




import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()


def    A						(__A   :    Dict   ,				__A   :    Dict   ,				__A   :    Optional[int]   ,				__A   :    Any="attention"       )   ->      int:







     """simple docstring"""


     UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
     UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
     UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
     UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
     return k, o, q, v


def    A						(__A   :    Tuple   ,				__A   :    Any   ,				__A   :    Optional[Any]   ,				__A   :    Dict=False       )   ->      Optional[Any]:







     """simple docstring"""


     if split_mlp_wi:
          UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
          UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
          UpperCAmelCase_          =							(wi_a, wi_a)
     else:
          UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

     UpperCAmelCase_          =							params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
     return wi, wo


def    A						(__A   :    Any   ,				__A   :    List[Any]   ,				__A   :    Any   ,				__A   :    Union[str, Any]       )   ->      Tuple:







     """simple docstring"""


     return params[F"""{prefix}/layers_{i}/{layer_name}/scale"""]


def    A						(__A   :    dict   ,				*, __A   :    int   ,				__A   :    bool       )   ->      List[str]:







     """simple docstring"""


     UpperCAmelCase_          =							traverse_util.flatten_dict(variables['''target''']       )
     UpperCAmelCase_          =							{'''/'''.join(__A       ): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     UpperCAmelCase_          =							'''encoder/layers_0/mlp/wi_0/kernel''' in old
     print('''Split MLP:'''   ,				__A       )

     UpperCAmelCase_          =							collections.OrderedDict()

     # Shared embeddings.
     UpperCAmelCase_          =							old['''token_embedder/embedding''']

     # Encoder.
     for i in range(__A       ):
          # Block i, layer 0 (Self Attention).
          UpperCAmelCase_          =							tax_layer_norm_lookup(__A   ,				__A   ,				'''encoder'''   ,				'''pre_attention_layer_norm'''       )
          UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							tax_attention_lookup(__A   ,				__A   ,				'''encoder'''   ,				'''attention'''       )
          UpperCAmelCase_          =							layer_norm
          UpperCAmelCase_          =							k.T
          UpperCAmelCase_          =							o.T
          UpperCAmelCase_          =							q.T
          UpperCAmelCase_          =							v.T

          # Block i, layer 1 (MLP).
          UpperCAmelCase_          =							tax_layer_norm_lookup(__A   ,				__A   ,				'''encoder'''   ,				'''pre_mlp_layer_norm'''       )
          UpperCAmelCase_     ,       UpperCAmelCase_          =							tax_mlp_lookup(__A   ,				__A   ,				'''encoder'''   ,				__A       )
          UpperCAmelCase_          =							layer_norm
          if split_mlp_wi:
               UpperCAmelCase_          =							wi[0].T
               UpperCAmelCase_          =							wi[1].T
          else:
               UpperCAmelCase_          =							wi.T
          UpperCAmelCase_          =							wo.T

     UpperCAmelCase_          =							old[
         '''encoder/relpos_bias/rel_embedding'''
     ].T
     UpperCAmelCase_          =							old['''encoder/encoder_norm/scale''']

     if not is_encoder_only:
          # Decoder.
          for i in range(__A       ):
               # Block i, layer 0 (Self Attention).
               UpperCAmelCase_          =							tax_layer_norm_lookup(__A   ,				__A   ,				'''decoder'''   ,				'''pre_self_attention_layer_norm'''       )
               UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							tax_attention_lookup(__A   ,				__A   ,				'''decoder'''   ,				'''self_attention'''       )
               UpperCAmelCase_          =							layer_norm
               UpperCAmelCase_          =							k.T
               UpperCAmelCase_          =							o.T
               UpperCAmelCase_          =							q.T
               UpperCAmelCase_          =							v.T

               # Block i, layer 1 (Cross Attention).
               UpperCAmelCase_          =							tax_layer_norm_lookup(__A   ,				__A   ,				'''decoder'''   ,				'''pre_cross_attention_layer_norm'''       )
               UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_     ,       UpperCAmelCase_          =							tax_attention_lookup(__A   ,				__A   ,				'''decoder'''   ,				'''encoder_decoder_attention'''       )
               UpperCAmelCase_          =							layer_norm
               UpperCAmelCase_          =							k.T
               UpperCAmelCase_          =							o.T
               UpperCAmelCase_          =							q.T
               UpperCAmelCase_          =							v.T

               # Block i, layer 2 (MLP).
               UpperCAmelCase_          =							tax_layer_norm_lookup(__A   ,				__A   ,				'''decoder'''   ,				'''pre_mlp_layer_norm'''       )
               UpperCAmelCase_     ,       UpperCAmelCase_          =							tax_mlp_lookup(__A   ,				__A   ,				'''decoder'''   ,				__A       )
               UpperCAmelCase_          =							layer_norm
               if split_mlp_wi:
                    UpperCAmelCase_          =							wi[0].T
                    UpperCAmelCase_          =							wi[1].T
               else:
                    UpperCAmelCase_          =							wi.T
               UpperCAmelCase_          =							wo.T

          UpperCAmelCase_          =							old['''decoder/decoder_norm/scale''']
          UpperCAmelCase_          =							old[
              '''decoder/relpos_bias/rel_embedding'''
          ].T

          # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
          if "decoder/logits_dense/kernel" in old:
               UpperCAmelCase_          =							old['''decoder/logits_dense/kernel'''].T

     return new


def    A						(__A   :    str   ,				__A   :    bool       )   ->      Optional[int]:







     """simple docstring"""


     UpperCAmelCase_          =							collections.OrderedDict([(k, torch.from_numpy(v.copy()       )) for (k, v) in converted_params.items()]       )

     # Add what is missing.
     if "encoder.embed_tokens.weight" not in state_dict:
          UpperCAmelCase_          =							state_dict['''shared.weight''']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               UpperCAmelCase_          =							state_dict['''shared.weight''']

          if "lm_head.weight" not in state_dict:  # For old 1.0 models.
               print('''Using shared word embeddings as lm_head.'''       )
               UpperCAmelCase_          =							state_dict['''shared.weight''']

     return state_dict


def    A						(__A   :    Dict   ,				__A   :    Optional[int]   ,				__A   :    Union[str, Any]   ,				__A   :    List[str]       )   ->      str:







     """simple docstring"""


     UpperCAmelCase_          =							checkpoints.load_tax_checkpoint(__A       )
     UpperCAmelCase_          =							convert_tax_to_pytorch(__A   ,				num_layers=config.num_layers   ,				is_encoder_only=__A       )
     UpperCAmelCase_          =							make_state_dict(__A   ,				__A       )
     model.load_state_dict(__A   ,				strict=__A       )


def    A						(__A   :    Any   ,				__A   :    List[Any]   ,				__A   :    Tuple   ,				__A   :    bool = False       )   ->      Tuple:







     """simple docstring"""


     UpperCAmelCase_          =							TaConfig.from_json_file(__A       )
     print(F"""Building PyTorch model from configuration: {config}"""       )
     # Non-v1.1 checkpoints could also use T5Model, but this works for all.
     # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
     if is_encoder_only:
          UpperCAmelCase_          =							TaEncoderModel(__A       )
     else:
          UpperCAmelCase_          =							TaForConditionalGeneration(__A       )

     # Load weights from tf checkpoint
     load_tax_weights_in_ta(__A   ,				__A   ,				__A   ,				__A       )

     # Save pytorch-model
     print(F"""Save PyTorch model to {pytorch_dump_path}"""       )
     model.save_pretrained(__A       )

     # Verify that we can load the checkpoint.
     model.from_pretrained(__A       )
     print('''Done'''       )


if __name__ == "__main__":
    snake_case_ : int    		=       argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
    # Required parameters
    parser.add_argument(
        "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
    )
    parser.add_argument(
        "--config_file",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
    )
    parser.add_argument(
        "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
    )
    parser.add_argument(
        "--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
    )
    snake_case_ : Tuple    		=       parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )












# 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 __snake_case			(			a ,  a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    List[Any]          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    Union[str, Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''}		)
   UpperCAmelCase__  :    Optional[Any]          =  IMAGE_TO_IMAGE_IMAGE_PARAMS







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							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					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

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







   def lowerCamelCase						(      self		:						Union[str, Any]					,						_snake_case		:						Any					,						_snake_case		:						Dict=0):

        """simple docstring"""


        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_snake_case).manual_seed(_snake_case)

        UpperCAmelCase_          =							2
        UpperCAmelCase_          =							randn_tensor(
            (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)					,						generator=_snake_case					,						device=torch.device(_snake_case)					,						)
        UpperCAmelCase_          =							floats_tensor(control_image.shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        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 lowerCamelCase						(      self		:						Any):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







class __snake_case			(			a ,  a ,  unittest.TestCase		):
   UpperCAmelCase__  :    str          =  StableDiffusionControlNetImgaImgPipeline
   UpperCAmelCase__  :    List[str]          =  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
   UpperCAmelCase__  :    Optional[Any]          =  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
   UpperCAmelCase__  :    str          =  frozenset([]		)  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess







   def lowerCamelCase						(      self		:						str):

        """simple docstring"""


        torch.manual_seed(0)
        UpperCAmelCase_          =							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(_snake_case		:						Optional[int]):
             if isinstance(_snake_case					,						torch.nn.Convad):
                  torch.nn.init.normal(m.weight)
                  m.bias.data.fill_(1.0)

        UpperCAmelCase_          =							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(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							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(_snake_case)

        torch.manual_seed(0)
        UpperCAmelCase_          =							DDIMScheduler(
            beta_start=0.0_0_0_8_5					,						beta_end=0.0_1_2					,						beta_schedule='''scaled_linear'''					,						clip_sample=_snake_case					,						set_alpha_to_one=_snake_case					,						)
        torch.manual_seed(0)
        UpperCAmelCase_          =							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)
        UpperCAmelCase_          =							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					,						)
        UpperCAmelCase_          =							CLIPTextModel(_snake_case)
        UpperCAmelCase_          =							CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''')

        UpperCAmelCase_          =							MultiControlNetModel([controlneta, controlneta])

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







   def lowerCamelCase						(      self		:						int					,						_snake_case		:						Union[str, Any]					,						_snake_case		:						str=0):

        """simple docstring"""


        if str(_snake_case).startswith('''mps'''):
             UpperCAmelCase_          =							torch.manual_seed(_snake_case)
        else:
             UpperCAmelCase_          =							torch.Generator(device=_snake_case).manual_seed(_snake_case)

        UpperCAmelCase_          =							2

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

        UpperCAmelCase_          =							floats_tensor(control_image[0].shape					,						rng=random.Random(_snake_case)).to(_snake_case)
        UpperCAmelCase_          =							image.cpu().permute(0					,						2					,						3					,						1)[0]
        UpperCAmelCase_          =							Image.fromarray(np.uinta(_snake_case)).convert('''RGB''').resize((64, 64))
        UpperCAmelCase_          =							{
            '''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 lowerCamelCase						(      self		:						Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_          =							self.get_dummy_components()
        UpperCAmelCase_          =							self.pipeline_class(**_snake_case)
        pipe.to(_snake_case)

        UpperCAmelCase_          =							1_0.0
        UpperCAmelCase_          =							4

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case)[0]

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

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

        UpperCAmelCase_          =							self.get_dummy_inputs(_snake_case)
        UpperCAmelCase_          =							steps
        UpperCAmelCase_          =							scale
        UpperCAmelCase_          =							pipe(**_snake_case					,						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 lowerCamelCase						(      self		:						Dict):

        """simple docstring"""


        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 lowerCamelCase						(      self		:						int):

        """simple docstring"""


        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						int):

        """simple docstring"""


        self._test_inference_batch_single_identical(expected_max_diff=2e-3)







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


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







   def lowerCamelCase						(      self		:						Optional[int]):

        """simple docstring"""


        UpperCAmelCase_          =							ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''')

        UpperCAmelCase_          =							StableDiffusionControlNetImgaImgPipeline.from_pretrained(
            '''runwayml/stable-diffusion-v1-5'''					,						safety_checker=_snake_case					,						controlnet=_snake_case)
        pipe.enable_model_cpu_offload()
        pipe.set_progress_bar_config(disable=_snake_case)

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

        UpperCAmelCase_          =							pipe(
            _snake_case					,						_snake_case					,						control_image=_snake_case					,						generator=_snake_case					,						output_type='''np'''					,						num_inference_steps=50					,						strength=0.6					,						)

        UpperCAmelCase_          =							output.images[0]

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

        UpperCAmelCase_          =							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






