

import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

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 transformers import EfficientNetForImageClassification, EfficientNetModel
      from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor


class      lowerCamelCase_ :




 def __init__(			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase=13  ,  _UpperCAmelCase=32  ,  _UpperCAmelCase=3  ,  _UpperCAmelCase=[3, 3, 5]  ,  _UpperCAmelCase=[32, 16, 24]  ,  _UpperCAmelCase=[16, 24, 40]  ,  _UpperCAmelCase=[1, 1, 2]  ,  _UpperCAmelCase=[1, 1, 2]  ,  _UpperCAmelCase=[1, 6, 6]  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=True  ,  _UpperCAmelCase=37  ,  _UpperCAmelCase="gelu"  ,  _UpperCAmelCase=10  ,  ):
   __UpperCAmelCase	:  Any		=     parent
   __UpperCAmelCase	:  int		=     batch_size
   __UpperCAmelCase	:  Any		=     image_size
   __UpperCAmelCase	:  Union[str, Any]		=     num_channels
   __UpperCAmelCase	:  str		=     kernel_sizes
   __UpperCAmelCase	:  List[Any]		=     in_channels
   __UpperCAmelCase	:  Any		=     out_channels
   __UpperCAmelCase	:  Tuple		=     strides
   __UpperCAmelCase	:  int		=     num_block_repeats
   __UpperCAmelCase	:  int		=     expand_ratios
   __UpperCAmelCase	:  Tuple		=     is_training
   __UpperCAmelCase	:  List[Any]		=     hidden_act
   __UpperCAmelCase	:  int		=     num_labels
   __UpperCAmelCase	:  Dict		=     use_labels




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

   __UpperCAmelCase	:  Tuple		=     None
   if self.use_labels:
     __UpperCAmelCase	:  int		=     ids_tensor([self.batch_size]  ,  self.num_labels							)

   __UpperCAmelCase	:  List[str]		=     self.get_config()
   return config, pixel_values, labels




 def     a       (			self							):
   return EfficientNetConfig(
       num_channels=self.num_channels  ,  kernel_sizes=self.kernel_sizes  ,  in_channels=self.in_channels  ,  out_channels=self.out_channels  ,  strides=self.strides  ,  num_block_repeats=self.num_block_repeats  ,  expand_ratios=self.expand_ratios  ,  hidden_act=self.hidden_act  ,  num_labels=self.num_labels  ,  )




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Dict		=     EfficientNetModel(config=_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  List[str]		=     model(_UpperCAmelCase							)
   # expected last hidden states: B, C, H // 4, W // 4
   self.parent.assertEqual(
       result.last_hidden_state.shape  ,  (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)  ,  )




 def     a       (			self  ,  _UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
   __UpperCAmelCase	:  Tuple		=     EfficientNetForImageClassification(_UpperCAmelCase							)
   model.to(_UpperCAmelCase							)
   model.eval()
   __UpperCAmelCase	:  Optional[Any]		=     model(_UpperCAmelCase  ,  labels=_UpperCAmelCase							)
   self.parent.assertEqual(result.logits.shape  ,  (self.batch_size, self.num_labels)							)




 def     a       (			self							):
   __UpperCAmelCase	:  List[str]		=     self.prepare_config_and_inputs()
   __UpperCAmelCase,			__UpperCAmelCase,			__UpperCAmelCase	:  List[str]		=     config_and_inputs
   __UpperCAmelCase	:  Tuple		=     {'pixel_values': pixel_values}
   return config, inputs_dict



@require_torch
class      lowerCamelCase_ (			_lowerCAmelCase	,				_lowerCAmelCase	,				unittest.TestCase):
 _lowerCAmelCase									=	(EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
 _lowerCAmelCase									=	(
     {'''feature-extraction''': EfficientNetModel, '''image-classification''': EfficientNetForImageClassification}
     if is_torch_available()
     else {}
 )

 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False
 _lowerCAmelCase									=	False




 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     EfficientNetModelTester(self							)
   __UpperCAmelCase	:  Any		=     ConfigTester(
       self  ,  config_class=_UpperCAmelCase  ,  has_text_modality=_UpperCAmelCase  ,  hidden_size=37							)




 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='EfficientNet does not use inputs_embeds'							)
 def     a       (			self							):
   pass




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




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




 def     a       (			self							):
   __UpperCAmelCase,			__UpperCAmelCase	:  Optional[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase	:  List[Any]		=     model_class(_UpperCAmelCase							)
     __UpperCAmelCase	:  List[Any]		=     inspect.signature(model.forward							)
     # signature.parameters is an OrderedDict => so arg_names order is deterministic
     __UpperCAmelCase	:  Optional[int]		=     [*signature.parameters.keys()]

     __UpperCAmelCase	:  Union[str, Any]		=     ['pixel_values']
     self.assertListEqual(arg_names[:1]  ,  _UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*_UpperCAmelCase							)




 def     a       (			self							):
   def check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							):
     __UpperCAmelCase	:  List[str]		=     model_class(_UpperCAmelCase							)
     model.to(_UpperCAmelCase							)
     model.eval()

     with torch.no_grad():
       __UpperCAmelCase	:  Optional[int]		=     model(**self._prepare_for_class(_UpperCAmelCase  ,  _UpperCAmelCase							)							)

     __UpperCAmelCase	:  List[str]		=     outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
     __UpperCAmelCase	:  Dict		=     sum(config.num_block_repeats							) * 4
     self.assertEqual(len(_UpperCAmelCase							)  ,  _UpperCAmelCase							)

     # EfficientNet'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]  ,  )

   __UpperCAmelCase,			__UpperCAmelCase	:  List[Any]		=     self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     __UpperCAmelCase	:  Optional[int]		=     True
     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)

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

     check_hidden_states_output(_UpperCAmelCase  ,  _UpperCAmelCase  ,  _UpperCAmelCase							)




 def     a       (			self							):
   __UpperCAmelCase	:  str		=     self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase							)




 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'							)
 def     a       (			self							):
   pass




 @slow
 def     a       (			self							):
   for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     __UpperCAmelCase	:  List[str]		=     EfficientNetModel.from_pretrained(_UpperCAmelCase							)
     self.assertIsNotNone(_UpperCAmelCase							)




 @is_pipeline_test
 @require_vision
 @slow
 def     a       (			self							):
   super().test_pipeline_image_classification()





def  _snake_case		()							->		Tuple:
  __UpperCAmelCase	:  List[Any]		=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
  return image







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




 @cached_property
 def     a       (			self							):
   return AutoImageProcessor.from_pretrained('google/efficientnet-b7'							) if is_vision_available() else None




 @slow
 def     a       (			self							):
   __UpperCAmelCase	:  Optional[int]		=     EfficientNetForImageClassification.from_pretrained('google/efficientnet-b7'							).to(_UpperCAmelCase							)

   __UpperCAmelCase	:  Dict		=     self.default_image_processor
   __UpperCAmelCase	:  List[str]		=     prepare_img()
   __UpperCAmelCase	:  int		=     image_processor(images=_UpperCAmelCase  ,  return_tensors='pt'							).to(_UpperCAmelCase							)

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

   # verify the logits
   __UpperCAmelCase	:  List[Any]		=     torch.Size((1, 1_000)							)
   self.assertEqual(outputs.logits.shape  ,  _UpperCAmelCase							)

   __UpperCAmelCase	:  Optional[Any]		=     torch.tensor([0.0_0_0_1, 0.0_0_0_2, 0.0_0_0_2]							).to(_UpperCAmelCase							)
   self.assertTrue(torch.allclose(outputs.logits[0, :3]  ,  _UpperCAmelCase  ,  atol=1E-4							)							)












from .balanced_parentheses import balanced_parentheses
from .stack import Stack







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       int:
  return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(SCREAMING_SNAKE_CASE__   ,   -1)







def      A__				(       SCREAMING_SNAKE_CASE__)  ->       str:
  if not balanced_parentheses(SCREAMING_SNAKE_CASE__):
    raise ValueError("""Mismatched parentheses""")
  __snake_case:		Stack[str]					    =		Stack()
  __snake_case:		Any					    =		[]
  for char in expression_str:
    if char.isalpha() or char.isdigit():
      postfix.append(SCREAMING_SNAKE_CASE__)
    elif char == "(":
      stack.push(SCREAMING_SNAKE_CASE__)
    elif char == ")":
      while not stack.is_empty() and stack.peek() != "(":
        postfix.append(stack.pop())
      stack.pop()
    else:
      while not stack.is_empty() and precedence(SCREAMING_SNAKE_CASE__) <= precedence(stack.peek()):
        postfix.append(stack.pop())
      stack.push(SCREAMING_SNAKE_CASE__)
  while not stack.is_empty():
    postfix.append(stack.pop())
  return " ".join(SCREAMING_SNAKE_CASE__)


if __name__ == "__main__":
      from doctest import testmod

      testmod()
      __UpperCAmelCase			:  Optional[int]										=				"a+b*(c^d-e)^(f+g*h)-i"

      print("Infix to Postfix Notation demonstration:\n")
      print("Infix notation: " + expression)
      print("Postfix notation: " + infix_to_postfix(expression))






