





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  __A   (   lowerCAmelCase      ,    lowerCAmelCase      ,    unittest.TestCase			):

			'''simple docstring'''







			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 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				torch.manual_seed(0							)
				lowerCamelCase__        =		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''')	,  attention_head_dim=(2, 4)	,  use_linear_projection=__lowerCAmelCase	,  addition_embed_type='''text_time'''	,  addition_time_embed_dim=8	,  transformer_layers_per_block=(1, 2)	,  projection_class_embeddings_input_dim=8_0	,  cross_attention_dim=6_4	,  )
				lowerCamelCase__        =		EulerDiscreteScheduler(
				    beta_start=0.0_0085	,  beta_end=0.012	,  steps_offset=1	,  beta_schedule='''scaled_linear'''	,  timestep_spacing='''leading'''	,  )
				torch.manual_seed(0							)
				lowerCamelCase__        =		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	,  sample_size=1_2_8	,  )
				torch.manual_seed(0							)
				lowerCamelCase__        =		CLIPTextConfig(
				    bos_token_id=0	,  eos_token_id=2	,  hidden_size=3_2	,  intermediate_size=3_7	,  layer_norm_eps=1E-05	,  num_attention_heads=4	,  num_hidden_layers=5	,  pad_token_id=1	,  vocab_size=1_0_0_0	,  hidden_act='''gelu'''	,  projection_dim=3_2	,  )
				lowerCamelCase__        =		CLIPTextModel(__lowerCAmelCase							)
				lowerCamelCase__        =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,  local_files_only=__lowerCAmelCase							)

				lowerCamelCase__        =		CLIPTextModelWithProjection(__lowerCAmelCase							)
				lowerCamelCase__        =		CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,  local_files_only=__lowerCAmelCase							)

				lowerCamelCase__        =		{
				    '''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 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase=0							):



				'''simple docstring'''







				if str(__lowerCAmelCase							).startswith('''mps'''							):
					lowerCamelCase__        =		torch.manual_seed(__lowerCAmelCase							)
				else:
					lowerCamelCase__        =		torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
				lowerCamelCase__        =		{
				    '''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 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		'''cpu'''  # ensure determinism for the device-dependent torch.Generator
				lowerCamelCase__        =		self.get_dummy_components()
				lowerCamelCase__        =		StableDiffusionXLPipeline(**__lowerCAmelCase							)
				lowerCamelCase__        =		sd_pipe.to(__lowerCAmelCase							)
				sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

				lowerCamelCase__        =		self.get_dummy_inputs(__lowerCAmelCase							)
				lowerCamelCase__        =		sd_pipe(**__lowerCAmelCase							).images
				lowerCamelCase__        =		image[0, -3:, -3:, -1]

				assert image.shape == (1, 6_4, 6_4, 3)
				lowerCamelCase__        =		np.array([0.5873, 0.6128, 0.4797, 0.5122, 0.5674, 0.4639, 0.5227, 0.5149, 0.4747]							)

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





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		self.get_dummy_components()
				lowerCamelCase__        =		StableDiffusionXLPipeline(**__lowerCAmelCase							)
				lowerCamelCase__        =		sd_pipe.to(__lowerCAmelCase							)
				lowerCamelCase__        =		sd_pipe.to(__lowerCAmelCase							)
				sd_pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

				# forward without prompt embeds
				lowerCamelCase__        =		self.get_dummy_inputs(__lowerCAmelCase							)
				lowerCamelCase__        =		3 * ['''this is a negative prompt''']
				lowerCamelCase__        =		negative_prompt
				lowerCamelCase__        =		3 * [inputs['''prompt''']]

				lowerCamelCase__        =		sd_pipe(**__lowerCAmelCase							)
				lowerCamelCase__        =		output.images[0, -3:, -3:, -1]

				# forward with prompt embeds
				lowerCamelCase__        =		self.get_dummy_inputs(__lowerCAmelCase							)
				lowerCamelCase__        =		3 * ['''this is a negative prompt''']
				lowerCamelCase__        =		3 * [inputs.pop('''prompt'''							)]

				(
				    (
				    lowerCamelCase__
				)    ,     (
				    lowerCamelCase__
				)    ,     (
				    lowerCamelCase__
				)    ,     (
				    lowerCamelCase__
				)    ,     
				)        =		sd_pipe.encode_prompt(__lowerCAmelCase	,  negative_prompt=__lowerCAmelCase							)

				lowerCamelCase__        =		sd_pipe(
				    **__lowerCAmelCase	,  prompt_embeds=__lowerCAmelCase	,  negative_prompt_embeds=__lowerCAmelCase	,  pooled_prompt_embeds=__lowerCAmelCase	,  negative_pooled_prompt_embeds=__lowerCAmelCase	,  )
				lowerCamelCase__        =		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 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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






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

			'''simple docstring'''







			def 		__lowerCamelCase    (   self							):



				'''simple docstring'''







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





			def 		__lowerCamelCase    (   self	,  __lowerCAmelCase	,  __lowerCAmelCase="cpu"	,  __lowerCAmelCase=torch.floataa	,  __lowerCAmelCase=0							):



				'''simple docstring'''







				lowerCamelCase__        =		torch.Generator(device=__lowerCAmelCase							).manual_seed(__lowerCAmelCase							)
				lowerCamelCase__        =		np.random.RandomState(__lowerCAmelCase							).standard_normal((1, 4, 6_4, 6_4)							)
				lowerCamelCase__        =		torch.from_numpy(__lowerCAmelCase							).to(device=__lowerCAmelCase	,  dtype=__lowerCAmelCase							)
				lowerCamelCase__        =		{
				    '''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 		__lowerCamelCase    (   self							):



				'''simple docstring'''







				lowerCamelCase__        =		DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''							)
				pipe.to(__lowerCAmelCase							)
				pipe.set_progress_bar_config(disable=__lowerCAmelCase							)

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

				assert image.shape == (1, 5_1_2, 5_1_2, 3)
				lowerCamelCase__        =		np.array([0.4_9493, 0.4_7896, 0.4_0798, 0.5_4214, 0.5_3212, 0.4_8202, 0.4_7656, 0.4_6329, 0.4_8506]							)
				assert np.abs(image_slice - expected_slice							).max() < 7E-3










import warnings

from .generation import FlaxGenerationMixin






class  __A   (   lowerCAmelCase			):

			'''simple docstring'''







			warnings.warn(
			    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
			    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""      ,    lowerCAmelCase      ,    )



