
"""simple docstring"""







import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference




class 				A__ (			_lowerCAmelCase ,  unittest.TestCase):
  A_		: Dict            =			ShapEPipeline
  A_		: Dict            =			['prompt']
  A_		: Union[str, Any]            =			['prompt']
  A_		: Any            =			[
      'num_images_per_prompt',
      'num_inference_steps',
      'generator',
      'latents',
      'guidance_scale',
      'frame_size',
      'output_type',
      'return_dict',
  ]
  A_		: str            =			False

  @property
  def 							__lowerCamelCase							(   self				):
   return 32

  @property
  def 							__lowerCamelCase							(   self				):
   return 32

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

  @property
  def 							__lowerCamelCase							(   self				):
   return 8

  @property
  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		Optional[Any]		    =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				)
   return tokenizer

  @property
  def 							__lowerCamelCase							(   self				):
   torch.manual_seed(0				)
   __lowerCAmelCase    :		str		    =   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=10_00 ,	)
   return CLIPTextModelWithProjection(_lowerCAmelCase				)

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

   __lowerCAmelCase    :		str		    =   {
       'num_attention_heads': 2,
       'attention_head_dim': 16,
       'embedding_dim': self.time_input_dim,
       'num_embeddings': 32,
       'embedding_proj_dim': self.text_embedder_hidden_size,
       'time_embed_dim': self.time_embed_dim,
       'num_layers': 1,
       'clip_embed_dim': self.time_input_dim * 2,
       'additional_embeddings': 0,
       'time_embed_act_fn': 'gelu',
       'norm_in_type': 'layer',
       'encoder_hid_proj_type': None,
       'added_emb_type': None,
   }

   __lowerCAmelCase    :		List[str]		    =   PriorTransformer(**_lowerCAmelCase				)
   return model

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

   __lowerCAmelCase    :		Union[str, Any]		    =   {
       'param_shapes': (
           (self.renderer_dim, 93),
           (self.renderer_dim, 8),
           (self.renderer_dim, 8),
           (self.renderer_dim, 8),
       ),
       'd_latent': self.time_input_dim,
       'd_hidden': self.renderer_dim,
       'n_output': 12,
       'background': (
           0.1,
           0.1,
           0.1,
       ),
   }
   __lowerCAmelCase    :		Union[str, Any]		    =   ShapERenderer(**_lowerCAmelCase				)
   return model

  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		Tuple		    =   self.dummy_prior
   __lowerCAmelCase    :		Tuple		    =   self.dummy_text_encoder
   __lowerCAmelCase    :		List[str]		    =   self.dummy_tokenizer
   __lowerCAmelCase    :		Any		    =   self.dummy_renderer

   __lowerCAmelCase    :		Tuple		    =   HeunDiscreteScheduler(
       beta_schedule='exp' ,	num_train_timesteps=10_24 ,	prediction_type='sample' ,	use_karras_sigmas=_lowerCAmelCase ,	clip_sample=_lowerCAmelCase ,	clip_sample_range=1.0 ,	)
   __lowerCAmelCase    :		List[str]		    =   {
       'prior': prior,
       'text_encoder': text_encoder,
       'tokenizer': tokenizer,
       'renderer': renderer,
       'scheduler': scheduler,
   }

   return components

  def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE=0				):







   if str(_lowerCAmelCase				).startswith('mps'				):
    __lowerCAmelCase    :		Optional[int]		    =   torch.manual_seed(_lowerCAmelCase				)
   else:
    __lowerCAmelCase    :		Optional[Any]		    =   torch.Generator(device=_lowerCAmelCase				).manual_seed(_lowerCAmelCase				)
   __lowerCAmelCase    :		int		    =   {
       'prompt': 'horse',
       'generator': generator,
       'num_inference_steps': 1,
       'frame_size': 32,
       'output_type': 'np',
   }
   return inputs

  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		int		    =   'cpu'

   __lowerCAmelCase    :		List[Any]		    =   self.get_dummy_components()

   __lowerCAmelCase    :		Dict		    =   self.pipeline_class(**_lowerCAmelCase				)
   __lowerCAmelCase    :		Optional[int]		    =   pipe.to(_lowerCAmelCase				)

   pipe.set_progress_bar_config(disable=_lowerCAmelCase				)

   __lowerCAmelCase    :		Dict		    =   pipe(**self.get_dummy_inputs(_lowerCAmelCase				)				)
   __lowerCAmelCase    :		str		    =   output.images[0]
   __lowerCAmelCase    :		Tuple		    =   image[0, -3:, -3:, -1]

   assert image.shape == (20, 32, 32, 3)

   __lowerCAmelCase    :		List[str]		    =   np.array(
       [
           0.0003_9216,
           0.0003_9216,
           0.0003_9216,
           0.0003_9216,
           0.0003_9216,
           0.0003_9216,
           0.0003_9216,
           0.0003_9216,
           0.0003_9216,
       ]				)

   assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2

  def 							__lowerCamelCase							(   self				):
   self._test_inference_batch_consistent(batch_sizes=[1, 2]				)

  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		Dict		    =   torch_device == 'cpu'
   __lowerCAmelCase    :		str		    =   True

   self._test_inference_batch_single_identical(
       batch_size=2 ,	test_max_difference=_lowerCAmelCase ,	relax_max_difference=_lowerCAmelCase ,	)







  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		Optional[int]		    =   self.get_dummy_components()
   __lowerCAmelCase    :		List[str]		    =   self.pipeline_class(**_lowerCAmelCase				)
   __lowerCAmelCase    :		str		    =   pipe.to(_lowerCAmelCase				)
   pipe.set_progress_bar_config(disable=_lowerCAmelCase				)

   __lowerCAmelCase    :		Dict		    =   1
   __lowerCAmelCase    :		Optional[int]		    =   2

   __lowerCAmelCase    :		List[Any]		    =   self.get_dummy_inputs(_lowerCAmelCase				)

   for key in inputs.keys():
    if key in self.batch_params:
     __lowerCAmelCase    :		Optional[Any]		    =   batch_size * [inputs[key]]

   __lowerCAmelCase    :		Any		    =   pipe(**_lowerCAmelCase ,	num_images_per_prompt=_lowerCAmelCase				)[0]

   assert images.shape[0] == batch_size * num_images_per_prompt




@slow
@require_torch_gpu
class 				A__ (			unittest.TestCase):

  def 							__lowerCamelCase							(   self				):
   super().tearDown()
   gc.collect()
   torch.cuda.empty_cache()







  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		Tuple		    =   load_numpy(
       'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
       '/shap_e/test_shap_e_np_out.npy'				)
   __lowerCAmelCase    :		Optional[int]		    =   ShapEPipeline.from_pretrained('openai/shap-e'				)
   __lowerCAmelCase    :		Tuple		    =   pipe.to(_lowerCAmelCase				)
   pipe.set_progress_bar_config(disable=_lowerCAmelCase				)

   __lowerCAmelCase    :		Dict		    =   torch.Generator(device=_lowerCAmelCase				).manual_seed(0				)

   __lowerCAmelCase    :		Dict		    =   pipe(
       'a shark' ,	generator=_lowerCAmelCase ,	guidance_scale=15.0 ,	num_inference_steps=64 ,	frame_size=64 ,	output_type='np' ,	).images[0]

   assert images.shape == (20, 64, 64, 3)

   assert_mean_pixel_difference(_lowerCAmelCase ,	_lowerCAmelCase				)

"""simple docstring"""







from __future__ import annotations

from collections import Counter
from random import random




class 				A__ :

		def __init__(   self				):
			__lowerCAmelCase    :		Any		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		Dict		    =   {}

		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE ,	_SCREAMING_SNAKE_CASE				):
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			if nodea not in self.connections:
				self.add_node(_SCREAMING_SNAKE_CASE				)
			__lowerCAmelCase    :		Optional[Any]		    =   probability

		def 							__lowerCamelCase							(   self				):
			return list(self.connections				)







		def 							__lowerCamelCase							(   self ,	_SCREAMING_SNAKE_CASE				):
			__lowerCAmelCase    :		List[str]		    =   0
			__lowerCAmelCase    :		List[Any]		    =   random()

			for dest in self.connections[node]:
				current_probability += self.connections[node][dest]
				if current_probability > random_value:
					return dest
			return ""



def 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[Any]		    =   MarkovChainGraphUndirectedUnweighted()

	for nodea, nodea, probability in transitions:
		graph.add_transition_probability(_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	)

	__lowerCAmelCase    :		str		    =   Counter(graph.get_nodes()	)
	__lowerCAmelCase    :		Tuple		    =   start

	for _ in range(_UpperCamelCase	):
		__lowerCAmelCase    :		int		    =   graph.transition(_UpperCamelCase	)
		visited[node] += 1

	return visited


if __name__ == "__main__":
							import doctest

							doctest.testmod()