





from __future__ import annotations

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					Tuple = 0 )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		key






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Dict )  ->	Optional[Any]:




           '''simple docstring'''


           assert isinstance(lowerCamelCase__		,lowerCamelCase__ ) and isinstance(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(lowerCamelCase__ ) ^ key ) for ch in content]






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					Optional[Any] )  ->	List[Any]:




           '''simple docstring'''


           assert isinstance(lowerCamelCase__		,lowerCamelCase__ ) and isinstance(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		key or self.__key or 1

           # make sure key is an appropriate size
           key %= 255

           return [chr(ord(lowerCamelCase__ ) ^ key ) for ch in content]






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					str		,lowerCamelCase__		:					Optional[int] = 0 )  ->	Optional[Any]:




           '''simple docstring'''


           assert isinstance(lowerCamelCase__		,lowerCamelCase__ ) and isinstance(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           SCREAMING_SNAKE_CASE	=		""""""

           for ch in content:
                 ans += chr(ord(lowerCamelCase__ ) ^ key )

           return ans






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Optional[int] = 0 )  ->	Optional[Any]:




           '''simple docstring'''


           assert isinstance(lowerCamelCase__		,lowerCamelCase__ ) and isinstance(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		key or self.__key or 1

           # make sure key can be any size
           while key > 255:
                 key -= 255

           # This will be returned
           SCREAMING_SNAKE_CASE	=		""""""

           for ch in content:
                 ans += chr(ord(lowerCamelCase__ ) ^ key )

           return ans






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any]		,lowerCamelCase__		:					List[Any]		,lowerCamelCase__		:					List[str] = 0 )  ->	Any:




           '''simple docstring'''


           assert isinstance(lowerCamelCase__		,lowerCamelCase__ ) and isinstance(lowerCamelCase__		,lowerCamelCase__ )

           try:
                 with open(lowerCamelCase__ ) as fin, open("""encrypt.out"""		,"""w+""" ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.encrypt_string(lowerCamelCase__		,lowerCamelCase__ ) )

           except OSError:
                 return False

           return True






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           assert isinstance(lowerCamelCase__		,lowerCamelCase__ ) and isinstance(lowerCamelCase__		,lowerCamelCase__ )

           try:
                 with open(lowerCamelCase__ ) as fin, open("""decrypt.out"""		,"""w+""" ) as fout:
                       # actual encrypt-process
                       for line in fin:
                             fout.write(self.decrypt_string(lowerCamelCase__		,lowerCamelCase__ ) )

           except OSError:
                 return False

           return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")









from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import ResNetConfig
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 TFResNetForImageClassification, TFResNetModel
      from transformers.models.resnet.modeling_tf_resnet import TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
      from PIL import Image

      from transformers import AutoImageProcessor

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					str		,lowerCamelCase__		:					int		,lowerCamelCase__		:					List[str]=3		,lowerCamelCase__		:					Optional[Any]=32		,lowerCamelCase__		:					Any=3		,lowerCamelCase__		:					Any=10		,lowerCamelCase__		:					Any=[10, 20, 30, 40]		,lowerCamelCase__		:					Any=[1, 1, 2, 1]		,lowerCamelCase__		:					Union[str, Any]=True		,lowerCamelCase__		:					List[str]=True		,lowerCamelCase__		:					Union[str, Any]="relu"		,lowerCamelCase__		:					Dict=3		,lowerCamelCase__		:					Any=None		,)  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		parent
           SCREAMING_SNAKE_CASE	=		batch_size
           SCREAMING_SNAKE_CASE	=		image_size
           SCREAMING_SNAKE_CASE	=		num_channels
           SCREAMING_SNAKE_CASE	=		embeddings_size
           SCREAMING_SNAKE_CASE	=		hidden_sizes
           SCREAMING_SNAKE_CASE	=		depths
           SCREAMING_SNAKE_CASE	=		is_training
           SCREAMING_SNAKE_CASE	=		use_labels
           SCREAMING_SNAKE_CASE	=		hidden_act
           SCREAMING_SNAKE_CASE	=		num_labels
           SCREAMING_SNAKE_CASE	=		scope
           SCREAMING_SNAKE_CASE	=		len(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

           SCREAMING_SNAKE_CASE	=		self.get_config()

           return config, pixel_values, labels






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




           '''simple docstring'''


           return ResNetConfig(
               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		,image_size=self.image_size		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					List[Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModel(config=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__ )
           # 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        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Union[str, Any]		,lowerCamelCase__		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.num_labels
           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification(lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		model(lowerCamelCase__		,labels=lowerCamelCase__ )
           self.parent.assertEqual(result.logits.shape		,(self.batch_size, self.num_labels) )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.prepare_config_and_inputs()
           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		config_and_inputs
           SCREAMING_SNAKE_CASE	=		{"""pixel_values""": pixel_values}
           return config, inputs_dict

@require_tf
class 	UpperCamelCase__				(   lowerCAmelCase_							,   lowerCAmelCase_							,   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Union[str, Any]				=    (TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
     __snake_case					:			int				=    (
         {"feature-extraction": TFResNetModel, "image-classification": TFResNetForImageClassification}
         if is_tf_available()
         else {}
     )

     __snake_case					:			Optional[int]				=    False
     __snake_case					:			int				=    False
     __snake_case					:			Optional[Any]				=    False
     __snake_case					:			Union[str, Any]				=    False
     __snake_case					:			str				=    False






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetModelTester(self )
           SCREAMING_SNAKE_CASE	=		ConfigTester(self		,config_class=lowerCamelCase__		,has_text_modality=lowerCamelCase__ )






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




           '''simple docstring'''


           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        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           return






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




           '''simple docstring'''


           pass






     @unittest.skip(reason="""ResNet does not support input and output embeddings""" )
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	Optional[int]:




           '''simple docstring'''


           pass






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs_for_common()

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

                 SCREAMING_SNAKE_CASE	=		["""pixel_values"""]
                 self.assertListEqual(arg_names[:1]		,lowerCamelCase__ )






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




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           def check_hidden_states_output(lowerCamelCase__		:					str		,lowerCamelCase__		:					Dict		,lowerCamelCase__		:					Dict ):
                 SCREAMING_SNAKE_CASE	=		model_class(lowerCamelCase__ )
                 SCREAMING_SNAKE_CASE	=		model(**self._prepare_for_class(lowerCamelCase__		,lowerCamelCase__ ) )

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

                 SCREAMING_SNAKE_CASE	=		self.model_tester.num_stages
                 self.assertEqual(len(lowerCamelCase__ )		,expected_num_stages + 1 )

                 # ResNet'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]		,)

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

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

                       check_hidden_states_output(lowerCamelCase__		,lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[Any]:




           '''simple docstring'''


           for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 SCREAMING_SNAKE_CASE	=		TFResNetModel.from_pretrained(lowerCamelCase__ )
                 self.assertIsNotNone(lowerCamelCase__ )



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


      '''simple docstring'''


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



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



     '''simple docstring'''

     @cached_property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Dict:




           '''simple docstring'''


           return (
               AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
               if is_vision_available()
               else None
           )






     @slow
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )

           SCREAMING_SNAKE_CASE	=		self.default_image_processor
           SCREAMING_SNAKE_CASE	=		prepare_img()
           SCREAMING_SNAKE_CASE	=		image_processor(images=lowerCamelCase__		,return_tensors="""tf""" )

           # forward pass
           SCREAMING_SNAKE_CASE	=		model(**lowerCamelCase__ )

           # verify the logits
           SCREAMING_SNAKE_CASE	=		tf.TensorShape((1, 1000) )
           self.assertEqual(outputs.logits.shape		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		tf.constant([-11.1069, -9.7877, -8.3777] )

           self.assertTrue(np.allclose(outputs.logits[0, :3].numpy()		,lowerCamelCase__		,atol=1e-4 ) )


