
import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()
class 			lowercase__(    lowerCAmelCase__			,						unittest.TestCase							):



 """simple docstring"""




 a      :str   					=     RePaintPipeline
 a      :List[str]   					=     IMAGE_INPAINTING_PARAMS - {'width', 'height', 'guidance_scale'}
 a      :List[Any]   					=     PipelineTesterMixin.required_optional_params - {
     'latents',
     'num_images_per_prompt',
     'callback',
     'callback_steps',
 }
 a      :Tuple   					=     IMAGE_INPAINTING_BATCH_PARAMS
 a      :List[Any]   					=     False
 def 						_lowercase  (	self  :      List[Any]	)	->					Any:
     torch.manual_seed(0	)
     torch.manual_seed(0	)
     lowercase_					  = UNetaDModel(
         block_out_channels=(3_2, 6_4)	,							layers_per_block=2	,							sample_size=3_2	,							in_channels=3	,							out_channels=3	,							down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,							up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,							)
     lowercase_					  = RePaintScheduler()
     lowercase_					  = {'''unet''': unet, '''scheduler''': scheduler}
     return components
 def 						_lowercase  (	self  :      int	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      str=0	)	->					List[str]:




     if str(_A	).startswith('''mps'''	):
         lowercase_					  = torch.manual_seed(_A	)
     else:
         lowercase_					  = torch.Generator(device=_A	).manual_seed(_A	)
     lowercase_					  = np.random.RandomState(_A	).standard_normal((1, 3, 3_2, 3_2)	)
     lowercase_					  = torch.from_numpy(_A	).to(device=_A	,							dtype=torch.floataa	)
     lowercase_					  = (image > 0).to(device=_A	,							dtype=torch.floataa	)
     lowercase_					  = {
         '''image''': image,
         '''mask_image''': mask,
         '''generator''': generator,
         '''num_inference_steps''': 5,
         '''eta''': 0.0,
         '''jump_length''': 2,
         '''jump_n_sample''': 2,
         '''output_type''': '''numpy''',
     }
     return inputs
 def 						_lowercase  (	self  :      int	)	->					int:
     lowercase_					  = '''cpu'''  # ensure determinism for the device-dependent torch.Generator
     lowercase_					  = self.get_dummy_components()
     lowercase_					  = RePaintPipeline(**_A	)
     lowercase_					  = sd_pipe.to(_A	)
     sd_pipe.set_progress_bar_config(disable=_A	)

     lowercase_					  = self.get_dummy_inputs(_A	)
     lowercase_					  = sd_pipe(**_A	).images
     lowercase_					  = image[0, -3:, -3:, -1]

     assert image.shape == (1, 3_2, 3_2, 3)
     lowercase_					  = np.array([1.00_00, 0.54_26, 0.54_97, 0.22_00, 1.00_00, 1.00_00, 0.56_23, 1.00_00, 0.62_74]	)

     assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3
 @skip_mps
 def 						_lowercase  (	self  :      Union[str, Any]	)	->					Tuple:
     return super().test_save_load_local()
 @unittest.skip('''non-deterministic pipeline'''	)
 def 						_lowercase  (	self  :      Optional[Any]	)	->					Tuple:
     return super().test_inference_batch_single_identical()
 @skip_mps
 def 						_lowercase  (	self  :      Union[str, Any]	)	->					List[str]:
     return super().test_dict_tuple_outputs_equivalent()
 @skip_mps
 def 						_lowercase  (	self  :      List[str]	)	->					Union[str, Any]:
     return super().test_save_load_optional_components()




 @skip_mps
 def 						_lowercase  (	self  :      Optional[Any]	)	->					Any:
     return super().test_attention_slicing_forward_pass()

@nightly
@require_torch_gpu
class 			lowercase__(    unittest.TestCase							):



 """simple docstring"""




 def 						_lowercase  (	self  :      List[Any]	)	->					Optional[Any]:
     super().tearDown()
     gc.collect()
     torch.cuda.empty_cache()




 def 						_lowercase  (	self  :      str	)	->					Union[str, Any]:
     lowercase_					  = load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
         '''repaint/celeba_hq_256.png'''	)
     lowercase_					  = load_image(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'''	)
     lowercase_					  = load_numpy(
         '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'''
         '''repaint/celeba_hq_256_result.npy'''	)

     lowercase_					  = '''google/ddpm-ema-celebahq-256'''
     lowercase_					  = UNetaDModel.from_pretrained(_A	)
     lowercase_					  = RePaintScheduler.from_pretrained(_A	)

     lowercase_					  = RePaintPipeline(unet=_A	,							scheduler=_A	).to(_A	)
     repaint.set_progress_bar_config(disable=_A	)
     repaint.enable_attention_slicing()

     lowercase_					  = torch.manual_seed(0	)
     lowercase_					  = repaint(
         _A	,							_A	,							num_inference_steps=2_5_0	,							eta=0.0	,							jump_length=1_0	,							jump_n_sample=1_0	,							generator=_A	,							output_type='''np'''	,							)
     lowercase_					  = output.images[0]

     assert image.shape == (2_5_6, 2_5_6, 3)
     assert np.abs(expected_image - image	).mean() < 1e-2






import unittest

import numpy as np

from transformers import ElectraConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.electra.modeling_flax_electra import (
						    FlaxElectraForCausalLM,
						    FlaxElectraForMaskedLM,
						    FlaxElectraForMultipleChoice,
						    FlaxElectraForPreTraining,
						    FlaxElectraForQuestionAnswering,
						    FlaxElectraForSequenceClassification,
						    FlaxElectraForTokenClassification,
						    FlaxElectraModel,
						)
class 			lowercase__(    unittest.TestCase							):



	"""simple docstring"""




	def __init__(	self  :      str	,							SCREAMING_SNAKE_CASE_  :      str	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_3	,							SCREAMING_SNAKE_CASE_  :      int=7	,							SCREAMING_SNAKE_CASE_  :      Tuple=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      List[Any]=True	,							SCREAMING_SNAKE_CASE_  :      Dict=True	,							SCREAMING_SNAKE_CASE_  :      Optional[Any]=9_9	,							SCREAMING_SNAKE_CASE_  :      Any=2_4	,							SCREAMING_SNAKE_CASE_  :      int=3_2	,							SCREAMING_SNAKE_CASE_  :      Any=5	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=3_7	,							SCREAMING_SNAKE_CASE_  :      Optional[int]="gelu"	,							SCREAMING_SNAKE_CASE_  :      Any=0.1	,							SCREAMING_SNAKE_CASE_  :      List[Any]=0.1	,							SCREAMING_SNAKE_CASE_  :      Dict=5_1_2	,							SCREAMING_SNAKE_CASE_  :      Optional[int]=1_6	,							SCREAMING_SNAKE_CASE_  :      List[Any]=2	,							SCREAMING_SNAKE_CASE_  :      List[str]=0.02	,							SCREAMING_SNAKE_CASE_  :      List[str]=4	,							)	->					Optional[int]:
					lowercase_					  = parent
					lowercase_					  = batch_size
					lowercase_					  = seq_length
					lowercase_					  = is_training
					lowercase_					  = use_attention_mask
					lowercase_					  = use_token_type_ids
					lowercase_					  = use_labels
					lowercase_					  = vocab_size
					lowercase_					  = hidden_size
					lowercase_					  = embedding_size
					lowercase_					  = num_hidden_layers
					lowercase_					  = num_attention_heads
					lowercase_					  = intermediate_size
					lowercase_					  = hidden_act
					lowercase_					  = hidden_dropout_prob
					lowercase_					  = attention_probs_dropout_prob
					lowercase_					  = max_position_embeddings
					lowercase_					  = type_vocab_size
					lowercase_					  = type_sequence_label_size
					lowercase_					  = initializer_range
					lowercase_					  = num_choices
	def 						_lowercase  (	self  :      Optional[int]	)	->					Tuple:
					lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.vocab_size	)

					lowercase_					  = None
					if self.use_attention_mask:
									lowercase_					  = random_attention_mask([self.batch_size, self.seq_length]	)

					lowercase_					  = None
					if self.use_token_type_ids:
									lowercase_					  = ids_tensor([self.batch_size, self.seq_length]	,							self.type_vocab_size	)

					lowercase_					  = ElectraConfig(
					    vocab_size=self.vocab_size	,							hidden_size=self.hidden_size	,							embedding_size=self.embedding_size	,							num_hidden_layers=self.num_hidden_layers	,							num_attention_heads=self.num_attention_heads	,							intermediate_size=self.intermediate_size	,							hidden_act=self.hidden_act	,							hidden_dropout_prob=self.hidden_dropout_prob	,							attention_probs_dropout_prob=self.attention_probs_dropout_prob	,							max_position_embeddings=self.max_position_embeddings	,							type_vocab_size=self.type_vocab_size	,							initializer_range=self.initializer_range	,							)

					return config, input_ids, token_type_ids, attention_mask




	def 						_lowercase  (	self  :      Any	)	->					str:
					lowercase_					  = self.prepare_config_and_inputs()
					lowercase_  ,     lowercase_  ,     lowercase_  ,     lowercase_					  = config_and_inputs
					lowercase_					  = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
					return config, inputs_dict

@require_flax
class 			lowercase__(    UpperCAmelCase			,						unittest.TestCase							):



	"""simple docstring"""




	a      :List[str]   					=     True

	a      :List[Any]   					=     (
	    (
	        FlaxElectraModel,
	        FlaxElectraForCausalLM,
	        FlaxElectraForMaskedLM,
	        FlaxElectraForPreTraining,
	        FlaxElectraForTokenClassification,
	        FlaxElectraForQuestionAnswering,
	        FlaxElectraForMultipleChoice,
	        FlaxElectraForSequenceClassification,
	    )
	    if is_flax_available()
	    else ()
	)
	def 						_lowercase  (	self  :      List[str]	)	->					Optional[Any]:
					lowercase_					  = FlaxElectraModelTester(self	)




	@slow
	def 						_lowercase  (	self  :      List[str]	)	->					int:
					for model_class_name in self.all_model_classes:
									if model_class_name == FlaxElectraForMaskedLM:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-generator'''	)
									else:
													lowercase_					  = model_class_name.from_pretrained('''google/electra-small-discriminator'''	)
									lowercase_					  = model(np.ones((1, 1)	)	)
									self.assertIsNotNone(SCREAMING_SNAKE_CASE_	)




