




'''simple docstring'''





import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
		from transformers.models.roberta.modeling_flax_roberta import (
		    FlaxRobertaForCausalLM,
		    FlaxRobertaForMaskedLM,
		    FlaxRobertaForMultipleChoice,
		    FlaxRobertaForQuestionAnswering,
		    FlaxRobertaForSequenceClassification,
		    FlaxRobertaForTokenClassification,
		    FlaxRobertaModel,
		)




class 			_snake_case		(     unittest.TestCase							):




			def __init__( self    ,							a__    ,							a__=13    ,							a__=7    ,							a__=True    ,							a__=True    ,							a__=True    ,							a__=True    ,							a__=99    ,							a__=32    ,							a__=5    ,							a__=4    ,							a__=37    ,							a__="gelu"    ,							a__=0.1    ,							a__=0.1    ,							a__=512    ,							a__=16    ,							a__=2    ,							a__=0.0_2    ,							a__=4    ,							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				parent
										snake_case_      =				batch_size
										snake_case_      =				seq_length
										snake_case_      =				is_training
										snake_case_      =				use_attention_mask
										snake_case_      =				use_token_type_ids
										snake_case_      =				use_labels
										snake_case_      =				vocab_size
										snake_case_      =				hidden_size
										snake_case_      =				num_hidden_layers
										snake_case_      =				num_attention_heads
										snake_case_      =				intermediate_size
										snake_case_      =				hidden_act
										snake_case_      =				hidden_dropout_prob
										snake_case_      =				attention_probs_dropout_prob
										snake_case_      =				max_position_embeddings
										snake_case_      =				type_vocab_size
										snake_case_      =				type_sequence_label_size
										snake_case_      =				initializer_range
										snake_case_      =				num_choices




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size							)

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

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

										snake_case_      =				RobertaConfig(
										    vocab_size=self.vocab_size    ,							hidden_size=self.hidden_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    ,							is_decoder=a__    ,							initializer_range=self.initializer_range    ,							)

										return config, input_ids, token_type_ids, attention_mask




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

										'''simple docstring'''




										snake_case_      =				self.prepare_config_and_inputs()
										snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				config_and_inputs
										snake_case_      =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict




			def        lowerCAmelCase__    ( self							)    ->    Optional[Any]:

										'''simple docstring'''




										snake_case_      =				self.prepare_config_and_inputs()
										snake_case_ ,				snake_case_ ,				snake_case_ ,				snake_case_      =				config_and_inputs

										snake_case_      =				True
										snake_case_      =				floats_tensor([self.batch_size, self.seq_length, self.hidden_size]							)
										snake_case_      =				ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2							)

										return (
										    config,
										    input_ids,
										    token_type_ids,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)




@require_flax
class 			_snake_case		(     lowercase_						,						unittest.TestCase							):
			lowerCAmelCase_					:				List[Any]							=   True

			lowerCAmelCase_					:				List[Any]							=   (
			    (
			        FlaxRobertaModel,
			        FlaxRobertaForCausalLM,
			        FlaxRobertaForMaskedLM,
			        FlaxRobertaForSequenceClassification,
			        FlaxRobertaForTokenClassification,
			        FlaxRobertaForMultipleChoice,
			        FlaxRobertaForQuestionAnswering,
			    )
			    if is_flax_available()
			    else ()
			)




			def        lowerCAmelCase__    ( self							)    ->    str:

										'''simple docstring'''




										snake_case_      =				FlaxRobertaModelTester(self							)




			@slow
			def        lowerCAmelCase__    ( self							)    ->    Dict:

										'''simple docstring'''




										for model_class_name in self.all_model_classes:
																	snake_case_      =				model_class_name.from_pretrained("roberta-base"    ,							from_pt=a__							)
																	snake_case_      =				model(np.ones((1, 1)							)							)
																	self.assertIsNotNone(a__							)










'''simple docstring'''





import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device


_SCREAMING_SNAKE_CASE							:       Any						=			False




class 			_snake_case		(     unittest.TestCase							):
			pass




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




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

										'''simple docstring'''




										snake_case_      =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"							)
										pipe.to(a__							)
										pipe.set_progress_bar_config(disable=a__							)

										snake_case_      =				load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"							)
										snake_case_      =				torch.manual_seed(0							)
										snake_case_      =				pipe(
										    image=a__    ,							generator=a__    ,							guidance_scale=7.5    ,							num_inference_steps=50    ,							output_type="numpy"    ,							).images

										snake_case_      =				image[0, 253:256, 253:256, -1]

										assert image.shape == (1, 512, 512, 3)
										snake_case_      =				np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5]							)

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




