

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 							UpperCAmelCase_						(     __lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		str             =       LDMTextToImagePipeline
							UpperCAmelCase__			:		Tuple             =       TEXT_TO_IMAGE_PARAMS - {
							    "negative_prompt",
							    "negative_prompt_embeds",
							    "cross_attention_kwargs",
							    "prompt_embeds",
							}
							UpperCAmelCase__			:		Any             =       PipelineTesterMixin.required_optional_params - {
							    "num_images_per_prompt",
							    "callback",
							    "callback_steps",
							}
							UpperCAmelCase__			:		Dict             =       TEXT_TO_IMAGE_BATCH_PARAMS
							UpperCAmelCase__			:		Tuple             =       False


							def 							__lowercase							(     self			)       ->       Dict:
												torch.manual_seed(0			)
												_a      :       List[Any]   =						UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4)			,				layers_per_block=2			,				sample_size=3_2			,				in_channels=4			,				out_channels=4			,				down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')			,				up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')			,				cross_attention_dim=3_2			,				)
												_a      :       int   =						DDIMScheduler(
												    beta_start=0.0_0085			,				beta_end=0.012			,				beta_schedule='''scaled_linear'''			,				clip_sample=_a			,				set_alpha_to_one=_a			,				)
												torch.manual_seed(0			)
												_a      :       List[Any]   =						AutoencoderKL(
												    block_out_channels=(3_2, 6_4)			,				in_channels=3			,				out_channels=3			,				down_block_types=('''DownEncoderBlock2D''', '''DownEncoderBlock2D''')			,				up_block_types=('''UpDecoderBlock2D''', '''UpDecoderBlock2D''')			,				latent_channels=4			,				)
												torch.manual_seed(0			)
												_a      :       Union[str, Any]   =						CLIPTextConfig(
												    bos_token_id=0			,				eos_token_id=2			,				hidden_size=3_2			,				intermediate_size=3_7			,				layer_norm_eps=1e-0_5			,				num_attention_heads=4			,				num_hidden_layers=5			,				pad_token_id=1			,				vocab_size=1_0_0_0			,				)
												_a      :       int   =						CLIPTextModel(_a			)
												_a      :       Dict   =						CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

												_a      :       List[Any]   =						{
												    '''unet''': unet,
												    '''scheduler''': scheduler,
												    '''vqvae''': vae,
												    '''bert''': text_encoder,
												    '''tokenizer''': tokenizer,
												}
												return components


							def 							__lowercase							(     self			,				_a			,				_a=0			)       ->       Optional[int]:
												if str(_a			).startswith('''mps'''			):
																	_a      :       List[str]   =						torch.manual_seed(_a			)
												else:
																	_a      :       int   =						torch.Generator(device=_a			).manual_seed(_a			)
												_a      :       Optional[int]   =						{
												    '''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 							__lowercase							(     self			)       ->       Any:
												_a      :       Optional[Any]   =						'''cpu'''  # ensure determinism for the device-dependent torch.Generator

												_a      :       int   =						self.get_dummy_components()
												_a      :       Any   =						LDMTextToImagePipeline(**_a			)
												pipe.to(_a			)
												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Optional[Any]   =						self.get_dummy_inputs(_a			)
												_a      :       List[str]   =						pipe(**_a			).images
												_a      :       Dict   =						image[0, -3:, -3:, -1]

												assert image.shape == (1, 1_6, 1_6, 3)
												_a      :       List[Any]   =						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 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       str:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


							def 							__lowercase							(     self			,				_a			,				_a=torch.floataa			,				_a=0			)       ->       str:
												_a      :       Any   =						torch.manual_seed(_a			)
												_a      :       Dict   =						np.random.RandomState(_a			).standard_normal((1, 4, 3_2, 3_2)			)
												_a      :       List[Any]   =						torch.from_numpy(_a			).to(device=_a			,				dtype=_a			)
												_a      :       Union[str, Any]   =						{
												    '''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 							__lowercase							(     self			)       ->       str:
												_a      :       Any   =						LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(_a			)
												pipe.set_progress_bar_config(disable=_a			)

												_a      :       Optional[int]   =						self.get_inputs(_a			)
												_a      :       Optional[Any]   =						pipe(**_a			).images
												_a      :       Union[str, Any]   =						image[0, -3:, -3:, -1].flatten()

												assert image.shape == (1, 2_5_6, 2_5_6, 3)
												_a      :       List[str]   =						np.array([0.5_1825, 0.5_2850, 0.5_2543, 0.5_4258, 0.5_2304, 0.5_2569, 0.5_4363, 0.5_5276, 0.5_6878]			)
												_a      :       List[Any]   =						np.abs(expected_slice - image_slice			).max()
												assert max_diff < 1e-3



@nightly
@require_torch_gpu
class 							UpperCAmelCase_						(     unittest.TestCase  ):
							"""simple docstring"""







							def 							__lowercase							(     self			)       ->       Dict:
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()


							def 							__lowercase							(     self			,				_a			,				_a=torch.floataa			,				_a=0			)       ->       int:
												_a      :       Union[str, Any]   =						torch.manual_seed(_a			)
												_a      :       str   =						np.random.RandomState(_a			).standard_normal((1, 4, 3_2, 3_2)			)
												_a      :       List[Any]   =						torch.from_numpy(_a			).to(device=_a			,				dtype=_a			)
												_a      :       Tuple   =						{
												    '''prompt''': '''A painting of a squirrel eating a burger''',
												    '''latents''': latents,
												    '''generator''': generator,
												    '''num_inference_steps''': 5_0,
												    '''guidance_scale''': 6.0,
												    '''output_type''': '''numpy''',
												}
												return inputs




							def 							__lowercase							(     self			)       ->       List[str]:
												_a      :       str   =						LDMTextToImagePipeline.from_pretrained('''CompVis/ldm-text2im-large-256'''			).to(_a			)
												pipe.set_progress_bar_config(disable=_a			)

												_a      :       List[str]   =						self.get_inputs(_a			)
												_a      :       int   =						pipe(**_a			).images[0]

												_a      :       Union[str, Any]   =						load_numpy(
												    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy'''			)
												_a      :       Dict   =						np.abs(expected_image - image			).max()
												assert max_diff < 1e-3






a__												=						'''Input must be a string of 8 numbers plus letter'''
a__												=						'''TRWAGMYFPDXBNJZSQVHLCKE'''



def   __UpperCAmelCase					(      __a :     str      )     ->	bool:

					"""simple docstring"""

					if not isinstance(__a						,__a      ):
										_a      :       List[str]   =						F"""Expected string as input, found {type(__a      ).__name__}"""
										raise TypeError(__a      )

					_a      :       List[Any]   =						spanish_id.replace('''-'''						,''''''      ).upper()
					if len(__a      ) != 9:
										raise ValueError(__a      )

					try:
										_a      :       Any   =						int(spanish_id_clean[0:8]      )
										_a      :       str   =						spanish_id_clean[8]
					except ValueError as ex:
										raise ValueError(__a      ) from ex

					if letter.isdigit():
										raise ValueError(__a      )

					return letter == LOOKUP_LETTERS[number % 23]


if __name__ == "__main__":
			import doctest

			doctest.testmod()



