



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       a							:



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



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Dict     		=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

						snake_case__	:      Optional[int]     		=	None
						if self.use_labels:
										snake_case__	:      Any     		=	ids_tensor([self.batch_size]   ,self.num_labels  )

						snake_case__	:      List[str]     		=	self.get_config()

						return config, pixel_values, labels



		def 	__lowerCamelCase			(     self     :Tuple  ):
						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 	__lowerCamelCase			(     self     :Dict   ,__lowercase     :Any   ,__lowercase     :Any   ,__lowercase     :List[Any]  ):
						snake_case__	:      Dict     		=	BitModel(config=__lowercase  )
						model.to(__lowercase  )
						model.eval()
						snake_case__	:      str     		=	model(__lowercase  )
						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 	__lowerCamelCase			(     self     :Optional[Any]   ,__lowercase     :Dict   ,__lowercase     :Union[str, Any]   ,__lowercase     :List[Any]  ):
						snake_case__	:      List[Any]     		=	self.num_labels
						snake_case__	:      Optional[int]     		=	BitForImageClassification(__lowercase  )
						model.to(__lowercase  )
						model.eval()
						snake_case__	:      Dict     		=	model(__lowercase   ,labels=__lowercase  )
						self.parent.assertEqual(result.logits.shape   ,(self.batch_size, self.num_labels)  )



		def 	__lowerCamelCase			(     self     :str   ,__lowercase     :Union[str, Any]   ,__lowercase     :Optional[Any]   ,__lowercase     :List[Any]  ):
						snake_case__	:      Any     		=	BitBackbone(config=__lowercase  )
						model.to(__lowercase  )
						model.eval()
						snake_case__	:      int     		=	model(__lowercase  )

						# 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
						snake_case__	:      List[Any]     		=	None
						snake_case__	:      Optional[Any]     		=	BitBackbone(config=__lowercase  )
						model.to(__lowercase  )
						model.eval()
						snake_case__	:      List[Any]     		=	model(__lowercase  )

						# 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 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      Tuple     		=	self.prepare_config_and_inputs()
						snake_case__		, snake_case__		, snake_case__	:      str     		=	config_and_inputs
						snake_case__	:      Optional[Any]     		=	{'''pixel_values''': pixel_values}
						return config, inputs_dict


@require_torch
class       a							(			__lowerCamelCase  ,							__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Optional[int]											=					(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		__lowerCAmelCase	:    str											=					(
		    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		__lowerCAmelCase	:    Optional[int]											=					False
		__lowerCAmelCase	:    int											=					False
		__lowerCAmelCase	:    Optional[Any]											=					False
		__lowerCAmelCase	:    str											=					False
		__lowerCAmelCase	:    Tuple											=					False



		def 	__lowerCamelCase			(     self     :Optional[Any]  ):
						snake_case__	:      Union[str, Any]     		=	BitModelTester(self  )
						snake_case__	:      str     		=	ConfigTester(self   ,config_class=__lowercase   ,has_text_modality=__lowercase  )



		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						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 	__lowerCamelCase			(     self     :Tuple  ):
						return



		@unittest.skip(reason='''Bit does not output attentions'''  )
		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						pass



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



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



		def 	__lowerCamelCase			(     self     :List[Any]  ):
						snake_case__		, snake_case__	:      Any     		=	self.model_tester.prepare_config_and_inputs_for_common()

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

										snake_case__	:      List[Any]     		=	['''pixel_values''']
										self.assertListEqual(arg_names[:1]   ,__lowercase  )



		def 	__lowerCamelCase			(     self     :str  ):
						snake_case__	:      Optional[int]     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowercase  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      Any     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_backbone(*__lowercase  )



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__		, snake_case__	:      Optional[Any]     		=	self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										snake_case__	:      Optional[int]     		=	model_class(config=__lowercase  )
										for name, module in model.named_modules():
														if isinstance(__lowercase   ,(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 	__lowerCamelCase			(     self     :Optional[int]  ):
						def check_hidden_states_output(__lowercase     :int   ,__lowercase     :List[Any]   ,__lowercase     :List[str]  ):
										snake_case__	:      Tuple     		=	model_class(__lowercase  )
										model.to(__lowercase  )
										model.eval()

										with torch.no_grad():
														snake_case__	:      Tuple     		=	model(**self._prepare_for_class(__lowercase   ,__lowercase  )  )

										snake_case__	:      Optional[Any]     		=	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

										snake_case__	:      Optional[int]     		=	self.model_tester.num_stages
										self.assertEqual(len(__lowercase  )   ,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]   ,)

						snake_case__		, snake_case__	:      Dict     		=	self.model_tester.prepare_config_and_inputs_for_common()
						snake_case__	:      List[Any]     		=	['''preactivation''', '''bottleneck''']
						for model_class in self.all_model_classes:
										for layer_type in layers_type:
														snake_case__	:      Any     		=	layer_type
														snake_case__	:      Optional[int]     		=	True
														check_hidden_states_output(__lowercase   ,__lowercase   ,__lowercase  )

														# check that output_hidden_states also work using config
														del inputs_dict["output_hidden_states"]
														snake_case__	:      Dict     		=	True

														check_hidden_states_output(__lowercase   ,__lowercase   ,__lowercase  )



		@unittest.skip(reason='''Bit does not use feedforward chunking'''  )
		def 	__lowerCamelCase			(     self     :Any  ):
						pass



		def 	__lowerCamelCase			(     self     :Dict  ):
						snake_case__	:      Any     		=	self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowercase  )






		@slow
		def 	__lowerCamelCase			(     self     :int  ):
						for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										snake_case__	:      Tuple     		=	BitModel.from_pretrained(__lowercase  )
										self.assertIsNotNone(__lowercase  )



def 			_lowerCAmelCase       (					)   ->					Union[str, Any]:


				"""simple docstring"""

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


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



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






		@slow
		def 	__lowerCamelCase			(     self     :Tuple  ):
						snake_case__	:      Optional[int]     		=	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]  ).to(__lowercase  )

						snake_case__	:      Optional[Any]     		=	self.default_image_processor
						snake_case__	:      Union[str, Any]     		=	prepare_img()
						snake_case__	:      str     		=	image_processor(images=__lowercase   ,return_tensors='''pt'''  ).to(__lowercase  )

						# forward pass
						with torch.no_grad():
										snake_case__	:      Union[str, Any]     		=	model(**__lowercase  )

						# verify the logits
						snake_case__	:      Optional[int]     		=	torch.Size((1, 1_0_0_0)  )
						self.assertEqual(outputs.logits.shape   ,__lowercase  )

						snake_case__	:      Union[str, Any]     		=	torch.tensor([[-0.6526, -0.5263, -1.4398]]  ).to(__lowercase  )

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


@require_torch
class       a							(			__lowerCamelCase  ,							unittest.TestCase				):
		__lowerCAmelCase	:    Tuple											=					(BitBackbone,) if is_torch_available() else ()
		__lowerCAmelCase	:    Optional[int]											=					BitConfig

		__lowerCAmelCase	:    List[Any]											=					False



		def 	__lowerCamelCase			(     self     :List[str]  ):
						snake_case__	:      Any     		=	BitModelTester(self  )






from importlib import import_module

from .logging import get_logger


A__								=						get_logger(__name__)


class       a							:



		def __init__(     self     :Optional[int]   ,__lowercase     :List[str]   ,__lowercase     :Any=None  ):
						snake_case__	:      List[str]     		=	attrs or []
						if module is not None:
										for key in module.__dict__:
														if key in attrs or not key.startswith('''__'''  ):
																		setattr(self   ,__lowercase   ,getattr(__lowercase   ,__lowercase  )  )
						snake_case__	:      Optional[Any]     		=	module._original_module if isinstance(__lowercase   ,_PatchedModuleObj  ) else module


class       a							:
		__lowerCAmelCase	:    Any											=					[]



		def __init__(     self     :List[str]   ,__lowercase     :Optional[Any]   ,__lowercase     :str   ,__lowercase     :Dict   ,__lowercase     :Any=None  ):
						snake_case__	:      Dict     		=	obj
						snake_case__	:      Dict     		=	target
						snake_case__	:      List[str]     		=	new
						snake_case__	:      int     		=	target.split('''.'''  )[0]
						snake_case__	:      List[str]     		=	{}
						snake_case__	:      Any     		=	attrs or []



		def __enter__(     self     :Tuple  ):
						*snake_case__		, snake_case__	:      str     		=	self.target.split('''.'''  )

						# Patch modules:
						# it's used to patch attributes of submodules like "os.path.join";
						# in this case we need to patch "os" and "os.path"

						for i in range(len(__lowercase  )  ):
										try:
														snake_case__	:      Any     		=	import_module('''.'''.join(submodules[: i + 1]  )  )
										except ModuleNotFoundError:
														continue
										# We iterate over all the globals in self.obj in case we find "os" or "os.path"
										for attr in self.obj.__dir__():
														snake_case__	:      Optional[int]     		=	getattr(self.obj   ,__lowercase  )
														# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
														# This allows to patch renamed modules like "from os import path as ospath".
														if obj_attr is submodule or (
														    (isinstance(__lowercase   ,_PatchedModuleObj  ) and obj_attr._original_module is submodule)
														):
																		snake_case__	:      List[Any]     		=	obj_attr
																		# patch at top level
																		setattr(self.obj   ,__lowercase   ,_PatchedModuleObj(__lowercase   ,attrs=self.attrs  )  )
																		snake_case__	:      List[Any]     		=	getattr(self.obj   ,__lowercase  )
																		# construct lower levels patches
																		for key in submodules[i + 1 :]:
																						setattr(__lowercase   ,__lowercase   ,_PatchedModuleObj(getattr(__lowercase   ,__lowercase   ,__lowercase  )   ,attrs=self.attrs  )  )
																						snake_case__	:      List[Any]     		=	getattr(__lowercase   ,__lowercase  )
																		# finally set the target attribute
																		setattr(__lowercase   ,__lowercase   ,self.new  )

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

						if submodules:  # if it's an attribute of a submodule like "os.path.join"
										try:
														snake_case__	:      int     		=	getattr(import_module('''.'''.join(__lowercase  )  )   ,__lowercase  )
										except (AttributeError, ModuleNotFoundError):
														return
										# We iterate over all the globals in self.obj in case we find "os.path.join"
										for attr in self.obj.__dir__():
														# We don't check for the name of the global, but rather if its value *is* "os.path.join".
														# This allows to patch renamed attributes like "from os.path import join as pjoin".
														if getattr(self.obj   ,__lowercase  ) is attr_value:
																		snake_case__	:      str     		=	getattr(self.obj   ,__lowercase  )
																		setattr(self.obj   ,__lowercase   ,self.new  )
						elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
										snake_case__	:      str     		=	globals()['''__builtins__'''][target_attr]
										setattr(self.obj   ,__lowercase   ,self.new  )
						else:
										raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule."""  )



		def __exit__(     self     :Tuple   ,*__lowercase     :Optional[int]  ):
						for attr in list(self.original  ):
										setattr(self.obj   ,__lowercase   ,self.original.pop(__lowercase  )  )



		def 	__lowerCamelCase			(     self     :Tuple  ):
						self.__enter__()
						self._active_patches.append(self  )






		def 	__lowerCamelCase			(     self     :Dict  ):
						try:
										self._active_patches.remove(self  )
						except ValueError:
										# If the patch hasn't been started this will fail
										return None

						return self.__exit__()

