






import unittest

import numpy as np
import torch
from torch import nn
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import KandinskyPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 					a					(				lowercase__							, unittest.TestCase     ):



      """simple docstring"""


      a						:				List[Any]							 =			KandinskyPriorPipeline
      a						:				Any							 =			['prompt']
      a						:				Union[str, Any]							 =			['prompt', 'negative_prompt']
      a						:				Dict							 =			[
          'num_images_per_prompt',
          'generator',
          'num_inference_steps',
          'latents',
          'negative_prompt',
          'guidance_scale',
          'output_type',
          'return_dict',
      ]
      a						:				Optional[int]							 =			False
      @property
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Optional[Any]:
        return 32
      @property
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   Tuple:
        return 32
      @property
      def 			UpperCAmelCase   (   self      :    Tuple      )							->   Dict:
        return self.time_input_dim
      @property
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   List[str]:
        return self.time_input_dim * 4
      @property
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   List[Any]:
        return 100
      @property
      def 			UpperCAmelCase   (   self      :    Any      )							->   Optional[int]:
        __UpperCAmelCase							:			Union[str, Any]     =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""      )
        return tokenizer
      @property
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   List[Any]:
        torch.manual_seed(0      )
        __UpperCAmelCase							:			Optional[Any]     =						CLIPTextConfig(
            bos_token_id=0	,   eos_token_id=2	,   hidden_size=self.text_embedder_hidden_size	,   projection_dim=self.text_embedder_hidden_size	,   intermediate_size=37	,   layer_norm_eps=1e-0_5	,   num_attention_heads=4	,   num_hidden_layers=5	,   pad_token_id=1	,   vocab_size=1000	,   )
        return CLIPTextModelWithProjection(__lowercase      )
      @property
      def 			UpperCAmelCase   (   self      :    List[Any]      )							->   Tuple:
        torch.manual_seed(0      )

        __UpperCAmelCase							:			List[Any]     =						{
            """num_attention_heads""": 2,
            """attention_head_dim""": 12,
            """embedding_dim""": self.text_embedder_hidden_size,
            """num_layers""": 1,
        }

        __UpperCAmelCase							:			Optional[int]     =						PriorTransformer(**__lowercase      )
        # clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
        __UpperCAmelCase							:			int     =						nn.Parameter(torch.ones(model.clip_std.shape      )      )
        return model
      @property
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   List[str]:
        torch.manual_seed(0      )
        __UpperCAmelCase							:			Dict     =						CLIPVisionConfig(
            hidden_size=self.text_embedder_hidden_size	,   image_size=224	,   projection_dim=self.text_embedder_hidden_size	,   intermediate_size=37	,   num_attention_heads=4	,   num_channels=3	,   num_hidden_layers=5	,   patch_size=14	,   )

        __UpperCAmelCase							:			Optional[int]     =						CLIPVisionModelWithProjection(__lowercase      )
        return model
      @property
      def 			UpperCAmelCase   (   self      :    Any      )							->   Optional[Any]:
        __UpperCAmelCase							:			Optional[int]     =						CLIPImageProcessor(
            crop_size=224	,   do_center_crop=__lowercase	,   do_normalize=__lowercase	,   do_resize=__lowercase	,   image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073]	,   image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711]	,   resample=3	,   size=224	,   )

        return image_processor
      def 			UpperCAmelCase   (   self      :    Dict      )							->   Dict:
        __UpperCAmelCase							:			Dict     =						self.dummy_prior
        __UpperCAmelCase							:			Optional[int]     =						self.dummy_image_encoder
        __UpperCAmelCase							:			Optional[Any]     =						self.dummy_text_encoder
        __UpperCAmelCase							:			Dict     =						self.dummy_tokenizer
        __UpperCAmelCase							:			Union[str, Any]     =						self.dummy_image_processor

        __UpperCAmelCase							:			Dict     =						UnCLIPScheduler(
            variance_type="""fixed_small_log"""	,   prediction_type="""sample"""	,   num_train_timesteps=1000	,   clip_sample=__lowercase	,   clip_sample_range=10.0	,   )

        __UpperCAmelCase							:			Optional[Any]     =						{
            """prior""": prior,
            """image_encoder""": image_encoder,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """scheduler""": scheduler,
            """image_processor""": image_processor,
        }

        return components
      def 			UpperCAmelCase   (   self      :    Tuple	,   __lowercase      :    Union[str, Any]	,   __lowercase      :    Tuple=0      )							->   List[str]:
        if str(__lowercase      ).startswith("""mps"""      ):
          __UpperCAmelCase							:			Optional[int]     =						torch.manual_seed(__lowercase      )
        else:
          __UpperCAmelCase							:			List[Any]     =						torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
        __UpperCAmelCase							:			Union[str, Any]     =						{
            """prompt""": """horse""",
            """generator""": generator,
            """guidance_scale""": 4.0,
            """num_inference_steps""": 2,
            """output_type""": """np""",
        }
        return inputs
      def 			UpperCAmelCase   (   self      :    List[str]      )							->   str:
        __UpperCAmelCase							:			List[str]     =						"""cpu"""

        __UpperCAmelCase							:			List[str]     =						self.get_dummy_components()

        __UpperCAmelCase							:			int     =						self.pipeline_class(**__lowercase      )
        __UpperCAmelCase							:			List[Any]     =						pipe.to(__lowercase      )

        pipe.set_progress_bar_config(disable=__lowercase      )

        __UpperCAmelCase							:			int     =						pipe(**self.get_dummy_inputs(__lowercase      )      )
        __UpperCAmelCase							:			List[Any]     =						output.image_embeds

        __UpperCAmelCase							:			Optional[Any]     =						pipe(
            **self.get_dummy_inputs(__lowercase      )	,   return_dict=__lowercase	,   )[0]

        __UpperCAmelCase							:			str     =						image[0, -10:]
        __UpperCAmelCase							:			List[Any]     =						image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        __UpperCAmelCase							:			int     =						np.array(
            [-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156]      )

        assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-2
        assert np.abs(image_from_tuple_slice.flatten() - expected_slice      ).max() < 1e-2
      @skip_mps
      def 			UpperCAmelCase   (   self      :    Optional[int]      )							->   List[str]:
        __UpperCAmelCase							:			List[Any]     =						torch_device == """cpu"""
        __UpperCAmelCase							:			int     =						True
        __UpperCAmelCase							:			Dict     =						False

        self._test_inference_batch_single_identical(
            test_max_difference=__lowercase	,   relax_max_difference=__lowercase	,   test_mean_pixel_difference=__lowercase	,   )




      @skip_mps
      def 			UpperCAmelCase   (   self      :    str      )							->   Tuple:
        __UpperCAmelCase							:			int     =						torch_device == """cpu"""
        __UpperCAmelCase							:			Dict     =						False

        self._test_attention_slicing_forward_pass(
            test_max_difference=__lowercase	,   test_mean_pixel_difference=__lowercase	,   )










a	: List[str]      =				[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
class 					a					:



      """simple docstring"""


      def __init__(   self      :    Union[str, Any]	,   __lowercase      :    List[str]      )							->   Optional[int]:
        __UpperCAmelCase							:			Dict     =						name
        __UpperCAmelCase							:			Optional[Any]     =						[]
        __UpperCAmelCase							:			List[Any]     =						[]
      def 			UpperCAmelCase   (   self      :    str	,   __lowercase      :    int      )							->   str:
        self.inbound.append(__lowercase      )
      def 			UpperCAmelCase   (   self      :    List[Any]	,   __lowercase      :    Optional[Any]      )							->   List[str]:
        self.outbound.append(__lowercase      )




      def __repr__(   self      :    Any      )							->   int:
        return f"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""






def        lowerCamelCase__   (					__lowerCamelCase    :  Dict						,	__lowerCamelCase    :  str=3						,	__lowerCamelCase    :  int=0.8_5	):
  __UpperCAmelCase							:			Optional[int]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			str     =						1

  __UpperCAmelCase							:			Union[str, Any]     =						{}
  for node in nodes:
    __UpperCAmelCase							:			Dict     =						len(node.outbound	)

  for i in range(__lowerCamelCase	):
    print(f"""======= Iteration {i + 1} ======="""	)
    for _, node in enumerate(__lowerCamelCase	):
      __UpperCAmelCase							:			int     =						(1 - d) + d * sum(
          ranks[ib] / outbounds[ib] for ib in node.inbound	)
    print(__lowerCamelCase	)






def        lowerCamelCase__   (					):
  __UpperCAmelCase							:			Optional[Any]     =						list(input("""Enter Names of the Nodes: """	).split()	)

  __UpperCAmelCase							:			Dict     =						[Node(__lowerCamelCase	) for name in names]

  for ri, row in enumerate(__lowerCamelCase	):
    for ci, col in enumerate(__lowerCamelCase	):
      if col == 1:
        nodes[ci].add_inbound(names[ri]	)
        nodes[ri].add_outbound(names[ci]	)

  print("""======= Nodes ======="""	)
  for node in nodes:
    print(__lowerCamelCase	)

  page_rank(__lowerCamelCase	)


if __name__ == "__main__":
       main()


