




import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 			a     (   _A	,   unittest.TestCase			):

     '''simple docstring'''





     lowerCAmelCase				:       List[str]   		=  UnCLIPPipeline
     lowerCAmelCase				:       Any   		=  TEXT_TO_IMAGE_PARAMS - {
         'negative_prompt',
         'height',
         'width',
         'negative_prompt_embeds',
         'guidance_scale',
         'prompt_embeds',
         'cross_attention_kwargs',
     }
     lowerCAmelCase				:       Union[str, Any]   		=  TEXT_TO_IMAGE_BATCH_PARAMS
     lowerCAmelCase				:       Dict   		=  [
         'generator',
         'return_dict',
         'prior_num_inference_steps',
         'decoder_num_inference_steps',
         'super_res_num_inference_steps',
     ]
     lowerCAmelCase				:       List[str]   		=  False
     @property
     def   lowerCamelCase_   (	self   :			List[str]				):
         return 32
     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         return 32
     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         return self.time_input_dim
     @property
     def   lowerCamelCase_   (	self   :			List[str]				):
         return self.time_input_dim * 4
     @property
     def   lowerCamelCase_   (	self   :			int				):
         return 1_00
     @property
     def   lowerCamelCase_   (	self   :			Tuple				):
         UpperCAmelCase_							 =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''				)
         return tokenizer
     @property
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         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=10_00					,    )
         return CLIPTextModelWithProjection(__snake_case				)
     @property
     def   lowerCamelCase_   (	self   :			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(**__snake_case				)
         return model
     @property
     def   lowerCamelCase_   (	self   :			Dict				):
         torch.manual_seed(0				)

         UpperCAmelCase_							 =     {
             '''clip_embeddings_dim''': self.text_embedder_hidden_size,
             '''time_embed_dim''': self.time_embed_dim,
             '''cross_attention_dim''': self.cross_attention_dim,
         }

         UpperCAmelCase_							 =     UnCLIPTextProjModel(**__snake_case				)
         return model
     @property
     def   lowerCamelCase_   (	self   :			Union[str, Any]				):
         torch.manual_seed(0				)

         UpperCAmelCase_							 =     {
             '''sample_size''': 32,
             # RGB in channels
             '''in_channels''': 3,
             # Out channels is double in channels because predicts mean and variance
             '''out_channels''': 6,
             '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
             '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
             '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
             '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
             '''layers_per_block''': 1,
             '''cross_attention_dim''': self.cross_attention_dim,
             '''attention_head_dim''': 4,
             '''resnet_time_scale_shift''': '''scale_shift''',
             '''class_embed_type''': '''identity''',
         }

         UpperCAmelCase_							 =     UNetaDConditionModel(**__snake_case				)
         return model
     @property
     def   lowerCamelCase_   (	self   :			Any				):
         return {
             "sample_size": 64,
             "layers_per_block": 1,
             "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
             "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
             "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
             "in_channels": 6,
             "out_channels": 3,
         }
     @property
     def   lowerCamelCase_   (	self   :			Tuple				):
         torch.manual_seed(0				)

         UpperCAmelCase_							 =     UNetaDModel(**self.dummy_super_res_kwargs				)
         return model
     @property
     def   lowerCamelCase_   (	self   :			str				):
         # seeded differently to get different unet than `self.dummy_super_res_first`
         torch.manual_seed(1				)

         UpperCAmelCase_							 =     UNetaDModel(**self.dummy_super_res_kwargs				)
         return model
     def   lowerCamelCase_   (	self   :			int				):
         UpperCAmelCase_							 =     self.dummy_prior
         UpperCAmelCase_							 =     self.dummy_decoder
         UpperCAmelCase_							 =     self.dummy_text_proj
         UpperCAmelCase_							 =     self.dummy_text_encoder
         UpperCAmelCase_							 =     self.dummy_tokenizer
         UpperCAmelCase_							 =     self.dummy_super_res_first
         UpperCAmelCase_							 =     self.dummy_super_res_last

         UpperCAmelCase_							 =     UnCLIPScheduler(
             variance_type='''fixed_small_log'''					,    prediction_type='''sample'''					,    num_train_timesteps=10_00					,    clip_sample_range=5.0					,    )

         UpperCAmelCase_							 =     UnCLIPScheduler(
             variance_type='''learned_range'''					,    prediction_type='''epsilon'''					,    num_train_timesteps=10_00					,    )

         UpperCAmelCase_							 =     UnCLIPScheduler(
             variance_type='''fixed_small_log'''					,    prediction_type='''epsilon'''					,    num_train_timesteps=10_00					,    )

         UpperCAmelCase_							 =     {
             '''prior''': prior,
             '''decoder''': decoder,
             '''text_proj''': text_proj,
             '''text_encoder''': text_encoder,
             '''tokenizer''': tokenizer,
             '''super_res_first''': super_res_first,
             '''super_res_last''': super_res_last,
             '''prior_scheduler''': prior_scheduler,
             '''decoder_scheduler''': decoder_scheduler,
             '''super_res_scheduler''': super_res_scheduler,
         }

         return components
     def   lowerCamelCase_   (	self   :			str					,    __snake_case   :			Optional[Any]					,    __snake_case   :			Union[str, Any]=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_							 =     {
             '''prompt''': '''horse''',
             '''generator''': generator,
             '''prior_num_inference_steps''': 2,
             '''decoder_num_inference_steps''': 2,
             '''super_res_num_inference_steps''': 2,
             '''output_type''': '''numpy''',
         }
         return inputs
     def   lowerCamelCase_   (	self   :			Tuple				):
         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.images

         UpperCAmelCase_							 =     pipe(
             **self.get_dummy_inputs(__snake_case				)					,    return_dict=__snake_case					,    )[0]

         UpperCAmelCase_							 =     image[0, -3:, -3:, -1]
         UpperCAmelCase_							 =     image_from_tuple[0, -3:, -3:, -1]

         assert image.shape == (1, 64, 64, 3)

         UpperCAmelCase_							 =     np.array(
             [
                 0.9_997,
                 0.9_988,
                 0.0_028,
                 0.9_997,
                 0.9_984,
                 0.9_965,
                 0.0_029,
                 0.9_986,
                 0.0_025,
             ]				)

         assert np.abs(image_slice.flatten() - expected_slice				).max() < 1E-2
         assert np.abs(image_from_tuple_slice.flatten() - expected_slice				).max() < 1E-2
     def   lowerCamelCase_   (	self   :			int				):
         UpperCAmelCase_							 =     torch.device('''cpu'''				)



         class 			a     :

              '''simple docstring'''





              lowerCAmelCase				:       Dict   		=  1


         UpperCAmelCase_							 =     self.get_dummy_components()

         UpperCAmelCase_							 =     self.pipeline_class(**__snake_case				)
         UpperCAmelCase_							 =     pipe.to(__snake_case				)

         UpperCAmelCase_							 =     components['''prior''']
         UpperCAmelCase_							 =     components['''decoder''']
         UpperCAmelCase_							 =     components['''super_res_first''']
         UpperCAmelCase_							 =     components['''tokenizer''']
         UpperCAmelCase_							 =     components['''text_encoder''']

         UpperCAmelCase_							 =     torch.Generator(device=__snake_case				).manual_seed(0				)
         UpperCAmelCase_							 =     prior.dtype
         UpperCAmelCase_							 =     1

         UpperCAmelCase_							 =     (batch_size, prior.config.embedding_dim)
         UpperCAmelCase_							 =     pipe.prepare_latents(
             __snake_case					,    dtype=__snake_case					,    device=__snake_case					,    generator=__snake_case					,    latents=__snake_case					,    scheduler=DummyScheduler()				)
         UpperCAmelCase_							 =     (batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
         UpperCAmelCase_							 =     pipe.prepare_latents(
             __snake_case					,    dtype=__snake_case					,    device=__snake_case					,    generator=__snake_case					,    latents=__snake_case					,    scheduler=DummyScheduler()				)

         UpperCAmelCase_							 =     (
             batch_size,
             super_res_first.config.in_channels // 2,
             super_res_first.config.sample_size,
             super_res_first.config.sample_size,
         )
         UpperCAmelCase_							 =     pipe.prepare_latents(
             __snake_case					,    dtype=__snake_case					,    device=__snake_case					,    generator=__snake_case					,    latents=__snake_case					,    scheduler=DummyScheduler()				)

         pipe.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     '''this is a prompt example'''

         UpperCAmelCase_							 =     torch.Generator(device=__snake_case				).manual_seed(0				)
         UpperCAmelCase_							 =     pipe(
             [prompt]					,    generator=__snake_case					,    prior_num_inference_steps=2					,    decoder_num_inference_steps=2					,    super_res_num_inference_steps=2					,    prior_latents=__snake_case					,    decoder_latents=__snake_case					,    super_res_latents=__snake_case					,    output_type='''np'''					,    )
         UpperCAmelCase_							 =     output.images

         UpperCAmelCase_							 =     tokenizer(
             __snake_case					,    padding='''max_length'''					,    max_length=tokenizer.model_max_length					,    return_tensors='''pt'''					,    )
         UpperCAmelCase_							 =     text_encoder(text_inputs.input_ids				)
         UpperCAmelCase_							 =     text_inputs.attention_mask

         UpperCAmelCase_							 =     torch.Generator(device=__snake_case				).manual_seed(0				)
         UpperCAmelCase_							 =     pipe(
             generator=__snake_case					,    prior_num_inference_steps=2					,    decoder_num_inference_steps=2					,    super_res_num_inference_steps=2					,    prior_latents=__snake_case					,    decoder_latents=__snake_case					,    super_res_latents=__snake_case					,    text_model_output=__snake_case					,    text_attention_mask=__snake_case					,    output_type='''np'''					,    )[0]

         # make sure passing text embeddings manually is identical
         assert np.abs(image - image_from_text				).max() < 1E-4
     @skip_mps
     def   lowerCamelCase_   (	self   :			str				):
         UpperCAmelCase_							 =     torch_device == '''cpu'''

         self._test_attention_slicing_forward_pass(test_max_difference=__snake_case					,    expected_max_diff=0.01				)
     @skip_mps
     def   lowerCamelCase_   (	self   :			Union[str, Any]				):
         UpperCAmelCase_							 =     torch_device == '''cpu'''
         UpperCAmelCase_							 =     True
         UpperCAmelCase_							 =     [
             '''prior_num_inference_steps''',
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         self._test_inference_batch_single_identical(
             test_max_difference=__snake_case					,    relax_max_difference=__snake_case					,    additional_params_copy_to_batched_inputs=__snake_case					,    )
     def   lowerCamelCase_   (	self   :			Any				):
         UpperCAmelCase_							 =     [
             '''prior_num_inference_steps''',
             '''decoder_num_inference_steps''',
             '''super_res_num_inference_steps''',
         ]

         if torch_device == "mps":
             # TODO: MPS errors with larger batch sizes
             UpperCAmelCase_							 =     [2, 3]
             self._test_inference_batch_consistent(
                 batch_sizes=__snake_case					,    additional_params_copy_to_batched_inputs=__snake_case					,    )
         else:
             self._test_inference_batch_consistent(
                 additional_params_copy_to_batched_inputs=__snake_case				)
     @skip_mps
     def   lowerCamelCase_   (	self   :			List[Any]				):
         return super().test_dict_tuple_outputs_equivalent()
     @skip_mps
     def   lowerCamelCase_   (	self   :			int				):
         return super().test_save_load_local()




     @skip_mps
     def   lowerCamelCase_   (	self   :			List[Any]				):
         return super().test_save_load_optional_components()



@nightly
class 			a     (   unittest.TestCase			):

     '''simple docstring'''





     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def   lowerCamelCase_   (	self   :			Dict				):
         UpperCAmelCase_							 =     load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/unclip/karlo_v1_alpha_horse_cpu.npy'''				)

         UpperCAmelCase_							 =     UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''				)
         pipeline.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     torch.manual_seed(0				)
         UpperCAmelCase_							 =     pipeline(
             '''horse'''					,    num_images_per_prompt=1					,    generator=__snake_case					,    output_type='''np'''					,    )

         UpperCAmelCase_							 =     output.images[0]

         assert image.shape == (2_56, 2_56, 3)
         assert np.abs(expected_image - image				).max() < 1E-1



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

     '''simple docstring'''





     def   lowerCamelCase_   (	self   :			Tuple				):
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()
     def   lowerCamelCase_   (	self   :			Union[str, Any]				):
         UpperCAmelCase_							 =     load_numpy(
             '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
             '''/unclip/karlo_v1_alpha_horse_fp16.npy'''				)

         UpperCAmelCase_							 =     UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''					,    torch_dtype=torch.floataa				)
         UpperCAmelCase_							 =     pipeline.to(__snake_case				)
         pipeline.set_progress_bar_config(disable=__snake_case				)

         UpperCAmelCase_							 =     torch.Generator(device='''cpu'''				).manual_seed(0				)
         UpperCAmelCase_							 =     pipeline(
             '''horse'''					,    generator=__snake_case					,    output_type='''np'''					,    )

         UpperCAmelCase_							 =     output.images[0]

         assert image.shape == (2_56, 2_56, 3)

         assert_mean_pixel_difference(__snake_case					,    __snake_case				)




     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         UpperCAmelCase_							 =     UnCLIPPipeline.from_pretrained('''kakaobrain/karlo-v1-alpha'''					,    torch_dtype=torch.floataa				)
         UpperCAmelCase_							 =     pipe.to(__snake_case				)
         pipe.set_progress_bar_config(disable=__snake_case				)
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         UpperCAmelCase_							 =     pipe(
             '''horse'''					,    num_images_per_prompt=1					,    prior_num_inference_steps=2					,    decoder_num_inference_steps=2					,    super_res_num_inference_steps=2					,    output_type='''np'''					,    )

         UpperCAmelCase_							 =     torch.cuda.max_memory_allocated()
         # make sure that less than 7 GB is allocated
         assert mem_bytes < 7 * 10**9













"""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"""])


