


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     __UpperCAmelCase							(       a__ ,					unittest.TestCase							):


     """simple docstring"""
     _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 	snake_case_  (	self    ):
        return 32


     @property
     def 	snake_case_  (	self    ):
        return 32


     @property
     def 	snake_case_  (	self    ):
        return self.time_input_dim


     @property
     def 	snake_case_  (	self    ):
        return self.time_input_dim * 4


     @property
     def 	snake_case_  (	self    ):
        return 100


     @property
     def 	snake_case_  (	self    ):
        __a					=  XLMRobertaTokenizerFast.from_pretrained("""YiYiXu/tiny-random-mclip-base"""    )
        return tokenizer


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  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  ,       )

        __a					=  MultilingualCLIP(__a    )
        __a					=  text_encoder.eval()

        return text_encoder


     @property
     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )

        __a					=  {
            """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,
        }

        __a					=  UNetaDConditionModel(**__a    )
        return model


     @property
     def 	snake_case_  (	self    ):
        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 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  VQModel(**self.dummy_movq_kwargs    )
        return model


     def 	snake_case_  (	self    ):
        __a					=  self.dummy_text_encoder
        __a					=  self.dummy_tokenizer
        __a					=  self.dummy_unet
        __a					=  self.dummy_movq

        __a					=  {
            """num_train_timesteps""": 1000,
            """beta_schedule""": """linear""",
            """beta_start""": 0.00085,
            """beta_end""": 0.012,
            """clip_sample""": False,
            """set_alpha_to_one""": False,
            """steps_offset""": 0,
            """prediction_type""": """epsilon""",
            """thresholding""": False,
        }

        __a					=  DDIMScheduler(**__a    )

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

        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        __a					=  floats_tensor((1, self.cross_attention_dim)  ,       rng=random.Random(__a    )    ).to(__a    )
        __a					=  floats_tensor((1, self.cross_attention_dim)  ,       rng=random.Random(seed + 1    )    ).to(__a    )
        # create init_image
        __a					=  floats_tensor((1, 3, 64, 64)  ,       rng=random.Random(__a    )    ).to(__a    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__a    )    ).convert("""RGB"""    ).resize((256, 256)    )

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

        __a					=  self.get_dummy_components()

        __a					=  self.pipeline_class(**__a    )
        __a					=  pipe.to(__a    )

        pipe.set_progress_bar_config(disable=__a    )

        __a					=  pipe(**self.get_dummy_inputs(__a    )    )
        __a					=  output.images

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

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

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

        __a					=  np.array(
            [0.61474943, 0.6073539, 0.43308544, 0.5928269, 0.47493595, 0.46755973, 0.4613838, 0.45368797, 0.50119233]    )
        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     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


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


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

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

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

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

        pipeline.set_progress_bar_config(disable=__a    )

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

        __a					=  pipeline(
            __a  ,       image=__a  ,       image_embeds=__a  ,       negative_image_embeds=__a  ,       generator=__a  ,       num_inference_steps=100  ,       height=768  ,       width=768  ,       strength=0.2  ,       output_type="""np"""  ,       )

        __a					=  output.images[0]

        assert image.shape == (768, 768, 3)

        assert_mean_pixel_difference(__a  ,       __a    )









'''simple docstring'''







from __future__ import annotations







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a = 0							)      ->				str:
          snake_case			:      List[Any]        =			key







     def 			snake_case_      (				self							,__a							,__a							)      ->				list[str]:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      Tuple        =			key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(__a							) ^ key							) for ch in content]







     def 			snake_case_      (				self							,__a							,__a							)      ->				list[str]:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      List[Any]        =			key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(__a							) ^ key							) for ch in content]







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				str:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      List[Any]        =			key or self.__key or 1

          # make sure key can be any size
          while key > 255:
               key -= 255

          # This will be returned
          snake_case			:      List[str]        =			""""""

          for ch in content:
               ans += chr(ord(__a							) ^ key							)

          return ans







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				str:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          snake_case			:      Any        =			key or self.__key or 1

          # make sure key can be any size
          while key > 255:
               key -= 255

          # This will be returned
          snake_case			:      List[str]        =			""""""

          for ch in content:
               ans += chr(ord(__a							) ^ key							)

          return ans







     def 			snake_case_      (				self							,__a							,__a = 0							)      ->				bool:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          try:
               with open(__a							) as fin, open("""encrypt.out"""							,"""w+"""							) as fout:
                    # actual encrypt-process
                    for line in fin:
                         fout.write(self.encrypt_string(__a							,__a							)							)

          except OSError:
               return False

          return True







     def 			snake_case_      (				self							,__a							,__a							)      ->				bool:
          assert isinstance(__a							,__a							) and isinstance(__a							,__a							)

          try:
               with open(__a							) as fin, open("""decrypt.out"""							,"""w+"""							) as fout:
                    # actual encrypt-process
                    for line in fin:
                         fout.write(self.decrypt_string(__a							,__a							)							)

          except OSError:
               return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")



