




import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()


class        __SCREAMING_SNAKE_CASE							(	lowerCamelCase     ,  lowerCamelCase     ,  unittest.TestCase						):
					_lowerCamelCase							=				StableDiffusionXLPipeline
					_lowerCamelCase							=				TEXT_TO_IMAGE_PARAMS
					_lowerCamelCase							=				TEXT_TO_IMAGE_BATCH_PARAMS
					_lowerCamelCase							=				TEXT_TO_IMAGE_IMAGE_PARAMS
					_lowerCamelCase							=				TEXT_TO_IMAGE_IMAGE_PARAMS

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						torch.manual_seed(0      )
						lowerCamelCase_							:	List[str]							     =							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')				, attention_head_dim=(2, 4)				, use_linear_projection=__lowercase				, addition_embed_type='text_time'				, addition_time_embed_dim=8				, transformer_layers_per_block=(1, 2)				, projection_class_embeddings_input_dim=80				, cross_attention_dim=64				, )
						lowerCamelCase_							:	Dict							     =							EulerDiscreteScheduler(
						    beta_start=0.0_00_85				, beta_end=0.0_12				, steps_offset=1				, beta_schedule='scaled_linear'				, timestep_spacing='leading'				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	str							     =							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				, sample_size=128				, )
						torch.manual_seed(0      )
						lowerCamelCase_							:	int							     =							CLIPTextConfig(
						    bos_token_id=0				, eos_token_id=2				, hidden_size=32				, intermediate_size=37				, layer_norm_eps=1E-05				, num_attention_heads=4				, num_hidden_layers=5				, pad_token_id=1				, vocab_size=1_000				, hidden_act='gelu'				, projection_dim=32				, )
						lowerCamelCase_							:	Any							     =							CLIPTextModel(__lowercase      )
						lowerCamelCase_							:	Union[str, Any]							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				, local_files_only=__lowercase      )

						lowerCamelCase_							:	List[str]							     =							CLIPTextModelWithProjection(__lowercase      )
						lowerCamelCase_							:	List[Any]							     =							CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'				, local_files_only=__lowercase      )

						lowerCamelCase_							:	Any							     =							{
						    'unet': unet,
						    'scheduler': scheduler,
						    'vae': vae,
						    'text_encoder': text_encoder,
						    'tokenizer': tokenizer,
						    'text_encoder_2': text_encoder_a,
						    'tokenizer_2': tokenizer_a,
						    # "safety_checker": None,
						    # "feature_extractor": None,
						}
						return components

					def 			lowercase_							(       self				, __lowercase				, __lowercase=0      ):







						'''simple docstring'''




						if str(__lowercase      ).startswith('mps'      ):
							lowerCamelCase_							:	Optional[int]							     =							torch.manual_seed(__lowercase      )
						else:
							lowerCamelCase_							:	Optional[int]							     =							torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
						lowerCamelCase_							:	Union[str, Any]							     =							{
						    'prompt': 'A painting of a squirrel eating a burger',
						    'generator': generator,
						    'num_inference_steps': 2,
						    'guidance_scale': 5.0,
						    'output_type': 'numpy',
						}
						return inputs

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							'cpu'  # ensure determinism for the device-dependent torch.Generator
						lowerCamelCase_							:	List[Any]							     =							self.get_dummy_components()
						lowerCamelCase_							:	str							     =							StableDiffusionXLPipeline(**__lowercase      )
						lowerCamelCase_							:	List[Any]							     =							sd_pipe.to(__lowercase      )
						sd_pipe.set_progress_bar_config(disable=__lowercase      )

						lowerCamelCase_							:	int							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	int							     =							sd_pipe(**__lowercase      ).images
						lowerCamelCase_							:	Optional[int]							     =							image[0, -3:, -3:, -1]

						assert image.shape == (1, 64, 64, 3)
						lowerCamelCase_							:	List[Any]							     =							np.array([0.58_73, 0.61_28, 0.47_97, 0.51_22, 0.56_74, 0.46_39, 0.52_27, 0.51_49, 0.47_47]      )

						assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1E-2

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	Any							     =							self.get_dummy_components()
						lowerCamelCase_							:	Optional[Any]							     =							StableDiffusionXLPipeline(**__lowercase      )
						lowerCamelCase_							:	str							     =							sd_pipe.to(__lowercase      )
						lowerCamelCase_							:	str							     =							sd_pipe.to(__lowercase      )
						sd_pipe.set_progress_bar_config(disable=__lowercase      )

						# forward without prompt embeds
						lowerCamelCase_							:	str							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	Optional[int]							     =							3 * ['this is a negative prompt']
						lowerCamelCase_							:	Dict							     =							negative_prompt
						lowerCamelCase_							:	Union[str, Any]							     =							3 * [inputs['prompt']]

						lowerCamelCase_							:	List[Any]							     =							sd_pipe(**__lowercase      )
						lowerCamelCase_							:	Any							     =							output.images[0, -3:, -3:, -1]

						# forward with prompt embeds
						lowerCamelCase_							:	str							     =							self.get_dummy_inputs(__lowercase      )
						lowerCamelCase_							:	Any							     =							3 * ['this is a negative prompt']
						lowerCamelCase_							:	Any							     =							3 * [inputs.pop('prompt'      )]

						(
						    (
						    lowerCamelCase_
						)     ,    (
						    lowerCamelCase_
						)     ,    (
						    lowerCamelCase_
						)     ,    (
						    lowerCamelCase_
						)     ,    
						)							:	Optional[int]							     =							sd_pipe.encode_prompt(__lowercase				, negative_prompt=__lowercase      )

						lowerCamelCase_							:	Union[str, Any]							     =							sd_pipe(
						    **__lowercase				, prompt_embeds=__lowercase				, negative_prompt_embeds=__lowercase				, pooled_prompt_embeds=__lowercase				, negative_pooled_prompt_embeds=__lowercase				, )
						lowerCamelCase_							:	Union[str, Any]							     =							output.images[0, -3:, -3:, -1]

						# make sure that it's equal
						assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()      ).max() < 1E-4

					def 			lowercase_							(       self      ):







						'''simple docstring'''




						super().test_attention_slicing_forward_pass(expected_max_diff=3E-3      )





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						super().test_inference_batch_single_identical(expected_max_diff=3E-3      )


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

					def 			lowercase_							(       self      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, __lowercase				, __lowercase="cpu"				, __lowercase=torch.floataa				, __lowercase=0      ):







						'''simple docstring'''




						lowerCamelCase_							:	int							     =							torch.Generator(device=__lowercase      ).manual_seed(__lowercase      )
						lowerCamelCase_							:	str							     =							np.random.RandomState(__lowercase      ).standard_normal((1, 4, 64, 64)      )
						lowerCamelCase_							:	Dict							     =							torch.from_numpy(__lowercase      ).to(device=__lowercase				, dtype=__lowercase      )
						lowerCamelCase_							:	Optional[int]							     =							{
						    'prompt': 'a photograph of an astronaut riding a horse',
						    'latents': latents,
						    'generator': generator,
						    'num_inference_steps': 3,
						    'guidance_scale': 7.5,
						    'output_type': 'numpy',
						}
						return inputs





					def 			lowercase_							(       self      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							DiffusionPipeline.from_pretrained('stabilityai/stable-diffusion-2-base'      )
						pipe.to(__lowercase      )
						pipe.set_progress_bar_config(disable=__lowercase      )

						lowerCamelCase_							:	Any							     =							self.get_inputs(__lowercase      )
						lowerCamelCase_							:	Any							     =							pipe(**__lowercase      ).images
						lowerCamelCase_							:	Optional[Any]							     =							image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						lowerCamelCase_							:	List[str]							     =							np.array([0.4_94_93, 0.4_78_96, 0.4_07_98, 0.5_42_14, 0.5_32_12, 0.4_82_02, 0.4_76_56, 0.4_63_29, 0.4_85_06]      )
						assert np.abs(image_slice - expected_slice      ).max() < 7E-3






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available


UpperCamelCase__  =		{}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__  =		['CpmTokenizer']

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase__  =		['CpmTokenizerFast']


if TYPE_CHECKING:
							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_cpm import CpmTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_cpm_fast import CpmTokenizerFast

else:
							import sys

							UpperCamelCase__  =		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
