





'''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 							_UpperCamelCase					:

       def __init__(		self: Tuple					,   _SCREAMING_SNAKE_CASE: List[Any]					,   _SCREAMING_SNAKE_CASE: int = 13					,   _SCREAMING_SNAKE_CASE: int = 64					,   _SCREAMING_SNAKE_CASE: int = 2					,   _SCREAMING_SNAKE_CASE: int = 3					,   _SCREAMING_SNAKE_CASE: int = 3					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: bool = True					,   _SCREAMING_SNAKE_CASE: int = 128					,   _SCREAMING_SNAKE_CASE: Tuple=[16, 32, 64, 128]					,   _SCREAMING_SNAKE_CASE: int = 7					,   _SCREAMING_SNAKE_CASE: int = 4					,   _SCREAMING_SNAKE_CASE: int = 37					,   _SCREAMING_SNAKE_CASE: str = "gelu"					,   _SCREAMING_SNAKE_CASE: float = 0.1					,   _SCREAMING_SNAKE_CASE: float = 0.1					,   _SCREAMING_SNAKE_CASE: int = 10					,   _SCREAMING_SNAKE_CASE: float = 0.02					,   _SCREAMING_SNAKE_CASE: int = 2					,   _SCREAMING_SNAKE_CASE: int = 1					,   _SCREAMING_SNAKE_CASE: int = 128					,   _SCREAMING_SNAKE_CASE: List[int] = [2, 2, 2, 2]					,   _SCREAMING_SNAKE_CASE: int = 2					,   _SCREAMING_SNAKE_CASE: int = 2					,   )       -> List[Any]:




              """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 lowercase (		self: Optional[int]		)       -> str:




              """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 lowercase (		self: List[str]		)       -> List[str]:




              """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=_SCREAMING_SNAKE_CASE					,   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 lowercase (		self: int					,   _SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: List[Any]		)       -> Union[str, Any]:




              """simple docstring"""


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

       def lowercase (		self: Tuple					,   _SCREAMING_SNAKE_CASE: str					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Any		)       -> Tuple:




              """simple docstring"""


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

              # test greyscale images
              UpperCamelCase_          =  1
              UpperCamelCase_          =  TFEfficientFormerForImageClassification(_SCREAMING_SNAKE_CASE		)

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



       def lowercase (		self: Union[str, Any]		)       -> Union[str, Any]:




              """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 							_UpperCamelCase					(	lowerCAmelCase_  ,						lowerCAmelCase_  ,						unittest.TestCase     ):
       _UpperCamelCase				:					Tuple         =   (
           (
               TFEfficientFormerModel,
               TFEfficientFormerForImageClassificationWithTeacher,
               TFEfficientFormerForImageClassification,
           )
           if is_tf_available()
           else ()
       )
       _UpperCamelCase				:					List[str]         =   (
           {
               '''feature-extraction''': TFEfficientFormerModel,
               '''image-classification''': (
                   TFEfficientFormerForImageClassification,
                   TFEfficientFormerForImageClassificationWithTeacher,
               ),
           }
           if is_tf_available()
           else {}
       )

       _UpperCamelCase				:					List[Any]         =   False

       _UpperCamelCase				:					Dict         =   False
       _UpperCamelCase				:					Union[str, Any]         =   False
       _UpperCamelCase				:					Optional[int]         =   False
       _UpperCamelCase				:					Tuple         =   False

       def lowercase (		self: Optional[int]		)       -> str:




              """simple docstring"""


              UpperCamelCase_          =  TFEfficientFormerModelTester(self		)
              UpperCamelCase_          =  ConfigTester(
                  self					,   config_class=_SCREAMING_SNAKE_CASE					,   has_text_modality=_SCREAMING_SNAKE_CASE					,   hidden_size=37		)

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




              """simple docstring"""


              self.config_tester.run_common_tests()

       @unittest.skip(reason="EfficientFormer does not use inputs_embeds"		)
       def lowercase (		self: Tuple		)       -> Any:




              """simple docstring"""


              pass

       @unittest.skip(reason="EfficientFormer does not support input and output embeddings"		)
       def lowercase (		self: List[str]		)       -> Union[str, Any]:




              """simple docstring"""


              pass

       def lowercase (		self: Union[str, Any]		)       -> int:




              """simple docstring"""


              UpperCamelCase_       , UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     UpperCamelCase_          =  model_class(_SCREAMING_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]					,   _SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Tuple		)       -> List[str]:




              """simple docstring"""


              def check_hidden_states_output(_SCREAMING_SNAKE_CASE: Tuple					,   _SCREAMING_SNAKE_CASE: Any					,   _SCREAMING_SNAKE_CASE: Tuple		):
                     UpperCamelCase_          =  model_class(_SCREAMING_SNAKE_CASE		)

                     UpperCamelCase_          =  model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)					,   training=_SCREAMING_SNAKE_CASE		)
                     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(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)

                     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(_SCREAMING_SNAKE_CASE					,   (list, tuple)		)
                            self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,   _SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  getattr(self.model_tester					,   "seq_length"					,   _SCREAMING_SNAKE_CASE		)
                            UpperCamelCase_          =  getattr(self.model_tester					,   "decoder_seq_length"					,   _SCREAMING_SNAKE_CASE		)

                            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(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

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

                     check_hidden_states_output(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Union[str, Any]					,   _SCREAMING_SNAKE_CASE: int					,   _SCREAMING_SNAKE_CASE: Optional[Any]					,   _SCREAMING_SNAKE_CASE: Optional[int]=False		)       -> Tuple:




              """simple docstring"""


              UpperCamelCase_          =  super()._prepare_for_class(_SCREAMING_SNAKE_CASE					,   _SCREAMING_SNAKE_CASE					,   return_labels=_SCREAMING_SNAKE_CASE		)

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

              return inputs_dict

       def lowercase (		self: List[str]		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)

       @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet"		)
       def lowercase (		self: Union[str, Any]		)       -> List[str]:




              """simple docstring"""


              UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE		)

       def lowercase (		self: Tuple		)       -> int:




              """simple docstring"""


              UpperCamelCase_          =  self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)

       @slow
       def lowercase (		self: Dict		)       -> int:




              """simple docstring"""


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

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




              """simple docstring"""


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

              UpperCamelCase_          =  getattr(self.model_tester					,   "seq_length"					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  getattr(self.model_tester					,   "encoder_seq_length"					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  getattr(self.model_tester					,   "key_length"					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  getattr(self.model_tester					,   "chunk_length"					,   _SCREAMING_SNAKE_CASE		)

              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(_SCREAMING_SNAKE_CASE		)

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

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

                     UpperCamelCase_          =  outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
                     self.assertEqual(len(_SCREAMING_SNAKE_CASE		)					,   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 lowercase (		self: Any		)       -> Dict:




              """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(_SCREAMING_SNAKE_CASE		)
                     # 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=_SCREAMING_SNAKE_CASE		)
                         for key, val in model.input_signature.items()
                         if key in model.dummy_inputs
                     }
                     UpperCamelCase_          =  model(_SCREAMING_SNAKE_CASE		)
                     self.assertTrue(outputs_dict is not None		)




def  lowerCAmelCase_ (							)     ->		int:
       UpperCamelCase_          =  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"  )
       return image







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

       @cached_property
       def lowercase (		self: Any		)       -> Optional[Any]:




              """simple docstring"""


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

       @slow
       def lowercase (		self: Optional[Any]		)       -> Any:




              """simple docstring"""


              UpperCamelCase_          =  TFEfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300"		)
              UpperCamelCase_          =  self.default_image_processor
              UpperCamelCase_          =  prepare_img()
              UpperCamelCase_          =  image_processor(images=_SCREAMING_SNAKE_CASE					,   return_tensors="tf"		)
              # forward pass
              UpperCamelCase_          =  model(**_SCREAMING_SNAKE_CASE					,   training=_SCREAMING_SNAKE_CASE		)
              # verify the logits
              UpperCamelCase_          =  tf.TensorShape((1, 1000)		)
              self.assertEqual(outputs.logits.shape					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tf.constant([-0.05_55, 0.48_25, -0.08_52]		)
              self.assertTrue(np.allclose(outputs.logits[0, :3]					,   _SCREAMING_SNAKE_CASE					,   atol=1e-4		)		)



       @slow
       def lowercase (		self: str		)       -> Optional[Any]:




              """simple docstring"""


              UpperCamelCase_          =  TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
                  "snap-research/efficientformer-l1-300"		)
              UpperCamelCase_          =  self.default_image_processor
              UpperCamelCase_          =  prepare_img()
              UpperCamelCase_          =  image_processor(images=_SCREAMING_SNAKE_CASE					,   return_tensors="tf"		)
              # forward pass
              UpperCamelCase_          =  model(**_SCREAMING_SNAKE_CASE					,   training=_SCREAMING_SNAKE_CASE		)
              # verify the logits
              UpperCamelCase_          =  tf.TensorShape((1, 1000)		)
              self.assertEqual(outputs.logits.shape					,   _SCREAMING_SNAKE_CASE		)
              UpperCamelCase_          =  tf.constant([-0.13_12, 0.43_53, -1.04_99]		)
              self.assertTrue(np.allclose(outputs.logits[0, :3]					,   _SCREAMING_SNAKE_CASE					,   atol=1e-4		)		)








import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
     # Initialising the CNN
     # (Sequential- Building the model layer by layer)
     _UpperCAmelCase        =				models.Sequential()

     # Step 1 - Convolution
     # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
     # (3,3) is the kernel size (filter matrix)
     classifier.add(
         layers.ConvaD(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

     # Step 2 - Pooling
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (3, 3), activation='relu'))
     classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

     # Step 3 - Flattening
     classifier.add(layers.Flatten())

     # Step 4 - Full connection
     classifier.add(layers.Dense(units=1_2_8, activation='relu'))
     classifier.add(layers.Dense(units=1, activation='sigmoid'))

     # Compiling the CNN
     classifier.compile(
         optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']
     )

     # Part 2 - Fitting the CNN to the images

     # Load Trained model weights

     # from keras.models import load_model
     # regressor=load_model('cnn.h5')

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(
         rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
     )

     classifier.save('cnn.h5')

     # Part 3 - Making new predictions

     _UpperCAmelCase        =				tf.keras.preprocessing.image.load_img(
         'dataset/single_prediction/image.png', target_size=(6_4, 6_4)
     )
     _UpperCAmelCase        =				tf.keras.preprocessing.image.img_to_array(test_image)
     _UpperCAmelCase        =				np.expand_dims(test_image, axis=0)
     _UpperCAmelCase        =				classifier.predict(test_image)
     # training_set.class_indices
     if result[0][0] == 0:
          _UpperCAmelCase        =				'Normal'
     if result[0][0] == 1:
          _UpperCAmelCase        =				'Abnormality detected'

