





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							:
      def __init__(		self      ,       a__      ,       a__=3      ,       a__=32      ,       a__=3      ,       a__=10      ,       a__=[10, 20, 30, 40]      ,       a__=[1, 1, 2, 1]      ,       a__=True      ,       a__=True      ,       a__="relu"      ,       a__=3      ,       a__=None      ,       ):
         A_     :				Optional[int]			      =	parent
         A_     :				Tuple			      =	batch_size
         A_     :				Optional[int]			      =	image_size
         A_     :				Optional[Any]			      =	num_channels
         A_     :				Union[str, Any]			      =	embeddings_size
         A_     :				List[Any]			      =	hidden_sizes
         A_     :				List[Any]			      =	depths
         A_     :				Tuple			      =	is_training
         A_     :				Tuple			      =	use_labels
         A_     :				Tuple			      =	hidden_act
         A_     :				str			      =	num_labels
         A_     :				List[Any]			      =	scope
         A_     :				Any			      =	len(a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]       )

         A_     :				List[str]			      =	None
         if self.use_labels:
            A_     :				Union[str, Any]			      =	ids_tensor([self.batch_size]      ,       self.num_labels       )

         A_     :				Dict			      =	self.get_config()

         return config, pixel_values, labels
      def 		_lowerCamelCase      (		self       ):
         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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
         A_     :				Optional[int]			      =	TFResNetModel(config=a__       )
         A_     :				Union[str, Any]			      =	model(a__       )
         # 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 		_lowerCamelCase      (		self      ,       a__      ,       a__      ,       a__       ):
         A_     :				Optional[Any]			      =	self.num_labels
         A_     :				str			      =	TFResNetForImageClassification(a__       )
         A_     :				Tuple			      =	model(a__      ,       labels=a__       )
         self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.num_labels)       )






      def 		_lowerCamelCase      (		self       ):
         A_     :				int			      =	self.prepare_config_and_inputs()
         A_      ,				A_      ,				A_     :				Tuple			      =	config_and_inputs
         A_     :				Optional[Any]			      =	{"""pixel_values""": pixel_values}
         return config, inputs_dict






@require_tf
class    _UpperCAmelCase							(		_lowerCamelCase			,       _lowerCamelCase			,       unittest.TestCase     ):
      a						       =							(TFResNetModel, TFResNetForImageClassification) if is_tf_available() else ()
      a						       =							(
          {'''feature-extraction''': TFResNetModel, '''image-classification''': TFResNetForImageClassification}
          if is_tf_available()
          else {}
      )

      a						       =							False
      a						       =							False
      a						       =							False
      a						       =							False
      a						       =							False
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[Any]			      =	TFResNetModelTester(self       )
         A_     :				str			      =	ConfigTester(self      ,       config_class=a__      ,       has_text_modality=a__       )
      def 		_lowerCamelCase      (		self       ):
         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 		_lowerCamelCase      (		self       ):
         return
      @unittest.skip(reason="""ResNet does not use inputs_embeds"""       )
      def 		_lowerCamelCase      (		self       ):
         pass
      @unittest.skip(reason="""ResNet does not support input and output embeddings"""       )
      def 		_lowerCamelCase      (		self       ):
         pass
      def 		_lowerCamelCase      (		self       ):
         A_      ,				A_     :				Dict			      =	self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
            A_     :				Tuple			      =	model_class(a__       )
            A_     :				List[Any]			      =	inspect.signature(model.call       )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            A_     :				Dict			      =	[*signature.parameters.keys()]

            A_     :				Optional[Any]			      =	["""pixel_values"""]
            self.assertListEqual(arg_names[:1]      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Tuple			      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a__       )
      def 		_lowerCamelCase      (		self       ):
         def check_hidden_states_output(a__      ,       a__      ,       a__       ):
            A_     :				Optional[Any]			      =	model_class(a__       )
            A_     :				Optional[Any]			      =	model(**self._prepare_for_class(a__      ,       a__       )       )

            A_     :				str			      =	outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

            A_     :				Dict			      =	self.model_tester.num_stages
            self.assertEqual(len(a__       )      ,       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]      ,       )

         A_      ,				A_     :				Tuple			      =	self.model_tester.prepare_config_and_inputs_for_common()
         A_     :				Optional[int]			      =	["""basic""", """bottleneck"""]
         for model_class in self.all_model_classes:
            for layer_type in layers_type:
               A_     :				List[Any]			      =	layer_type
               A_     :				List[str]			      =	True
               check_hidden_states_output(a__      ,       a__      ,       a__       )

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

               check_hidden_states_output(a__      ,       a__      ,       a__       )
      def 		_lowerCamelCase      (		self       ):
         A_     :				Optional[int]			      =	self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_image_classification(*a__       )






      @slow
      def 		_lowerCamelCase      (		self       ):
         for model_name in TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
            A_     :				int			      =	TFResNetModel.from_pretrained(a__       )
            self.assertIsNotNone(a__       )






def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Union[str, Any]			      =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""     )
   return image







@require_tf
@require_vision
class    _UpperCAmelCase							(		unittest.TestCase     ):
      @cached_property
      def 		_lowerCamelCase      (		self       ):
         return (
             AutoImageProcessor.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )
             if is_vision_available()
             else None
         )






      @slow
      def 		_lowerCamelCase      (		self       ):
         A_     :				Union[str, Any]			      =	TFResNetForImageClassification.from_pretrained(TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST[0]       )

         A_     :				str			      =	self.default_image_processor
         A_     :				List[Any]			      =	prepare_img()
         A_     :				Union[str, Any]			      =	image_processor(images=a__      ,       return_tensors="""tf"""       )

         # forward pass
         A_     :				Union[str, Any]			      =	model(**a__       )

         # verify the logits
         A_     :				int			      =	tf.TensorShape((1, 1000)       )
         self.assertEqual(outputs.logits.shape      ,       a__       )

         A_     :				List[Any]			      =	tf.constant([-11.1069, -9.7877, -8.3777]       )

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













_lowerCAmelCase 				=     """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def     _lowerCAmelCase		(  ):
   '''simple docstring'''


   A_     :				Any			      =	input("""Enter message: """     )
   A_     :				Dict			      =	input("""Enter key [alphanumeric]: """     )
   A_     :				Dict			      =	input("""Encrypt/Decrypt [e/d]: """     )

   if mode.lower().startswith("""e"""     ):
      A_     :				Union[str, Any]			      =	"""encrypt"""
      A_     :				Optional[int]			      =	encrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )
   elif mode.lower().startswith("""d"""     ):
      A_     :				str			      =	"""decrypt"""
      A_     :				Dict			      =	decrypt_message(_lowerCAmelCase  ,_lowerCAmelCase     )

   print(f"""\n{mode.title()}ed message:"""     )
   print(_lowerCAmelCase     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""encrypt"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   return translate_message(_lowerCAmelCase  ,_lowerCAmelCase  ,"""decrypt"""     )



def     _lowerCAmelCase		(  _lowerCAmelCase  ,_lowerCAmelCase  ,_lowerCAmelCase     ):
   '''simple docstring'''


   A_     :				Dict			      =	[]
   A_     :				List[Any]			      =	0
   A_     :				int			      =	key.upper()

   for symbol in message:
      A_     :				Optional[int]			      =	LETTERS.find(symbol.upper()     )
      if num != -1:
         if mode == "encrypt":
            num += LETTERS.find(key[key_index]     )
         elif mode == "decrypt":
            num -= LETTERS.find(key[key_index]     )

         num %= len(_lowerCAmelCase     )

         if symbol.isupper():
            translated.append(LETTERS[num]     )
         elif symbol.islower():
            translated.append(LETTERS[num].lower()     )

         key_index += 1
         if key_index == len(_lowerCAmelCase     ):
            A_     :				Dict			      =	0
      else:
         translated.append(_lowerCAmelCase     )
   return "".join(_lowerCAmelCase     )


if __name__ == "__main__":
      main()






