


from __future__ import annotations

import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available

from ...test_modeling_tf_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_tf_bert import TFBertModelTester
from ..clip.test_modeling_tf_clip import TFCLIPVisionModelTester
from ..deit.test_modeling_tf_deit import TFDeiTModelTester
from ..roberta.test_modeling_tf_roberta import TFRobertaModelTester
from ..vit.test_modeling_tf_vit import TFViTModelTester


if is_tf_available():
		from transformers import (
		    TFBertModel,
		    TFCLIPVisionModel,
		    TFDeiTModel,
		    TFRobertaModel,
		    TFVisionTextDualEncoderModel,
		    TFViTModel,
		    VisionTextDualEncoderConfig,
		)

if is_vision_available():
		from PIL import Image

		from transformers import VisionTextDualEncoderProcessor

def        lowerCAmelCase__ (			a__:		Union[str, Any]       )  ->		Any:


							'''simple docstring'''





							if isinstance(lowerCamelCase_			,  collections.abc.Iterable       ):
														return x
							return (x, x)

@require_tf
class 		__a      :
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) ->       int:




									"""simple docstring"""





									pass
		def 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





									pass
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									pass
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=None			,    **_SCREAMING_SNAKE_CASE						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			VisionTextDualEncoderConfig.from_vision_text_configs(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel(_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(output['text_embeds'].shape			,    (input_ids.shape[0], config.projection_dim)						)
									self.assertEqual(output['image_embeds'].shape			,    (pixel_values.shape[0], config.projection_dim)						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=None			,    **_SCREAMING_SNAKE_CASE						) ->       str:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE			,    text_model=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(output['text_embeds'].shape			,    (input_ids.shape[0], model.config.projection_dim)						)
									self.assertEqual(output['image_embeds'].shape			,    (pixel_values.shape[0], model.config.projection_dim)						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=None			,    **_SCREAMING_SNAKE_CASE						) ->       Tuple:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			{'vision_model': vision_model, 'text_model': text_model}
									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(**_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE						)

									self.assertEqual(output['text_embeds'].shape			,    (input_ids.shape[0], model.config.projection_dim)						)
									self.assertEqual(output['image_embeds'].shape			,    (pixel_values.shape[0], model.config.projection_dim)						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=None			,    **_SCREAMING_SNAKE_CASE						) ->       List[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE			,    text_model=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			output[0].numpy()

									with tempfile.TemporaryDirectory() as tmpdirname:
																model.save_pretrained(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase 		=			TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase 		=			model(input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase 		=			after_output[0].numpy()
																_UpperCAmelCase 		=			np.amax(np.abs(out_a - out_a						)						)
																self.assertLessEqual(_SCREAMING_SNAKE_CASE			,    1e-5						)
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=None			,    **_SCREAMING_SNAKE_CASE						) ->       Dict:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE			,    text_model=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			model(
									    input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    output_attentions=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			output.vision_model_output.attentions
									self.assertEqual(len(_SCREAMING_SNAKE_CASE						)			,    vision_config.num_hidden_layers						)

									# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase 		=			to_atuple(vision_model.config.image_size						)
									_UpperCAmelCase 		=			to_atuple(vision_model.config.patch_size						)
									_UpperCAmelCase 		=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									_UpperCAmelCase 		=			num_patches + 1
									self.assertEqual(vision_attentions[0].shape[-3:]			,    (vision_config.num_attention_heads, seq_len, seq_len)						)

									_UpperCAmelCase 		=			output.text_model_output.attentions
									self.assertEqual(len(_SCREAMING_SNAKE_CASE						)			,    text_config.num_hidden_layers						)

									self.assertEqual(
									    text_attentions[0].shape[-3:]			,    (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,    )
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			np.abs((a - b)						).max()
									self.assertLessEqual(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    f'''Difference between torch and flax is {diff} (>= {tol}).'''						)
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_model(**_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.prepare_config_and_inputs()
									self.check_model_from_pretrained_configs(**_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.prepare_config_and_inputs()
									self.check_vision_text_dual_encoder_from_pretrained(**_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Dict:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.prepare_config_and_inputs()
									self.check_save_load(**_SCREAMING_SNAKE_CASE						)
		def 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.prepare_config_and_inputs()
									self.check_vision_text_output_attention(**_SCREAMING_SNAKE_CASE						)






		@slow
		def 	UpperCAmelCase__				(			self						) ->       int:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.get_pretrained_model_and_inputs()

									_UpperCAmelCase 		=			model_a(**_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			outputs[0].numpy()

									with tempfile.TemporaryDirectory() as tmp_dirname:
																model_a.save_pretrained(_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase 		=			TFVisionTextDualEncoderModel.from_pretrained(_SCREAMING_SNAKE_CASE						)

																_UpperCAmelCase 		=			model_a(**_SCREAMING_SNAKE_CASE						)
																_UpperCAmelCase 		=			after_outputs[0].numpy()
																_UpperCAmelCase 		=			np.amax(np.abs(out_a - out_a						)						)
																self.assertLessEqual(_SCREAMING_SNAKE_CASE			,    1e-5						)



@require_tf
class 		__a      (				_a    ,      unittest.TestCase			):
		def 	UpperCAmelCase__				(			self						) ->       Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    'hf-internal-testing/tiny-random-vit'			,    'hf-internal-testing/tiny-random-bert'						)
									_UpperCAmelCase 		=			13
									_UpperCAmelCase 		=			floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]						)
									_UpperCAmelCase 		=			ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size						)
									_UpperCAmelCase 		=			random_attention_mask([batch_size, 4]						)
									_UpperCAmelCase 		=			{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

									return model, inputs
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) ->       List[str]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFViTModel(_SCREAMING_SNAKE_CASE			,    name='vision_model'						)
									_UpperCAmelCase 		=			TFBertModel(_SCREAMING_SNAKE_CASE			,    name='text_model'						)
									return vision_model, text_model






		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFViTModelTester(self						)
									_UpperCAmelCase 		=			TFBertModelTester(self						)
									_UpperCAmelCase 		=			vit_model_tester.prepare_config_and_inputs()
									_UpperCAmelCase 		=			bert_model_tester.prepare_config_and_inputs()

									_UpperCAmelCase 		=			vision_config_and_inputs

									(
									    _UpperCAmelCase				
									) 		=			text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}



@require_tf
class 		__a      (				_a    ,      unittest.TestCase			):
		def 	UpperCAmelCase__				(			self						) ->       str:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    'Rocketknight1/tiny-random-deit-tf'			,    'hf-internal-testing/tiny-random-roberta'						)
									_UpperCAmelCase 		=			13
									_UpperCAmelCase 		=			floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]						)
									_UpperCAmelCase 		=			ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size						)
									_UpperCAmelCase 		=			random_attention_mask([batch_size, 4]						)
									_UpperCAmelCase 		=			{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

									return model, inputs
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE=None			,    **_SCREAMING_SNAKE_CASE						) ->       List[str]:




									"""simple docstring"""





									_UpperCAmelCase 		=			self.get_vision_text_model(_SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel(vision_model=_SCREAMING_SNAKE_CASE			,    text_model=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			model(
									    input_ids=_SCREAMING_SNAKE_CASE			,    pixel_values=_SCREAMING_SNAKE_CASE			,    attention_mask=_SCREAMING_SNAKE_CASE			,    output_attentions=_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase 		=			output.vision_model_output.attentions
									self.assertEqual(len(_SCREAMING_SNAKE_CASE						)			,    vision_config.num_hidden_layers						)

									# in DEiT, the seq_len equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
									_UpperCAmelCase 		=			to_atuple(vision_model.config.image_size						)
									_UpperCAmelCase 		=			to_atuple(vision_model.config.patch_size						)
									_UpperCAmelCase 		=			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									_UpperCAmelCase 		=			num_patches + 2
									self.assertEqual(vision_attentions[0].shape[-3:]			,    (vision_config.num_attention_heads, seq_len, seq_len)						)

									_UpperCAmelCase 		=			output.text_model_output.attentions
									self.assertEqual(len(_SCREAMING_SNAKE_CASE						)			,    text_config.num_hidden_layers						)

									self.assertEqual(
									    text_attentions[0].shape[-3:]			,    (text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])			,    )
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) ->       Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFDeiTModel(_SCREAMING_SNAKE_CASE			,    name='vision_model'						)
									_UpperCAmelCase 		=			TFRobertaModel(_SCREAMING_SNAKE_CASE			,    name='text_model'						)
									return vision_model, text_model






		def 	UpperCAmelCase__				(			self						) ->       Any:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFDeiTModelTester(self						)
									_UpperCAmelCase 		=			TFRobertaModelTester(self						)
									_UpperCAmelCase 		=			vit_model_tester.prepare_config_and_inputs()
									_UpperCAmelCase 		=			bert_model_tester.prepare_config_and_inputs()

									_UpperCAmelCase 		=			vision_config_and_inputs

									(
									    _UpperCAmelCase				
									) 		=			text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}



@require_tf
class 		__a      (				_a    ,      unittest.TestCase			):
		def 	UpperCAmelCase__				(			self						) ->       Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel.from_vision_text_pretrained(
									    'Rocketknight1/tiny-random-clip-tf'			,    'hf-internal-testing/tiny-random-bert'						)
									_UpperCAmelCase 		=			13
									_UpperCAmelCase 		=			floats_tensor(
									    [
									        batch_size,
									        model.vision_model.config.num_channels,
									        model.vision_model.config.image_size,
									        model.vision_model.config.image_size,
									    ]						)
									_UpperCAmelCase 		=			ids_tensor([batch_size, 4]			,    model.text_model.config.vocab_size						)
									_UpperCAmelCase 		=			random_attention_mask([batch_size, 4]						)
									_UpperCAmelCase 		=			{'pixel_values': pixel_values, 'input_ids': input_ids, 'attention_mask': attention_mask}

									return model, inputs
		def 	UpperCAmelCase__				(			self			,    _SCREAMING_SNAKE_CASE			,    _SCREAMING_SNAKE_CASE						) ->       Union[str, Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFCLIPVisionModel(_SCREAMING_SNAKE_CASE			,    name='vision_model'						)
									_UpperCAmelCase 		=			TFBertModel(_SCREAMING_SNAKE_CASE			,    name='text_model'						)
									return vision_model, text_model






		def 	UpperCAmelCase__				(			self						) ->       Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFCLIPVisionModelTester(self						)
									_UpperCAmelCase 		=			TFBertModelTester(self						)
									_UpperCAmelCase 		=			clip_model_tester.prepare_config_and_inputs()
									_UpperCAmelCase 		=			bert_model_tester.prepare_config_and_inputs()

									_UpperCAmelCase 		=			vision_config_and_inputs

									(
									    _UpperCAmelCase				
									) 		=			text_config_and_inputs

									return {
									    "text_config": text_config,
									    "vision_config": vision_config,
									    "pixel_values": pixel_values,
									    "attention_mask": input_mask,
									    "input_ids": input_ids,
									    "text_token_type_ids": token_type_ids,
									    "text_sequence_labels": sequence_labels,
									    "text_token_labels": token_labels,
									    "text_choice_labels": choice_labels,
									}



@require_vision
@require_tf
class 		__a      (				unittest.TestCase			):
		@slow
		def 	UpperCAmelCase__				(			self						) ->       List[Any]:




									"""simple docstring"""





									_UpperCAmelCase 		=			TFVisionTextDualEncoderModel.from_pretrained(
									    'clip-italian/clip-italian'			,    logit_scale_init_value=1.0			,    from_pt=_SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase 		=			VisionTextDualEncoderProcessor.from_pretrained('clip-italian/clip-italian'						)

									_UpperCAmelCase 		=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'						)
									_UpperCAmelCase 		=			processor(
									    text=['una foto di un gatto', 'una foto di un cane']			,    images=_SCREAMING_SNAKE_CASE			,    padding=_SCREAMING_SNAKE_CASE			,    return_tensors='np'						)

									_UpperCAmelCase 		=			model(**_SCREAMING_SNAKE_CASE						)

									# verify the logits
									self.assertEqual(outputs.logits_per_image.shape			,    (inputs.pixel_values.shape[0], inputs.input_ids.shape[0])						)
									self.assertEqual(
									    outputs.logits_per_text.shape			,    (inputs.input_ids.shape[0], inputs.pixel_values.shape[0])			,    )

									_UpperCAmelCase 		=			np.array([[1.2284727, 0.3104122]]						)

									self.assertTrue(np.allclose(outputs.logits_per_image.numpy()			,    _SCREAMING_SNAKE_CASE			,    atol=1e-3						)						)




import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizer

from diffusers import (
    AltDiffusionImgaImgPipeline,
    AutoencoderKL,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()






class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           super().tearDown()
           gc.collect()
           torch.cuda.empty_cache()
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		1
           _lowercase       :							Any      		=		3
           _lowercase       :							Tuple      		=		(32, 32)

           _lowercase       :							Tuple      		=		floats_tensor((batch_size, num_channels) + sizes,      rng=random.Random(0)).to(lowerCamelCase)
           return image
       @property
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Dict      		=		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,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							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,      )
           return model
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

           torch.manual_seed(0)
           _lowercase       :							Optional[int]      		=		RobertaSeriesConfig(
               hidden_size=32,      project_dim=32,      intermediate_size=37,      layer_norm_eps=1E-05,      num_attention_heads=4,      num_hidden_layers=5,      pad_token_id=1,      vocab_size=50_06,      )
           return RobertaSeriesModelWithTransformation(lowerCamelCase)
       @property
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           def extract(*lowerCamelCase,      **lowerCamelCase):






               class _lowerCamelCase:
                      def __init__(					self)       ->   Optional[Any]:




                          """simple docstring"""

                          _lowercase       :							Optional[int]      		=		torch.ones([0])




                      def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   int:




                          """simple docstring"""

                          self.pixel_values.to(lowerCamelCase)
                          return self


               return Out()

           return extract
       def 			UpperCamelCase     (					self)       ->   Tuple:




           """simple docstring"""

           _lowercase       :							Any      		=		'cpu'  # ensure determinism for the device-dependent torch.Generator
           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Union[str, Any]      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							Optional[Any]      		=		self.dummy_vae
           _lowercase       :							List[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Any      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Tuple      		=		77

           _lowercase       :							int      		=		self.dummy_image.to(lowerCamelCase)
           _lowercase       :							int      		=		init_image / 2 + 0.5

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[Any]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Optional[int]      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							Optional[Any]      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Dict      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Any      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      )

           _lowercase       :							Optional[int]      		=		output.images

           _lowercase       :							Optional[Any]      		=		torch.Generator(device=lowerCamelCase).manual_seed(0)
           _lowercase       :							Optional[Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      guidance_scale=6.0,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      return_dict=lowerCamelCase,      )[0]

           _lowercase       :							Optional[int]      		=		image[0, -3:, -3:, -1]
           _lowercase       :							Dict      		=		image_from_tuple[0, -3:, -3:, -1]

           assert image.shape == (1, 32, 32, 3)
           _lowercase       :							int      		=		np.array([0.4_4_2_7, 0.3_7_3_1, 0.4_2_4_9, 0.4_9_4_1, 0.4_5_4_6, 0.4_1_4_8, 0.4_1_9_3, 0.4_6_6_6, 0.4_4_9_9])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 5E-3
           assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 5E-3
       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   str:




           """simple docstring"""

           _lowercase       :							List[Any]      		=		self.dummy_cond_unet
           _lowercase       :							Tuple      		=		PNDMScheduler(skip_prk_steps=lowerCamelCase)
           _lowercase       :							str      		=		self.dummy_vae
           _lowercase       :							Optional[Any]      		=		self.dummy_text_encoder
           _lowercase       :							Optional[Any]      		=		XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
           _lowercase       :							Optional[Any]      		=		77

           _lowercase       :							str      		=		self.dummy_image.to(lowerCamelCase)

           # put models in fp16
           _lowercase       :							List[str]      		=		unet.half()
           _lowercase       :							List[Any]      		=		vae.half()
           _lowercase       :							Any      		=		bert.half()

           # make sure here that pndm scheduler skips prk
           _lowercase       :							Union[str, Any]      		=		AltDiffusionImgaImgPipeline(
               unet=lowerCamelCase,      scheduler=lowerCamelCase,      vae=lowerCamelCase,      text_encoder=lowerCamelCase,      tokenizer=lowerCamelCase,      safety_checker=lowerCamelCase,      feature_extractor=self.dummy_extractor,      )
           _lowercase       :							List[str]      		=		VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor,      do_normalize=lowerCamelCase)
           _lowercase       :							Any      		=		alt_pipe.to(lowerCamelCase)
           alt_pipe.set_progress_bar_config(disable=lowerCamelCase)

           _lowercase       :							int      		=		'A painting of a squirrel eating a burger'
           _lowercase       :							Optional[Any]      		=		torch.manual_seed(0)
           _lowercase       :							Union[str, Any]      		=		alt_pipe(
               [prompt],      generator=lowerCamelCase,      num_inference_steps=2,      output_type='np',      image=lowerCamelCase,      ).images

           assert image.shape == (1, 32, 32, 3)




       @unittest.skipIf(torch_device != 'cuda',      'This test requires a GPU')
       def 			UpperCamelCase     (					self)       ->   Optional[int]:




           """simple docstring"""

           _lowercase       :							int      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           # resize to resolution that is divisible by 8 but not 16 or 32
           _lowercase       :							str      		=		init_image.resize((7_60, 5_04))

           _lowercase       :							Optional[int]      		=		'BAAI/AltDiffusion'
           _lowercase       :							str      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							List[str]      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							Any      		=		torch.manual_seed(0)
           _lowercase       :							Dict      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							List[str]      		=		output.images[0]

           _lowercase       :							Tuple      		=		image[2_55:2_58, 3_83:3_86, -1]

           assert image.shape == (5_04, 7_60, 3)
           _lowercase       :							Optional[Any]      		=		np.array([0.9_3_5_8, 0.9_3_9_7, 0.9_5_9_9, 0.9_9_0_1, 1.0_0_0_0, 1.0_0_0_0, 0.9_8_8_2, 1.0_0_0_0, 1.0_0_0_0])

           assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2






@slow
@require_torch_gpu
class _lowerCamelCase(						unittest.TestCase   ):
       def 			UpperCamelCase     (					self)       ->   Optional[Any]:




           """simple docstring"""

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




       def 			UpperCamelCase     (					self)       ->   List[Any]:




           """simple docstring"""

           _lowercase       :							Union[str, Any]      		=		load_image(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
               '/img2img/sketch-mountains-input.jpg')
           _lowercase       :							str      		=		init_image.resize((7_68, 5_12))
           _lowercase       :							Any      		=		load_numpy(
               'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy')

           _lowercase       :							str      		=		'BAAI/AltDiffusion'
           _lowercase       :							Optional[Any]      		=		AltDiffusionImgaImgPipeline.from_pretrained(
               lowerCamelCase,      safety_checker=lowerCamelCase,      )
           pipe.to(lowerCamelCase)
           pipe.set_progress_bar_config(disable=lowerCamelCase)
           pipe.enable_attention_slicing()

           _lowercase       :							int      		=		'A fantasy landscape, trending on artstation'

           _lowercase       :							List[Any]      		=		torch.manual_seed(0)
           _lowercase       :							int      		=		pipe(
               prompt=lowerCamelCase,      image=lowerCamelCase,      strength=0.7_5,      guidance_scale=7.5,      generator=lowerCamelCase,      output_type='np',      )
           _lowercase       :							Union[str, Any]      		=		output.images[0]

           assert image.shape == (5_12, 7_68, 3)
           # img2img is flaky across GPUs even in fp32, so using MAE here
           assert np.abs(expected_image - image).max() < 1E-2


