



'''simple docstring'''



from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor
class 							UpperCAmelCase__	:



 def __init__(	self		,						lowercase		,						lowercase=3		,						lowercase=3_2		,						lowercase=3		,						lowercase=1_0		,						lowercase=[1_0, 2_0, 3_0, 4_0]		,						lowercase=[1, 1, 2, 1]		,						lowercase=True		,						lowercase=True		,						lowercase="relu"		,						lowercase=3		,						lowercase=None		,						)	->				Optional[int]:
  __UpperCamelCase					   =	parent
  __UpperCamelCase					   =	batch_size
  __UpperCamelCase					   =	image_size
  __UpperCamelCase					   =	num_channels
  __UpperCamelCase					   =	embeddings_size
  __UpperCamelCase					   =	hidden_sizes
  __UpperCamelCase					   =	depths
  __UpperCamelCase					   =	is_training
  __UpperCamelCase					   =	use_labels
  __UpperCamelCase					   =	hidden_act
  __UpperCamelCase					   =	num_labels
  __UpperCamelCase					   =	scope
  __UpperCamelCase					   =	len(_snake_case      )



 def  __lowerCamelCase					(	self      )	->				str:
  __UpperCamelCase					   =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

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

  __UpperCamelCase					   =	self.get_config()
  return config, pixel_values, labels



 def  __lowerCamelCase					(	self      )	->				Dict:
  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      )	->				List[Any]:
  __UpperCamelCase					   =	TFRegNetModel(config=_snake_case      )
  __UpperCamelCase					   =	model(_snake_case		,						training=_snake_case      )
  # 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 // 3_2, self.image_size // 3_2)		,						)



 def  __lowerCamelCase					(	self		,						lowercase		,						lowercase		,						lowercase      )	->				Union[str, Any]:
  __UpperCamelCase					   =	self.num_labels
  __UpperCamelCase					   =	TFRegNetForImageClassification(_snake_case      )
  __UpperCamelCase					   =	model(_snake_case		,						labels=_snake_case		,						training=_snake_case      )
  self.parent.assertEqual(result.logits.shape		,						(self.batch_size, self.num_labels)      )




 def  __lowerCamelCase					(	self      )	->				Any:
  __UpperCamelCase					   =	self.prepare_config_and_inputs()
  __UpperCamelCase   ,		__UpperCamelCase   ,		__UpperCamelCase					   =	config_and_inputs
  __UpperCamelCase					   =	{"""pixel_values""": pixel_values}
  return config, inputs_dict


@require_tf
class 							UpperCAmelCase__	(     UpperCAmelCase_				,      UpperCAmelCase_				,      unittest.TestCase):
 __SCREAMING_SNAKE_CASE          =					(TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
 __SCREAMING_SNAKE_CASE          =					(
     {"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
     if is_tf_available()
     else {}
 )

 __SCREAMING_SNAKE_CASE          =					False
 __SCREAMING_SNAKE_CASE          =					False
 __SCREAMING_SNAKE_CASE          =					False
 __SCREAMING_SNAKE_CASE          =					False
 __SCREAMING_SNAKE_CASE          =					False



 def  __lowerCamelCase					(	self      )	->				int:
  __UpperCamelCase					   =	TFRegNetModelTester(self      )
  __UpperCamelCase					   =	ConfigTester(self		,						config_class=_snake_case		,						has_text_modality=_snake_case      )



 def  __lowerCamelCase					(	self      )	->				Optional[int]:
  return



 @unittest.skip(reason="""RegNet does not use inputs_embeds"""      )
 def  __lowerCamelCase					(	self      )	->				Union[str, Any]:
  pass



 @unittest.skipIf(
     not is_tf_available() or len(tf.config.list_physical_devices("""GPU"""      )      ) == 0		,						reason="""TF does not support backprop for grouped convolutions on CPU."""		,						)
 @slow
 def  __lowerCamelCase					(	self      )	->				List[Any]:
  super().test_keras_fit()



 @unittest.skip(reason="""RegNet does not support input and output embeddings"""      )
 def  __lowerCamelCase					(	self      )	->				Any:
  pass



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

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

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



 def  __lowerCamelCase					(	self      )	->				Optional[Any]:
  __UpperCamelCase					   =	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*_snake_case      )



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


  def check_hidden_states_output(lowercase		,						lowercase		,						lowercase      ):
   __UpperCamelCase					   =	model_class(_snake_case      )
   __UpperCamelCase					   =	model(**self._prepare_for_class(_snake_case		,						_snake_case      )		,						training=_snake_case      )

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

   __UpperCamelCase					   =	self.model_tester.num_stages
   self.assertEqual(len(_snake_case      )		,						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]		,						)

  __UpperCamelCase   ,		__UpperCamelCase					   =	self.model_tester.prepare_config_and_inputs_for_common()
  __UpperCamelCase					   =	["""basic""", """bottleneck"""]
  for model_class in self.all_model_classes:
   for layer_type in layers_type:
    __UpperCamelCase					   =	layer_type
    __UpperCamelCase					   =	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"]
    __UpperCamelCase					   =	True

    check_hidden_states_output(_snake_case		,						_snake_case		,						_snake_case      )



 def  __lowerCamelCase					(	self      )	->				Tuple:
  __UpperCamelCase   ,		__UpperCamelCase					   =	self.model_tester.prepare_config_and_inputs_for_common()

  def check_equivalence(lowercase		,						lowercase		,						lowercase		,						lowercase={}      ):
   __UpperCamelCase					   =	model(_snake_case		,						return_dict=_snake_case		,						**_snake_case      )
   __UpperCamelCase					   =	model(_snake_case		,						return_dict=_snake_case		,						**_snake_case      ).to_tuple()

   def recursive_check(lowercase		,						lowercase      ):
    if isinstance(_snake_case		,						(List, Tuple)      ):
     for tuple_iterable_value, dict_iterable_value in zip(_snake_case		,						_snake_case      ):
      recursive_check(_snake_case		,						_snake_case      )
    elif tuple_object is None:
     return
    else:
     self.assertTrue(
         all(tf.equal(_snake_case		,						_snake_case      )      )		,						msg=(
             """Tuple and dict output are not equal. Difference:"""
             f" {tf.math.reduce_max(tf.abs(tuple_object - dict_object      )      )}"
         )		,						)

    recursive_check(_snake_case		,						_snake_case      )

  for model_class in self.all_model_classes:
   __UpperCamelCase					   =	model_class(_snake_case      )

   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case      )
   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case      )
   check_equivalence(_snake_case		,						_snake_case		,						_snake_case      )

   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case		,						return_labels=_snake_case      )
   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case		,						return_labels=_snake_case      )
   check_equivalence(_snake_case		,						_snake_case		,						_snake_case      )

   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case      )
   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case      )
   check_equivalence(_snake_case		,						_snake_case		,						_snake_case		,						{"""output_hidden_states""": True}      )

   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case		,						return_labels=_snake_case      )
   __UpperCamelCase					   =	self._prepare_for_class(_snake_case		,						_snake_case		,						return_labels=_snake_case      )
   check_equivalence(_snake_case		,						_snake_case		,						_snake_case		,						{"""output_hidden_states""": True}      )



 def  __lowerCamelCase					(	self      )	->				Dict:
  __UpperCamelCase					   =	self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_classification(*_snake_case      )




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


  for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __UpperCamelCase					   =	TFRegNetModel.from_pretrained(_snake_case      )
   self.assertIsNotNone(_snake_case      )







def _lowercase (	):



 '''simple docstring'''



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






@require_tf
@require_vision
class 							UpperCAmelCase__	(     unittest.TestCase):



 @cached_property
 def  __lowerCamelCase					(	self      )	->				List[Any]:
  return (
      AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )
      if is_vision_available()
      else None
  )




 @slow
 def  __lowerCamelCase					(	self      )	->				int:
  __UpperCamelCase					   =	TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]      )

  __UpperCamelCase					   =	self.default_image_processor
  __UpperCamelCase					   =	prepare_img()
  __UpperCamelCase					   =	image_processor(images=_snake_case		,						return_tensors="""tf"""      )

  # forward pass
  __UpperCamelCase					   =	model(**_snake_case		,						training=_snake_case      )

  # verify the logits
  __UpperCamelCase					   =	tf.TensorShape((1, 1_0_0_0)      )
  self.assertEqual(outputs.logits.shape		,						_snake_case      )

  __UpperCamelCase					   =	tf.constant([-0.4_180, -1.5_051, -3.4_836]      )

  tf.debugging.assert_near(outputs.logits[0, :3]		,						_snake_case		,						atol=1E-4      )










from __future__ import annotations

from typing import Any


def A    (							__UpperCamelCase       )    ->   int:
			if not postfix_notation:
						return 0

			A__	  =     {'+', '-', '*', '/'}
			A__	  =     []

			for token in postfix_notation:
						if token in operations:
									A__  ,			A__	  =     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()




