


'''simple docstring'''
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        lowercase     (							A__     ,		A__     ,		A__     ,		unittest.TestCase ):
		_a   			=    StableDiffusionControlNetImgaImgPipeline
		_a   			=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
		_a   			=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		_a   			=    IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} )
		_a   			=    IMAGE_TO_IMAGE_IMAGE_PARAMS




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__       :List[Any]							 =			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__       :Optional[int]							 =			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__       :Optional[int]							 =			DDIMScheduler(
							    beta_start=0.00085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=UpperCamelCase_   ,       set_alpha_to_one=UpperCamelCase_   ,       )
							torch.manual_seed(0							)
							UpperCamelCase__       :Optional[int]							 =			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__       :Any							 =			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__       :Tuple							 =			CLIPTextModel(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__       :Union[str, Any]							 =			{
							    '''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   ,       UpperCamelCase_   ,       UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__       :Tuple							 =			torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__       :Dict							 =			torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)

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

							'''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							):

							'''simple docstring'''


							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self._test_inference_batch_single_identical(expected_max_diff=2e-3							)



class        lowercase     (							A__     ,		A__     ,		unittest.TestCase ):
		_a   			=    StableDiffusionControlNetImgaImgPipeline
		_a   			=    TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
		_a   			=    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
		_a   			=    frozenset([] )  # TO_DO: add image_params once refactored VaeImageProcessor.preprocess




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__       :List[Any]							 =			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(UpperCamelCase_							):
												if isinstance(UpperCamelCase_   ,       torch.nn.Convad							):
																	torch.nn.init.normal(m.weight							)
																	m.bias.data.fill_(1.0							)

							UpperCamelCase__       :Any							 =			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(UpperCamelCase_							)

							torch.manual_seed(0							)
							UpperCamelCase__       :Union[str, Any]							 =			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(UpperCamelCase_							)

							torch.manual_seed(0							)
							UpperCamelCase__       :Optional[Any]							 =			DDIMScheduler(
							    beta_start=0.00085   ,       beta_end=0.012   ,       beta_schedule='''scaled_linear'''   ,       clip_sample=UpperCamelCase_   ,       set_alpha_to_one=UpperCamelCase_   ,       )
							torch.manual_seed(0							)
							UpperCamelCase__       :List[str]							 =			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__       :Optional[Any]							 =			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__       :str							 =			CLIPTextModel(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__       :Dict							 =			MultiControlNetModel([controlneta, controlneta]							)

							UpperCamelCase__       :Optional[Any]							 =			{
							    '''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   ,       UpperCamelCase_   ,       UpperCamelCase_=0							):

							'''simple docstring'''


							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__       :List[Any]							 =			torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__       :Optional[Any]							 =			torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)

							UpperCamelCase__       :str							 =			2

							UpperCamelCase__       :Optional[Any]							 =			[
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,       generator=UpperCamelCase_   ,       device=torch.device(UpperCamelCase_							)   ,       ),
							    randn_tensor(
							        (1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor)   ,       generator=UpperCamelCase_   ,       device=torch.device(UpperCamelCase_							)   ,       ),
							]

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

							'''simple docstring'''


							UpperCamelCase__       :Union[str, Any]							 =			self.get_dummy_components()
							UpperCamelCase__       :List[str]							 =			self.pipeline_class(**UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)

							UpperCamelCase__       :List[str]							 =			10.0
							UpperCamelCase__       :Optional[int]							 =			4

							UpperCamelCase__       :Dict							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Optional[int]							 =			steps
							UpperCamelCase__       :Optional[Any]							 =			scale
							UpperCamelCase__       :Any							 =			pipe(**UpperCamelCase_							)[0]

							UpperCamelCase__       :Optional[Any]							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Tuple							 =			steps
							UpperCamelCase__       :Any							 =			scale
							UpperCamelCase__       :str							 =			pipe(**UpperCamelCase_   ,       control_guidance_start=0.1   ,       control_guidance_end=0.2							)[0]

							UpperCamelCase__       :Tuple							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :List[str]							 =			steps
							UpperCamelCase__       :str							 =			scale
							UpperCamelCase__       :int							 =			pipe(**UpperCamelCase_   ,       control_guidance_start=[0.1, 0.3]   ,       control_guidance_end=[0.2, 0.7]							)[0]

							UpperCamelCase__       :int							 =			self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__       :Union[str, Any]							 =			steps
							UpperCamelCase__       :Optional[int]							 =			scale
							UpperCamelCase__       :Optional[Any]							 =			pipe(**UpperCamelCase_   ,       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							):

							'''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							):

							'''simple docstring'''


							self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							self._test_inference_batch_single_identical(expected_max_diff=2e-3							)




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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



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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


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




		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							UpperCamelCase__       :Any							 =			ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny'''							)

							UpperCamelCase__       :Optional[int]							 =			StableDiffusionControlNetImgaImgPipeline.from_pretrained(
							    '''runwayml/stable-diffusion-v1-5'''   ,       safety_checker=UpperCamelCase_   ,       controlnet=UpperCamelCase_							)
							pipe.enable_model_cpu_offload()
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

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

							UpperCamelCase__       :str							 =			pipe(
							    UpperCamelCase_   ,       UpperCamelCase_   ,       control_image=UpperCamelCase_   ,       generator=UpperCamelCase_   ,       output_type='''np'''   ,       num_inference_steps=50   ,       strength=0.6   ,       )

							UpperCamelCase__       :Optional[int]							 =			output.images[0]

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

							UpperCamelCase__       :Union[str, Any]							 =			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



'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__snake_case											=					logging.get_logger(__name__)

__snake_case											=					{
    '''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/config.json''',
    '''distilbert-base-uncased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/config.json''',
    '''distilbert-base-cased-distilled-squad''': (
        '''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'''
    ),
    '''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json''',
    '''distilbert-base-multilingual-cased''': (
        '''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'''
    ),
    '''distilbert-base-uncased-finetuned-sst-2-english''': (
        '''https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'''
    ),
}
class        lowercase     (							A__ ):

		"""simple docstring"""

		_a   			=    'distilbert'
		_a   			=    {
		    'hidden_size': 'dim',
		    'num_attention_heads': 'n_heads',
		    'num_hidden_layers': 'n_layers',
		}




		def __init__(		self   ,       UpperCamelCase_=30522   ,       UpperCamelCase_=512   ,       UpperCamelCase_=False   ,       UpperCamelCase_=6   ,       UpperCamelCase_=12   ,       UpperCamelCase_=768   ,       UpperCamelCase_=4 * 768   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_="gelu"   ,       UpperCamelCase_=0.02   ,       UpperCamelCase_=0.1   ,       UpperCamelCase_=0.2   ,       UpperCamelCase_=0   ,       **UpperCamelCase_   ,       ):

							'''simple docstring'''


							UpperCamelCase__       :List[Any]							 =			vocab_size
							UpperCamelCase__       :Dict							 =			max_position_embeddings
							UpperCamelCase__       :str							 =			sinusoidal_pos_embds
							UpperCamelCase__       :Any							 =			n_layers
							UpperCamelCase__       :str							 =			n_heads
							UpperCamelCase__       :Tuple							 =			dim
							UpperCamelCase__       :str							 =			hidden_dim
							UpperCamelCase__       :Dict							 =			dropout
							UpperCamelCase__       :int							 =			attention_dropout
							UpperCamelCase__       :Optional[Any]							 =			activation
							UpperCamelCase__       :Optional[int]							 =			initializer_range
							UpperCamelCase__       :Union[str, Any]							 =			qa_dropout
							UpperCamelCase__       :Dict							 =			seq_classif_dropout
							super().__init__(**UpperCamelCase_   ,       pad_token_id=UpperCamelCase_							)



class        lowercase     (							A__ ):

		"""simple docstring"""

		@property
		def    lowerCAmelCase__    (		self							):

							'''simple docstring'''


							if self.task == "multiple-choice":
												UpperCamelCase__       :str							 =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												UpperCamelCase__       :str							 =			{0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							    ]							)