



"""simple docstring"""






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__  :




	'''simple docstring'''





	def __init__(	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						str=3			,     _SCREAMING_SNAKE_CASE:						List[str]=32			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=3			,     _SCREAMING_SNAKE_CASE:						Dict=10			,     _SCREAMING_SNAKE_CASE:						str=[8, 16, 32, 64]			,     _SCREAMING_SNAKE_CASE:						int=[1, 1, 2, 1]			,     _SCREAMING_SNAKE_CASE:						Optional[int]=True			,     _SCREAMING_SNAKE_CASE:						List[str]=True			,     _SCREAMING_SNAKE_CASE:						str="relu"			,     _SCREAMING_SNAKE_CASE:						str=3			,     _SCREAMING_SNAKE_CASE:						str=None			,     _SCREAMING_SNAKE_CASE:						List[str]=["stage2", "stage3", "stage4"]			,     _SCREAMING_SNAKE_CASE:						Union[str, Any]=[2, 3, 4]			,     _SCREAMING_SNAKE_CASE:						Dict=1			,     )							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			parent
				__lowerCAmelCase :    str				=			batch_size
				__lowerCAmelCase :    Union[str, Any]				=			image_size
				__lowerCAmelCase :    int				=			num_channels
				__lowerCAmelCase :    int				=			embeddings_size
				__lowerCAmelCase :    Optional[Any]				=			hidden_sizes
				__lowerCAmelCase :    str				=			depths
				__lowerCAmelCase :    int				=			is_training
				__lowerCAmelCase :    str				=			use_labels
				__lowerCAmelCase :    List[Any]				=			hidden_act
				__lowerCAmelCase :    List[str]				=			num_labels
				__lowerCAmelCase :    Tuple				=			scope
				__lowerCAmelCase :    Dict				=			len(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			out_features
				__lowerCAmelCase :    Any				=			out_indices
				__lowerCAmelCase :    Dict				=			num_groups

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			int:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

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

				return config, pixel_values, labels

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			int:

				"""simple docstring"""

				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 				_SCREAMING_SNAKE_CASE  (	self:						Tuple			,     _SCREAMING_SNAKE_CASE:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str)							->			Tuple:

				"""simple docstring"""

				__lowerCAmelCase :    str				=			BitModel(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    Dict				=			model(_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(
				    result.last_hidden_state.shape			,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,     )

	def 				_SCREAMING_SNAKE_CASE  (	self:						str			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						Any			,     _SCREAMING_SNAKE_CASE:						List[str])							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			self.num_labels
				__lowerCAmelCase :    Optional[Any]				=			BitForImageClassification(_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    Tuple				=			model(_SCREAMING_SNAKE_CASE			,     labels=_SCREAMING_SNAKE_CASE)
				self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels))

	def 				_SCREAMING_SNAKE_CASE  (	self:						List[Any]			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str)							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			BitBackbone(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    Optional[int]				=			model(_SCREAMING_SNAKE_CASE)

				# 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 :    Tuple				=			None
				__lowerCAmelCase :    Tuple				=			BitBackbone(config=_SCREAMING_SNAKE_CASE)
				model.to(_SCREAMING_SNAKE_CASE)
				model.eval()
				__lowerCAmelCase :    str				=			model(_SCREAMING_SNAKE_CASE)

				# 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 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			str:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			self.prepare_config_and_inputs()
				__lowerCAmelCase		,      __lowerCAmelCase		,      __lowerCAmelCase :    int				=			config_and_inputs
				__lowerCAmelCase :    List[str]				=			{"pixel_values": pixel_values}
				return config, inputs_dict






@require_torch
class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE        =      (
	    {'feature-extraction': BitModel, 'image-classification': BitForImageClassification}
	    if is_torch_available()
	    else {}
	)

	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False
	SCREAMING_SNAKE_CASE        =      False

	def 				_SCREAMING_SNAKE_CASE  (	self:						Optional[Any])							->			Any:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			BitModelTester(self)
				__lowerCAmelCase :    Union[str, Any]				=			ConfigTester(self			,     config_class=_SCREAMING_SNAKE_CASE			,     has_text_modality=_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Any:

				"""simple docstring"""

				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 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			int:

				"""simple docstring"""

				return

	@unittest.skip(reason="Bit does not output attentions")
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			List[str]:

				"""simple docstring"""

				pass

	@unittest.skip(reason="Bit does not use inputs_embeds")
	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			int:

				"""simple docstring"""

				pass

	@unittest.skip(reason="Bit does not support input and output embeddings")
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Optional[Any]:

				"""simple docstring"""

				pass

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Tuple				=			self.model_tester.prepare_config_and_inputs_for_common()

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

							__lowerCAmelCase :    List[Any]				=			["pixel_values"]
							self.assertListEqual(arg_names[:1]			,     _SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    List[Any]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						Dict)							->			Optional[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*_SCREAMING_SNAKE_CASE)

	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Any:

				"""simple docstring"""

				__lowerCAmelCase		,      __lowerCAmelCase :    Any				=			self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
							__lowerCAmelCase :    List[str]				=			model_class(config=_SCREAMING_SNAKE_CASE)
							for name, module in model.named_modules():
										if isinstance(_SCREAMING_SNAKE_CASE			,     (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 				_SCREAMING_SNAKE_CASE  (	self:						List[Any])							->			Dict:

				"""simple docstring"""

				def check_hidden_states_output(_SCREAMING_SNAKE_CASE:						Optional[int]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						Tuple):
							__lowerCAmelCase :    Optional[int]				=			model_class(_SCREAMING_SNAKE_CASE)
							model.to(_SCREAMING_SNAKE_CASE)
							model.eval()

							with torch.no_grad():
										__lowerCAmelCase :    Dict				=			model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE))

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

							__lowerCAmelCase :    str				=			self.model_tester.num_stages
							self.assertEqual(len(_SCREAMING_SNAKE_CASE)			,     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		,      __lowerCAmelCase :    Optional[Any]				=			self.model_tester.prepare_config_and_inputs_for_common()
				__lowerCAmelCase :    Dict				=			["preactivation", "bottleneck"]
				for model_class in self.all_model_classes:
							for layer_type in layers_type:
										__lowerCAmelCase :    Optional[Any]				=			layer_type
										__lowerCAmelCase :    str				=			True
										check_hidden_states_output(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

										# check that output_hidden_states also work using config
										del inputs_dict["output_hidden_states"]
										__lowerCAmelCase :    Union[str, Any]				=			True

										check_hidden_states_output(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

	@unittest.skip(reason="Bit does not use feedforward chunking")
	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			List[Any]:

				"""simple docstring"""

				pass

	def 				_SCREAMING_SNAKE_CASE  (	self:						Union[str, Any])							->			List[Any]:

				"""simple docstring"""

				__lowerCAmelCase :    Optional[int]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE)







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						str)							->			Dict:

				"""simple docstring"""

				for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
							__lowerCAmelCase :    Tuple				=			BitModel.from_pretrained(_SCREAMING_SNAKE_CASE)
							self.assertIsNotNone(_SCREAMING_SNAKE_CASE)






def      _lowercase							(    )		->  int:
			__lowerCAmelCase :    Optional[int]				=			Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"      )
			return image



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




	'''simple docstring'''





	@cached_property
	def 				_SCREAMING_SNAKE_CASE  (	self:						List[str])							->			Dict:

				"""simple docstring"""

				return (
				    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) if is_vision_available() else None
				)







	@slow
	def 				_SCREAMING_SNAKE_CASE  (	self:						int)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Any				=			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    int				=			self.default_image_processor
				__lowerCAmelCase :    str				=			prepare_img()
				__lowerCAmelCase :    Any				=			image_processor(images=_SCREAMING_SNAKE_CASE			,     return_tensors="pt").to(_SCREAMING_SNAKE_CASE)

				# forward pass
				with torch.no_grad():
							__lowerCAmelCase :    str				=			model(**_SCREAMING_SNAKE_CASE)

				# verify the logits
				__lowerCAmelCase :    Dict				=			torch.Size((1, 1000))
				self.assertEqual(outputs.logits.shape			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    int				=			torch.tensor([[-0.6526, -0.5263, -1.4398]]).to(_SCREAMING_SNAKE_CASE)

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






@require_torch
class      A__  (    __SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):




	'''simple docstring'''





	SCREAMING_SNAKE_CASE        =      (BitBackbone,) if is_torch_available() else ()
	SCREAMING_SNAKE_CASE        =      BitConfig

	SCREAMING_SNAKE_CASE        =      False

	def 				_SCREAMING_SNAKE_CASE  (	self:						Any)							->			Dict:

				"""simple docstring"""

				__lowerCAmelCase :    Dict				=			BitModelTester(self)




"""simple docstring"""






import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin



class      A__  (    __SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE    ,						__SCREAMING_SNAKE_CASE				):




	'''simple docstring'''





	@register_to_config
	def __init__(	self:						List[str]			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						float			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						int			,     _SCREAMING_SNAKE_CASE:						str			,     _SCREAMING_SNAKE_CASE:						bool = False			,     )							->			Any:

				"""simple docstring"""

				super().__init__()

				__lowerCAmelCase :    str				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			nn.Embedding(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    str				=			False

				__lowerCAmelCase :    Optional[Any]				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaConfig(
				    vocab_size=_SCREAMING_SNAKE_CASE			,     d_model=_SCREAMING_SNAKE_CASE			,     num_heads=_SCREAMING_SNAKE_CASE			,     d_kv=_SCREAMING_SNAKE_CASE			,     d_ff=_SCREAMING_SNAKE_CASE			,     dropout_rate=_SCREAMING_SNAKE_CASE			,     feed_forward_proj=_SCREAMING_SNAKE_CASE			,     is_decoder=_SCREAMING_SNAKE_CASE			,     is_encoder_decoder=_SCREAMING_SNAKE_CASE			,     )

				__lowerCAmelCase :    str				=			nn.ModuleList()
				for lyr_num in range(_SCREAMING_SNAKE_CASE):
							__lowerCAmelCase :    int				=			TaBlock(_SCREAMING_SNAKE_CASE)
							self.encoders.append(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Dict				=			TaLayerNorm(_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase :    Any				=			nn.Dropout(p=_SCREAMING_SNAKE_CASE)







	def 				_SCREAMING_SNAKE_CASE  (	self:						Any			,     _SCREAMING_SNAKE_CASE:						Tuple			,     _SCREAMING_SNAKE_CASE:						str)							->			List[str]:

				"""simple docstring"""

				__lowerCAmelCase :    Union[str, Any]				=			self.token_embedder(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    Optional[Any]				=			encoder_input_tokens.shape[1]
				__lowerCAmelCase :    List[Any]				=			torch.arange(_SCREAMING_SNAKE_CASE			,     device=encoder_input_tokens.device)
				x += self.position_encoding(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase :    List[Any]				=			self.dropout_pre(_SCREAMING_SNAKE_CASE)

				# inverted the attention mask
				__lowerCAmelCase :    List[Any]				=			encoder_input_tokens.size()
				__lowerCAmelCase :    Any				=			self.get_extended_attention_mask(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)

				for lyr in self.encoders:
							__lowerCAmelCase :    Union[str, Any]				=			lyr(_SCREAMING_SNAKE_CASE			,     _SCREAMING_SNAKE_CASE)[0]
				__lowerCAmelCase :    int				=			self.layer_norm(_SCREAMING_SNAKE_CASE)

				return self.dropout_post(_SCREAMING_SNAKE_CASE), encoder_inputs_mask