
import random
import unittest

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

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

from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class  __UpperCamelCase						(       a_  ,   unittest.TestCase						):
     __A  :     str            =      KandinskyVaaPriorEmbaEmbPipeline
     __A  :     Dict            =      ['prompt', 'image']
     __A  :     List[Any]            =      ['prompt', 'image']
     __A  :     List[Any]            =      [
         'num_images_per_prompt',
         'strength',
         'generator',
         'num_inference_steps',
         'latents',
         'negative_prompt',
         'guidance_scale',
         'output_type',
         'return_dict',
     ]
     __A  :     List[str]            =      False





     @property
     def 		UpperCamelCase(    self				):
        return 32





     @property
     def 		UpperCamelCase(    self				):
        return 32





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





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





     @property
     def 		UpperCamelCase(    self				):
        return 100





     @property
     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
        return tokenizer





     @property
     def 		UpperCamelCase(    self				):
        torch.manual_seed(0				)
        _UpperCAmelCase											=      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-05							,       num_attention_heads=4							,       num_hidden_layers=5							,       pad_token_id=1							,       vocab_size=1000							,       )
        return CLIPTextModelWithProjection(_snake_case				)





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

        _UpperCAmelCase											=      {
            '''num_attention_heads''': 2,
            '''attention_head_dim''': 12,
            '''embedding_dim''': self.text_embedder_hidden_size,
            '''num_layers''': 1,
        }

        _UpperCAmelCase											=      PriorTransformer(**_snake_case				)
        # 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											=      nn.Parameter(torch.ones(model.clip_std.shape				)				)
        return model





     @property
     def 		UpperCamelCase(    self				):
        torch.manual_seed(0				)
        _UpperCAmelCase											=      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											=      CLIPVisionModelWithProjection(_snake_case				)
        return model





     @property
     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      CLIPImageProcessor(
            crop_size=224							,       do_center_crop=_snake_case							,       do_normalize=_snake_case							,       do_resize=_snake_case							,       image_mean=[0.48145466, 0.4578275, 0.40821073]							,       image_std=[0.26862954, 0.26130258, 0.27577711]							,       resample=3							,       size=224							,       )

        return image_processor





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      self.dummy_prior
        _UpperCAmelCase											=      self.dummy_image_encoder
        _UpperCAmelCase											=      self.dummy_text_encoder
        _UpperCAmelCase											=      self.dummy_tokenizer
        _UpperCAmelCase											=      self.dummy_image_processor

        _UpperCAmelCase											=      UnCLIPScheduler(
            variance_type='''fixed_small_log'''							,       prediction_type='''sample'''							,       num_train_timesteps=1000							,       clip_sample=_snake_case							,       clip_sample_range=10.0							,       )

        _UpperCAmelCase											=      {
            '''prior''': prior,
            '''image_encoder''': image_encoder,
            '''text_encoder''': text_encoder,
            '''tokenizer''': tokenizer,
            '''scheduler''': scheduler,
            '''image_processor''': image_processor,
        }

        return components





     def 		UpperCamelCase(    self							,       _UpperCamelCase							,       _UpperCamelCase=0				):
        if str(_snake_case				).startswith('''mps'''				):
           _UpperCAmelCase											=      torch.manual_seed(_snake_case				)
        else:
           _UpperCAmelCase											=      torch.Generator(device=_snake_case				).manual_seed(_snake_case				)

        _UpperCAmelCase											=      floats_tensor((1, 3, 64, 64)							,       rng=random.Random(_snake_case				)				).to(_snake_case				)
        _UpperCAmelCase											=      image.cpu().permute(0							,       2							,       3							,       1				)[0]
        _UpperCAmelCase											=      Image.fromarray(np.uinta(_snake_case				)				).convert('''RGB'''				).resize((256, 256)				)

        _UpperCAmelCase											=      {
            '''prompt''': '''horse''',
            '''image''': init_image,
            '''strength''': 0.5,
            '''generator''': generator,
            '''guidance_scale''': 4.0,
            '''num_inference_steps''': 2,
            '''output_type''': '''np''',
        }
        return inputs





     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      '''cpu'''

        _UpperCAmelCase											=      self.get_dummy_components()

        _UpperCAmelCase											=      self.pipeline_class(**_snake_case				)
        _UpperCAmelCase											=      pipe.to(_snake_case				)

        pipe.set_progress_bar_config(disable=_snake_case				)

        _UpperCAmelCase											=      pipe(**self.get_dummy_inputs(_snake_case				)				)
        _UpperCAmelCase											=      output.image_embeds

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

        _UpperCAmelCase											=      image[0, -10:]
        _UpperCAmelCase											=      image_from_tuple[0, -10:]

        assert image.shape == (1, 32)

        _UpperCAmelCase											=      np.array(
            [
                0.1071284,
                1.3330271,
                0.61260223,
                -0.6691065,
                -0.3846852,
                -1.0303661,
                0.22716111,
                0.03348901,
                0.30040675,
                -0.24805029,
            ]				)

        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				):
        _UpperCAmelCase											=      torch_device == '''cpu'''
        _UpperCAmelCase											=      True
        _UpperCAmelCase											=      False

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





     @skip_mps
     def 		UpperCamelCase(    self				):
        _UpperCAmelCase											=      torch_device == '''cpu'''
        _UpperCAmelCase											=      False

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






def     A__					(						SCREAMING_SNAKE_CASE_      :			Optional[int]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     List[str]:






   """simple docstring"""







   print('''\nVertex Distance'''	)
   for i in range(SCREAMING_SNAKE_CASE_	):
      if dist[i] != float('''inf'''	):
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  int(dist[i]	)							,  end='''\t'''	)
      else:
         print(SCREAMING_SNAKE_CASE_							,  '''\t'''							,  '''INF'''							,  end='''\t'''	)
      print()
def     A__					(						SCREAMING_SNAKE_CASE_      :			Any							,  SCREAMING_SNAKE_CASE_      :			Optional[Any]							,  SCREAMING_SNAKE_CASE_      :			str	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      float('''inf'''	)
   _UpperCAmelCase											=      -1
   for i in range(SCREAMING_SNAKE_CASE_	):
      if (not vset[i]) and mdist[i] < min_val:
         _UpperCAmelCase											=      i
         _UpperCAmelCase											=      mdist[i]
   return min_ind
def     A__					(						SCREAMING_SNAKE_CASE_      :			str							,  SCREAMING_SNAKE_CASE_      :			Dict							,  SCREAMING_SNAKE_CASE_      :			int	)     ->     Any:






   """simple docstring"""







   _UpperCAmelCase											=      [float('''inf'''	) for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      [False for _ in range(SCREAMING_SNAKE_CASE_	)]
   _UpperCAmelCase											=      0.0

   for _ in range(v - 1	):
      _UpperCAmelCase											=      min_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)
      _UpperCAmelCase											=      True

      for i in range(SCREAMING_SNAKE_CASE_	):
         if (
             (not vset[i])
             and graph[u][i] != float('''inf'''	)
             and mdist[u] + graph[u][i] < mdist[i]
         ):
            _UpperCAmelCase											=      mdist[u] + graph[u][i]

   print_dist(SCREAMING_SNAKE_CASE_							,  SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
   UpperCAmelCase_											=  int(input("Enter number of vertices: ").strip())
   UpperCAmelCase_											=  int(input("Enter number of edges: ").strip())

   UpperCAmelCase_											=  [[float("inf") for i in range(V)] for j in range(V)]

   for i in range(V):
      UpperCAmelCase_											=  0.0

   for i in range(E):
      print("\nEdge ", i + 1)
      UpperCAmelCase_											=  int(input("Enter source:").strip())
      UpperCAmelCase_											=  int(input("Enter destination:").strip())
      UpperCAmelCase_											=  float(input("Enter weight:").strip())
      UpperCAmelCase_											=  weight

   UpperCAmelCase_											=  int(input("\nEnter shortest path source:").strip())
   dijkstra(graph, V, gsrc)