



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 	lowerCamelCase_    ( UpperCamelCase_						):






						'''simple docstring'''
						a__   =  ["image_processor", "tokenizer"]
						a__   =  "AutoImageProcessor"
						a__   =  "AutoTokenizer"







						def __init__(	self      :			List[str]	,	__lowerCamelCase      :			List[str]=None	,	__lowerCamelCase      :			Optional[Any]=None	,	**__lowerCamelCase      :			Union[str, Any] )      ->				Any:
									A							:					str              =						None
									if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."	,	__a	,	)
												A							:					Any              =						kwargs.pop("feature_extractor" )

									A							:					Any              =						image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
												raise ValueError("You need to specify an `image_processor`." )
									if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`." )

									super().__init__(__a	,	__a )
									A							:					List[str]              =						self.image_processor
									A							:					int              =						False







						def __call__(	self      :			Optional[Any]	,	*__lowerCamelCase      :			List[Any]	,	**__lowerCamelCase      :			Optional[int] )      ->				List[Any]:






									if self._in_target_context_manager:
												return self.current_processor(*__a	,	**__a )

									A							:					Tuple              =						kwargs.pop("images"	,	__a )
									A							:					str              =						kwargs.pop("text"	,	__a )
									if len(__a ) > 0:
												A							:					Union[str, Any]              =						args[0]
												A							:					Optional[Any]              =						args[1:]

									if images is None and text is None:
												raise ValueError("You need to specify either an `images` or `text` input to process." )

									if images is not None:
												A							:					int              =						self.image_processor(__a	,	*__a	,	**__a )
									if text is not None:
												A							:					Tuple              =						self.tokenizer(__a	,	**__a )

									if text is None:
												return inputs
									elif images is None:
												return encodings
									else:
												A							:					List[Any]              =						encodings['input_ids']
												return inputs







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int	,	*__lowerCamelCase      :			List[Any]	,	**__lowerCamelCase      :			List[Any] )      ->				Tuple:
									return self.tokenizer.batch_decode(*__a	,	**__a )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int]	,	*__lowerCamelCase      :			Any	,	**__lowerCamelCase      :			Optional[int] )      ->				int:
									return self.tokenizer.decode(*__a	,	**__a )







						@contextmanager
						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[Any] )      ->				Any:
									warnings.warn(
									    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
									    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
									    "your images inputs, or in a separate call." )
									A							:					List[str]              =						True
									A							:					Optional[Any]              =						self.tokenizer
									yield
									A							:					Dict              =						self.image_processor
									A							:					List[Any]              =						False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			Dict=False	,	__lowerCamelCase      :			Optional[Any]=None )      ->				Optional[Any]:






									if added_vocab is None:
												A							:					int              =						self.tokenizer.get_added_vocab()

									A							:					Any              =						{}

									while tokens:
												A							:					Any              =						re.search(r"<s_(.*?)>"	,	__a	,	re.IGNORECASE )
												if start_token is None:
															break
												A							:					int              =						start_token.group(1 )
												A							:					Tuple              =						re.search(rF"""</s_{key}>"""	,	__a	,	re.IGNORECASE )
												A							:					Any              =						start_token.group()
												if end_token is None:
															A							:					int              =						tokens.replace(__a	,	"" )
												else:
															A							:					str              =						end_token.group()
															A							:					Optional[int]              =						re.escape(__a )
															A							:					Tuple              =						re.escape(__a )
															A							:					Optional[int]              =						re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""	,	__a	,	re.IGNORECASE )
															if content is not None:
																		A							:					Union[str, Any]              =						content.group(1 ).strip()
																		if r"<s_" in content and r"</s_" in content:  # non-leaf node
																					A							:					Union[str, Any]              =						self.tokenajson(__a	,	is_inner_value=__a	,	added_vocab=__a )
																					if value:
																								if len(__a ) == 1:
																											A							:					Optional[Any]              =						value[0]
																								A							:					Union[str, Any]              =						value
																		else:  # leaf nodes
																					A							:					Any              =						[]
																					for leaf in content.split(r"<sep/>" ):
																								A							:					List[Any]              =						leaf.strip()
																								if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																											A							:					Optional[Any]              =						leaf[1:-2]  # for categorical special tokens
																								output[key].append(__a )
																					if len(output[key] ) == 1:
																								A							:					Optional[Any]              =						output[key][0]

															A							:					str              =						tokens[tokens.find(__a ) + len(__a ) :].strip()
															if tokens[:6] == r"<sep/>":  # non-leaf nodes
																		return [output] + self.tokenajson(tokens[6:]	,	is_inner_value=__a	,	added_vocab=__a )

									if len(__a ):
												return [output] if is_inner_value else output
									else:
												return [] if is_inner_value else {"text_sequence": tokens}







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Optional[Any]:
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."	,	__a	,	)
									return self.image_processor_class







						@property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Any:
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."	,	__a	,	)
									return self.image_processor




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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Optional[Any]:
									A							:					Optional[Any]              =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

									A							:					Tuple              =						self.get_config()

									return config, pixel_values, labels







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any]	,	__lowerCamelCase      :			Any	,	__lowerCamelCase      :			Optional[Any]	,	__lowerCamelCase      :			str )      ->				List[Any]:
									A							:					Dict              =						BitBackbone(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					Optional[Any]              =						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
									A							:					Optional[Any]              =						None
									A							:					Optional[int]              =						BitBackbone(config=__lowerCamelCase )
									model.to(__lowerCamelCase )
									model.eval()
									A							:					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 			SCREAMING_SNAKE_CASE__		(	self      :			Tuple )      ->				Dict:
									A							:					List[str]              =						self.prepare_config_and_inputs()
									A       , A       , A							:					Tuple              =						config_and_inputs
									A							:					Tuple              =						{"pixel_values": pixel_values}
									return config, inputs_dict







@require_torch
class 	lowerCamelCase_    ( _A						,_A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
						a__   =  (
						    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
						    if is_torch_available()
						    else {}
						)

						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False
						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Dict:
									A							:					Any              =						BitModelTester(self )
									A							:					Optional[int]              =						ConfigTester(self	,	config_class=__lowerCamelCase	,	has_text_modality=__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				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 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Union[str, Any]:
									return







						@unittest.skip(reason="Bit does not output attentions" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				List[str]:
									pass







						@unittest.skip(reason="Bit does not use inputs_embeds" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Dict )      ->				Optional[Any]:
									pass







						@unittest.skip(reason="Bit does not support input and output embeddings" )
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				List[str]:
									pass







						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				str:
									A       , A							:					int              =						self.model_tester.prepare_config_and_inputs_for_common()

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

												A							:					List[str]              =						["pixel_values"]
												self.assertListEqual(arg_names[:1]	,	__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Union[str, Any]:
									A							:					List[Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[str]:
									A							:					int              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__lowerCamelCase )







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[Any] )      ->				Optional[int]:
									A       , A							:					List[str]              =						self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												A							:					Optional[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 			SCREAMING_SNAKE_CASE__		(	self      :			Union[str, Any] )      ->				Optional[Any]:
									def check_hidden_states_output(__lowerCamelCase      :			str	,	__lowerCamelCase      :			Tuple	,	__lowerCamelCase      :			Union[str, Any] ):
												A							:					Dict              =						model_class(__lowerCamelCase )
												model.to(__lowerCamelCase )
												model.eval()

												with torch.no_grad():
															A							:					List[Any]              =						model(**self._prepare_for_class(__lowerCamelCase	,	__lowerCamelCase ) )

												A							:					List[str]              =						outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

												A							:					List[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]	,	)

									A       , A							:					Optional[int]              =						self.model_tester.prepare_config_and_inputs_for_common()
									A							:					Dict              =						["preactivation", "bottleneck"]
									for model_class in self.all_model_classes:
												for layer_type in layers_type:
															A							:					Dict              =						layer_type
															A							:					Union[str, Any]              =						True
															check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )

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

															check_hidden_states_output(__lowerCamelCase	,	__lowerCamelCase	,	__lowerCamelCase )







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







						def 			SCREAMING_SNAKE_CASE__		(	self      :			List[str] )      ->				Optional[int]:
									A							:					Union[str, Any]              =						self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				List[Any]:
									for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												A							:					Optional[Any]              =						BitModel.from_pretrained(__lowerCamelCase )
												self.assertIsNotNone(__lowerCamelCase )







def 	UpperCAmelCase   (						):
			A							:					Tuple              =						Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
			return image

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






						'''simple docstring'''







						@cached_property
						def 			SCREAMING_SNAKE_CASE__		(	self      :			int )      ->				Union[str, Any]:
									return (
									    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ) if is_vision_available() else None
									)







						@slow
						def 			SCREAMING_SNAKE_CASE__		(	self      :			Any )      ->				Dict:
									A							:					Union[str, Any]              =						BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(__lowerCamelCase )

									A							:					List[Any]              =						self.default_image_processor
									A							:					List[Any]              =						prepare_img()
									A							:					Tuple              =						image_processor(images=__lowerCamelCase	,	return_tensors="pt" ).to(__lowerCamelCase )

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

									# verify the logits
									A							:					str              =						torch.Size((1, 10_00) )
									self.assertEqual(outputs.logits.shape	,	__lowerCamelCase )

									A							:					Optional[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_    ( _A						,unittest.TestCase						):






						'''simple docstring'''
						a__   =  (BitBackbone,) if is_torch_available() else ()
						a__   =  BitConfig

						a__   =  False







						def 			SCREAMING_SNAKE_CASE__		(	self      :			Optional[int] )      ->				Optional[int]:
									A							:					Union[str, Any]              =						BitModelTester(self )