
"""simple docstring"""





from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
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 TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
    from PIL import Image

    from transformers import ConvNextImageProcessor


class A_      :




  '''simple docstring'''







  def __init__(						self							,  lowercase_							,  lowercase_=13							,  lowercase_=32							,  lowercase_=3							,  lowercase_=4							,  lowercase_=[10, 20, 30, 40]							,  lowercase_=[2, 2, 3, 2]							,  lowercase_=True							,  lowercase_=True							,  lowercase_=37							,  lowercase_="gelu"							,  lowercase_=10							,  lowercase_=0.02							,  lowercase_=3							,  lowercase_=None							,  ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       parent
     UpperCAmelCase_   :   int     	=       batch_size
     UpperCAmelCase_   :   int     	=       image_size
     UpperCAmelCase_   :   Union[str, Any]     	=       num_channels
     UpperCAmelCase_   :   Any     	=       num_stages
     UpperCAmelCase_   :   Dict     	=       hidden_sizes
     UpperCAmelCase_   :   List[Any]     	=       depths
     UpperCAmelCase_   :   Tuple     	=       is_training
     UpperCAmelCase_   :   Union[str, Any]     	=       use_labels
     UpperCAmelCase_   :   List[str]     	=       intermediate_size
     UpperCAmelCase_   :   Union[str, Any]     	=       hidden_act
     UpperCAmelCase_   :   Tuple     	=       type_sequence_label_size
     UpperCAmelCase_   :   Any     	=       initializer_range
     UpperCAmelCase_   :   Union[str, Any]     	=       scope







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   List[str]     	=       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

     UpperCAmelCase_   :   Any     	=       None
     if self.use_labels:
        UpperCAmelCase_   :   str     	=       ids_tensor([self.batch_size]							,  self.type_sequence_label_size    )

     UpperCAmelCase_   :   int     	=       self.get_config()

     return config, pixel_values, labels







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return ConvNextConfig(
         num_channels=self.num_channels							,  hidden_sizes=self.hidden_sizes							,  depths=self.depths							,  num_stages=self.num_stages							,  hidden_act=self.hidden_act							,  is_decoder=lowercase_							,  initializer_range=self.initializer_range							,  )







  def 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       TFConvNextModel(config=lowercase_    )
     UpperCAmelCase_   :   Tuple     	=       model(lowercase_							,  training=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 			UpperCamelCase__   (						self							,  lowercase_							,  lowercase_							,  lowercase_    ):

     """simple docstring"""
     UpperCAmelCase_   :   Union[str, Any]     	=       self.type_sequence_label_size
     UpperCAmelCase_   :   int     	=       TFConvNextForImageClassification(lowercase_    )
     UpperCAmelCase_   :   int     	=       model(lowercase_							,  labels=lowercase_							,  training=lowercase_    )
     self.parent.assertEqual(result.logits.shape							,  (self.batch_size, self.type_sequence_label_size)    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Any     	=       self.prepare_config_and_inputs()
     UpperCAmelCase_	,  UpperCAmelCase_	,  UpperCAmelCase_   :   Any     	=       config_and_inputs
     UpperCAmelCase_   :   List[Any]     	=       {"pixel_values": pixel_values}
     return config, inputs_dict




@require_tf
class A_      (lowercase__							,lowercase__							,unittest.TestCase ):




  '''simple docstring'''
  SCREAMING_SNAKE_CASE__     :		str    	=						(TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
  SCREAMING_SNAKE_CASE__     :		Dict    	=						(
      {"""feature-extraction""": TFConvNextModel, """image-classification""": TFConvNextForImageClassification}
      if is_tf_available()
      else {}
  )

  SCREAMING_SNAKE_CASE__     :		Tuple    	=						False
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						False
  SCREAMING_SNAKE_CASE__     :		Union[str, Any]    	=						False
  SCREAMING_SNAKE_CASE__     :		str    	=						False
  SCREAMING_SNAKE_CASE__     :		Any    	=						False







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   str     	=       TFConvNextModelTester(self    )
     UpperCAmelCase_   :   Union[str, Any]     	=       ConfigTester(
         self							,  config_class=lowercase_							,  has_text_modality=lowercase_							,  hidden_size=37							,  )







  @unittest.skip(reason="ConvNext does not use inputs_embeds"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     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 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     super().test_keras_fit()







  @unittest.skip(reason="ConvNext does not support input and output embeddings"    )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     pass







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Optional[int]     	=       self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCAmelCase_   :   int     	=       ["pixel_values"]
        self.assertListEqual(arg_names[:1]							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   int     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*lowercase_    )







  @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."							,  )
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     super().test_dataset_conversion()







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     def check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_    ):
        UpperCAmelCase_   :   Dict     	=       model_class(lowercase_    )

        UpperCAmelCase_   :   List[str]     	=       model(**self._prepare_for_class(lowercase_							,  lowercase_    )    )
        UpperCAmelCase_   :   List[Any]     	=       outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

        UpperCAmelCase_   :   List[Any]     	=       self.model_tester.num_stages
        self.assertEqual(len(lowercase_    )							,  expected_num_stages + 1    )

        # ConvNext'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 // 4, self.model_tester.image_size // 4]							,  )

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

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

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

        check_hidden_states_output(lowercase_							,  lowercase_							,  lowercase_    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_	,  UpperCAmelCase_   :   Union[str, Any]     	=       self.model_tester.prepare_config_and_inputs_for_common()

     def check_equivalence(lowercase_							,  lowercase_							,  lowercase_							,  lowercase_={}    ):
        UpperCAmelCase_   :   Optional[int]     	=       model(lowercase_							,  return_dict=lowercase_							,  **lowercase_    )
        UpperCAmelCase_   :   Optional[Any]     	=       model(lowercase_							,  return_dict=lowercase_							,  **lowercase_    ).to_tuple()

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

           recursive_check(lowercase_							,  lowercase_    )

     for model_class in self.all_model_classes:
        UpperCAmelCase_   :   Optional[int]     	=       model_class(lowercase_    )

        UpperCAmelCase_   :   Optional[int]     	=       self._prepare_for_class(lowercase_							,  lowercase_    )
        UpperCAmelCase_   :   int     	=       self._prepare_for_class(lowercase_							,  lowercase_    )
        check_equivalence(lowercase_							,  lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   Optional[Any]     	=       self._prepare_for_class(lowercase_							,  lowercase_							,  return_labels=lowercase_    )
        UpperCAmelCase_   :   List[str]     	=       self._prepare_for_class(lowercase_							,  lowercase_							,  return_labels=lowercase_    )
        check_equivalence(lowercase_							,  lowercase_							,  lowercase_    )

        UpperCAmelCase_   :   str     	=       self._prepare_for_class(lowercase_							,  lowercase_    )
        UpperCAmelCase_   :   Tuple     	=       self._prepare_for_class(lowercase_							,  lowercase_    )
        check_equivalence(lowercase_							,  lowercase_							,  lowercase_							,  {"output_hidden_states": True}    )

        UpperCAmelCase_   :   Any     	=       self._prepare_for_class(lowercase_							,  lowercase_							,  return_labels=lowercase_    )
        UpperCAmelCase_   :   Optional[Any]     	=       self._prepare_for_class(lowercase_							,  lowercase_							,  return_labels=lowercase_    )
        check_equivalence(lowercase_							,  lowercase_							,  lowercase_							,  {"output_hidden_states": True}    )







  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Dict     	=       self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*lowercase_    )







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       TFConvNextModel.from_pretrained("facebook/convnext-tiny-224"    )
     self.assertIsNotNone(lowercase_    )







def       __a	(						):
   UpperCAmelCase_   :   Tuple     	=       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
   return image


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




  '''simple docstring'''







  @cached_property
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     return ConvNextImageProcessor.from_pretrained("facebook/convnext-tiny-224"    ) if is_vision_available() else None







  @slow
  def 			UpperCamelCase__   (						self    ):

     """simple docstring"""
     UpperCAmelCase_   :   Optional[Any]     	=       TFConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224"    )

     UpperCAmelCase_   :   str     	=       self.default_image_processor
     UpperCAmelCase_   :   Optional[Any]     	=       prepare_img()
     UpperCAmelCase_   :   Dict     	=       image_processor(images=lowercase_							,  return_tensors="tf"    )

     # forward pass
     UpperCAmelCase_   :   Optional[int]     	=       model(**lowercase_    )

     # verify the logits
     UpperCAmelCase_   :   List[str]     	=       tf.TensorShape((1, 1000)    )
     self.assertEqual(outputs.logits.shape							,  lowercase_    )

     UpperCAmelCase_   :   Any     	=       tf.constant([-0.02_60, -0.47_39, 0.19_11]    )

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




from __future__ import annotations

import random
import string







class 							lowercase						:




							def __init__(							self  ,A__ = None):
												lowercase		      =		passcode or self.__passcode_creator()
												lowercase		      =		self.__make_key_list()
												lowercase		      =		self.__make_shift_key()




							def __str__(							self):
												return "".join(self.__passcode)




							def 					A__ (							self  ,A__):
												for i in range(1  ,len(A__)  ,2):
																	iterlist[i] *= -1
												return iterlist




							def 					A__ (							self):
												lowercase		      =		string.ascii_letters + string.digits
												lowercase		      =		[random.choice(A__) for _ in range(random.randint(1_0  ,2_0))]
												return password




							def 					A__ (							self):
												lowercase		      =		(
												    string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
												)

												lowercase		      =		[]

												# creates points known as breakpoints to break the key_list_options at those
												# points and pivot each substring
												lowercase		      =		sorted(set(self.__passcode))
												lowercase		      =		[]

												# algorithm for creating a new shuffled list, keys_l, out of key_list_options
												for i in key_list_options:
																	temp_list.extend(A__)

																	# checking breakpoints at which to pivot temporary sublist and add it into
																	# keys_l
																	if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1])
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
												return keys_l




							def 					A__ (							self):
												lowercase		      =		sum(self.__neg_pos([ord(A__) for x in self.__passcode]))
												return num if num > 0 else len(self.__passcode)




							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# decoding shift like Caesar cipher algorithm implementing negative shift or
												# reverse shift or left shift
												for i in encoded_message:
																	lowercase		      =		self.__key_list.index(A__)
																	decoded_message += self.__key_list[
																	    (position - self.__shift_key) % -len(self.__key_list)
																	]

												return decoded_message





							def 					A__ (							self  ,A__):
												lowercase		      =		''''''

												# encoding shift like Caesar cipher algorithm implementing positive shift or
												# forward shift or right shift
												for i in plaintext:
																	lowercase		      =		self.__key_list.index(A__)
																	encoded_message += self.__key_list[
																	    (position + self.__shift_key) % len(self.__key_list)
																	]

												return encoded_message



def 			UpperCamelCase       (				lowerCAmelCase__ = "Hello, this is a modified Caesar cipher"						):
					'''simple docstring'''
					lowercase		      =		ShuffledShiftCipher()
					return cipa.decrypt(cipa.encrypt(lowerCAmelCase__						)						)


if __name__ == "__main__":
							import doctest

							doctest.testmod()




