





'''simple docstring'''






import gc
import random
import unittest

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

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

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class  _A (					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   List[Any]				     =   StableDiffusionXLImgaImgPipeline
		lowercase__:   Any				     =   TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
		lowercase__:   Optional[Any]				     =   PipelineTesterMixin.required_optional_params - {'''latents'''}
		lowercase__:   Union[str, Any]				     =   TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		lowercase__:   Dict				     =   IMAGE_TO_IMAGE_IMAGE_PARAMS
		lowercase__:   int				     =   IMAGE_TO_IMAGE_IMAGE_PARAMS

		def 	lowercase__     (   self					:   List[str]			)					->    Tuple:


					"""simple docstring"""

					torch.manual_seed(0			)
					__snake_case					:      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=__magic_name__  ,      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  ,      )
					__snake_case					:      List[Any]							      =     EulerDiscreteScheduler(
					    beta_start=0.00085  ,      beta_end=0.012  ,      steps_offset=1  ,      beta_schedule="""scaled_linear"""  ,      timestep_spacing="""leading"""  ,      )
					torch.manual_seed(0			)
					__snake_case					:      List[Any]							      =     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=1_28  ,      )
					torch.manual_seed(0			)
					__snake_case					:      str							      =     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=10_00  ,      hidden_act="""gelu"""  ,      projection_dim=32  ,      )
					__snake_case					:      Tuple							      =     CLIPTextModel(__magic_name__			)
					__snake_case					:      List[str]							      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,      local_files_only=__magic_name__			)

					__snake_case					:      Dict							      =     CLIPTextModelWithProjection(__magic_name__			)
					__snake_case					:      List[str]							      =     CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""  ,      local_files_only=__magic_name__			)

					__snake_case					:      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					:   Dict  ,      __magic_name__					:   Any  ,      __magic_name__					:   Any=0			)					->    Any:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     floats_tensor((1, 3, 32, 32)  ,      rng=random.Random(__magic_name__			)			).to(__magic_name__			)
					__snake_case					:      Dict							      =     image / 2 + 0.5
					if str(__magic_name__			).startswith("""mps"""			):
								__snake_case					:      Tuple							      =     torch.manual_seed(__magic_name__			)
					else:
								__snake_case					:      List[str]							      =     torch.Generator(device=__magic_name__			).manual_seed(__magic_name__			)
					__snake_case					:      Dict							      =     {
					    """prompt""": """A painting of a squirrel eating a burger""",
					    """image""": image,
					    """generator""": generator,
					    """num_inference_steps""": 2,
					    """guidance_scale""": 5.0,
					    """output_type""": """numpy""",
					    """strength""": 0.75,
					}
					return inputs

		def 	lowercase__     (   self					:   str			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     """cpu"""  # ensure determinism for the device-dependent torch.Generator
					__snake_case					:      Any							      =     self.get_dummy_components()
					__snake_case					:      Tuple							      =     StableDiffusionXLImgaImgPipeline(**__magic_name__			)
					__snake_case					:      List[str]							      =     sd_pipe.to(__magic_name__			)
					sd_pipe.set_progress_bar_config(disable=__magic_name__			)

					__snake_case					:      Tuple							      =     self.get_dummy_inputs(__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     sd_pipe(**__magic_name__			).images
					__snake_case					:      Optional[int]							      =     image[0, -3:, -3:, -1]

					assert image.shape == (1, 32, 32, 3)

					__snake_case					:      str							      =     np.array([0.4656, 0.4840, 0.4439, 0.6698, 0.5574, 0.4524, 0.5799, 0.5943, 0.5165]			)

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

		def 	lowercase__     (   self					:   Dict			)					->    Dict:


					"""simple docstring"""

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

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


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict			)					->    Optional[Any]:


					"""simple docstring"""

					pass

		def 	lowercase__     (   self					:   str			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.get_dummy_components()
					__snake_case					:      Union[str, Any]							      =     StableDiffusionXLImgaImgPipeline(**__magic_name__			)
					__snake_case					:      Optional[Any]							      =     sd_pipe.to(__magic_name__			)
					__snake_case					:      int							      =     sd_pipe.to(__magic_name__			)
					sd_pipe.set_progress_bar_config(disable=__magic_name__			)

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

					__snake_case					:      List[str]							      =     sd_pipe(**__magic_name__			)
					__snake_case					:      int							      =     output.images[0, -3:, -3:, -1]

					# forward with prompt embeds
					__snake_case					:      int							      =     self.get_dummy_inputs(__magic_name__			)
					__snake_case					:      List[Any]							      =     3 * ["""this is a negative prompt"""]
					__snake_case					:      Optional[Any]							      =     3 * [inputs.pop("""prompt"""			)]

					(
					    (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     
					)					:      int							      =     sd_pipe.encode_prompt(__magic_name__  ,      negative_prompt=__magic_name__			)

					__snake_case					:      Any							      =     sd_pipe(
					    **__magic_name__  ,      prompt_embeds=__magic_name__  ,      negative_prompt_embeds=__magic_name__  ,      pooled_prompt_embeds=__magic_name__  ,      negative_pooled_prompt_embeds=__magic_name__  ,      )
					__snake_case					:      str							      =     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





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

		def 	lowercase__     (   self					:   int			)					->    Dict:


					"""simple docstring"""

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

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   str="cpu"  ,      __magic_name__					:   List[str]=torch.floataa  ,      __magic_name__					:   str=0			)					->    str:


					"""simple docstring"""

					__snake_case					:      Dict							      =     torch.Generator(device=__magic_name__			).manual_seed(__magic_name__			)
					__snake_case					:      Union[str, Any]							      =     np.random.RandomState(__magic_name__			).standard_normal((1, 4, 64, 64)			)
					__snake_case					:      Dict							      =     torch.from_numpy(__magic_name__			).to(device=__magic_name__  ,      dtype=__magic_name__			)
					__snake_case					:      Tuple							      =     {
					    """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					:   Any			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      str							      =     DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base"""			)
					pipe.to(__magic_name__			)
					pipe.set_progress_bar_config(disable=__magic_name__			)

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

					assert image.shape == (1, 5_12, 5_12, 3)
					__snake_case					:      List[Any]							      =     np.array([0.49493, 0.47896, 0.40798, 0.54214, 0.53212, 0.48202, 0.47656, 0.46329, 0.48506]			)
					assert np.abs(image_slice - expected_slice			).max() < 7E-3







'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


__lowerCAmelCase		     =  {
    '''configuration_ernie''': ['''ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ErnieConfig''', '''ErnieOnnxConfig'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase		     =  [
							    '''ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''ErnieForCausalLM''',
							    '''ErnieForMaskedLM''',
							    '''ErnieForMultipleChoice''',
							    '''ErnieForNextSentencePrediction''',
							    '''ErnieForPreTraining''',
							    '''ErnieForQuestionAnswering''',
							    '''ErnieForSequenceClassification''',
							    '''ErnieForTokenClassification''',
							    '''ErnieModel''',
							    '''ErniePreTrainedModel''',
							]

if TYPE_CHECKING:
							from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_ernie import (
														    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    ErnieForCausalLM,
														    ErnieForMaskedLM,
														    ErnieForMultipleChoice,
														    ErnieForNextSentencePrediction,
														    ErnieForPreTraining,
														    ErnieForQuestionAnswering,
														    ErnieForSequenceClassification,
														    ErnieForTokenClassification,
														    ErnieModel,
														    ErniePreTrainedModel,
														)

else:
							import sys

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




