
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 	lowerCamelCase						(    A_				,					unittest.TestCase    ):
     UpperCAmelCase__							:				List[str] 	=						KandinskyPipeline
     UpperCAmelCase__							:				Optional[Any] 	=						[
         "prompt",
         "image_embeds",
         "negative_image_embeds",
     ]
     UpperCAmelCase__							:				Any 	=						["prompt", "negative_prompt", "image_embeds", "negative_image_embeds"]
     UpperCAmelCase__							:				Any 	=						[
         "generator",
         "height",
         "width",
         "latents",
         "guidance_scale",
         "negative_prompt",
         "num_inference_steps",
         "return_dict",
         "guidance_scale",
         "num_images_per_prompt",
         "output_type",
         "return_dict",
     ]
     UpperCAmelCase__							:				Any 	=						False





     @property
     def 						UpperCAmelCase(self :       Optional[int]  )		->		Tuple:
         return 3_2





     @property
     def 						UpperCAmelCase(self :       List[str]  )		->		Optional[Any]:
         return 3_2





     @property
     def 						UpperCAmelCase(self :       List[str]  )		->		Optional[int]:
         return self.time_input_dim





     @property
     def 						UpperCAmelCase(self :       Tuple  )		->		Tuple:
         return self.time_input_dim * 4





     @property
     def 						UpperCAmelCase(self :       str  )		->		str:
         return 1_0_0





     @property
     def 						UpperCAmelCase(self :       List[str]  )		->		Optional[int]:
         snake_case						=   XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"  )
         return tokenizer





     @property
     def 						UpperCAmelCase(self :       int  )		->		Dict:
         torch.manual_seed(0  )
         snake_case						=   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						=   MultilingualCLIP(_A  )
         snake_case						=   text_encoder.eval()

         return text_encoder





     @property
     def 						UpperCAmelCase(self :       Tuple  )		->		List[Any]:
         torch.manual_seed(0  )

         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,
         }

         snake_case						=   UNetaDConditionModel(**_A  )
         return model





     @property
     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Optional[Any]:
         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 						UpperCAmelCase(self :       Optional[Any]  )		->		List[str]:
         torch.manual_seed(0  )
         snake_case						=   VQModel(**self.dummy_movq_kwargs  )
         return model





     def 						UpperCAmelCase(self :       Any  )		->		Optional[int]:
         snake_case						=   self.dummy_text_encoder
         snake_case						=   self.dummy_tokenizer
         snake_case						=   self.dummy_unet
         snake_case						=   self.dummy_movq

         snake_case						=   DDIMScheduler(
             num_train_timesteps=1_0_0_0							,       beta_schedule="linear"							,       beta_start=0.0_00_85							,       beta_end=0.0_12							,       clip_sample=_A							,       set_alpha_to_one=_A							,       steps_offset=1							,       prediction_type="epsilon"							,       thresholding=_A							,       )

         snake_case						=   {
             "text_encoder": text_encoder,
             "tokenizer": tokenizer,
             "unet": unet,
             "scheduler": scheduler,
             "movq": movq,
         }
         return components





     def 						UpperCAmelCase(self :       str							,       _A :       Tuple							,       _A :       List[str]=0  )		->		Tuple:
         snake_case						=   floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(_A  )  ).to(_A  )
         snake_case						=   floats_tensor((1, self.cross_attention_dim)							,       rng=random.Random(seed + 1  )  ).to(_A  )
         if str(_A  ).startswith("mps"  ):
             snake_case						=   torch.manual_seed(_A  )
         else:
             snake_case						=   torch.Generator(device=_A  ).manual_seed(_A  )
         snake_case						=   {
             "prompt": "horse",
             "image_embeds": image_embeds,
             "negative_image_embeds": negative_image_embeds,
             "generator": generator,
             "height": 6_4,
             "width": 6_4,
             "guidance_scale": 4.0,
             "num_inference_steps": 2,
             "output_type": "np",
         }
         return inputs





     def 						UpperCAmelCase(self :       Optional[Any]  )		->		Tuple:
         snake_case						=   "cpu"

         snake_case						=   self.get_dummy_components()

         snake_case						=   self.pipeline_class(**_A  )
         snake_case						=   pipe.to(_A  )

         pipe.set_progress_bar_config(disable=_A  )

         snake_case						=   pipe(**self.get_dummy_inputs(_A  )  )
         snake_case						=   output.images

         snake_case						=   pipe(
             **self.get_dummy_inputs(_A  )							,       return_dict=_A							,       )[0]

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

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

         snake_case						=   np.array(
             [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]  )

         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 	lowerCamelCase						(    unittest.TestCase    ):





     def 						UpperCAmelCase(self :       Dict  )		->		Dict:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()





     def 						UpperCAmelCase(self :       Union[str, Any]  )		->		Dict:
         snake_case						=   load_numpy(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
             "/kandinsky/kandinsky_text2img_cat_fp16.npy"  )

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

         snake_case						=   KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"							,       torch_dtype=torch.floataa  )
         snake_case						=   pipeline.to(_A  )
         pipeline.set_progress_bar_config(disable=_A  )

         snake_case						=   "red cat, 4k photo"

         snake_case						=   torch.Generator(device="cuda"  ).manual_seed(0  )
         snake_case	,				snake_case						=   pipe_prior(
             _A							,       generator=_A							,       num_inference_steps=5							,       negative_prompt=""							,       ).to_tuple()

         snake_case						=   torch.Generator(device="cuda"  ).manual_seed(0  )
         snake_case						=   pipeline(
             _A							,       image_embeds=_A							,       negative_image_embeds=_A							,       generator=_A							,       num_inference_steps=1_0_0							,       output_type="np"							,       )

         snake_case						=   output.images[0]

         assert image.shape == (5_1_2, 5_1_2, 3)

         assert_mean_pixel_difference(_A							,       _A  )



_A       =					{
    "+": lambda x, y: x + y,
    "-": lambda x, y: x - y,
    "*": lambda x, y: x * y,
    "/": lambda x, y: x / y,
}



def 		lowercase_     (  A__						)				->				Union[str, Any]:
    """simple docstring"""

    return c.isdigit()



def 		lowercase_     (  A__						)				->				int:
    """simple docstring"""

    snake_case						=   []

    # iterate over the string in reverse order
    for c in expression.split()[::-1]:
        # push operand to stack
        if is_operand(A__						):
            stack.append(int(A__						)						)

        else:
            # pop values from stack can calculate the result
            # push the result onto the stack again
            snake_case						=   stack.pop()
            snake_case						=   stack.pop()
            stack.append(calc[c](A__					,						A__						)						)

    return stack.pop()


# Driver code
if __name__ == "__main__":
   _A       =					"+ 9 * 2 6"
   print(evaluate(test_expression))

   _A       =					"/ * 10 2 + 4 1 "
   print(evaluate(test_expression))

