






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 				_UpperCamelCase      :


      def __init__(						self       ,    __UpperCamelCase       ,    __UpperCamelCase=3       ,    __UpperCamelCase=3_2       ,    __UpperCamelCase=3       ,    __UpperCamelCase=1_0       ,    __UpperCamelCase=[8, 1_6, 3_2, 6_4]       ,    __UpperCamelCase=[1, 1, 2, 1]       ,    __UpperCamelCase=True       ,    __UpperCamelCase=True       ,    __UpperCamelCase="relu"       ,    __UpperCamelCase=3       ,    __UpperCamelCase=None       ,    __UpperCamelCase=["stage2", "stage3", "stage4"]       ,    __UpperCamelCase=[2, 3, 4]       ,    __UpperCamelCase=1       ,    )->						Any:
           __lowerCAmelCase             =       parent
           __lowerCAmelCase             =       batch_size
           __lowerCAmelCase             =       image_size
           __lowerCAmelCase             =       num_channels
           __lowerCAmelCase             =       embeddings_size
           __lowerCAmelCase             =       hidden_sizes
           __lowerCAmelCase             =       depths
           __lowerCAmelCase             =       is_training
           __lowerCAmelCase             =       use_labels
           __lowerCAmelCase             =       hidden_act
           __lowerCAmelCase             =       num_labels
           __lowerCAmelCase             =       scope
           __lowerCAmelCase             =       len(__UpperCamelCase						)
           __lowerCAmelCase             =       out_features
           __lowerCAmelCase             =       out_indices
           __lowerCAmelCase             =       num_groups


      def 			__UpperCAmelCase		(						self						)->						Tuple:
           __lowerCAmelCase             =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

           __lowerCAmelCase             =       self.get_config()

           return config, pixel_values, labels


      def 			__UpperCAmelCase		(						self						)->						int:
           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 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Tuple:
           __lowerCAmelCase             =       BitModel(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)
           self.parent.assertEqual(
               result.last_hidden_state.shape       ,    (self.batch_size, self.hidden_sizes[-1], self.image_size // 3_2, self.image_size // 3_2)       ,    )


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						Optional[int]:
           __lowerCAmelCase             =       self.num_labels
           __lowerCAmelCase             =       BitForImageClassification(__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase       ,    labels=__UpperCamelCase						)
           self.parent.assertEqual(result.logits.shape       ,    (self.batch_size, self.num_labels)						)


      def 			__UpperCAmelCase		(						self       ,    __UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)->						int:
           __lowerCAmelCase             =       BitBackbone(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)

           # 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             =       None
           __lowerCAmelCase             =       BitBackbone(config=__UpperCamelCase						)
           model.to(__UpperCamelCase						)
           model.eval()
           __lowerCAmelCase             =       model(__UpperCamelCase						)

           # 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 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       self.prepare_config_and_inputs()
           __lowerCAmelCase      ,			__lowerCAmelCase      ,			__lowerCAmelCase             =       config_and_inputs
           __lowerCAmelCase             =       {"pixel_values": pixel_values}
           return config, inputs_dict



@require_torch
class 				_UpperCamelCase      (a_				,		a_				,		unittest.TestCase      ):
      snake_case_	=				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
      snake_case_	=				(
          {"""feature-extraction""": BitModel, """image-classification""": BitForImageClassification}
          if is_torch_available()
          else {}
      )

      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False
      snake_case_	=				False


      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           __lowerCAmelCase             =       BitModelTester(self						)
           __lowerCAmelCase             =       ConfigTester(self       ,    config_class=__UpperCamelCase       ,    has_text_modality=__UpperCamelCase						)


      def 			__UpperCAmelCase		(						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 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           return


      @unittest.skip(reason="Bit does not output attentions"						)
      def 			__UpperCAmelCase		(						self						)->						str:
           pass


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


      @unittest.skip(reason="Bit does not support input and output embeddings"						)
      def 			__UpperCAmelCase		(						self						)->						Dict:
           pass


      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

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

                __lowerCAmelCase             =       ["pixel_values"]
                self.assertListEqual(arg_names[:1]       ,    __UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Any:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_backbone(*__UpperCamelCase						)


      def 			__UpperCAmelCase		(						self						)->						List[Any]:
           __lowerCAmelCase      ,			__lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs_for_common()

           for model_class in self.all_model_classes:
                __lowerCAmelCase             =       model_class(config=__UpperCamelCase						)
                for name, module in model.named_modules():
                     if isinstance(__UpperCamelCase       ,    (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 			__UpperCAmelCase		(						self						)->						List[Any]:
           def check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						):
                __lowerCAmelCase             =       model_class(__UpperCamelCase						)
                model.to(__UpperCamelCase						)
                model.eval()

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

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

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

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

                     check_hidden_states_output(__UpperCamelCase       ,    __UpperCamelCase       ,    __UpperCamelCase						)


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


      def 			__UpperCAmelCase		(						self						)->						Dict:
           __lowerCAmelCase             =       self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*__UpperCamelCase						)


      @slow
      def 			__UpperCAmelCase		(						self						)->						Optional[Any]:
           for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                __lowerCAmelCase             =       BitModel.from_pretrained(__UpperCamelCase						)
                self.assertIsNotNone(__UpperCamelCase						)





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



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


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


      @slow
      def 			__UpperCAmelCase		(						self						)->						Union[str, Any]:
           __lowerCAmelCase             =       BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(__UpperCamelCase						)

           __lowerCAmelCase             =       self.default_image_processor
           __lowerCAmelCase             =       prepare_img()
           __lowerCAmelCase             =       image_processor(images=__UpperCamelCase       ,    return_tensors="pt"						).to(__UpperCamelCase						)

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

           # verify the logits
           __lowerCAmelCase             =       torch.Size((1, 1_0_0_0)						)
           self.assertEqual(outputs.logits.shape       ,    __UpperCamelCase						)

           __lowerCAmelCase             =       torch.tensor([[-0.6_5_2_6, -0.5_2_6_3, -1.4_3_9_8]]						).to(__UpperCamelCase						)

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



@require_torch
class 				_UpperCamelCase      (a_				,		unittest.TestCase      ):
      snake_case_	=				(BitBackbone,) if is_torch_available() else ()
      snake_case_	=				BitConfig

      snake_case_	=				False


      def 			__UpperCAmelCase		(						self						)->						Optional[int]:
           __lowerCAmelCase             =       BitModelTester(self						)














import os

# Precomputes a list of the 100 first triangular numbers
lowerCamelCase      :       str    =						[int(0.5 * n * (n + 1)) for n in range(1, 101)]





def    __lowerCAmelCase   (    ):
     __lowerCAmelCase             =       os.path.dirname(os.path.realpath(__snake_case							)							)
     __lowerCAmelCase             =       os.path.join(__snake_case       ,			"words.txt"							)

     __lowerCAmelCase             =       ""
     with open(__snake_case							) as f:
          __lowerCAmelCase             =       f.readline()

     __lowerCAmelCase             =       [word.strip("\""							) for word in words.strip("\r\n"							).split(","							)]
     __lowerCAmelCase             =       [
         word
         for word in [sum(ord(__snake_case							) - 64 for x in word							) for word in words]
         if word in TRIANGULAR_NUMBERS
     ]
     return len(__snake_case							)


if __name__ == "__main__":
 print(solution())






