

'''simple docstring'''



import inspect
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch
import torch.utils.checkpoint

from ...models import UNetaDModel, VQModel
from ...schedulers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
)
from ...utils import PIL_INTERPOLATION, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       Union[str, Any]       ):
					__a      :	Dict    =  image.size
					__a      :	Optional[int]    =  (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
					__a      :	str    =  image.resize((w, h)						,				resample=PIL_INTERPOLATION['lanczos']       )
					__a      :	Optional[int]    =  np.array(_SCREAMING_SNAKE_CASE       ).astype(np.floataa       ) / 255.0
					__a      :	str    =  image[None].transpose(0						,				3						,				1						,				2       )
					__a      :	str    =  torch.from_numpy(_SCREAMING_SNAKE_CASE       )
					return 2.0 * image - 1.0





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a						,    __a						,    __a						,    ):

						'''simple docstring'''





						super().__init__()
						self.register_modules(vqvae=__a						,    unet=__a						,    scheduler=__a							)





	@torch.no_grad()
	def __call__(       self						,    __a = None						,    __a = 1						,    __a = 100						,    __a = 0.0						,    __a = None						,    __a = "pil"						,    __a = True						,    ):

						'''simple docstring'''





						if isinstance(__a						,    PIL.Image.Image							):
											__a      :	Union[str, Any]    =  1
						elif isinstance(__a						,    torch.Tensor							):
											__a      :	Any    =  image.shape[0]
						else:
											raise ValueError(f"""`image` has to be of type `PIL.Image.Image` or `torch.Tensor` but is {type(__a							)}"""							)

						if isinstance(__a						,    PIL.Image.Image							):
											__a      :	List[str]    =  preprocess(__a							)

						__a      :	List[Any]    =  image.shape[-2:]

						# in_channels should be 6: 3 for latents, 3 for low resolution image
						__a      :	str    =  (batch_size, self.unet.config.in_channels // 2, height, width)
						__a      :	Tuple    =  next(self.unet.parameters()							).dtype

						__a      :	Tuple    =  randn_tensor(__a						,    generator=__a						,    device=self.device						,    dtype=__a							)

						__a      :	Union[str, Any]    =  image.to(device=self.device						,    dtype=__a							)

						# set timesteps and move to the correct device
						self.scheduler.set_timesteps(__a						,    device=self.device							)
						__a      :	Dict    =  self.scheduler.timesteps

						# scale the initial noise by the standard deviation required by the scheduler
						__a      :	Union[str, Any]    =  latents * self.scheduler.init_noise_sigma

						# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature.
						# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
						# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
						# and should be between [0, 1]
						__a      :	str    =  'eta' in set(inspect.signature(self.scheduler.step							).parameters.keys()							)
						__a      :	str    =  {}
						if accepts_eta:
											__a      :	Optional[Any]    =  eta

						for t in self.progress_bar(__a							):
											# concat latents and low resolution image in the channel dimension.
											__a      :	Optional[Any]    =  torch.cat([latents, image]						,    dim=1							)
											__a      :	Any    =  self.scheduler.scale_model_input(__a						,    __a							)
											# predict the noise residual
											__a      :	int    =  self.unet(__a						,    __a							).sample
											# compute the previous noisy sample x_t -> x_t-1
											__a      :	Optional[int]    =  self.scheduler.step(__a						,    __a						,    __a						,    **__a							).prev_sample

						# decode the image latents with the VQVAE
						__a      :	Union[str, Any]    =  self.vqvae.decode(__a							).sample
						__a      :	List[Any]    =  torch.clamp(__a						,    -1.0						,    1.0							)
						__a      :	Dict    =  image / 2 + 0.5
						__a      :	Tuple    =  image.cpu().permute(0						,    2						,    3						,    1							).numpy()

						if output_type == "pil":
											__a      :	Union[str, Any]    =  self.numpy_to_pil(__a							)

						if not return_dict:
											return (image,)

						return ImagePipelineOutput(images=__a							)







'''simple docstring'''



import unittest

from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers import (
	    DebertaVaForMaskedLM,
	    DebertaVaForMultipleChoice,
	    DebertaVaForQuestionAnswering,
	    DebertaVaForSequenceClassification,
	    DebertaVaForTokenClassification,
	    DebertaVaModel,
	)
	from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST





class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	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=0.02						,    __a=False						,    __a=True						,    __a="None"						,    __a=3						,    __a=4						,    __a=None						,    ):

						'''simple docstring'''





						__a      :	int    =  parent
						__a      :	Union[str, Any]    =  batch_size
						__a      :	Optional[int]    =  seq_length
						__a      :	List[str]    =  is_training
						__a      :	Any    =  use_input_mask
						__a      :	Optional[int]    =  use_token_type_ids
						__a      :	Any    =  use_labels
						__a      :	List[str]    =  vocab_size
						__a      :	str    =  hidden_size
						__a      :	List[str]    =  num_hidden_layers
						__a      :	str    =  num_attention_heads
						__a      :	Optional[int]    =  intermediate_size
						__a      :	Tuple    =  hidden_act
						__a      :	Union[str, Any]    =  hidden_dropout_prob
						__a      :	Dict    =  attention_probs_dropout_prob
						__a      :	Optional[int]    =  max_position_embeddings
						__a      :	Dict    =  type_vocab_size
						__a      :	Any    =  type_sequence_label_size
						__a      :	Dict    =  initializer_range
						__a      :	Optional[Any]    =  num_labels
						__a      :	Optional[Any]    =  num_choices
						__a      :	Union[str, Any]    =  relative_attention
						__a      :	List[str]    =  position_biased_input
						__a      :	List[Any]    =  pos_att_type
						__a      :	Tuple    =  scope




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size							)

						__a      :	List[Any]    =  None
						if self.use_input_mask:
											__a      :	Any    =  ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2							)

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

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

						__a      :	Optional[int]    =  self.get_config()

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




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						return 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						,    initializer_range=self.initializer_range						,    relative_attention=self.relative_attention						,    position_biased_input=self.position_biased_input						,    pos_att_type=self.pos_att_type						,    )




	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						self.parent.assertListEqual(list(result.loss.size()							)						,    []							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	Dict    =  DebertaVaModel(config=__a							)
						model.to(__a							)
						model.eval()
						__a      :	Optional[int]    =  model(__a						,    attention_mask=__a						,    token_type_ids=__a							)[0]
						__a      :	str    =  model(__a						,    token_type_ids=__a							)[0]
						__a      :	Optional[int]    =  model(__a							)[0]

						self.parent.assertListEqual(list(sequence_output.size()							)						,    [self.batch_size, self.seq_length, self.hidden_size]							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	int    =  DebertaVaForMaskedLM(config=__a							)
						model.to(__a							)
						model.eval()
						__a      :	List[Any]    =  model(__a						,    attention_mask=__a						,    token_type_ids=__a						,    labels=__a							)

						self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.vocab_size)							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.num_labels
						__a      :	List[Any]    =  DebertaVaForSequenceClassification(__a							)
						model.to(__a							)
						model.eval()
						__a      :	Any    =  model(__a						,    attention_mask=__a						,    token_type_ids=__a						,    labels=__a							)
						self.parent.assertListEqual(list(result.logits.size()							)						,    [self.batch_size, self.num_labels]							)
						self.check_loss_output(__a							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	Any    =  self.num_labels
						__a      :	Dict    =  DebertaVaForTokenClassification(config=__a							)
						model.to(__a							)
						model.eval()
						__a      :	str    =  model(__a						,    attention_mask=__a						,    token_type_ids=__a						,    labels=__a							)
						self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.seq_length, self.num_labels)							)




	def 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	List[str]    =  DebertaVaForQuestionAnswering(config=__a							)
						model.to(__a							)
						model.eval()
						__a      :	str    =  model(
						    __a						,    attention_mask=__a						,    token_type_ids=__a						,    start_positions=__a						,    end_positions=__a						,    )
						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 				__UpperCAmelCase							(       self						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a						,    __a							):

						'''simple docstring'''





						__a      :	Optional[int]    =  DebertaVaForMultipleChoice(config=__a							)
						model.to(__a							)
						model.eval()
						__a      :	Any    =  input_ids.unsqueeze(1							).expand(-1						,    self.num_choices						,    -1							).contiguous()
						__a      :	Optional[Any]    =  token_type_ids.unsqueeze(1							).expand(-1						,    self.num_choices						,    -1							).contiguous()
						__a      :	Optional[int]    =  input_mask.unsqueeze(1							).expand(-1						,    self.num_choices						,    -1							).contiguous()
						__a      :	int    =  model(
						    __a						,    attention_mask=__a						,    token_type_ids=__a						,    labels=__a						,    )
						self.parent.assertEqual(result.logits.shape						,    (self.batch_size, self.num_choices)							)





	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





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





@require_torch
class 							__UpperCamelCase    (		lowerCAmelCase_		,				lowerCAmelCase_		,				unittest.TestCase ):
	A_           =   (
	    (
	        DebertaVaModel,
	        DebertaVaForMaskedLM,
	        DebertaVaForSequenceClassification,
	        DebertaVaForTokenClassification,
	        DebertaVaForQuestionAnswering,
	        DebertaVaForMultipleChoice,
	    )
	    if is_torch_available()
	    else ()
	)
	A_           =   (
	    {
	        "feature-extraction": DebertaVaModel,
	        "fill-mask": DebertaVaForMaskedLM,
	        "question-answering": DebertaVaForQuestionAnswering,
	        "text-classification": DebertaVaForSequenceClassification,
	        "token-classification": DebertaVaForTokenClassification,
	        "zero-shot": DebertaVaForSequenceClassification,
	    }
	    if is_torch_available()
	    else {}
	)

	A_           =   True
	A_           =   False
	A_           =   False
	A_           =   False
	A_           =   False




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Union[str, Any]    =  DebertaVaModelTester(self							)
						__a      :	List[str]    =  ConfigTester(self						,    config_class=__a						,    hidden_size=37							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						self.config_tester.run_common_tests()




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[Any]    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_model(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Dict    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_sequence_classification(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	int    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_masked_lm(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[Any]    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_question_answering(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Tuple    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_token_classification(*__a							)




	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	List[str]    =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_multiple_choice(*__a							)





	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											__a      :	str    =  DebertaVaModel.from_pretrained(__a							)
											self.assertIsNotNone(__a							)





@require_torch
@require_sentencepiece
@require_tokenizers
class 							__UpperCamelCase    (		unittest.TestCase ):




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

						'''simple docstring'''





						pass





	@slow
	def 				__UpperCAmelCase							(       self							):

						'''simple docstring'''





						__a      :	Optional[int]    =  DebertaVaModel.from_pretrained('microsoft/deberta-v2-xlarge'							)

						__a      :	Optional[Any]    =  torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]							)
						__a      :	str    =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]							)
						with torch.no_grad():
											__a      :	int    =  model(__a						,    attention_mask=__a							)[0]
						# compare the actual values for a slice.
						__a      :	str    =  torch.tensor(
						    [[[0.2356, 0.1948, 0.0369], [-0.1063, 0.3586, -0.5152], [-0.6399, -0.0259, -0.2525]]]							)
						self.assertTrue(torch.allclose(output[:, 1:4, 1:4]						,    __a						,    atol=1E-4							)						,    f"""{output[:, 1:4, 1:4]}"""							)



