


# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/

import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    ControlNetModel,
    DDIMScheduler,
    StableDiffusionControlNetImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
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 (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
)


enable_full_determinism()


class     __UpperCAmelCase							(       __A ,					__A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      StableDiffusionControlNetImgaImgPipeline
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     _lowerCamelCase   =      IMAGE_TO_IMAGE_IMAGE_PARAMS.union({"""control_image"""}							)
     _lowerCamelCase   =      IMAGE_TO_IMAGE_IMAGE_PARAMS


     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  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""")  ,       cross_attention_dim=32  ,       )
        torch.manual_seed(0    )
        __a					=  ControlNetModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       in_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       cross_attention_dim=32  ,       conditioning_embedding_out_channels=(16, 32)  ,       )
        torch.manual_seed(0    )
        __a					=  DDIMScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_one=__A  ,       )
        torch.manual_seed(0    )
        __a					=  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  ,       )
        torch.manual_seed(0    )
        __a					=  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=1000  ,       )
        __a					=  CLIPTextModel(__A    )
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

        __a					=  {
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )

        __a					=  2
        __a					=  randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,       generator=__A  ,       device=torch.device(__A    )  ,       )
        __a					=  floats_tensor(control_image.shape  ,       rng=random.Random(__A    )    ).to(__A    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__A    )    ).convert("""RGB"""    ).resize((64, 64)    )
        __a					=  {
            """prompt""": """A painting of a squirrel eating a burger""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
            """image""": image,
            """control_image""": control_image,
        }

        return inputs


     def 	snake_case_  (	self    ):
        return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3    )


     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()  ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,       )
     def 	snake_case_  (	self    ):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )


     def 	snake_case_  (	self    ):
        self._test_inference_batch_single_identical(expected_max_diff=2E-3    )







class     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      StableDiffusionControlNetImgaImgPipeline
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width"""}
     _lowerCamelCase   =      TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     _lowerCamelCase   =      frozenset([]							)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess


     def 	snake_case_  (	self    ):
        torch.manual_seed(0    )
        __a					=  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""")  ,       cross_attention_dim=32  ,       )
        torch.manual_seed(0    )

        def init_weights(__A    ):
           if isinstance(__A  ,       torch.nn.Convad    ):
              torch.nn.init.normal(m.weight    )
              m.bias.data.fill_(1.0    )

        __a					=  ControlNetModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       in_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       cross_attention_dim=32  ,       conditioning_embedding_out_channels=(16, 32)  ,       )
        controlneta.controlnet_down_blocks.apply(__A    )

        torch.manual_seed(0    )
        __a					=  ControlNetModel(
            block_out_channels=(32, 64)  ,       layers_per_block=2  ,       in_channels=4  ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")  ,       cross_attention_dim=32  ,       conditioning_embedding_out_channels=(16, 32)  ,       )
        controlneta.controlnet_down_blocks.apply(__A    )

        torch.manual_seed(0    )
        __a					=  DDIMScheduler(
            beta_start=0.00085  ,       beta_end=0.012  ,       beta_schedule="""scaled_linear"""  ,       clip_sample=__A  ,       set_alpha_to_one=__A  ,       )
        torch.manual_seed(0    )
        __a					=  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  ,       )
        torch.manual_seed(0    )
        __a					=  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=1000  ,       )
        __a					=  CLIPTextModel(__A    )
        __a					=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""    )

        __a					=  MultiControlNetModel([controlneta, controlneta]    )

        __a					=  {
            """unet""": unet,
            """controlnet""": controlnet,
            """scheduler""": scheduler,
            """vae""": vae,
            """text_encoder""": text_encoder,
            """tokenizer""": tokenizer,
            """safety_checker""": None,
            """feature_extractor""": None,
        }
        return components


     def 	snake_case_  (	self  ,       __A  ,       __A=0    ):
        if str(__A    ).startswith("""mps"""    ):
           __a					=  torch.manual_seed(__A    )
        else:
           __a					=  torch.Generator(device=__A    ).manual_seed(__A    )

        __a					=  2

        __a					=  [
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,       generator=__A  ,       device=torch.device(__A    )  ,       ),
            randn_tensor(
                (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)  ,       generator=__A  ,       device=torch.device(__A    )  ,       ),
        ]

        __a					=  floats_tensor(control_image[0].shape  ,       rng=random.Random(__A    )    ).to(__A    )
        __a					=  image.cpu().permute(0  ,       2  ,       3  ,       1    )[0]
        __a					=  Image.fromarray(np.uinta(__A    )    ).convert("""RGB"""    ).resize((64, 64)    )
        __a					=  {
            """prompt""": """A painting of a squirrel eating a burger""",
            """generator""": generator,
            """num_inference_steps""": 2,
            """guidance_scale""": 6.0,
            """output_type""": """numpy""",
            """image""": image,
            """control_image""": control_image,
        }

        return inputs


     def 	snake_case_  (	self    ):
        __a					=  self.get_dummy_components()
        __a					=  self.pipeline_class(**__A    )
        pipe.to(__A    )

        __a					=  10.0
        __a					=  4

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  steps
        __a					=  scale
        __a					=  pipe(**__A    )[0]

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  steps
        __a					=  scale
        __a					=  pipe(**__A  ,       control_guidance_start=0.1  ,       control_guidance_end=0.2    )[0]

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  steps
        __a					=  scale
        __a					=  pipe(**__A  ,       control_guidance_start=[0.1, 0.3]  ,       control_guidance_end=[0.2, 0.7]    )[0]

        __a					=  self.get_dummy_inputs(__A    )
        __a					=  steps
        __a					=  scale
        __a					=  pipe(**__A  ,       control_guidance_start=0.4  ,       control_guidance_end=[0.5, 0.8]    )[0]

        # make sure that all outputs are different
        assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
        assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3
        assert np.sum(np.abs(output_a - output_a    )    ) > 1E-3


     def 	snake_case_  (	self    ):
        return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3    )


     @unittest.skipIf(
         torch_device != """cuda""" or not is_xformers_available()  ,       reason="""XFormers attention is only available with CUDA and `xformers` installed"""  ,       )
     def 	snake_case_  (	self    ):
        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )


     def 	snake_case_  (	self    ):
        self._test_inference_batch_single_identical(expected_max_diff=2E-3    )


     def 	snake_case_  (	self    ):
        __a					=  self.get_dummy_components()
        __a					=  self.pipeline_class(**__A    )
        pipe.to(__A    )
        pipe.set_progress_bar_config(disable=__A    )
        with tempfile.TemporaryDirectory() as tmpdir:
           try:
              # save_pretrained is not implemented for Multi-ControlNet
              pipe.save_pretrained(__A    )
           except NotImplementedError:
              pass







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


     """simple docstring"""


     def 	snake_case_  (	self    ):
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()


     def 	snake_case_  (	self    ):
        __a					=  ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny"""    )

        __a					=  StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            """runwayml/stable-diffusion-v1-5"""  ,       safety_checker=__A  ,       controlnet=__A    )
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=__A    )

        __a					=  torch.Generator(device="""cpu"""    ).manual_seed(0    )
        __a					=  """evil space-punk bird"""
        __a					=  load_image(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png"""    ).resize((512, 512)    )
        __a					=  load_image(
            """https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png"""    ).resize((512, 512)    )

        __a					=  pipe(
            __A  ,       __A  ,       control_image=__A  ,       generator=__A  ,       output_type="""np"""  ,       num_inference_steps=50  ,       strength=0.6  ,       )

        __a					=  output.images[0]

        assert image.shape == (512, 512, 3)

        __a					=  load_numpy(
            """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy"""    )

        assert np.abs(expected_image - image    ).max() < 9E-2







from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

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



