


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

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


if is_tf_available():
     import tensorflow as tf

     from transformers import (
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
         TFDebertaVaModel,
     )







class 		_snake_case  :




 def __init__(		self ,  _lowerCamelCase ,  _lowerCamelCase=13 ,  _lowerCamelCase=7 ,  _lowerCamelCase=True ,  _lowerCamelCase=True ,  _lowerCamelCase=True ,  _lowerCamelCase=True ,  _lowerCamelCase=99 ,  _lowerCamelCase=32 ,  _lowerCamelCase=2 ,  _lowerCamelCase=4 ,  _lowerCamelCase=37 ,  _lowerCamelCase="gelu" ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=0.1 ,  _lowerCamelCase=512 ,  _lowerCamelCase=16 ,  _lowerCamelCase=2 ,  _lowerCamelCase=0.02 ,  _lowerCamelCase=False ,  _lowerCamelCase=True ,  _lowerCamelCase="None" ,  _lowerCamelCase=3 ,  _lowerCamelCase=4 ,  _lowerCamelCase=None ,  ):
       a						:Any										=	parent
       a						:Optional[int]										=	batch_size
       a						:Dict										=	seq_length
       a						:List[Any]										=	is_training
       a						:str										=	use_input_mask
       a						:Union[str, Any]										=	use_token_type_ids
       a						:Optional[Any]										=	use_labels
       a						:List[str]										=	vocab_size
       a						:Any										=	hidden_size
       a						:Tuple										=	num_hidden_layers
       a						:Union[str, Any]										=	num_attention_heads
       a						:Union[str, Any]										=	intermediate_size
       a						:str										=	hidden_act
       a						:str										=	hidden_dropout_prob
       a						:List[Any]										=	attention_probs_dropout_prob
       a						:Union[str, Any]										=	max_position_embeddings
       a						:Optional[int]										=	type_vocab_size
       a						:Optional[int]										=	type_sequence_label_size
       a						:Dict										=	initializer_range
       a						:int										=	num_labels
       a						:Dict										=	num_choices
       a						:Dict										=	relative_attention
       a						:Optional[int]										=	position_biased_input
       a						:str										=	pos_att_type
       a						:Union[str, Any]										=	scope




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Dict										=	ids_tensor([self.batch_size, self.seq_length] ,  self.vocab_size   )

       a						:Union[str, Any]										=	None
       if self.use_input_mask:
             a						:List[str]										=	random_attention_mask([self.batch_size, self.seq_length]   )

       a						:List[Any]										=	None
       if self.use_token_type_ids:
             a						:Optional[int]										=	ids_tensor([self.batch_size, self.seq_length] ,  self.type_vocab_size   )

       a						:Optional[Any]										=	None
       a						:Tuple										=	None
       a						:Dict										=	None
       if self.use_labels:
             a						:Union[str, Any]										=	ids_tensor([self.batch_size] ,  self.type_sequence_label_size   )
             a						:List[str]										=	ids_tensor([self.batch_size, self.seq_length] ,  self.num_labels   )

       a						:Union[str, Any]										=	DebertaVaConfig(
           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 ,  relative_attention=self.relative_attention ,  position_biased_input=self.position_biased_input ,  initializer_range=self.initializer_range ,  return_dict=_lowerCamelCase ,  )

       return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:str										=	TFDebertaVaModel(config=_lowerCamelCase   )
       a						:int										=	{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}

       a						:Optional[int]										=	[input_ids, input_mask]
       a						:Any										=	model(_lowerCamelCase   )

       a						:Optional[Any]										=	model(_lowerCamelCase   )

       self.parent.assertEqual(result.last_hidden_state.shape ,  (self.batch_size, self.seq_length, self.hidden_size)   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Dict										=	TFDebertaVaForMaskedLM(config=_lowerCamelCase   )
       a						:List[str]										=	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       a						:Optional[Any]										=	model(_lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.vocab_size)   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:Any										=	self.num_labels
       a						:Any										=	TFDebertaVaForSequenceClassification(config=_lowerCamelCase   )
       a						:Dict										=	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       a						:Any										=	model(_lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.num_labels)   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:str										=	self.num_labels
       a						:Union[str, Any]										=	TFDebertaVaForTokenClassification(config=_lowerCamelCase   )
       a						:Tuple										=	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }
       a						:Optional[Any]										=	model(_lowerCamelCase   )
       self.parent.assertEqual(result.logits.shape ,  (self.batch_size, self.seq_length, self.num_labels)   )




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase ,  _lowerCamelCase   ):
       a						:List[Any]										=	TFDebertaVaForQuestionAnswering(config=_lowerCamelCase   )
       a						:Dict										=	{
           '''input_ids''': input_ids,
           '''attention_mask''': input_mask,
           '''token_type_ids''': token_type_ids,
       }

       a						:Optional[Any]										=	model(_lowerCamelCase   )
       self.parent.assertEqual(result.start_logits.shape ,  (self.batch_size, self.seq_length)   )
       self.parent.assertEqual(result.end_logits.shape ,  (self.batch_size, self.seq_length)   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.prepare_config_and_inputs()
       (
           (
           a
       )							,    (
           a
       )							,    (
           a
       )							,    (
           a
       )							,    (
           a
       )							,    (
           a
       )							,    (
           a
       )							,    
       )						:Optional[int]										=	config_and_inputs
       a						:Dict										=	{'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
       return config, inputs_dict







@require_tf
class 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	(
     (
         TFDebertaVaModel,
         TFDebertaVaForMaskedLM,
         TFDebertaVaForQuestionAnswering,
         TFDebertaVaForSequenceClassification,
         TFDebertaVaForTokenClassification,
     )
     if is_tf_available()
     else ()
 )
 SCREAMING_SNAKE_CASE__				=	(
     {
         'feature-extraction': TFDebertaVaModel,
         'fill-mask': TFDebertaVaForMaskedLM,
         'question-answering': TFDebertaVaForQuestionAnswering,
         'text-classification': TFDebertaVaForSequenceClassification,
         'token-classification': TFDebertaVaForTokenClassification,
         'zero-shot': TFDebertaVaForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )

 SCREAMING_SNAKE_CASE__				=	False
 SCREAMING_SNAKE_CASE__				=	False




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Tuple										=	TFDebertaVaModelTester(self   )
       a						:int										=	ConfigTester(self ,  config_class=_lowerCamelCase ,  hidden_size=37   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       self.config_tester.run_common_tests()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Dict										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_masked_lm(*_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*_lowerCamelCase   )




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''   )
       self.assertIsNotNone(_lowerCamelCase   )







@require_tf
class 		_snake_case  (				unittest.TestCase							):




 @unittest.skip(reason='''Model not available yet'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       pass




 @slow
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:List[str]										=	TFDebertaVaModel.from_pretrained('''kamalkraj/deberta-v2-xlarge'''   )
       a						:int										=	tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]   )
       a						:str										=	tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]   )
       a						:Any										=	model(_lowerCamelCase ,  attention_mask=_lowerCamelCase   )[0]

       a						:Optional[int]										=	tf.constant(
           [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]   )
       tf.debugging.assert_near(output[:, 1:4, 1:4] ,  _lowerCamelCase ,  atol=1e-4   )









import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
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, skip_mps

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 PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class 		_snake_case  (				_snake_case						,   _snake_case						,   unittest.TestCase							):
 SCREAMING_SNAKE_CASE__				=	CycleDiffusionPipeline
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
     'negative_prompt',
     'height',
     'width',
     'negative_prompt_embeds',
 }
 SCREAMING_SNAKE_CASE__				=	PipelineTesterMixin.required_optional_params - {'latents'}
 SCREAMING_SNAKE_CASE__				=	TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'source_prompt'}							)
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS
 SCREAMING_SNAKE_CASE__				=	IMAGE_TO_IMAGE_IMAGE_PARAMS




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.manual_seed(0   )
       a						:Optional[Any]										=	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 ,  )
       a						:List[str]										=	DDIMScheduler(
           beta_start=0.0_0085 ,  beta_end=0.012 ,  beta_schedule='''scaled_linear''' ,  num_train_timesteps=1000 ,  clip_sample=_lowerCamelCase ,  set_alpha_to_one=_lowerCamelCase ,  )
       torch.manual_seed(0   )
       a						:List[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 ,  )
       torch.manual_seed(0   )
       a						:Dict										=	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=1000 ,  )
       a						:str										=	CLIPTextModel(_lowerCamelCase   )
       a						:List[str]										=	CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''   )

       a						:Union[str, Any]										=	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''vae''': vae,
           '''text_encoder''': text_encoder,
           '''tokenizer''': tokenizer,
           '''safety_checker''': None,
           '''feature_extractor''': None,
       }
       return components




 def 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       a						:Tuple										=	floats_tensor((1, 3, 32, 32) ,  rng=random.Random(_lowerCamelCase   )   ).to(_lowerCamelCase   )
       a						:Tuple										=	image / 2 + 0.5
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:List[str]										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Any										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:int										=	{
           '''prompt''': '''An astronaut riding an elephant''',
           '''source_prompt''': '''An astronaut riding a horse''',
           '''image''': image,
           '''generator''': generator,
           '''num_inference_steps''': 2,
           '''eta''': 0.1,
           '''strength''': 0.8,
           '''guidance_scale''': 3,
           '''source_guidance_scale''': 1,
           '''output_type''': '''numpy''',
       }
       return inputs




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Union[str, Any]										=	'''cpu'''  # ensure determinism for the device-dependent torch.Generator

       a						:Optional[Any]										=	self.get_dummy_components()
       a						:Dict										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Optional[Any]										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:List[str]										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Any										=	pipe(**_lowerCamelCase   )
       a						:List[Any]										=	output.images

       a						:str										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:List[Any]										=	np.array([0.4459, 0.4943, 0.4544, 0.6643, 0.5474, 0.4327, 0.5701, 0.5959, 0.5179]   )

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




 @unittest.skipIf(torch_device != '''cuda''' ,  '''This test requires a GPU'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Optional[int]										=	self.get_dummy_components()
       for name, module in components.items():
             if hasattr(_lowerCamelCase ,  '''half'''   ):
                   a						:Union[str, Any]										=	module.half()
       a						:List[Any]										=	CycleDiffusionPipeline(**_lowerCamelCase   )
       a						:Dict										=	pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )

       a						:Tuple										=	self.get_dummy_inputs(_lowerCamelCase   )
       a						:Optional[int]										=	pipe(**_lowerCamelCase   )
       a						:Optional[Any]										=	output.images

       a						:List[Any]										=	images[0, -3:, -3:, -1]

       assert images.shape == (1, 32, 32, 3)
       a						:str										=	np.array([0.3506, 0.4543, 0.446, 0.4575, 0.5195, 0.4155, 0.5273, 0.518, 0.4116]   )

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




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_local()




 @unittest.skip('''non-deterministic pipeline'''   )
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_inference_batch_single_identical()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_dict_tuple_outputs_equivalent()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_save_load_optional_components()




 @skip_mps
 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       return super().test_attention_slicing_forward_pass()







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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'''   )
       a						:Optional[Any]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:List[str]										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:Tuple										=	CycleDiffusionPipeline.from_pretrained(
           _lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase ,  torch_dtype=torch.floataa ,  revision='''fp16'''   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[Any]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:str										=	torch.manual_seed(0   )
       a						:List[Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:int										=	output.images

       # the values aren't exactly equal, but the images look the same visually
       assert np.abs(image - expected_image   ).max() < 5e-1




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:str										=	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/cycle-diffusion/black_colored_car.png'''   )
       a						:Optional[int]										=	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'''   )
       a						:List[str]										=	init_image.resize((512, 512)   )

       a						:List[str]										=	'''CompVis/stable-diffusion-v1-4'''
       a						:Any										=	DDIMScheduler.from_pretrained(_lowerCamelCase ,  subfolder='''scheduler'''   )
       a						:int										=	CycleDiffusionPipeline.from_pretrained(_lowerCamelCase ,  scheduler=_lowerCamelCase ,  safety_checker=_lowerCamelCase   )

       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       pipe.enable_attention_slicing()

       a						:Optional[int]										=	'''A black colored car'''
       a						:Any										=	'''A blue colored car'''

       a						:Optional[int]										=	torch.manual_seed(0   )
       a						:Union[str, Any]										=	pipe(
           prompt=_lowerCamelCase ,  source_prompt=_lowerCamelCase ,  image=_lowerCamelCase ,  num_inference_steps=100 ,  eta=0.1 ,  strength=0.85 ,  guidance_scale=3 ,  source_guidance_scale=1 ,  generator=_lowerCamelCase ,  output_type='''np''' ,  )
       a						:Optional[int]										=	output.images

       assert np.abs(image - expected_image   ).max() < 2e-2





