f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.')

     lowercase__  :   Optional[Any]    		=      backbone_featmap_shape
     lowercase__  :   List[str]    		=      neck_ignore_stages

     if readout_type != "project":
       raise ValueError("""Readout type must be 'project' when using `DPT-hybrid` mode.""")
   else:
     lowercase__  :   Optional[int]    		=      None
     lowercase__  :   Union[str, Any]    		=      None
     lowercase__  :   Tuple    		=      []

   lowercase__  :   List[str]    		=      num_hidden_layers
   lowercase__  :   Union[str, Any]    		=      num_attention_heads
   lowercase__  :   List[Any]    		=      intermediate_size
   lowercase__  :   Optional[Any]    		=      hidden_act
   lowercase__  :   int    		=      hidden_dropout_prob
   lowercase__  :   str    		=      attention_probs_dropout_prob
   lowercase__  :   Dict    		=      initializer_range
   lowercase__  :   Tuple    		=      layer_norm_eps
   lowercase__  :   List[str]    		=      image_size
   lowercase__  :   Tuple    		=      patch_size
   lowercase__  :   Tuple    		=      num_channels
   lowercase__  :   Dict    		=      qkv_bias
   lowercase__  :   List[Any]    		=      backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
     raise ValueError("""Readout_type must be one of ['ignore', 'add', 'project']""")
   lowercase__  :   Dict    		=      readout_type
   lowercase__  :   Tuple    		=      reassemble_factors
   lowercase__  :   Any    		=      neck_hidden_sizes
   lowercase__  :   Dict    		=      fusion_hidden_size
   lowercase__  :   Tuple    		=      head_in_index
   lowercase__  :   Tuple    		=      use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   lowercase__  :   Tuple    		=      use_auxiliary_head
   lowercase__  :   int    		=      auxiliary_loss_weight
   lowercase__  :   Union[str, Any]    		=      semantic_loss_ignore_index
   lowercase__  :   str    		=      semantic_classifier_dropout



 def      lowercase__     (					self):


   '''simple docstring'''




   lowercase__  :   Optional[int]    		=      copy.deepcopy(self.__dict__)

   if output["backbone_config"] is not None:
     lowercase__  :   Optional[Any]    		=      self.backbone_config.to_dict()

   lowercase__  :   Dict    		=      self.__class__.model_type
   return output






