

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    A_				( _lowerCamelCase	,       unittest.TestCase  ):


    '''simple docstring'''






    a__       							=    KandinskyVaaPriorEmbaEmbPipeline
    a__       							=    ["prompt", "image"]
    a__       							=    ["prompt", "image"]
    a__       							=    [
        "num_images_per_prompt",
        "strength",
        "generator",
        "num_inference_steps",
        "latents",
        "negative_prompt",
        "guidance_scale",
        "output_type",
        "return_dict",
    ]
    a__       							=    False


    @property
    def        lowerCAmelCase_  (self					)       -> Optional[Any]:
        return 32


    @property
    def        lowerCAmelCase_  (self					)       -> Tuple:
        return 32


    @property
    def        lowerCAmelCase_  (self					)       -> int:
        return self.time_input_dim


    @property
    def        lowerCAmelCase_  (self					)       -> Optional[int]:
        return self.time_input_dim * 4


    @property
    def        lowerCAmelCase_  (self					)       -> Any:
        return 100


    @property
    def        lowerCAmelCase_  (self					)       -> str:
        __UpperCAmelCase			   =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''					)
        return tokenizer


    @property
    def        lowerCAmelCase_  (self					)       -> List[str]:
        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=1_000       , )
        return CLIPTextModelWithProjection(__UpperCamelCase					)


    @property
    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        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(**__UpperCamelCase					)
        # 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        lowerCAmelCase_  (self					)       -> int:
        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(__UpperCamelCase					)
        return model


    @property
    def        lowerCAmelCase_  (self					)       -> int:
        __UpperCAmelCase			   =     CLIPImageProcessor(
            crop_size=224       , do_center_crop=__UpperCamelCase       , do_normalize=__UpperCamelCase       , do_resize=__UpperCamelCase       , image_mean=[0.48145466, 0.4578275, 0.40821073]       , image_std=[0.26862954, 0.26130258, 0.27577711]       , resample=3       , size=224       , )

        return image_processor


    def        lowerCAmelCase_  (self					)       -> List[str]:
        __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=1_000       , clip_sample=__UpperCamelCase       , 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        lowerCAmelCase_  (self       , lowercase__       , lowercase__=0					)       -> Any:







        if str(__UpperCamelCase					).startswith('''mps'''					):
            __UpperCAmelCase			   =     torch.manual_seed(__UpperCamelCase					)
        else:
            __UpperCAmelCase			   =     torch.Generator(device=__UpperCamelCase					).manual_seed(__UpperCamelCase					)

        __UpperCAmelCase			   =     floats_tensor((1, 3, 64, 64)       , rng=random.Random(__UpperCamelCase					)					).to(__UpperCamelCase					)
        __UpperCAmelCase			   =     image.cpu().permute(0       , 2       , 3       , 1					)[0]
        __UpperCAmelCase			   =     Image.fromarray(np.uinta(__UpperCamelCase					)					).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        lowerCAmelCase_  (self					)       -> List[str]:
        __UpperCAmelCase			   =     'cpu'

        __UpperCAmelCase			   =     self.get_dummy_components()

        __UpperCAmelCase			   =     self.pipeline_class(**__UpperCamelCase					)
        __UpperCAmelCase			   =     pipe.to(__UpperCamelCase					)

        pipe.set_progress_bar_config(disable=__UpperCamelCase					)

        __UpperCAmelCase			   =     pipe(**self.get_dummy_inputs(__UpperCamelCase					)					)
        __UpperCAmelCase			   =     output.image_embeds

        __UpperCAmelCase			   =     pipe(
            **self.get_dummy_inputs(__UpperCamelCase					)       , return_dict=__UpperCamelCase       , )[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        lowerCAmelCase_  (self					)       -> Optional[int]:
        __UpperCAmelCase			   =     torch_device == 'cpu'
        __UpperCAmelCase			   =     True
        __UpperCAmelCase			   =     False

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






    @skip_mps
    def        lowerCAmelCase_  (self					)       -> Union[str, Any]:
        __UpperCAmelCase			   =     torch_device == 'cpu'
        __UpperCAmelCase			   =     False

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







from __future__ import annotations

from collections import deque
from queue import Queue
from timeit import timeit

A_						:     Optional[int]			  =				{
    'A': ['B', 'C'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F'],
    'D': ['B'],
    'E': ['B', 'F'],
    'F': ['C', 'E'],
}


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     Queue()
    queue.put(SCREAMING_SNAKE_CASE )
    while not queue.empty():
        __UpperCAmelCase			   =     queue.get()
        for w in graph[v]:
            if w not in explored:
                explored.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.put(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       list[str]:






    '''simple docstring'''
    __UpperCAmelCase			   =     {start}
    __UpperCAmelCase			   =     [start]
    __UpperCAmelCase			   =     deque([start] )
    while queue:
        __UpperCAmelCase			   =     queue.popleft()
        for child in graph[v]:
            if child not in visited:
                visited.add(SCREAMING_SNAKE_CASE )
                result.append(SCREAMING_SNAKE_CASE )
                queue.append(SCREAMING_SNAKE_CASE )
    return result


def  __a    (				SCREAMING_SNAKE_CASE ) ->       None:






    '''simple docstring'''
    __UpperCAmelCase			   =     f'''from __main__ import G, {name}'''
    __UpperCAmelCase			   =     1_0_0_0_0
    __UpperCAmelCase			   =     timeit(f'''{name}(G, \'A\')'''    ,				setup=SCREAMING_SNAKE_CASE    ,				number=SCREAMING_SNAKE_CASE )
    print(f'''{name:<35} finished {number} runs in {res:.5f} seconds''' )


if __name__ == "__main__":
       import doctest

       doctest.testmod()

       benchmark_function('breadth_first_search')
       benchmark_function('breadth_first_search_with_deque')
       # breadth_first_search                finished 10000 runs in 0.20999 seconds
       # breadth_first_search_with_deque     finished 10000 runs in 0.01421 seconds




