






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        lowerCAmelCase_				:







				"""simple docstring"""




				def __init__( self     :Dict  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Optional[Any]=3  ,     lowerCamelCase__     :Tuple=32  ,     lowerCamelCase__     :str=3  ,     lowerCamelCase__     :str=10  ,     lowerCamelCase__     :Dict=[8, 16, 32, 64]  ,     lowerCamelCase__     :Optional[int]=[1, 1, 2, 1]  ,     lowerCamelCase__     :Tuple=True  ,     lowerCamelCase__     :List[str]=True  ,     lowerCamelCase__     :List[str]="relu"  ,     lowerCamelCase__     :Tuple=3  ,     lowerCamelCase__     :Optional[int]=None  ,     lowerCamelCase__     :List[Any]=["stage2", "stage3", "stage4"]  ,     lowerCamelCase__     :Any=[2, 3, 4]  ,     lowerCamelCase__     :Dict=1  ,     ):
					UpperCamelCase__ :List[str]						=      parent
					UpperCamelCase__ :Union[str, Any]						=      batch_size
					UpperCamelCase__ :str						=      image_size
					UpperCamelCase__ :str						=      num_channels
					UpperCamelCase__ :Dict						=      embeddings_size
					UpperCamelCase__ :List[str]						=      hidden_sizes
					UpperCamelCase__ :str						=      depths
					UpperCamelCase__ :int						=      is_training
					UpperCamelCase__ :List[Any]						=      use_labels
					UpperCamelCase__ :Any						=      hidden_act
					UpperCamelCase__ :str						=      num_labels
					UpperCamelCase__ :Optional[Any]						=      scope
					UpperCamelCase__ :str						=      len(lowerCamelCase__     )
					UpperCamelCase__ :List[str]						=      out_features
					UpperCamelCase__ :Optional[Any]						=      out_indices
					UpperCamelCase__ :str						=      num_groups







				def 				__a   ( self     :Optional[int]     ):
					UpperCamelCase__ :str						=      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]     )

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

					UpperCamelCase__ :Union[str, Any]						=      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     :Optional[int]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :str     ):
					UpperCamelCase__ :List[Any]						=      BitModel(config=lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :List[str]						=      model(lowerCamelCase__     )
					self.parent.assertEqual(
					    result.last_hidden_state.shape  ,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)  ,     )







				def 				__a   ( self     :int  ,     lowerCamelCase__     :Optional[int]  ,     lowerCamelCase__     :Tuple  ,     lowerCamelCase__     :int     ):
					UpperCamelCase__ :Tuple						=      self.num_labels
					UpperCamelCase__ :int						=      BitForImageClassification(lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :int						=      model(lowerCamelCase__  ,     labels=lowerCamelCase__     )
					self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.num_labels)     )







				def 				__a   ( self     :Optional[int]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :Union[str, Any]  ,     lowerCamelCase__     :str     ):
					UpperCamelCase__ :Tuple						=      BitBackbone(config=lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :int						=      model(lowerCamelCase__     )

					# 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
					UpperCamelCase__ :int						=      None
					UpperCamelCase__ :Any						=      BitBackbone(config=lowerCamelCase__     )
					model.to(lowerCamelCase__     )
					model.eval()
					UpperCamelCase__ :List[Any]						=      model(lowerCamelCase__     )

					# 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     :Tuple     ):
					UpperCamelCase__ :Dict						=      self.prepare_config_and_inputs()
					UpperCamelCase__				,		UpperCamelCase__				,		UpperCamelCase__ :Optional[Any]						=      config_and_inputs
					UpperCamelCase__ :Any						=      {"""pixel_values""": pixel_values}
					return config, inputs_dict

@require_torch
class        lowerCAmelCase_				(      lowercase						,      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   List[str]        =			(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				_snake_case				:   Optional[int]        =			(
				    {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				_snake_case				:   str        =			False
				_snake_case				:   Any        =			False
				_snake_case				:   Tuple        =			False
				_snake_case				:   Dict        =			False
				_snake_case				:   Optional[Any]        =			False







				def 				__a   ( self     :str     ):
					UpperCamelCase__ :List[str]						=      BitModelTester(self     )
					UpperCamelCase__ :Optional[int]						=      ConfigTester(self  ,     config_class=lowerCamelCase__  ,     has_text_modality=lowerCamelCase__     )







				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     :Any     ):
					return







				@unittest.skip(reason="""Bit does not output attentions"""     )
				def 				__a   ( self     :List[str]     ):
					pass







				@unittest.skip(reason="""Bit does not use inputs_embeds"""     )
				def 				__a   ( self     :int     ):
					pass







				@unittest.skip(reason="""Bit does not support input and output embeddings"""     )
				def 				__a   ( self     :Any     ):
					pass







				def 				__a   ( self     :Any     ):
					UpperCamelCase__				,		UpperCamelCase__ :Optional[int]						=      self.model_tester.prepare_config_and_inputs_for_common()

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

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







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :int						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*lowerCamelCase__     )







				def 				__a   ( self     :Optional[Any]     ):
					UpperCamelCase__ :Tuple						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_backbone(*lowerCamelCase__     )







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

					for model_class in self.all_model_classes:
						UpperCamelCase__ :int						=      model_class(config=lowerCamelCase__     )
						for name, module in model.named_modules():
							if isinstance(lowerCamelCase__  ,     (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__     :Dict  ,     lowerCamelCase__     :Dict  ,     lowerCamelCase__     :Any     ):
						UpperCamelCase__ :Optional[int]						=      model_class(lowerCamelCase__     )
						model.to(lowerCamelCase__     )
						model.eval()

						with torch.no_grad():
							UpperCamelCase__ :str						=      model(**self._prepare_for_class(lowerCamelCase__  ,     lowerCamelCase__     )     )

						UpperCamelCase__ :Any						=      outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						UpperCamelCase__ :Optional[Any]						=      self.model_tester.num_stages
						self.assertEqual(len(lowerCamelCase__     )  ,     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]  ,     )

					UpperCamelCase__				,		UpperCamelCase__ :str						=      self.model_tester.prepare_config_and_inputs_for_common()
					UpperCamelCase__ :Optional[int]						=      ["""preactivation""", """bottleneck"""]
					for model_class in self.all_model_classes:
						for layer_type in layers_type:
							UpperCamelCase__ :Tuple						=      layer_type
							UpperCamelCase__ :Optional[int]						=      True
							check_hidden_states_output(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )

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

							check_hidden_states_output(lowerCamelCase__  ,     lowerCamelCase__  ,     lowerCamelCase__     )







				@unittest.skip(reason="""Bit does not use feedforward chunking"""     )
				def 				__a   ( self     :Dict     ):
					pass







				def 				__a   ( self     :List[Any]     ):
					UpperCamelCase__ :List[str]						=      self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__     )







				@slow
				def 				__a   ( self     :Optional[int]     ):
					for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						UpperCamelCase__ :str						=      BitModel.from_pretrained(lowerCamelCase__     )
						self.assertIsNotNone(lowerCamelCase__     )




def 				A		(					)		-> Tuple:
	UpperCamelCase__ :Optional[Any]						=      Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""  )
	return image

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







				"""simple docstring"""




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







				@slow
				def 				__a   ( self     :Any     ):
					UpperCamelCase__ :Optional[Any]						=      BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]     ).to(lowerCamelCase__     )

					UpperCamelCase__ :Dict						=      self.default_image_processor
					UpperCamelCase__ :Union[str, Any]						=      prepare_img()
					UpperCamelCase__ :List[Any]						=      image_processor(images=lowerCamelCase__  ,     return_tensors="""pt"""     ).to(lowerCamelCase__     )

					# forward pass
					with torch.no_grad():
						UpperCamelCase__ :Any						=      model(**lowerCamelCase__     )

					# verify the logits
					UpperCamelCase__ :Tuple						=      torch.Size((1, 10_00)     )
					self.assertEqual(outputs.logits.shape  ,     lowerCamelCase__     )

					UpperCamelCase__ :List[Any]						=      torch.tensor([[-0.6526, -0.5263, -1.4398]]     ).to(lowerCamelCase__     )

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

@require_torch
class        lowerCAmelCase_				(      lowercase						,      unittest.TestCase	):







				"""simple docstring"""




				_snake_case				:   Dict        =			(BitBackbone,) if is_torch_available() else ()
				_snake_case				:   List[Any]        =			BitConfig

				_snake_case				:   List[str]        =			False







				def 				__a   ( self     :Dict     ):
					UpperCamelCase__ :Tuple						=      BitModelTester(self     )




"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCAmelCase							:   List[Any]				=				{
    "configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCAmelCase							:   int				=				[
     "LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "LiltForQuestionAnswering",
     "LiltForSequenceClassification",
     "LiltForTokenClassification",
     "LiltModel",
     "LiltPreTrainedModel",
 ]

if TYPE_CHECKING:
 from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_lilt import (
      LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
      LiltPreTrainedModel,
  )

else:
 import sys

 _lowerCAmelCase							:   Optional[int]				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
