
"""simple docstring"""

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        _lowercase   (				__UpperCAmelCase				,       unittest.TestCase		):
	lowercase_        =	KandinskyVaaPriorEmbaEmbPipeline
	lowercase_        =	['prompt', 'image']
	lowercase_        =	['prompt', 'image']
	lowercase_        =	[
	    'num_images_per_prompt',
	    'strength',
	    'generator',
	    'num_inference_steps',
	    'latents',
	    'negative_prompt',
	    'guidance_scale',
	    'output_type',
	    'return_dict',
	]
	lowercase_        =	False




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




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




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




	@property
	def 			_UpperCamelCase    ( self					)		->							Any:
			return self.time_input_dim * 4




	@property
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			return 100




	@property
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			lowerCamelCase     :			Tuple		=    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)
			return tokenizer




	@property
	def 			_UpperCamelCase    ( self					)		->							Optional[int]:
			torch.manual_seed(0					)
			lowerCamelCase     :			Dict		=    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(snake_case__					)




	@property
	def 			_UpperCamelCase    ( self					)		->							int:
			torch.manual_seed(0					)

			lowerCamelCase     :			int		=    {
			    "num_attention_heads": 2,
			    "attention_head_dim": 12,
			    "embedding_dim": self.text_embedder_hidden_size,
			    "num_layers": 1,
			}

			lowerCamelCase     :			Optional[int]		=    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
			lowerCamelCase     :			List[str]		=    nn.Parameter(torch.ones(model.clip_std.shape					)					)
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							Union[str, Any]:
			torch.manual_seed(0					)
			lowerCamelCase     :			Optional[int]		=    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		, )

			lowerCamelCase     :			Any		=    CLIPVisionModelWithProjection(snake_case__					)
			return model




	@property
	def 			_UpperCamelCase    ( self					)		->							List[str]:
			lowerCamelCase     :			List[str]		=    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					)		->							Dict:
			lowerCamelCase     :			str		=    self.dummy_prior
			lowerCamelCase     :			List[Any]		=    self.dummy_image_encoder
			lowerCamelCase     :			List[str]		=    self.dummy_text_encoder
			lowerCamelCase     :			int		=    self.dummy_tokenizer
			lowerCamelCase     :			Union[str, Any]		=    self.dummy_image_processor

			lowerCamelCase     :			List[Any]		=    UnCLIPScheduler(
			    variance_type='fixed_small_log'		, prediction_type='sample'		, num_train_timesteps=1000		, clip_sample=snake_case__		, clip_sample_range=10.0		, )

			lowerCamelCase     :			List[str]		=    {
			    "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					)		->							List[Any]:
			if str(snake_case__					).startswith('mps'					):
					lowerCamelCase     :			List[Any]		=    torch.manual_seed(snake_case__					)
			else:
					lowerCamelCase     :			int		=    torch.Generator(device=snake_case__					).manual_seed(snake_case__					)

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

			lowerCamelCase     :			Optional[int]		=    {
			    "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					)		->							Any:
			lowerCamelCase     :			str		=    "cpu"

			lowerCamelCase     :			Union[str, Any]		=    self.get_dummy_components()

			lowerCamelCase     :			List[str]		=    self.pipeline_class(**snake_case__					)
			lowerCamelCase     :			int		=    pipe.to(snake_case__					)

			pipe.set_progress_bar_config(disable=snake_case__					)

			lowerCamelCase     :			Optional[int]		=    pipe(**self.get_dummy_inputs(snake_case__					)					)
			lowerCamelCase     :			Union[str, Any]		=    output.image_embeds

			lowerCamelCase     :			str		=    pipe(
			    **self.get_dummy_inputs(snake_case__					)		, return_dict=snake_case__		, )[0]

			lowerCamelCase     :			Tuple		=    image[0, -10:]
			lowerCamelCase     :			Any		=    image_from_tuple[0, -10:]

			assert image.shape == (1, 32)

			lowerCamelCase     :			Optional[int]		=    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					)		->							str:
			lowerCamelCase     :			List[str]		=    torch_device == "cpu"
			lowerCamelCase     :			int		=    True
			lowerCamelCase     :			Dict		=    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					)		->							List[str]:
			lowerCamelCase     :			Any		=    torch_device == "cpu"
			lowerCamelCase     :			Union[str, Any]		=    False

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














"""simple docstring"""



import shutil

import requests


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      bool = False       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  Union[str, Any]				=				"https://api.nasa.gov/planetary/apod"
     return requests.get(snake_case       , params={"api_key": api_key}							).json()


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str       , snake_case    :      str = "."							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[Any]				=				get_apod_data(snake_case							)
     UpperCAmelCase__		:  str				=				apod_data["url"]
     UpperCAmelCase__		:  Tuple				=				img_url.split("/"							)[-1]
     UpperCAmelCase__		:  Dict				=				requests.get(snake_case       , stream=snake_case							)

     with open(f'{path}/{img_name}'       , "wb+"							) as img_file:
          shutil.copyfileobj(response.raw       , snake_case							)
     del response
     return apod_data


def      SCREAMING_SNAKE_CASE__    (   snake_case    :      str							)->						dict:






     '''simple docstring'''



     UpperCAmelCase__		:  List[str]				=				"https://images-api.nasa.gov/search"
     return requests.get(snake_case       , params={"q": query}							).json()


if __name__ == "__main__":
 print(save_apod("""YOUR API KEY"""))
 _lowerCAmelCase     :     List[Any]            =	get_archive_data("""apollo 2011""")["""collection"""]["""items"""]
 print(apollo_aaaa_items[0]["""data"""][0]["""description"""])


