
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 		__magic_name__ (    a_,					a_,					unittest.TestCase     ):
		_lowerCAmelCase		=						StableDiffusionXLImgaImgPipeline
		_lowerCAmelCase		=						TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
		_lowerCAmelCase		=						PipelineTesterMixin.required_optional_params - {"latents"}
		_lowerCAmelCase		=						TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		_lowerCAmelCase		=						IMAGE_TO_IMAGE_IMAGE_PARAMS
		_lowerCAmelCase		=						IMAGE_TO_IMAGE_IMAGE_PARAMS





		def    _A				(				self     :						Dict					):
							torch.manual_seed(0					)
							lowerCAmelCase		:       str							=  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=_a    ,	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		:       Optional[Any]							=  EulerDiscreteScheduler(
							    beta_start=0.0_0_0_8_5    ,	beta_end=0.0_1_2    ,	steps_offset=1    ,	beta_schedule='''scaled_linear'''    ,	timestep_spacing='''leading'''    ,	)
							torch.manual_seed(0					)
							lowerCAmelCase		:       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    ,	sample_size=1_2_8    ,	)
							torch.manual_seed(0					)
							lowerCAmelCase		:       Dict							=  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		:       str							=  CLIPTextModel(_a					)
							lowerCAmelCase		:       List[str]							=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    ,	local_files_only=_a					)

							lowerCAmelCase		:       Any							=  CLIPTextModelWithProjection(_a					)
							lowerCAmelCase		:       Union[str, Any]							=  CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''    ,	local_files_only=_a					)

							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    _A				(				self     :						Optional[int]    ,	lowerCamelCase__     :						List[str]    ,	lowerCamelCase__     :						Any=0					):
							lowerCAmelCase		:       Dict							=  floats_tensor((1, 3, 3_2, 3_2)    ,	rng=random.Random(_a					)					).to(_a					)
							lowerCAmelCase		:       List[Any]							=  image / 2 + 0.5
							if str(_a					).startswith('''mps'''					):
												lowerCAmelCase		:       Optional[int]							=  torch.manual_seed(_a					)
							else:
												lowerCAmelCase		:       str							=  torch.Generator(device=_a					).manual_seed(_a					)
							lowerCAmelCase		:       Optional[int]							=  {
							    "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": 0.7_5,
							}
							return inputs





		def    _A				(				self     :						List[Any]					):
							lowerCAmelCase		:       Dict							=  "cpu"  # ensure determinism for the device-dependent torch.Generator
							lowerCAmelCase		:       Optional[int]							=  self.get_dummy_components()
							lowerCAmelCase		:       str							=  StableDiffusionXLImgaImgPipeline(**_a					)
							lowerCAmelCase		:       List[Any]							=  sd_pipe.to(_a					)
							sd_pipe.set_progress_bar_config(disable=_a					)

							lowerCAmelCase		:       Optional[Any]							=  self.get_dummy_inputs(_a					)
							lowerCAmelCase		:       Tuple							=  sd_pipe(**_a					).images
							lowerCAmelCase		:       Dict							=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 3_2, 3_2, 3)

							lowerCAmelCase		:       Tuple							=  np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]					)

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





		def    _A				(				self     :						Optional[Any]					):
							super().test_attention_slicing_forward_pass(expected_max_diff=3E-3					)





		def    _A				(				self     :						Optional[Any]					):
							super().test_inference_batch_single_identical(expected_max_diff=3E-3					)





		def    _A				(				self     :						Dict					):
							pass





		def    _A				(				self     :						Optional[Any]					):
							lowerCAmelCase		:       Optional[Any]							=  self.get_dummy_components()
							lowerCAmelCase		:       Optional[Any]							=  StableDiffusionXLImgaImgPipeline(**_a					)
							lowerCAmelCase		:       Any							=  sd_pipe.to(_a					)
							lowerCAmelCase		:       Any							=  sd_pipe.to(_a					)
							sd_pipe.set_progress_bar_config(disable=_a					)

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

							lowerCAmelCase		:       Optional[int]							=  sd_pipe(**_a					)
							lowerCAmelCase		:       List[Any]							=  output.images[0, -3:, -3:, -1]

							# forward with prompt embeds
							lowerCAmelCase		:       Any							=  self.get_dummy_inputs(_a					)
							lowerCAmelCase		:       List[str]							=  3 * ["this is a negative prompt"]
							lowerCAmelCase		:       Optional[Any]							=  3 * [inputs.pop('''prompt'''					)]

							(
							    lowerCAmelCase       
							)		:       List[Any]							=  sd_pipe.encode_prompt(_a    ,	negative_prompt=_a					)

							lowerCAmelCase		:       int							=  sd_pipe(
							    **_a    ,	prompt_embeds=_a    ,	negative_prompt_embeds=_a    ,	pooled_prompt_embeds=_a    ,	negative_pooled_prompt_embeds=_a    ,	)
							lowerCAmelCase		:       Dict							=  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 		__magic_name__ (    unittest.TestCase     ):





		def    _A				(				self     :						List[Any]					):
							super().tearDown()
							gc.collect()
							torch.cuda.empty_cache()





		def    _A				(				self     :						int    ,	lowerCamelCase__     :						Dict    ,	lowerCamelCase__     :						Optional[int]="cpu"    ,	lowerCamelCase__     :						int=torch.floataa    ,	lowerCamelCase__     :						Tuple=0					):
							lowerCAmelCase		:       Optional[int]							=  torch.Generator(device=_a					).manual_seed(_a					)
							lowerCAmelCase		:       Any							=  np.random.RandomState(_a					).standard_normal((1, 4, 6_4, 6_4)					)
							lowerCAmelCase		:       List[str]							=  torch.from_numpy(_a					).to(device=_a    ,	dtype=_a					)
							lowerCAmelCase		:       List[Any]							=  {
							    "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    _A				(				self     :						List[Any]					):
							lowerCAmelCase		:       List[Any]							=  DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''					)
							pipe.to(_a					)
							pipe.set_progress_bar_config(disable=_a					)

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

							assert image.shape == (1, 5_1_2, 5_1_2, 3)
							lowerCAmelCase		:       Optional[Any]							=  np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]					)
							assert np.abs(image_slice - expected_slice					).max() < 7E-3


"""simple docstring"""




from typing import TYPE_CHECKING

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


_A     :			Optional[Any]  						=			{
    """configuration_bridgetower""": [
        """BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BridgeTowerConfig""",
        """BridgeTowerTextConfig""",
        """BridgeTowerVisionConfig""",
    ],
    """processing_bridgetower""": ["""BridgeTowerProcessor"""],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			Any  						=			["""BridgeTowerImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A     :			List[Any]  						=			[
       """BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """BridgeTowerForContrastiveLearning""",
       """BridgeTowerForImageAndTextRetrieval""",
       """BridgeTowerForMaskedLM""",
       """BridgeTowerModel""",
       """BridgeTowerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_bridgetower import (
       BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       BridgeTowerConfig,
       BridgeTowerTextConfig,
       BridgeTowerVisionConfig,
   )
   from .processing_bridgetower import BridgeTowerProcessor

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .image_processing_bridgetower import BridgeTowerImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_bridgetower import (
          BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
          BridgeTowerForContrastiveLearning,
          BridgeTowerForImageAndTextRetrieval,
          BridgeTowerForMaskedLM,
          BridgeTowerModel,
          BridgeTowerPreTrainedModel,
      )


else:
   import sys

   _A     :			Any  						=			_LazyModule(__name__, globals()["""__file__"""], _import_structure)


