


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





     def  a_       (    self					:				List[Any]							):
        __lowerCamelCase:				int     =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

        __lowerCamelCase:				int     =	self.get_config()

        return config, pixel_values, labels





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





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



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

     UpperCAmelCase__ :  List[str]						      =	False
     UpperCAmelCase__ :  Any						      =	False
     UpperCAmelCase__ :  Optional[int]						      =	False
     UpperCAmelCase__ :  Optional[int]						      =	False
     UpperCAmelCase__ :  List[str]						      =	False





     def  a_       (    self					:				str							):
        __lowerCamelCase:				int     =	BitModelTester(self							)
        __lowerCamelCase:				Optional[int]     =	ConfigTester(self    ,	config_class=lowercase_    ,	has_text_modality=lowercase_							)





     def  a_       (    self					:				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					:				List[str]							):
        return





     @unittest.skip(reason="""Bit does not output attentions"""							)
     def  a_       (    self					:				Any							):
        pass





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





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





     def  a_       (    self					:				Dict							):
        __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

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

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





     def  a_       (    self					:				Any							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase_							)





     def  a_       (    self					:				Tuple							):
        __lowerCamelCase:				Any     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_backbone(*lowercase_							)





     def  a_       (    self					:				Any							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				List[Any]     =	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  a_       (    self					:				Optional[Any]							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				int    ,	SCREAMING_SNAKE_CASE_					:				int							):
           __lowerCamelCase:				Optional[Any]     =	model_class(lowercase_							)
           model.to(lowercase_							)
           model.eval()

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

           __lowerCamelCase:				str     =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

           __lowerCamelCase:				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]    ,	)

        __lowerCamelCase:				List[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:				Union[str, Any]     =	layer_type
              __lowerCamelCase:				Optional[Any]     =	True
              check_hidden_states_output(lowercase_    ,	lowercase_    ,	lowercase_							)

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

              check_hidden_states_output(lowercase_    ,	lowercase_    ,	lowercase_							)





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





     def  a_       (    self					:				Optional[Any]							):
        __lowerCamelCase:				Tuple     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase_							)







     @slow
     def  a_       (    self					:				Tuple							):
        for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				List[str]     =	BitModel.from_pretrained(lowercase_							)
           self.assertIsNotNone(lowercase_							)



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



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





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







     @slow
     def  a_       (    self					:				int							):
        __lowerCamelCase:				List[str]     =	BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]							).to(lowercase_							)

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

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				str     =	model(**lowercase_							)

        # verify the logits
        __lowerCamelCase:				Optional[int]     =	torch.Size((1, 1000)							)
        self.assertEqual(outputs.logits.shape    ,	lowercase_							)

        __lowerCamelCase:				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					(			_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Union[str, Any]						      =	(BitBackbone,) if is_torch_available() else ()
     UpperCAmelCase__ :  Any						      =	BitConfig

     UpperCAmelCase__ :  Optional[Any]						      =	False





     def  a_       (    self					:				List[Any]							):
        __lowerCamelCase:				Union[str, Any]     =	BitModelTester(self							)






def     __lowerCAmelCase						(							snake_case	:    int = 1000000 )       ->							int:
   __lowerCamelCase:				Union[str, Any]     =	set(range(3						,      snake_case						,      2 ) )
   primes.add(2 )
   for p in range(3						,      snake_case						,      2 ):
      if p not in primes:
         continue
      primes.difference_update(set(range(p * p						,      snake_case						,      snake_case ) ) )

   __lowerCamelCase:				Any     =	[float(snake_case ) for n in range(limit + 1 )]

   for p in primes:
      for n in range(snake_case						,      limit + 1						,      snake_case ):
         phi[n] *= 1 - 1 / p

   return int(sum(phi[2:] ) )


if __name__ == "__main__":
   print(F"""{solution() = }""")


