
"""simple docstring"""

import inspect
import unittest
from typing import List

import numpy as np

from transformers import EfficientFormerConfig
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 (
          TFEfficientFormerForImageClassification,
          TFEfficientFormerForImageClassificationWithTeacher,
          TFEfficientFormerModel,
      )
      from transformers.models.efficientformer.modeling_tf_efficientformer import (
          TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
      )


if is_vision_available():
      from PIL import Image

      from transformers import EfficientFormerImageProcessor

class __lowerCAmelCase		:





     def __init__(     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase = 13     ,					__UpperCAmelCase = 64     ,					__UpperCAmelCase = 2     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = 3     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = True     ,					__UpperCAmelCase = 128     ,					__UpperCAmelCase=[16, 32, 64, 128]     ,					__UpperCAmelCase = 7     ,					__UpperCAmelCase = 4     ,					__UpperCAmelCase = 37     ,					__UpperCAmelCase = "gelu"     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 0.1     ,					__UpperCAmelCase = 10     ,					__UpperCAmelCase = 0.0_2     ,					__UpperCAmelCase = 2     ,					__UpperCAmelCase = 1     ,					__UpperCAmelCase = 128     ,					__UpperCAmelCase = [2, 2, 2, 2]     ,					__UpperCAmelCase = 2     ,					__UpperCAmelCase = 2     ,					):

      '''simple docstring'''




      __UpperCamelCase      =	parent
      __UpperCamelCase      =	batch_size
      __UpperCamelCase      =	image_size
      __UpperCamelCase      =	patch_size
      __UpperCamelCase      =	num_channels
      __UpperCamelCase      =	is_training
      __UpperCamelCase      =	use_labels
      __UpperCamelCase      =	hidden_size
      __UpperCamelCase      =	num_hidden_layers
      __UpperCamelCase      =	num_attention_heads
      __UpperCamelCase      =	intermediate_size
      __UpperCamelCase      =	hidden_act
      __UpperCamelCase      =	hidden_dropout_prob
      __UpperCamelCase      =	attention_probs_dropout_prob
      __UpperCamelCase      =	type_sequence_label_size
      __UpperCamelCase      =	initializer_range
      __UpperCamelCase      =	encoder_stride
      __UpperCamelCase      =	num_attention_outputs
      __UpperCamelCase      =	embed_dim
      __UpperCamelCase      =	embed_dim + 1
      __UpperCamelCase      =	resolution
      __UpperCamelCase      =	depths
      __UpperCamelCase      =	hidden_sizes
      __UpperCamelCase      =	dim
      __UpperCamelCase      =	mlp_expansion_ratio





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __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.type_sequence_label_size				)

      __UpperCamelCase      =	self.get_config()

      return config, pixel_values, labels





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return EfficientFormerConfig(
          image_size=self.image_size     ,					patch_size=self.patch_size     ,					num_channels=self.num_channels     ,					hidden_size=self.hidden_size     ,					num_hidden_layers=self.num_hidden_layers     ,					num_attention_heads=self.num_attention_heads     ,					intermediate_size=self.intermediate_size     ,					hidden_act=self.hidden_act     ,					hidden_dropout_prob=self.hidden_dropout_prob     ,					attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,					is_decoder=lowercase_     ,					initializer_range=self.initializer_range     ,					encoder_stride=self.encoder_stride     ,					resolution=self.resolution     ,					depths=self.depths     ,					hidden_sizes=self.hidden_sizes     ,					dim=self.dim     ,					mlp_expansion_ratio=self.mlp_expansion_ratio     ,					)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	TFEfficientFormerModel(config=lowercase_				)
      __UpperCamelCase      =	model(lowercase_     ,					training=lowercase_				)
      self.parent.assertEqual(result.last_hidden_state.shape     ,					(self.batch_size, self.seq_length, self.hidden_size)				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):

      '''simple docstring'''




      __UpperCamelCase      =	self.type_sequence_label_size
      __UpperCamelCase      =	TFEfficientFormerForImageClassification(lowercase_				)
      __UpperCamelCase      =	model(lowercase_     ,					labels=lowercase_     ,					training=lowercase_				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)				)

      # test greyscale images
      __UpperCamelCase      =	1
      __UpperCamelCase      =	TFEfficientFormerForImageClassification(lowercase_				)

      __UpperCamelCase      =	floats_tensor([self.batch_size, 1, self.image_size, self.image_size]				)
      __UpperCamelCase      =	model(lowercase_     ,					labels=lowercase_				)
      self.parent.assertEqual(result.logits.shape     ,					(self.batch_size, self.type_sequence_label_size)				)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.prepare_config_and_inputs()
      __UpperCamelCase  ,     __UpperCamelCase  ,     __UpperCamelCase      =	config_and_inputs
      __UpperCamelCase      =	{'pixel_values': pixel_values}
      return config, inputs_dict


@require_tf
class __lowerCAmelCase		(				a_      ,  a_      ,  unittest.TestCase      ):
     lowercase						       =						(
         (
             TFEfficientFormerModel,
             TFEfficientFormerForImageClassificationWithTeacher,
             TFEfficientFormerForImageClassification,
         )
         if is_tf_available()
         else ()
     )
     lowercase						       =						(
         {
             '''feature-extraction''': TFEfficientFormerModel,
             '''image-classification''': (
                 TFEfficientFormerForImageClassification,
                 TFEfficientFormerForImageClassificationWithTeacher,
             ),
         }
         if is_tf_available()
         else {}
     )

     lowercase						       =						False

     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False
     lowercase						       =						False





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFEfficientFormerModelTester(self				)
      __UpperCamelCase      =	ConfigTester(
          self     ,					config_class=lowercase_     ,					has_text_modality=lowercase_     ,					hidden_size=37				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      self.config_tester.run_common_tests()





     @unittest.skip(reason='EfficientFormer does not use inputs_embeds'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     @unittest.skip(reason='EfficientFormer does not support input and output embeddings'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      pass





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	model_class(lowercase_				)
       __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]     ,					lowercase_				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      def check_hidden_states_output(__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase				):
       __UpperCamelCase      =	model_class(lowercase_				)

       __UpperCamelCase      =	model(**self._prepare_for_class(lowercase_     ,					lowercase_				)     ,					training=lowercase_				)
       __UpperCamelCase      =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

       __UpperCamelCase      =	getattr(
           self.model_tester     ,					'expected_num_hidden_layers'     ,					self.model_tester.num_hidden_layers + 1				)
       self.assertEqual(len(lowercase_				)     ,					lowercase_				)

       if hasattr(self.model_tester     ,					'encoder_seq_length'				):
        __UpperCamelCase      =	self.model_tester.encoder_seq_length
        if hasattr(self.model_tester     ,					'chunk_length'				) and self.model_tester.chunk_length > 1:
         __UpperCamelCase      =	seq_length * self.model_tester.chunk_length
       else:
        __UpperCamelCase      =	self.model_tester.seq_length

       self.assertListEqual(
           list(hidden_states[-1].shape[-2:]				)     ,					[seq_length, self.model_tester.hidden_size]     ,					)

       if config.is_encoder_decoder:
        __UpperCamelCase      =	outputs.decoder_hidden_states

        self.asseretIsInstance(lowercase_     ,					(list, tuple)				)
        self.assertEqual(len(lowercase_				)     ,					lowercase_				)
        __UpperCamelCase      =	getattr(self.model_tester     ,					'seq_length'     ,					lowercase_				)
        __UpperCamelCase      =	getattr(self.model_tester     ,					'decoder_seq_length'     ,					lowercase_				)

        self.assertListEqual(
            list(hidden_states[-1].shape[-2:]				)     ,					[decoder_seq_length, self.model_tester.hidden_size]     ,					)

      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	True
       check_hidden_states_output(lowercase_     ,					lowercase_     ,					lowercase_				)

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

       check_hidden_states_output(lowercase_     ,					lowercase_     ,					lowercase_				)





     def 						UpperCAmelCase (     self     ,					__UpperCAmelCase     ,					__UpperCAmelCase     ,					__UpperCAmelCase=False				):

      '''simple docstring'''




      __UpperCamelCase      =	super()._prepare_for_class(lowercase_     ,					lowercase_     ,					return_labels=lowercase_				)

      if return_labels:
       if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
        del inputs_dict["labels"]

      return inputs_dict





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*lowercase_				)





     @unittest.skip(reason='EfficientFormer does not implement masked image modeling yet'				)
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*lowercase_				)





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __UpperCamelCase      =	TFEfficientFormerModel.from_pretrained(lowercase_				)
       self.assertIsNotNone(lowercase_				)





     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()
      __UpperCamelCase      =	True

      __UpperCamelCase      =	getattr(self.model_tester     ,					'seq_length'     ,					lowercase_				)
      __UpperCamelCase      =	getattr(self.model_tester     ,					'encoder_seq_length'     ,					lowercase_				)
      __UpperCamelCase      =	getattr(self.model_tester     ,					'key_length'     ,					lowercase_				)
      __UpperCamelCase      =	getattr(self.model_tester     ,					'chunk_length'     ,					lowercase_				)

      if chunk_length is not None and hasattr(self.model_tester     ,					'num_hashes'				):
       __UpperCamelCase      =	encoder_seq_length * self.model_tester.num_hashes

      for model_class in self.all_model_classes:
       __UpperCamelCase      =	True
       __UpperCamelCase      =	False
       __UpperCamelCase      =	True
       __UpperCamelCase      =	model_class(lowercase_				)

       __UpperCamelCase      =	model(**self._prepare_for_class(lowercase_     ,					lowercase_				)     ,					training=lowercase_				)
       __UpperCamelCase      =	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(lowercase_				)     ,					self.model_tester.num_attention_outputs				)

       # check that output_attentions also work using config
       del inputs_dict["output_attentions"]
       __UpperCamelCase      =	True
       __UpperCamelCase      =	model_class(lowercase_				)
       __UpperCamelCase      =	model(**self._prepare_for_class(lowercase_     ,					lowercase_				)     ,					training=lowercase_				)

       __UpperCamelCase      =	outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
       self.assertEqual(len(lowercase_				)     ,					self.model_tester.num_attention_outputs				)

       if chunk_length is not None:
        self.assertListEqual(
            list(attentions[0].shape[-4:]				)     ,					[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length]     ,					)
       else:
        self.assertListEqual(
            list(attentions[0].shape[-3:]				)     ,					[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]     ,					)







     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase  ,     __UpperCamelCase      =	self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
       # Prepare our model
       __UpperCamelCase      =	model_class(lowercase_				)
       # These are maximally general inputs for the model, with multiple None dimensions
       # Hopefully this will catch any conditionals that fail for flexible shapes
       __UpperCamelCase      =	{
           key: tf.keras.Input(shape=val.shape[1:]     ,					dtype=val.dtype     ,					name=lowercase_				)
           for key, val in model.input_signature.items()
           if key in model.dummy_inputs
       }
       __UpperCamelCase      =	model(lowercase_				)
       self.assertTrue(outputs_dict is not None				)






def 							A						(    )				->     int:






 """simple docstring"""




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





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





     @cached_property
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      return (
          EfficientFormerImageProcessor.from_pretrained('snap-research/efficientformer-l1-300'				)
          if is_vision_available()
          else None
      )





     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFEfficientFormerForImageClassification.from_pretrained('snap-research/efficientformer-l1-300'				)
      __UpperCamelCase      =	self.default_image_processor
      __UpperCamelCase      =	prepare_img()
      __UpperCamelCase      =	image_processor(images=lowercase_     ,					return_tensors='tf'				)
      # forward pass
      __UpperCamelCase      =	model(**lowercase_     ,					training=lowercase_				)
      # verify the logits
      __UpperCamelCase      =	tf.TensorShape((1, 1000)				)
      self.assertEqual(outputs.logits.shape     ,					lowercase_				)
      __UpperCamelCase      =	tf.constant([-0.0_5_5_5, 0.4_8_2_5, -0.0_8_5_2]				)
      self.assertTrue(np.allclose(outputs.logits[0, :3]     ,					lowercase_     ,					atol=1E-4				)				)







     @slow
     def 						UpperCAmelCase (     self				):

      '''simple docstring'''




      __UpperCamelCase      =	TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
          'snap-research/efficientformer-l1-300'				)
      __UpperCamelCase      =	self.default_image_processor
      __UpperCamelCase      =	prepare_img()
      __UpperCamelCase      =	image_processor(images=lowercase_     ,					return_tensors='tf'				)
      # forward pass
      __UpperCamelCase      =	model(**lowercase_     ,					training=lowercase_				)
      # verify the logits
      __UpperCamelCase      =	tf.TensorShape((1, 1000)				)
      self.assertEqual(outputs.logits.shape     ,					lowercase_				)
      __UpperCamelCase      =	tf.constant([-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9]				)
      self.assertTrue(np.allclose(outputs.logits[0, :3]     ,					lowercase_     ,					atol=1E-4				)				)





"""simple docstring"""

from __future__ import annotations

from typing import Any






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

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

 for token in postfix_notation:
  if token in operations:
   __UpperCamelCase  ,     __UpperCamelCase      =	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(snake_case		)		)

 return stack.pop()


if __name__ == "__main__":
      import doctest

      doctest.testmod()



