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




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     return 32




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     return 32




   @property
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     return self.time_input_dim




   @property
   def 			__a		(					self    :       Any   ):



     """simple docstring"""






     return self.time_input_dim * 4




   @property
   def 			__a		(					self    :       Tuple   ):



     """simple docstring"""






     return 1_00




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""   )
     return tokenizer




   @property
   def 			__a		(					self    :       List[Any]   ):



     """simple docstring"""






     torch.manual_seed(0   )
     SCREAMING_SNAKE_CASE__    							=   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 ,			)

     SCREAMING_SNAKE_CASE__    							=   MultilingualCLIP(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   text_encoder.eval()

     return text_encoder




   @property
   def 			__a		(					self    :       Optional[Any]   ):



     """simple docstring"""






     torch.manual_seed(0   )

     SCREAMING_SNAKE_CASE__    							=   {
         """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,
     }

     SCREAMING_SNAKE_CASE__    							=   UNetaDConditionModel(**_lowercase   )
     return model




   @property
   def 			__a		(					self    :       Tuple   ):



     """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    :       Optional[int]   ):



     """simple docstring"""






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




   def 			__a		(					self    :       int   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   self.dummy_text_encoder
     SCREAMING_SNAKE_CASE__    							=   self.dummy_tokenizer
     SCREAMING_SNAKE_CASE__    							=   self.dummy_unet
     SCREAMING_SNAKE_CASE__    							=   self.dummy_movq

     SCREAMING_SNAKE_CASE__    							=   {
         """num_train_timesteps""": 10_00,
         """beta_schedule""": """linear""",
         """beta_start""": 0.0_00_85,
         """beta_end""": 0.0_12,
         """clip_sample""": False,
         """set_alpha_to_one""": False,
         """steps_offset""": 0,
         """prediction_type""": """epsilon""",
         """thresholding""": False,
     }

     SCREAMING_SNAKE_CASE__    							=   DDIMScheduler(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   {
         """text_encoder""": text_encoder,
         """tokenizer""": tokenizer,
         """unet""": unet,
         """scheduler""": scheduler,
         """movq""": movq,
     }

     return components




   def 			__a		(					self    :       Any ,			_lowercase    :       Union[str, Any] ,			_lowercase    :       Optional[Any]=0   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, self.cross_attention_dim) ,			rng=random.Random(_lowercase   )   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, self.cross_attention_dim) ,			rng=random.Random(seed + 1   )   ).to(_lowercase   )
     # create init_image
     SCREAMING_SNAKE_CASE__    							=   floats_tensor((1, 3, 64, 64) ,			rng=random.Random(_lowercase   )   ).to(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   image.cpu().permute(0 ,			2 ,			3 ,			1   )[0]
     SCREAMING_SNAKE_CASE__    							=   Image.fromarray(np.uinta(_lowercase   )   ).convert("""RGB"""   ).resize((2_56, 2_56)   )

     if str(_lowercase   ).startswith("""mps"""   ):
       SCREAMING_SNAKE_CASE__    							=   torch.manual_seed(_lowercase   )
     else:
       SCREAMING_SNAKE_CASE__    							=   torch.Generator(device=_lowercase   ).manual_seed(_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   {
         """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 			__a		(					self    :       str   ):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__    							=   """cpu"""

     SCREAMING_SNAKE_CASE__    							=   self.get_dummy_components()

     SCREAMING_SNAKE_CASE__    							=   self.pipeline_class(**_lowercase   )
     SCREAMING_SNAKE_CASE__    							=   pipe.to(_lowercase   )

     pipe.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   pipe(**self.get_dummy_inputs(_lowercase   )   )
     SCREAMING_SNAKE_CASE__    							=   output.images

     SCREAMING_SNAKE_CASE__    							=   pipe(
         **self.get_dummy_inputs(_lowercase   ) ,			return_dict=_lowercase ,			)[0]

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

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

     SCREAMING_SNAKE_CASE__    							=   np.array(
         [0.61_47_49_43, 0.6_07_35_39, 0.43_30_85_44, 0.5_92_82_69, 0.47_49_35_95, 0.46_75_59_73, 0.4_61_38_38, 0.45_36_87_97, 0.50_11_92_33]   )
     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 			__a		(					self    :       Dict   ):



     """simple docstring"""






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




   def 			__a		(					self    :       List[str]   ):



     """simple docstring"""






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

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

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

     SCREAMING_SNAKE_CASE__    							=   KandinskyImgaImgPipeline.from_pretrained(
         """kandinsky-community/kandinsky-2-1""" ,			torch_dtype=torch.floataa   )
     SCREAMING_SNAKE_CASE__    							=   pipeline.to(_lowercase   )

     pipeline.set_progress_bar_config(disable=_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   torch.Generator(device="""cpu"""   ).manual_seed(0   )
     SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   pipe_prior(
         _lowercase ,			generator=_lowercase ,			num_inference_steps=5 ,			negative_prompt="""""" ,			).to_tuple()

     SCREAMING_SNAKE_CASE__    							=   pipeline(
         _lowercase ,			image=_lowercase ,			image_embeds=_lowercase ,			negative_image_embeds=_lowercase ,			generator=_lowercase ,			num_inference_steps=1_00 ,			height=7_68 ,			width=7_68 ,			strength=0.2 ,			output_type="""np""" ,			)

     SCREAMING_SNAKE_CASE__    							=   output.images[0]

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

     assert_mean_pixel_difference(_lowercase ,			_lowercase   )

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase    :				List[Any]							     =	logging.get_logger(__name__)

__lowerCamelCase    :				str							     =	{
    '''facebook/timesformer''': '''https://huggingface.co/facebook/timesformer/resolve/main/config.json''',
}







class __snake_case       (		lowerCamelCase_	):
   lowerCAmelCase_														=							"timesformer"




   def __init__(					self    :       Any ,			_lowercase    :       Tuple=2_24 ,			_lowercase    :       Tuple=16 ,			_lowercase    :       List[Any]=3 ,			_lowercase    :       str=8 ,			_lowercase    :       Optional[Any]=7_68 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       List[str]=12 ,			_lowercase    :       Dict=30_72 ,			_lowercase    :       List[str]="gelu" ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       List[str]=0.0 ,			_lowercase    :       Tuple=0.02 ,			_lowercase    :       List[Any]=1E-6 ,			_lowercase    :       List[str]=True ,			_lowercase    :       Dict="divided_space_time" ,			_lowercase    :       Union[str, Any]=0 ,			**_lowercase    :       Union[str, Any] ,			):



     """simple docstring"""






     super().__init__(**_lowercase   )

     SCREAMING_SNAKE_CASE__    							=   image_size
     SCREAMING_SNAKE_CASE__    							=   patch_size
     SCREAMING_SNAKE_CASE__    							=   num_channels
     SCREAMING_SNAKE_CASE__    							=   num_frames

     SCREAMING_SNAKE_CASE__    							=   hidden_size
     SCREAMING_SNAKE_CASE__    							=   num_hidden_layers
     SCREAMING_SNAKE_CASE__    							=   num_attention_heads
     SCREAMING_SNAKE_CASE__    							=   intermediate_size
     SCREAMING_SNAKE_CASE__    							=   hidden_act
     SCREAMING_SNAKE_CASE__    							=   hidden_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   attention_probs_dropout_prob
     SCREAMING_SNAKE_CASE__    							=   initializer_range
     SCREAMING_SNAKE_CASE__    							=   layer_norm_eps
     SCREAMING_SNAKE_CASE__    							=   qkv_bias

     SCREAMING_SNAKE_CASE__    							=   attention_type
     SCREAMING_SNAKE_CASE__    							=   drop_path_rate
