"""simple docstring"""
from __future__ import annotations

from typing import Any







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->Dict:







  if not postfix_notation:
    return 0

  a__:					Union[str, Any]      							=     {'+', '-', '*', '/'}
  a__:					List[str]      							=     []

  for token in postfix_notation:
    if token in operations:
      a__							,   a__:					List[Any]      							=     stack.pop(), stack.pop()
      if token == "+":
        stack.append(a + b       )
      elif token == "-":
        stack.append(a - b       )
      elif token == "*":
        stack.append(a * b       )
      else:
        if a * b < 0 and a % b != 0:
          stack.append(a // b + 1       )
        else:
          stack.append(a // b       )
    else:
      stack.append(int(UpperCamelCase__       )       )

  return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()



"""simple docstring"""
import inspect
import unittest

from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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 RegNetForImageClassification, RegNetModel
 from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor




class        __snake_case	:



      def __init__(						self   ,      lowercase   ,      lowercase=3   ,      lowercase=32   ,      lowercase=3   ,      lowercase=10   ,      lowercase=[10, 20, 30, 40]   ,      lowercase=[1, 1, 2, 1]   ,      lowercase=True   ,      lowercase=True   ,      lowercase="relu"   ,      lowercase=3   ,      lowercase=None   ,      )     ->      Any:





        '''simple docstring'''





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



      def   lowerCamelCase_       (						self)     ->      int:





        '''simple docstring'''





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

        a__:					str      							=     None
        if self.use_labels:
          a__:					Dict      							=     ids_tensor([self.batch_size]   ,      self.num_labels)

        a__:					int      							=     self.get_config()

        return config, pixel_values, labels



      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        return RegNetConfig(
            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   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      Any:





        '''simple docstring'''





        a__:					Dict      							=     RegNetModel(config=lowercase)
        model.to(lowercase)
        model.eval()
        a__:					List[str]      							=     model(lowercase)
        # expected last hidden states: B, C, H // 32, W // 32
        self.parent.assertEqual(
            result.last_hidden_state.shape   ,      (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)   ,      )



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Tuple      							=     self.num_labels
        a__:					Tuple      							=     RegNetForImageClassification(lowercase)
        model.to(lowercase)
        model.eval()
        a__:					Any      							=     model(lowercase   ,      labels=lowercase)
        self.parent.assertEqual(result.logits.shape   ,      (self.batch_size, self.num_labels))



      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        a__:					int      							=     self.prepare_config_and_inputs()
        a__							,   a__							,   a__:					List[Any]      							=     config_and_inputs
        a__:					Union[str, Any]      							=     {'pixel_values': pixel_values}
        return config, inputs_dict







@require_torch
class        __snake_case	(					__lowerCAmelCase	,				__lowerCAmelCase	,				unittest.TestCase    ):
      a__				=		(RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
      a__				=		(
          {"""feature-extraction""": RegNetModel, """image-classification""": RegNetForImageClassification}
          if is_torch_available()
          else {}
      )

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



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__:					Dict      							=     RegNetModelTester(self)
        a__:					Dict      							=     ConfigTester(self   ,      config_class=lowercase   ,      has_text_modality=lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''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   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        return



      @unittest.skip(reason='RegNet does not use inputs_embeds')
      def   lowerCamelCase_       (						self)     ->      List[str]:





        '''simple docstring'''





        pass



      @unittest.skip(reason='RegNet does not support input and output embeddings')
      def   lowerCamelCase_       (						self)     ->      Optional[Any]:





        '''simple docstring'''





        pass



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__							,   a__:					str      							=     self.model_tester.prepare_config_and_inputs_for_common()

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

          a__:					List[str]      							=     ['pixel_values']
          self.assertListEqual(arg_names[:1]   ,      lowercase)



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





        '''simple docstring'''





        a__:					Any      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowercase)



      def   lowerCamelCase_       (						self)     ->      Dict:





        '''simple docstring'''





        a__							,   a__:					int      							=     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          a__:					int      							=     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   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        def check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase):
          a__:					int      							=     model_class(lowercase)
          model.to(lowercase)
          model.eval()

          with torch.no_grad():
            a__:					int      							=     model(**self._prepare_for_class(lowercase   ,      lowercase))

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

          a__:					List[str]      							=     self.model_tester.num_stages
          self.assertEqual(len(lowercase)   ,      expected_num_stages + 1)

          # RegNet'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 // 2, self.model_tester.image_size // 2]   ,      )

        a__							,   a__:					Optional[Any]      							=     self.model_tester.prepare_config_and_inputs_for_common()
        a__:					Dict      							=     ['basic', 'bottleneck']
        for model_class in self.all_model_classes:
          for layer_type in layers_type:
            a__:					Dict      							=     layer_type
            a__:					Dict      							=     True
            check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase)

            # check that output_hidden_states also work using config
            del inputs_dict["output_hidden_states"]
            a__:					str      							=     True

            check_hidden_states_output(lowercase   ,      lowercase   ,      lowercase)



      def   lowerCamelCase_       (						self)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*lowercase)



      @slow
      def   lowerCamelCase_       (						self)     ->      str:





        '''simple docstring'''





        for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          a__:					Tuple      							=     RegNetModel.from_pretrained(lowercase)
          self.assertIsNotNone(lowercase)







def 						__a       (     )     ->Dict:
  a__:					str      							=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
  return image




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



      @cached_property
      def   lowerCamelCase_       (						self)     ->      Any:





        '''simple docstring'''





        return (
            AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0])
            if is_vision_available()
            else None
        )



      @slow
      def   lowerCamelCase_       (						self)     ->      Optional[int]:





        '''simple docstring'''





        a__:					Tuple      							=     RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(lowercase)

        a__:					Tuple      							=     self.default_image_processor
        a__:					str      							=     prepare_img()
        a__:					Optional[int]      							=     image_processor(images=lowercase   ,      return_tensors='pt').to(lowercase)

        # forward pass
        with torch.no_grad():
          a__:					List[Any]      							=     model(**lowercase)

        # verify the logits
        a__:					Tuple      							=     torch.Size((1, 10_00))
        self.assertEqual(outputs.logits.shape   ,      lowercase)

        a__:					Optional[Any]      							=     torch.tensor([-0.4180, -1.5051, -3.4836]).to(lowercase)

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


