


"""simple docstring"""







import unittest

import numpy as np

from transformers import RoFormerConfig, 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():
		import jax.numpy as jnp

		from transformers.models.roformer.modeling_flax_roformer import (
		    FlaxRoFormerForMaskedLM,
		    FlaxRoFormerForMultipleChoice,
		    FlaxRoFormerForQuestionAnswering,
		    FlaxRoFormerForSequenceClassification,
		    FlaxRoFormerForTokenClassification,
		    FlaxRoFormerModel,
		)




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





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




	def   UpperCAmelCase_      ( self			:				Tuple   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size   )

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

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

						__SCREAMING_SNAKE_CASE						   =				RoFormerConfig(
						    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=UpperCAmelCase__			,			initializer_range=self.initializer_range			,			)

						return config, input_ids, token_type_ids, attention_mask




	def   UpperCAmelCase_      ( self			:				str   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				self.prepare_config_and_inputs()
						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				config_and_inputs
						__SCREAMING_SNAKE_CASE						   =				{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
						return config, inputs_dict




@require_flax
class  UpperCamelCase_	( UpperCamelCase			,  unittest.TestCase):

	"""simple docstring"""





	snake_case__				:  Union[str, Any]    					=			True

	snake_case__				:  Optional[int]    					=			(
	    (
	        FlaxRoFormerModel,
	        FlaxRoFormerForMaskedLM,
	        FlaxRoFormerForSequenceClassification,
	        FlaxRoFormerForTokenClassification,
	        FlaxRoFormerForMultipleChoice,
	        FlaxRoFormerForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def   UpperCAmelCase_      ( self			:				str   )       ->	Tuple:
						__SCREAMING_SNAKE_CASE						   =				FlaxRoFormerModelTester(self   )




	@slow
	def   UpperCAmelCase_      ( self			:				int   )       ->	Any:
						for model_class_name in self.all_model_classes:
											__SCREAMING_SNAKE_CASE						   =				model_class_name.from_pretrained("junnyu/roformer_chinese_small"			,			from_pt=UpperCAmelCase__   )
											__SCREAMING_SNAKE_CASE						   =				model(np.ones((1, 1)   )   )
											self.assertIsNotNone(UpperCAmelCase__   )




@require_flax
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	@slow
	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	Optional[Any]:
						__SCREAMING_SNAKE_CASE						   =				FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base"   )
						__SCREAMING_SNAKE_CASE						   =				jnp.array([[0, 1, 2, 3, 4, 5]]   )
						__SCREAMING_SNAKE_CASE						   =				model(UpperCAmelCase__   )[0]

						__SCREAMING_SNAKE_CASE						   =				5_0_0_0_0

						__SCREAMING_SNAKE_CASE						   =				(1, 6, vocab_size)
						self.assertEqual(output.shape			,			UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				jnp.array(
						    [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]   )

						self.assertTrue(jnp.allclose(output[:, :3, :3]			,			UpperCAmelCase__			,			atol=1E-4   )   )




"""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


a__				:							Tuple						=     False




class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	pass




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

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				int   )       ->	int:
						__SCREAMING_SNAKE_CASE						   =				VersatileDiffusionImageVariationPipeline.from_pretrained("shi-labs/versatile-diffusion"   )
						pipe.to(UpperCAmelCase__   )
						pipe.set_progress_bar_config(disable=UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE						   =				load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"   )
						__SCREAMING_SNAKE_CASE						   =				torch.manual_seed(0   )
						__SCREAMING_SNAKE_CASE						   =				pipe(
						    image=UpperCAmelCase__			,			generator=UpperCAmelCase__			,			guidance_scale=7.5			,			num_inference_steps=5_0			,			output_type="numpy"			,			).images

						__SCREAMING_SNAKE_CASE						   =				image[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

						assert image.shape == (1, 5_1_2, 5_1_2, 3)
						__SCREAMING_SNAKE_CASE						   =				np.array([0.0_441, 0.0_469, 0.0_507, 0.0_575, 0.0_632, 0.0_650, 0.0_865, 0.0_909, 0.0_945]   )

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