




from __future__ import annotations

import unittest

from transformers import XGLMConfig, XGLMTokenizer, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers.models.xglm.modeling_tf_xglm import (
     TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
     TFXGLMForCausalLM,
     TFXGLMModel,
 )



@require_tf
class       a					:

     '''simple docstring'''





     lowerCAmelCase    :	Union[str, Any]       		=  XGLMConfig
     lowerCAmelCase    :	Union[str, Any]       		=  {}
     lowerCAmelCase    :	Union[str, Any]       		=  'gelu'
     def __init__(     self       :      List[Any] ,   __snake_case       :      List[str] ,   __snake_case       :      List[Any]=14 ,   __snake_case       :      List[str]=7 ,   __snake_case       :      Optional[int]=True ,   __snake_case       :      Dict=True ,   __snake_case       :      List[str]=True ,   __snake_case       :      Dict=99 ,   __snake_case       :      str=32 ,   __snake_case       :      List[str]=2 ,   __snake_case       :      Union[str, Any]=4 ,   __snake_case       :      Optional[Any]=37 ,   __snake_case       :      Optional[int]="gelu" ,   __snake_case       :      List[str]=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Union[str, Any]=5_12 ,   __snake_case       :      str=0.02 ,   ):
         UpperCAmelCase_										=					parent
         UpperCAmelCase_										=					batch_size
         UpperCAmelCase_										=					seq_length
         UpperCAmelCase_										=					is_training
         UpperCAmelCase_										=					use_input_mask
         UpperCAmelCase_										=					use_labels
         UpperCAmelCase_										=					vocab_size
         UpperCAmelCase_										=					d_model
         UpperCAmelCase_										=					num_hidden_layers
         UpperCAmelCase_										=					num_attention_heads
         UpperCAmelCase_										=					ffn_dim
         UpperCAmelCase_										=					activation_function
         UpperCAmelCase_										=					activation_dropout
         UpperCAmelCase_										=					attention_dropout
         UpperCAmelCase_										=					max_position_embeddings
         UpperCAmelCase_										=					initializer_range
         UpperCAmelCase_										=					None
         UpperCAmelCase_										=					0
         UpperCAmelCase_										=					2
         UpperCAmelCase_										=					1
     def   lowerCamelCase_       (     self       :      Dict    ):
         return XGLMConfig.from_pretrained('''facebook/xglm-564M'''    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					tf.clip_by_value(
             ids_tensor([self.batch_size, self.seq_length] ,   self.vocab_size    ) ,   clip_value_min=0 ,   clip_value_max=3    )

         UpperCAmelCase_										=					None
         if self.use_input_mask:
             UpperCAmelCase_										=					random_attention_mask([self.batch_size, self.seq_length]    )

         UpperCAmelCase_										=					self.get_config()

         UpperCAmelCase_										=					floats_tensor([self.num_hidden_layers, self.num_attention_heads] ,   2    )

         return (
             config,
             input_ids,
             input_mask,
             head_mask,
         )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         return XGLMConfig(
             vocab_size=self.vocab_size ,   d_model=self.hidden_size ,   num_layers=self.num_hidden_layers ,   attention_heads=self.num_attention_heads ,   ffn_dim=self.ffn_dim ,   activation_function=self.activation_function ,   activation_dropout=self.activation_dropout ,   attention_dropout=self.attention_dropout ,   max_position_embeddings=self.max_position_embeddings ,   initializer_range=self.initializer_range ,   use_cache=__snake_case ,   bos_token_id=self.bos_token_id ,   eos_token_id=self.eos_token_id ,   pad_token_id=self.pad_token_id ,   return_dict=__snake_case ,   )




     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.prepare_config_and_inputs()

         (
             (
             UpperCAmelCase_
         )			,							(
             UpperCAmelCase_
         )			,							(
             UpperCAmelCase_
         )			,							(
             UpperCAmelCase_
         )			,							
         )										=					config_and_inputs

         UpperCAmelCase_										=					{
             '''input_ids''': input_ids,
             '''head_mask''': head_mask,
         }

         return config, inputs_dict



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

     '''simple docstring'''





     lowerCAmelCase    :	Tuple       		=  (TFXGLMModel, TFXGLMForCausalLM) if is_tf_available() else ()
     lowerCAmelCase    :	Dict       		=  (TFXGLMForCausalLM,) if is_tf_available() else ()
     lowerCAmelCase    :	List[str]       		=  (
         {'feature-extraction': TFXGLMModel, 'text-generation': TFXGLMForCausalLM} if is_tf_available() else {}
     )
     lowerCAmelCase    :	Tuple       		=  False
     lowerCAmelCase    :	int       		=  False
     lowerCAmelCase    :	Dict       		=  False
     def   lowerCamelCase_       (     self       :      Optional[Any]    ):
         UpperCAmelCase_										=					TFXGLMModelTester(self    )
         UpperCAmelCase_										=					ConfigTester(self ,   config_class=__snake_case ,   n_embd=37    )
     def   lowerCamelCase_       (     self       :      int    ):
         self.config_tester.run_common_tests()
     @slow
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         for model_name in TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             UpperCAmelCase_										=					TFXGLMModel.from_pretrained(__snake_case    )
             self.assertIsNotNone(__snake_case    )




     @unittest.skip(reason='''Currently, model embeddings are going to undergo a major refactor.'''    )
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         super().test_resize_token_embeddings()



@require_tf
class       a					(       unittest.TestCase      ):

     '''simple docstring'''





     @slow
     def   lowerCamelCase_       (     self       :      Tuple ,   __snake_case       :      int=True    ):
         UpperCAmelCase_										=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )
         UpperCAmelCase_										=					tf.convert_to_tensor([[2, 2_68, 98_65]] ,   dtype=tf.intaa    )  # The dog
         # </s> The dog is a very friendly dog. He is very affectionate and loves to play with other
         # fmt: off
         UpperCAmelCase_										=					[2, 2_68, 98_65, 67, 11, 19_88, 5_72_52, 98_65, 5, 9_84, 67, 19_88, 21_38_38, 16_58, 53, 7_04_46, 33, 66_57, 2_78, 15_81]
         # fmt: on
         UpperCAmelCase_										=					model.generate(__snake_case ,   do_sample=__snake_case ,   num_beams=1    )
         if verify_outputs:
             self.assertListEqual(output_ids[0].numpy().tolist() ,   __snake_case    )
     @slow
     def   lowerCamelCase_       (     self       :      Any    ):
         UpperCAmelCase_										=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''    )
         UpperCAmelCase_										=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )

         tf.random.set_seed(0    )
         UpperCAmelCase_										=					tokenizer('''Today is a nice day and''' ,   return_tensors='''tf'''    )
         UpperCAmelCase_										=					tokenized.input_ids
         # forces the generation to happen on CPU, to avoid GPU-related quirks (and assure same output regardless of the available devices)
         with tf.device(''':/CPU:0'''    ):
             UpperCAmelCase_										=					model.generate(__snake_case ,   do_sample=__snake_case ,   seed=[7, 0]    )
         UpperCAmelCase_										=					tokenizer.decode(output_ids[0] ,   skip_special_tokens=__snake_case    )

         UpperCAmelCase_										=					(
             '''Today is a nice day and warm evening here over Southern Alberta!! Today when they closed schools due'''
         )
         self.assertEqual(__snake_case ,   __snake_case    )




     @slow
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					TFXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''    )
         UpperCAmelCase_										=					XGLMTokenizer.from_pretrained('''facebook/xglm-564M'''    )

         UpperCAmelCase_										=					'''left'''

         # use different length sentences to test batching
         UpperCAmelCase_										=					[
             '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
             '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
             '''regardless of whether left padding is applied or not. When''',
             '''Hello, my dog is a little''',
         ]

         UpperCAmelCase_										=					tokenizer(__snake_case ,   return_tensors='''tf''' ,   padding=__snake_case    )
         UpperCAmelCase_										=					inputs['''input_ids''']

         UpperCAmelCase_										=					model.generate(input_ids=__snake_case ,   attention_mask=inputs['''attention_mask'''] ,   max_new_tokens=12    )

         UpperCAmelCase_										=					tokenizer(sentences[0] ,   return_tensors='''tf'''    ).input_ids
         UpperCAmelCase_										=					model.generate(input_ids=__snake_case ,   max_new_tokens=12    )

         UpperCAmelCase_										=					tokenizer(sentences[1] ,   return_tensors='''tf'''    ).input_ids
         UpperCAmelCase_										=					model.generate(input_ids=__snake_case ,   max_new_tokens=12    )

         UpperCAmelCase_										=					tokenizer.batch_decode(__snake_case ,   skip_special_tokens=__snake_case    )
         UpperCAmelCase_										=					tokenizer.decode(output_non_padded[0] ,   skip_special_tokens=__snake_case    )
         UpperCAmelCase_										=					tokenizer.decode(output_padded[0] ,   skip_special_tokens=__snake_case    )

         UpperCAmelCase_										=					[
             '''This is an extremelly long sentence that only exists to test the ability of the model to cope with '''
             '''left-padding, such as in batched generation. The output for the sequence below should be the same '''
             '''regardless of whether left padding is applied or not. When left padding is applied, the sequence will be '''
             '''a single''',
             '''Hello, my dog is a little bit of a shy one, but he is very friendly''',
         ]
         self.assertListEqual(__snake_case ,   __snake_case    )
         self.assertListEqual(__snake_case ,   [non_padded_sentence, padded_sentence]    )











# 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





