




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowerCamelCase      			=						{
    'configuration_electra': ['ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ElectraConfig', 'ElectraOnnxConfig'],
    'tokenization_electra': ['ElectraTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      			=						['ElectraTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      			=						[
	    'ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'ElectraForCausalLM',
	    'ElectraForMaskedLM',
	    'ElectraForMultipleChoice',
	    'ElectraForPreTraining',
	    'ElectraForQuestionAnswering',
	    'ElectraForSequenceClassification',
	    'ElectraForTokenClassification',
	    'ElectraModel',
	    'ElectraPreTrainedModel',
	    'load_tf_weights_in_electra',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      			=						[
	    'TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFElectraForMaskedLM',
	    'TFElectraForMultipleChoice',
	    'TFElectraForPreTraining',
	    'TFElectraForQuestionAnswering',
	    'TFElectraForSequenceClassification',
	    'TFElectraForTokenClassification',
	    'TFElectraModel',
	    'TFElectraPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowerCamelCase      			=						[
	    'FlaxElectraForCausalLM',
	    'FlaxElectraForMaskedLM',
	    'FlaxElectraForMultipleChoice',
	    'FlaxElectraForPreTraining',
	    'FlaxElectraForQuestionAnswering',
	    'FlaxElectraForSequenceClassification',
	    'FlaxElectraForTokenClassification',
	    'FlaxElectraModel',
	    'FlaxElectraPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_electra import ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP, ElectraConfig, ElectraOnnxConfig
	from .tokenization_electra import ElectraTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_electra_fast import ElectraTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_electra import (
		    ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ElectraForCausalLM,
		    ElectraForMaskedLM,
		    ElectraForMultipleChoice,
		    ElectraForPreTraining,
		    ElectraForQuestionAnswering,
		    ElectraForSequenceClassification,
		    ElectraForTokenClassification,
		    ElectraModel,
		    ElectraPreTrainedModel,
		    load_tf_weights_in_electra,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_electra import (
		    TF_ELECTRA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFElectraForMaskedLM,
		    TFElectraForMultipleChoice,
		    TFElectraForPreTraining,
		    TFElectraForQuestionAnswering,
		    TFElectraForSequenceClassification,
		    TFElectraForTokenClassification,
		    TFElectraModel,
		    TFElectraPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_electra import (
		    FlaxElectraForCausalLM,
		    FlaxElectraForMaskedLM,
		    FlaxElectraForMultipleChoice,
		    FlaxElectraForPreTraining,
		    FlaxElectraForQuestionAnswering,
		    FlaxElectraForSequenceClassification,
		    FlaxElectraForTokenClassification,
		    FlaxElectraModel,
		    FlaxElectraPreTrainedModel,
		)

else:
	import sys

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











# 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       a					(       _A     ,       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	List[Any]       		=  StableDiffusionControlNetImgaImgPipeline
     lowerCAmelCase    :	List[str]       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     lowerCAmelCase    :	Union[str, Any]       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase    :	Optional[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'}      )
     lowerCAmelCase    :	List[Any]       		=  IMAGE_TO_IMAGE_IMAGE_PARAMS
     def   lowerCamelCase_       (     self       :      Dict    ):
         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=0.00_085 ,   beta_end=0.012 ,   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=10_00 ,   )
         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       :      Optional[int] ,   __snake_case       :      Any=0    ):
         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       :      Union[str, Any]    ):
         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       :      Optional[Any]    ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )




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



class       a					(       _A     ,       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Dict       		=  StableDiffusionControlNetImgaImgPipeline
     lowerCAmelCase    :	List[Any]       		=  TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
     lowerCAmelCase    :	Any       		=  TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
     lowerCAmelCase    :	Optional[int]       		=  frozenset([]      )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         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       :      Tuple    ):
             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=0.00_085 ,   beta_end=0.012 ,   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=10_00 ,   )
         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       :      Optional[Any] ,   __snake_case       :      Any ,   __snake_case       :      Optional[Any]=0    ):
         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       :      List[Any]    ):
         UpperCAmelCase_										=					self.get_dummy_components()
         UpperCAmelCase_										=					self.pipeline_class(**__snake_case    )
         pipe.to(__snake_case    )

         UpperCAmelCase_										=					10.0
         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       :      Optional[int]    ):
         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       :      List[Any]    ):
         self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3    )
     def   lowerCamelCase_       (     self       :      List[Any]    ):
         self._test_inference_batch_single_identical(expected_max_diff=2E-3    )




     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         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       a					(       unittest.TestCase      ):

     '''simple docstring'''





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




     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         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((5_12, 5_12)    )
         UpperCAmelCase_										=					load_image(
             '''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png'''    ).resize((5_12, 5_12)    )

         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 == (5_12, 5_12, 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





