

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  lowercase				:




				def __init__(					self      ,   snake_case      ,   snake_case=3      ,   snake_case=32      ,   snake_case=3      ,   snake_case=10      ,   snake_case=[8, 16, 32, 64]      ,   snake_case=[1, 1, 2, 1]      ,   snake_case=True      ,   snake_case=True      ,   snake_case="relu"      ,   snake_case=3      ,   snake_case=None      ,   snake_case=["stage2", "stage3", "stage4"]      ,   snake_case=[2, 3, 4]      ,   snake_case=1      ,   ):
											snake_case_							=    parent
											snake_case_							=    batch_size
											snake_case_							=    image_size
											snake_case_							=    num_channels
											snake_case_							=    embeddings_size
											snake_case_							=    hidden_sizes
											snake_case_							=    depths
											snake_case_							=    is_training
											snake_case_							=    use_labels
											snake_case_							=    hidden_act
											snake_case_							=    num_labels
											snake_case_							=    scope
											snake_case_							=    len(snake_case       )
											snake_case_							=    out_features
											snake_case_							=    out_indices
											snake_case_							=    num_groups




				def     a							(					self       ):
											snake_case_							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

											snake_case_							=    None
											if self.use_labels:
																		snake_case_							=    ids_tensor([self.batch_size]      ,   self.num_labels       )

											snake_case_							=    self.get_config()

											return config, pixel_values, labels




				def     a							(					self       ):
											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      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    BitModel(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(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     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    self.num_labels
											snake_case_							=    BitForImageClassification(snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(snake_case      ,   labels=snake_case       )
											self.parent.assertEqual(result.logits.shape      ,   (self.batch_size, self.num_labels)       )




				def     a							(					self      ,   snake_case      ,   snake_case      ,   snake_case       ):
											snake_case_							=    BitBackbone(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(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
											snake_case_							=    None
											snake_case_							=    BitBackbone(config=snake_case       )
											model.to(snake_case       )
											model.eval()
											snake_case_							=    model(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     a							(					self       ):
											snake_case_							=    self.prepare_config_and_inputs()
											snake_case_				,    snake_case_				,    snake_case_							=    config_and_inputs
											snake_case_							=    {'pixel_values': pixel_values}
											return config, inputs_dict







@require_torch
class  lowercase				(       lowercase_	,  lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Any       			=			(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE					: List[str]       			=			(
				    {'''feature-extraction''': BitModel, '''image-classification''': BitForImageClassification}
				    if is_torch_available()
				    else {}
				)

				__SCREAMING_SNAKE_CASE					: List[Any]       			=			False
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			False
				__SCREAMING_SNAKE_CASE					: str       			=			False
				__SCREAMING_SNAKE_CASE					: List[Any]       			=			False
				__SCREAMING_SNAKE_CASE					: List[str]       			=			False




				def     a							(					self       ):
											snake_case_							=    BitModelTester(self       )
											snake_case_							=    ConfigTester(self      ,   config_class=snake_case      ,   has_text_modality=snake_case       )




				def     a							(					self       ):
											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       ):
											return




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




				@unittest.skip(reason='Bit does not use inputs_embeds'       )
				def     a							(					self       ):
											pass




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




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

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

																		snake_case_							=    ['pixel_values']
																		self.assertListEqual(arg_names[:1]      ,   snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*snake_case       )




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_backbone(*snake_case       )




				def     a							(					self       ):
											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		snake_case_							=    model_class(config=snake_case       )
																		for name, module in model.named_modules():
																									if isinstance(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     a							(					self       ):
											def check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case       ):
																		snake_case_							=    model_class(snake_case       )
																		model.to(snake_case       )
																		model.eval()

																		with torch.no_grad():
																									snake_case_							=    model(**self._prepare_for_class(snake_case      ,   snake_case       )       )

																		snake_case_							=    outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

																		snake_case_							=    self.model_tester.num_stages
																		self.assertEqual(len(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]      ,   )

											snake_case_				,    snake_case_							=    self.model_tester.prepare_config_and_inputs_for_common()
											snake_case_							=    ['preactivation', 'bottleneck']
											for model_class in self.all_model_classes:
																		for layer_type in layers_type:
																									snake_case_							=    layer_type
																									snake_case_							=    True
																									check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case       )

																									# check that output_hidden_states also work using config
																									del inputs_dict["output_hidden_states"]
																									snake_case_							=    True

																									check_hidden_states_output(snake_case      ,   snake_case      ,   snake_case       )




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




				def     a							(					self       ):
											snake_case_							=    self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*snake_case       )




				@slow
				def     a							(					self       ):
											for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																		snake_case_							=    BitModel.from_pretrained(snake_case       )
																		self.assertIsNotNone(snake_case       )





def 					__lowerCamelCase	( ):







							'''simple docstring'''






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







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




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




				@slow
				def     a							(					self       ):
											snake_case_							=    BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]       ).to(snake_case       )

											snake_case_							=    self.default_image_processor
											snake_case_							=    prepare_img()
											snake_case_							=    image_processor(images=snake_case      ,   return_tensors='pt'       ).to(snake_case       )

											# forward pass
											with torch.no_grad():
																		snake_case_							=    model(**snake_case       )

											# verify the logits
											snake_case_							=    torch.Size((1, 1000)       )
											self.assertEqual(outputs.logits.shape      ,   snake_case       )

											snake_case_							=    torch.tensor([[-0.65_26, -0.52_63, -1.43_98]]       ).to(snake_case       )

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







@require_torch
class  lowercase				(       lowercase_	,  unittest.TestCase			):
				__SCREAMING_SNAKE_CASE					: Dict       			=			(BitBackbone,) if is_torch_available() else ()
				__SCREAMING_SNAKE_CASE					: Optional[int]       			=			BitConfig

				__SCREAMING_SNAKE_CASE					: List[Any]       			=			False




				def     a							(					self       ):
											snake_case_							=    BitModelTester(self       )







import numpy as np





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return 1 / (1 + np.exp(-vector      ))





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return vector * sigmoid(UpperCamelCase__      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()




