




import inspect
import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel
		from transformers.models.vit_hybrid.modeling_vit_hybrid import VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image


class 							__UpperCamelCase			:





			'''simple docstring'''



			def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_=13       , UpperCAmelCase_=64       , UpperCAmelCase_=2       , UpperCAmelCase_=3       , UpperCAmelCase_=True       , UpperCAmelCase_=True       , UpperCAmelCase_=32       , UpperCAmelCase_=5       , UpperCAmelCase_=4       , UpperCAmelCase_=37       , UpperCAmelCase_="gelu"       , UpperCAmelCase_=0.1       , UpperCAmelCase_=0.1       , UpperCAmelCase_=10       , UpperCAmelCase_=0.02       , UpperCAmelCase_=[1, 16, 4, 4]       , UpperCAmelCase_=None       , ):
										lowerCAmelCase   =		parent
										lowerCAmelCase   =		batch_size
										lowerCAmelCase   =		image_size
										lowerCAmelCase   =		patch_size
										lowerCAmelCase   =		num_channels
										lowerCAmelCase   =		is_training
										lowerCAmelCase   =		use_labels
										lowerCAmelCase   =		hidden_size
										lowerCAmelCase   =		num_hidden_layers
										lowerCAmelCase   =		num_attention_heads
										lowerCAmelCase   =		intermediate_size
										lowerCAmelCase   =		hidden_act
										lowerCAmelCase   =		hidden_dropout_prob
										lowerCAmelCase   =		attention_probs_dropout_prob
										lowerCAmelCase   =		type_sequence_label_size
										lowerCAmelCase   =		initializer_range
										lowerCAmelCase   =		scope
										lowerCAmelCase   =		backbone_featmap_shape

										# in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
										# the number of patches is based on the feature map of the backbone, which by default uses an output stride
										# of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
										lowerCAmelCase   =		(self.image_size // 32) ** 2
										lowerCAmelCase   =		num_patches + 1



			def       __snake_case		( self						):
										lowerCAmelCase   =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

										lowerCAmelCase   =		None
										if self.use_labels:
																	lowerCAmelCase   =		ids_tensor([self.batch_size]       , self.type_sequence_label_size						)

										lowerCAmelCase   =		self.get_config()

										return config, pixel_values, labels



			def       __snake_case		( self						):
										lowerCAmelCase   =		{
										    '''global_padding''': '''same''',
										    '''layer_type''': '''bottleneck''',
										    '''depths''': [3, 4, 9],
										    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
										    '''embedding_dynamic_padding''': True,
										    '''hidden_sizes''': [4, 8, 16, 32],
										    '''num_groups''': 2,
										}

										return ViTHybridConfig(
										    image_size=self.image_size       , patch_size=self.patch_size       , num_channels=self.num_channels       , 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       , is_decoder=UpperCAmelCase_       , initializer_range=self.initializer_range       , backbone_featmap_shape=self.backbone_featmap_shape       , backbone_config=UpperCAmelCase_       , )



			def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
										lowerCAmelCase   =		ViTHybridModel(config=UpperCAmelCase_						)
										model.to(UpperCAmelCase_						)
										model.eval()
										lowerCAmelCase   =		model(UpperCAmelCase_						)
										self.parent.assertEqual(result.last_hidden_state.shape       , (self.batch_size, self.seq_length, self.hidden_size)						)



			def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_						):
										lowerCAmelCase   =		self.type_sequence_label_size
										lowerCAmelCase   =		ViTHybridForImageClassification(UpperCAmelCase_						)
										model.to(UpperCAmelCase_						)
										model.eval()
										lowerCAmelCase   =		model(UpperCAmelCase_       , labels=UpperCAmelCase_						)
										self.parent.assertEqual(result.logits.shape       , (self.batch_size, self.type_sequence_label_size)						)






			def       __snake_case		( self						):
										lowerCAmelCase   =		self.prepare_config_and_inputs()
										lowerCAmelCase   =		config_and_inputs
										lowerCAmelCase   =		{'''pixel_values''': pixel_values}
										return config, inputs_dict




@require_torch
class 							__UpperCamelCase			( UpperCamelCase_     ,					UpperCamelCase_     ,					unittest.TestCase  ):





			'''simple docstring'''



			__a							: Any										=(ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
			__a							: Dict										=(
			    {"""feature-extraction""": ViTHybridModel, """image-classification""": ViTHybridForImageClassification}
			    if is_torch_available()
			    else {}
			)
			__a							: str										=False
			__a							: Optional[int]										=False
			__a							: Union[str, Any]										=False



			def       __snake_case		( self						):
										lowerCAmelCase   =		ViTHybridModelTester(self						)
										lowerCAmelCase   =		ConfigTester(self       , config_class=UpperCAmelCase_       , has_text_modality=UpperCAmelCase_       , hidden_size=37						)



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



			@unittest.skip(reason='''ViT does not use inputs_embeds'''						)
			def       __snake_case		( self						):
										pass



			def       __snake_case		( self						):
										lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowerCAmelCase   =		model_class(UpperCAmelCase_						)
																	self.assertIsInstance(model.get_input_embeddings()       , (nn.Module)						)
																	lowerCAmelCase   =		model.get_output_embeddings()
																	self.assertTrue(x is None or isinstance(UpperCAmelCase_       , nn.Linear						)						)



			def       __snake_case		( self						):
										lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
																	lowerCAmelCase   =		model_class(UpperCAmelCase_						)
																	lowerCAmelCase   =		inspect.signature(model.forward						)
																	# signature.parameters is an OrderedDict => so arg_names order is deterministic
																	lowerCAmelCase   =		[*signature.parameters.keys()]

																	lowerCAmelCase   =		['''pixel_values''']
																	self.assertListEqual(arg_names[:1]       , UpperCAmelCase_						)



			def       __snake_case		( self						):
										lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*UpperCAmelCase_						)



			def       __snake_case		( self						):
										lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_						)



			def       __snake_case		( self						):
										lowerCAmelCase   =		self.model_tester.prepare_config_and_inputs_for_common()

										lowerCAmelCase   =		_config_zero_init(UpperCAmelCase_						)
										for model_class in self.all_model_classes:
																	lowerCAmelCase   =		model_class(config=UpperCAmelCase_						)
																	# Skip the check for the backbone
																	for name, module in model.named_modules():
																								if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
																															lowerCAmelCase   =		[F"""{name}.{key}""" for key in module.state_dict().keys()]
																															break

																	for name, param in model.named_parameters():
																								if param.requires_grad:
																															if name in backbone_params:
																																						continue
																															self.assertIn(
																															    ((param.data.mean() * 1E9).round() / 1E9).item()       , [0.0, 1.0]       , msg=F"""Parameter {name} of model {model_class} seems not properly initialized"""       , )






			@slow
			def       __snake_case		( self						):
										for model_name in VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	lowerCAmelCase   =		ViTHybridModel.from_pretrained(UpperCAmelCase_						)
																	self.assertIsNotNone(UpperCAmelCase_						)




def       UpperCAmelCase       (     ):
							lowerCAmelCase   =		Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
							return image


@require_torch
@require_vision
class 							__UpperCamelCase			( unittest.TestCase  ):





			'''simple docstring'''



			@cached_property
			def       __snake_case		( self						):
										return (
										    ViTHybridImageProcessor.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)
										    if is_vision_available()
										    else None
										)



			@slow
			def       __snake_case		( self						):
										lowerCAmelCase   =		ViTHybridForImageClassification.from_pretrained(VIT_HYBRID_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(
										    UpperCAmelCase_						)

										lowerCAmelCase   =		self.default_image_processor
										lowerCAmelCase   =		prepare_img()
										lowerCAmelCase   =		image_processor(images=UpperCAmelCase_       , return_tensors='''pt'''						).to(UpperCAmelCase_						)

										# forward pass
										with torch.no_grad():
																	lowerCAmelCase   =		model(**UpperCAmelCase_						)

										# verify the logits
										lowerCAmelCase   =		torch.Size((1, 10_00)						)
										self.assertEqual(outputs.logits.shape       , UpperCAmelCase_						)

										lowerCAmelCase   =		torch.tensor([-1.9090, -0.4993, -0.2389]						).to(UpperCAmelCase_						)

										self.assertTrue(torch.allclose(outputs.logits[0, :3]       , UpperCAmelCase_       , atol=1E-4						)						)






			@slow
			@require_accelerate
			def       __snake_case		( self						):
										lowerCAmelCase   =		ViTHybridImageProcessor.from_pretrained('''google/vit-hybrid-base-bit-384'''						)
										lowerCAmelCase   =		ViTHybridForImageClassification.from_pretrained('''google/vit-hybrid-base-bit-384'''       , device_map='''auto'''						)

										lowerCAmelCase   =		prepare_img()

										lowerCAmelCase   =		image_processor(images=UpperCAmelCase_       , return_tensors='''pt'''						)
										lowerCAmelCase   =		model(**UpperCAmelCase_						)
										lowerCAmelCase   =		outputs.logits
										# model predicts one of the 1000 ImageNet classes
										lowerCAmelCase   =		logits.argmax(-1						).item()

										self.assertTrue(model.config.idalabel[predicted_class_idx]       , '''tabby, tabby cat'''						)









# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

import math
from typing import Union

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin


class 							__UpperCamelCase			( __UpperCAmelCase     ,					__UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Any										=1



				@register_to_config
				def __init__( self       , UpperCAmelCase_=20_00       , UpperCAmelCase_=0.1       , UpperCAmelCase_=20       , UpperCAmelCase_=1E-3						):
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None
											lowerCAmelCase   =		None



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		torch.linspace(1       , self.config.sampling_eps       , UpperCAmelCase_       , device=UpperCAmelCase_						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_       , UpperCAmelCase_=None						):
											if self.timesteps is None:
																		raise ValueError(
																		    '''`self.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''						)

											# TODO(Patrick) better comments + non-PyTorch
											# postprocess model score
											lowerCAmelCase   =		(
											    -0.25 * t**2 * (self.config.beta_max - self.config.beta_min) - 0.5 * t * self.config.beta_min
											)
											lowerCAmelCase   =		torch.sqrt(1.0 - torch.exp(2.0 * log_mean_coeff						)						)
											lowerCAmelCase   =		std.flatten()
											while len(std.shape						) < len(score.shape						):
																		lowerCAmelCase   =		std.unsqueeze(-1						)
											lowerCAmelCase   =		-score / std

											# compute
											lowerCAmelCase   =		-1.0 / len(self.timesteps						)

											lowerCAmelCase   =		self.config.beta_min + t * (self.config.beta_max - self.config.beta_min)
											lowerCAmelCase   =		beta_t.flatten()
											while len(beta_t.shape						) < len(x.shape						):
																		lowerCAmelCase   =		beta_t.unsqueeze(-1						)
											lowerCAmelCase   =		-0.5 * beta_t * x

											lowerCAmelCase   =		torch.sqrt(UpperCAmelCase_						)
											lowerCAmelCase   =		drift - diffusion**2 * score
											lowerCAmelCase   =		x + drift * dt

											# add noise
											lowerCAmelCase   =		randn_tensor(x.shape       , layout=x.layout       , generator=UpperCAmelCase_       , device=x.device       , dtype=x.dtype						)
											lowerCAmelCase   =		x_mean + diffusion * math.sqrt(-dt						) * noise

											return x, x_mean






				def __len__( self						):
											return self.config.num_train_timesteps



