
import inspect
import unittest

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

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


if is_torch_available():
		import torch
		from torch import nn

		from transformers import BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
		from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
		from PIL import Image


class 		__magic_name__ :





		def __init__(				self     :						Optional[int]    ,	lowerCamelCase__     :						Dict    ,	lowerCamelCase__     :						Union[str, Any]=3    ,	lowerCamelCase__     :						Tuple=3_2    ,	lowerCamelCase__     :						int=3    ,	lowerCamelCase__     :						Optional[int]=1_0    ,	lowerCamelCase__     :						List[str]=[8, 1_6, 3_2, 6_4]    ,	lowerCamelCase__     :						List[Any]=[1, 1, 2, 1]    ,	lowerCamelCase__     :						Dict=True    ,	lowerCamelCase__     :						int=True    ,	lowerCamelCase__     :						int="relu"    ,	lowerCamelCase__     :						Tuple=3    ,	lowerCamelCase__     :						List[str]=None    ,	lowerCamelCase__     :						Tuple=["stage2", "stage3", "stage4"]    ,	lowerCamelCase__     :						List[str]=[2, 3, 4]    ,	lowerCamelCase__     :						Tuple=1    ,	):
							lowerCAmelCase		:       int							=  parent
							lowerCAmelCase		:       int							=  batch_size
							lowerCAmelCase		:       Tuple							=  image_size
							lowerCAmelCase		:       Dict							=  num_channels
							lowerCAmelCase		:       Dict							=  embeddings_size
							lowerCAmelCase		:       List[str]							=  hidden_sizes
							lowerCAmelCase		:       List[str]							=  depths
							lowerCAmelCase		:       Optional[int]							=  is_training
							lowerCAmelCase		:       Optional[Any]							=  use_labels
							lowerCAmelCase		:       Optional[Any]							=  hidden_act
							lowerCAmelCase		:       Union[str, Any]							=  num_labels
							lowerCAmelCase		:       Dict							=  scope
							lowerCAmelCase		:       Optional[int]							=  len(_a					)
							lowerCAmelCase		:       Optional[int]							=  out_features
							lowerCAmelCase		:       Optional[int]							=  out_indices
							lowerCAmelCase		:       Optional[int]							=  num_groups





		def    _A				(				self     :						Optional[int]					):
							lowerCAmelCase		:       Dict							=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

							lowerCAmelCase		:       Tuple							=  None
							if self.use_labels:
												lowerCAmelCase		:       List[str]							=  ids_tensor([self.batch_size]    ,	self.num_labels					)

							lowerCAmelCase		:       str							=  self.get_config()

							return config, pixel_values, labels





		def    _A				(				self     :						List[Any]					):
							return BitConfig(
							    num_channels=self.num_channels    ,	embeddings_size=self.embeddings_size    ,	hidden_sizes=self.hidden_sizes    ,	depths=self.depths    ,	hidden_act=self.hidden_act    ,	num_labels=self.num_labels    ,	out_features=self.out_features    ,	out_indices=self.out_indices    ,	num_groups=self.num_groups    ,	)





		def    _A				(				self     :						str    ,	lowerCamelCase__     :						Any    ,	lowerCamelCase__     :						List[Any]    ,	lowerCamelCase__     :						Tuple					):
							lowerCAmelCase		:       Union[str, Any]							=  BitModel(config=_a					)
							model.to(_a					)
							model.eval()
							lowerCAmelCase		:       List[str]							=  model(_a					)
							self.parent.assertEqual(
							    result.last_hidden_state.shape    ,	(self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)    ,	)





		def    _A				(				self     :						Optional[Any]    ,	lowerCamelCase__     :						Any    ,	lowerCamelCase__     :						Any    ,	lowerCamelCase__     :						Any					):
							lowerCAmelCase		:       List[Any]							=  self.num_labels
							lowerCAmelCase		:       Optional[int]							=  BitForImageClassification(_a					)
							model.to(_a					)
							model.eval()
							lowerCAmelCase		:       List[str]							=  model(_a    ,	labels=_a					)
							self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_labels)					)





		def    _A				(				self     :						Any    ,	lowerCamelCase__     :						List[Any]    ,	lowerCamelCase__     :						str    ,	lowerCamelCase__     :						Dict					):
							lowerCAmelCase		:       int							=  BitBackbone(config=_a					)
							model.to(_a					)
							model.eval()
							lowerCAmelCase		:       Tuple							=  model(_a					)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps					)    ,	len(config.out_features					)					)
							self.parent.assertListEqual(list(result.feature_maps[0].shape					)    ,	[self.batch_size, self.hidden_sizes[1], 4, 4]					)

							# verify channels
							self.parent.assertEqual(len(model.channels					)    ,	len(config.out_features					)					)
							self.parent.assertListEqual(model.channels    ,	config.hidden_sizes[1:]					)

							# verify backbone works with out_features=None
							lowerCAmelCase		:       Union[str, Any]							=  None
							lowerCAmelCase		:       List[Any]							=  BitBackbone(config=_a					)
							model.to(_a					)
							model.eval()
							lowerCAmelCase		:       List[Any]							=  model(_a					)

							# verify feature maps
							self.parent.assertEqual(len(result.feature_maps					)    ,	1					)
							self.parent.assertListEqual(list(result.feature_maps[0].shape					)    ,	[self.batch_size, self.hidden_sizes[-1], 1, 1]					)

							# verify channels
							self.parent.assertEqual(len(model.channels					)    ,	1					)
							self.parent.assertListEqual(model.channels    ,	[config.hidden_sizes[-1]]					)





		def    _A				(				self     :						List[Any]					):
							lowerCAmelCase		:       Any							=  self.prepare_config_and_inputs()
							lowerCAmelCase		:       Optional[Any]							=  config_and_inputs
							lowerCAmelCase		:       Optional[int]							=  {"pixel_values": pixel_values}
							return config, inputs_dict



@require_torch
class 		__magic_name__ (    a_,					a_,					unittest.TestCase     ):
		_lowerCAmelCase		=						(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		_lowerCAmelCase		=						(
		    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		_lowerCAmelCase		=						False
		_lowerCAmelCase		=						False
		_lowerCAmelCase		=						False
		_lowerCAmelCase		=						False
		_lowerCAmelCase		=						False





		def    _A				(				self     :						str					):
							lowerCAmelCase		:       int							=  BitModelTester(self					)
							lowerCAmelCase		:       Dict							=  ConfigTester(self    ,	config_class=_a    ,	has_text_modality=_a					)





		def    _A				(				self     :						List[Any]					):
							self.create_and_test_config_common_properties()
							self.config_tester.create_and_test_config_to_json_string()
							self.config_tester.create_and_test_config_to_json_file()
							self.config_tester.create_and_test_config_from_and_save_pretrained()
							self.config_tester.create_and_test_config_with_num_labels()
							self.config_tester.check_config_can_be_init_without_params()
							self.config_tester.check_config_arguments_init()





		def    _A				(				self     :						Optional[int]					):
							return





		@unittest.skip(reason='''Bit does not output attentions'''					)
		def    _A				(				self     :						Any					):
							pass





		@unittest.skip(reason='''Bit does not use inputs_embeds'''					)
		def    _A				(				self     :						List[Any]					):
							pass





		@unittest.skip(reason='''Bit does not support input and output embeddings'''					)
		def    _A				(				self     :						int					):
							pass





		def    _A				(				self     :						Tuple					):
							lowerCAmelCase		:       Union[str, Any]							=  self.model_tester.prepare_config_and_inputs_for_common()

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

												lowerCAmelCase		:       Optional[int]							=  ["pixel_values"]
												self.assertListEqual(arg_names[:1]    ,	_a					)





		def    _A				(				self     :						Any					):
							lowerCAmelCase		:       Optional[Any]							=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*_a					)





		def    _A				(				self     :						str					):
							lowerCAmelCase		:       Tuple							=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_backbone(*_a					)





		def    _A				(				self     :						List[Any]					):
							lowerCAmelCase		:       Any							=  self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
												lowerCAmelCase		:       Optional[Any]							=  model_class(config=_a					)
												for name, module in model.named_modules():
																	if isinstance(_a    ,	(nn.BatchNormad, nn.GroupNorm)					):
																						self.assertTrue(
																						    torch.all(module.weight == 1					)    ,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,	)
																						self.assertTrue(
																						    torch.all(module.bias == 0					)    ,	msg=f'''Parameter {name} of model {model_class} seems not properly initialized'''    ,	)





		def    _A				(				self     :						Tuple					):
							def check_hidden_states_output(lowerCamelCase__     :						int    ,	lowerCamelCase__     :						str    ,	lowerCamelCase__     :						List[Any]					):
												lowerCAmelCase		:       List[str]							=  model_class(_a					)
												model.to(_a					)
												model.eval()

												with torch.no_grad():
																	lowerCAmelCase		:       Union[str, Any]							=  model(**self._prepare_for_class(_a    ,	_a					)					)

												lowerCAmelCase		:       Tuple							=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												lowerCAmelCase		:       Tuple							=  self.model_tester.num_stages
												self.assertEqual(len(_a					)    ,	expected_num_stages + 1					)

												# Bit's feature maps are of shape (batch_size, num_channels, height, width)
												self.assertListEqual(
												    list(hidden_states[0].shape[-2:]					)    ,	[self.model_tester.image_size // 4, self.model_tester.image_size // 4]    ,	)

							lowerCAmelCase		:       List[str]							=  self.model_tester.prepare_config_and_inputs_for_common()
							lowerCAmelCase		:       Optional[Any]							=  ["preactivation", "bottleneck"]
							for model_class in self.all_model_classes:
												for layer_type in layers_type:
																	lowerCAmelCase		:       Any							=  layer_type
																	lowerCAmelCase		:       List[Any]							=  True
																	check_hidden_states_output(_a    ,	_a    ,	_a					)

																	# check that output_hidden_states also work using config
																	del inputs_dict["output_hidden_states"]
																	lowerCAmelCase		:       List[Any]							=  True

																	check_hidden_states_output(_a    ,	_a    ,	_a					)





		@unittest.skip(reason='''Bit does not use feedforward chunking'''					)
		def    _A				(				self     :						Union[str, Any]					):
							pass





		def    _A				(				self     :						List[str]					):
							lowerCAmelCase		:       Union[str, Any]							=  self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_image_classification(*_a					)





		@slow
		def    _A				(				self     :						Dict					):
							for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												lowerCAmelCase		:       Dict							=  BitModel.from_pretrained(_a					)
												self.assertIsNotNone(_a					)



def      UpperCAmelCase__				(							):


					'''simple docstring'''

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


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





		@cached_property
		def    _A				(				self     :						int					):
							return (
							    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					) if is_vision_available() else None
							)





		@slow
		def    _A				(				self     :						Any					):
							lowerCAmelCase		:       List[str]							=  BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]					).to(_a					)

							lowerCAmelCase		:       Optional[int]							=  self.default_image_processor
							lowerCAmelCase		:       Optional[int]							=  prepare_img()
							lowerCAmelCase		:       Dict							=  image_processor(images=_a    ,	return_tensors='''pt'''					).to(_a					)

							# forward pass
							with torch.no_grad():
												lowerCAmelCase		:       List[str]							=  model(**_a					)

							# verify the logits
							lowerCAmelCase		:       Dict							=  torch.Size((1, 1_0_0_0)					)
							self.assertEqual(outputs.logits.shape    ,	_a					)

							lowerCAmelCase		:       List[Any]							=  torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]					).to(_a					)

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



@require_torch
class 		__magic_name__ (    a_,					unittest.TestCase     ):
		_lowerCAmelCase		=						(BitBackbone,) if is_torch_available() else ()
		_lowerCAmelCase		=						BitConfig

		_lowerCAmelCase		=						False





		def    _A				(				self     :						Optional[int]					):
							lowerCAmelCase		:       List[str]							=  BitModelTester(self					)


"""simple docstring"""




import warnings

from ...utils import logging
from .image_processing_owlvit import OwlViTImageProcessor


_A     :			Optional[int]  						=			logging.get_logger(__name__)

class 						a__		( a_		):
   def __init__(     self      ,       *_a      ,       **_a							):
          warnings.warn(
              "The class OwlViTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
              " use OwlViTImageProcessor instead."      ,       _a      ,       )
          super().__init__(*_a      ,       **_a							)


