
"""simple docstring"""



import gc
import unittest

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

from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_numpy,
    nightly,
    require_torch_gpu,
    slow,
    torch_device,
)

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


enable_full_determinism()
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase      ,				unittest.TestCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Optional[int]							=					LDMTextToImagePipeline
				SCREAMING_SNAKE_CASE_:							Dict							=					TEXT_TO_IMAGE_PARAMS - {
				    "negative_prompt",
				    "negative_prompt_embeds",
				    "cross_attention_kwargs",
				    "prompt_embeds",
				}
				SCREAMING_SNAKE_CASE_:							List[Any]							=					PipelineTesterMixin.required_optional_params - {
				    "num_images_per_prompt",
				    "callback",
				    "callback_steps",
				}
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					TEXT_TO_IMAGE_BATCH_PARAMS
				SCREAMING_SNAKE_CASE_:							List[str]							=					False


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											torch.manual_seed(0  )
											_lowerCAmelCase  	=			UNetaDConditionModel(
											    block_out_channels=(32, 64)				,     layers_per_block=2				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')				,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')				,     cross_attention_dim=32				,     )
											_lowerCAmelCase  	=			DDIMScheduler(
											    beta_start=0.00085				,     beta_end=0.012				,     beta_schedule='scaled_linear'				,     clip_sample=UpperCAmelCase_				,     set_alpha_to_one=UpperCAmelCase_				,     )
											torch.manual_seed(0  )
											_lowerCAmelCase  	=			AutoencoderKL(
											    block_out_channels=(32, 64)				,     in_channels=3				,     out_channels=3				,     down_block_types=('DownEncoderBlock2D', 'DownEncoderBlock2D')				,     up_block_types=('UpDecoderBlock2D', 'UpDecoderBlock2D')				,     latent_channels=4				,     )
											torch.manual_seed(0  )
											_lowerCAmelCase  	=			CLIPTextConfig(
											    bos_token_id=0				,     eos_token_id=2				,     hidden_size=32				,     intermediate_size=37				,     layer_norm_eps=1E-0_5				,     num_attention_heads=4				,     num_hidden_layers=5				,     pad_token_id=1				,     vocab_size=1_000				,     )
											_lowerCAmelCase  	=			CLIPTextModel(UpperCAmelCase_  )
											_lowerCAmelCase  	=			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )

											_lowerCAmelCase  	=			{
											    'unet': unet,
											    'scheduler': scheduler,
											    'vqvae': vae,
											    'bert': text_encoder,
											    'tokenizer': tokenizer,
											}
											return components


				def 				__lowerCamelCase	(					self	:			Tuple				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			Optional[int]=0  )		->  Any:







											"""simple docstring"""




											if str(UpperCAmelCase_  ).startswith('mps'  ):
																		_lowerCAmelCase  	=			torch.manual_seed(UpperCAmelCase_  )
											else:
																		_lowerCAmelCase  	=			torch.Generator(device=UpperCAmelCase_  ).manual_seed(UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







				def 				__lowerCamelCase	(					self	:			Tuple  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			'cpu'  # ensure determinism for the device-dependent torch.Generator

											_lowerCAmelCase  	=			self.get_dummy_components()
											_lowerCAmelCase  	=			LDMTextToImagePipeline(**UpperCAmelCase_  )
											pipe.to(UpperCAmelCase_  )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_  )

											_lowerCAmelCase  	=			self.get_dummy_inputs(UpperCAmelCase_  )
											_lowerCAmelCase  	=			pipe(**UpperCAmelCase_  ).images
											_lowerCAmelCase  	=			image[0, -3:, -3:, -1]

											assert image.shape == (1, 16, 16, 3)
											_lowerCAmelCase  	=			np.array([0.6101, 0.6156, 0.5622, 0.4895, 0.6661, 0.3804, 0.5748, 0.6136, 0.5014]  )

											assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3






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






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			int  )		->  Any:







											"""simple docstring"""




											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 				__lowerCamelCase	(					self	:			Dict				,     UpperCAmelCase_	:			Any				,     UpperCAmelCase_	:			str=torch.floataa				,     UpperCAmelCase_	:			Optional[Any]=0  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.manual_seed(UpperCAmelCase_  )
											_lowerCAmelCase  	=			np.random.RandomState(UpperCAmelCase_  ).standard_normal((1, 4, 32, 32)  )
											_lowerCAmelCase  	=			torch.from_numpy(UpperCAmelCase_  ).to(device=UpperCAmelCase_				,     dtype=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 3,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







				def 				__lowerCamelCase	(					self	:			Any  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'  ).to(UpperCAmelCase_  )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_  )

											_lowerCAmelCase  	=			self.get_inputs(UpperCAmelCase_  )
											_lowerCAmelCase  	=			pipe(**UpperCAmelCase_  ).images
											_lowerCAmelCase  	=			image[0, -3:, -3:, -1].flatten()

											assert image.shape == (1, 256, 256, 3)
											_lowerCAmelCase  	=			np.array([0.51825, 0.52850, 0.52543, 0.54258, 0.52304, 0.52569, 0.54363, 0.55276, 0.56878]  )
											_lowerCAmelCase  	=			np.abs(expected_slice - image_slice  ).max()
											assert max_diff < 1E-3






@nightly
@require_torch_gpu
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  str:







											"""simple docstring"""




											super().tearDown()
											gc.collect()
											torch.cuda.empty_cache()


				def 				__lowerCamelCase	(					self	:			Any				,     UpperCAmelCase_	:			Optional[Any]				,     UpperCAmelCase_	:			Union[str, Any]=torch.floataa				,     UpperCAmelCase_	:			int=0  )		->  Tuple:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.manual_seed(UpperCAmelCase_  )
											_lowerCAmelCase  	=			np.random.RandomState(UpperCAmelCase_  ).standard_normal((1, 4, 32, 32)  )
											_lowerCAmelCase  	=			torch.from_numpy(UpperCAmelCase_  ).to(device=UpperCAmelCase_				,     dtype=UpperCAmelCase_  )
											_lowerCAmelCase  	=			{
											    'prompt': 'A painting of a squirrel eating a burger',
											    'latents': latents,
											    'generator': generator,
											    'num_inference_steps': 50,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs







				def 				__lowerCamelCase	(					self	:			List[str]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			LDMTextToImagePipeline.from_pretrained('CompVis/ldm-text2im-large-256'  ).to(UpperCAmelCase_  )
											pipe.set_progress_bar_config(disable=UpperCAmelCase_  )

											_lowerCAmelCase  	=			self.get_inputs(UpperCAmelCase_  )
											_lowerCAmelCase  	=			pipe(**UpperCAmelCase_  ).images[0]

											_lowerCAmelCase  	=			load_numpy(
											    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'  )
											_lowerCAmelCase  	=			np.abs(expected_image - image  ).max()
											assert max_diff < 1E-3








"""simple docstring"""



import unittest

import numpy as np
import torch

from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class        _SCREAMING_SNAKE_CASE  (  unittest.TestCase							):






				'''simple docstring'''



				def 				__lowerCamelCase	(					self	:			Any  )		->  Dict:







											"""simple docstring"""




											_lowerCAmelCase  	=			10


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[Any]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
											_lowerCAmelCase  	=			[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
											self.assertEqual(truncate_or_pad(UpperCAmelCase_				,     self.block_size				,     0  )				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			str  )		->  Optional[int]:







											"""simple docstring"""




											_lowerCAmelCase  	=			'It was the year of Our Lord one thousand seven hundred and\n        seventy-five.\n\nSpiritual revelations were conceded to England at that\n        favoured period, as at this.'
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     []  )


				def 				__lowerCamelCase	(					self	:			Dict  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			''
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )
											self.assertEqual(UpperCAmelCase_				,     []  )
											self.assertEqual(UpperCAmelCase_				,     []  )


				def 				__lowerCamelCase	(					self	:			Optional[int]  )		->  List[str]:







											"""simple docstring"""




											_lowerCAmelCase  	=			(
											    'It was the year of Our Lord one thousand seven hundred and '
											    'seventy-five\n\nSpiritual revelations were conceded to England '
											    'at that favoured period, as at this.\n@highlight\n\nIt was the best of times'
											)
											_lowerCAmelCase	,		_lowerCAmelCase  	=			process_story(UpperCAmelCase_  )

											_lowerCAmelCase  	=			[
											    'It was the year of Our Lord one thousand seven hundred and seventy-five.',
											    'Spiritual revelations were conceded to England at that favoured period, as at this.',
											]
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )

											_lowerCAmelCase  	=			['It was the best of times.']
											self.assertEqual(UpperCAmelCase_				,     UpperCAmelCase_  )


				def 				__lowerCamelCase	(					self	:			List[str]  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([1, 2, 3, 4]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     0  ).numpy()				,     expected.numpy()  )


				def 				__lowerCamelCase	(					self	:			int  )		->  str:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([1, 2, 3, 4, 23, 23, 23]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1, 0, 0, 0]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     23  ).numpy()				,     expected.numpy()  )


				def 				__lowerCamelCase	(					self	:			int  )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			torch.tensor([8, 2, 3, 4, 1, 1, 1]  )
											_lowerCAmelCase  	=			torch.tensor([1, 1, 1, 1, 0, 0, 0]  )
											np.testing.assert_array_equal(build_mask(UpperCAmelCase_				,     1  ).numpy()				,     expected.numpy()  )







				def 				__lowerCamelCase	(					self	:			str  )		->  int:







											"""simple docstring"""




											_lowerCAmelCase  	=			101
											_lowerCAmelCase  	=			torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]]  )
											_lowerCAmelCase  	=			torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]]  )

											_lowerCAmelCase  	=			compute_token_type_ids(UpperCAmelCase_				,     UpperCAmelCase_  )
											np.testing.assert_array_equal(UpperCAmelCase_				,     UpperCAmelCase_  )






