


import copy
import inspect
import unittest

from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor


if is_torch_available():
      import torch

      from transformers import TimmBackbone, TimmBackboneConfig

from ...test_pipeline_mixin import PipelineTesterMixin







class        lowerCamelCase__	:






   """simple docstring"""






   def __init__(							self      ,						snake_case      ,						snake_case=None      ,						snake_case=None      ,						snake_case=None      ,						snake_case="resnet50"      ,						snake_case=3      ,						snake_case=32      ,						snake_case=3      ,						snake_case=True      ,						snake_case=True      ,						):

      '''simple docstring'''


      UpperCamelCase__					=       parent
      UpperCamelCase__					=       out_indices if out_indices is not None else [4]
      UpperCamelCase__					=       stage_names
      UpperCamelCase__					=       out_features
      UpperCamelCase__					=       backbone
      UpperCamelCase__					=       batch_size
      UpperCamelCase__					=       image_size
      UpperCamelCase__					=       num_channels
      UpperCamelCase__					=       use_pretrained_backbone
      UpperCamelCase__					=       is_training




   def 				snake_case__				(							self     ):

      '''simple docstring'''


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

      return config, pixel_values




   def 				snake_case__				(							self     ):

      '''simple docstring'''


      return TimmBackboneConfig(
          image_size=self.image_size      ,						num_channels=self.num_channels      ,						out_features=self.out_features      ,						out_indices=self.out_indices      ,						stage_names=self.stage_names      ,						use_pretrained_backbone=self.use_pretrained_backbone      ,						backbone=self.backbone      ,						)




   def 				snake_case__				(							self      ,						snake_case      ,						snake_case     ):

      '''simple docstring'''


      UpperCamelCase__					=       TimmBackbone(config=snake_case     )
      model.to(snake_case     )
      model.eval()
      with torch.no_grad():
         UpperCamelCase__					=       model(snake_case     )
      self.parent.assertEqual(
          result.feature_map[-1].shape      ,						(self.batch_size, model.channels[-1], 14, 14)      ,						)







   def 				snake_case__				(							self     ):

      '''simple docstring'''


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







@require_torch
@require_timm
class        lowerCamelCase__	(							UpperCAmelCase					,	UpperCAmelCase					,	UpperCAmelCase					,	unittest.TestCase      ):






   """simple docstring"""






   _UpperCamelCase		:					int    =		(TimmBackbone,) if is_torch_available() else ()
   _UpperCamelCase		:					Optional[int]    =		{'feature-extraction': TimmBackbone} if is_torch_available() else {}
   _UpperCamelCase		:					List[Any]    =		False
   _UpperCamelCase		:					Optional[int]    =		False
   _UpperCamelCase		:					Tuple    =		False
   _UpperCamelCase		:					List[Any]    =		False




   def 				snake_case__				(							self     ):

      '''simple docstring'''


      UpperCamelCase__					=       TimmBackboneModelTester(self     )
      UpperCamelCase__					=       ConfigTester(self      ,						config_class=snake_case      ,						has_text_modality=snake_case     )




   def 				snake_case__				(							self     ):

      '''simple docstring'''


      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 				snake_case__				(							self     ):

      '''simple docstring'''


      UpperCamelCase__					=       "resnet18"
      UpperCamelCase__					=       "microsoft/resnet-18"

      UpperCamelCase__					=       AutoBackbone.from_pretrained(snake_case      ,						use_timm_backbone=snake_case     )
      UpperCamelCase__					=       AutoBackbone.from_pretrained(snake_case     )

      self.assertEqual(len(timm_model.out_features     )      ,						len(transformers_model.out_features     )     )
      self.assertEqual(len(timm_model.stage_names     )      ,						len(transformers_model.stage_names     )     )
      self.assertEqual(timm_model.channels      ,						transformers_model.channels     )
      # Out indices are set to the last layer by default. For timm models, we don't know
      # the number of layers in advance, so we set it to (-1,), whereas for transformers
      # models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
      self.assertEqual(timm_model.out_indices      ,						(-1,)     )
      self.assertEqual(transformers_model.out_indices      ,						[len(timm_model.stage_names     ) - 1]     )

      UpperCamelCase__					=       AutoBackbone.from_pretrained(snake_case      ,						use_timm_backbone=snake_case      ,						out_indices=[1, 2, 3]     )
      UpperCamelCase__					=       AutoBackbone.from_pretrained(snake_case      ,						out_indices=[1, 2, 3]     )

      self.assertEqual(timm_model.out_indices      ,						transformers_model.out_indices     )
      self.assertEqual(len(timm_model.out_features     )      ,						len(transformers_model.out_features     )     )
      self.assertEqual(timm_model.channels      ,						transformers_model.channels     )




   @unittest.skip("TimmBackbone doesn't support feed forward chunking"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("TimmBackbone initialization is managed on the timm side"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("TimmBackbone models doesn't have inputs_embeds"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("TimmBackbone models doesn't have inputs_embeds"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("model weights aren't tied in TimmBackbone."     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("model weights aren't tied in TimmBackbone."     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone"     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("TimmBackbone doesn't have hidden size info in its configuration."     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("TimmBackbone doesn't support output_attentions."     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("Safetensors is not supported by timm."     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."     )
   def 				snake_case__				(							self     ):

      '''simple docstring'''


      pass




   def 				snake_case__				(							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(snake_case     )
         UpperCamelCase__					=       inspect.signature(model.forward     )
         # signature.parameters is an OrderedDict => so arg_names order is deterministic
         UpperCamelCase__					=       [*signature.parameters.keys()]

         UpperCamelCase__					=       ["pixel_values"]
         self.assertListEqual(arg_names[:1]      ,						snake_case     )




   def 				snake_case__				(							self     ):

      '''simple docstring'''


      UpperCamelCase__,      UpperCamelCase__					=       self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase__					=       True
      UpperCamelCase__					=       self.has_attentions

      # no need to test all models as different heads yield the same functionality
      UpperCamelCase__					=       self.all_model_classes[0]
      UpperCamelCase__					=       model_class(snake_case     )
      model.to(snake_case     )

      UpperCamelCase__					=       self._prepare_for_class(snake_case      ,						snake_case     )
      UpperCamelCase__					=       model(**snake_case     )
      UpperCamelCase__					=       outputs[0][-1]

      # Encoder-/Decoder-only models
      UpperCamelCase__					=       outputs.hidden_states[0]
      hidden_states.retain_grad()

      if self.has_attentions:
         UpperCamelCase__					=       outputs.attentions[0]
         attentions.retain_grad()

      output.flatten()[0].backward(retain_graph=snake_case     )

      self.assertIsNotNone(hidden_states.grad     )

      if self.has_attentions:
         self.assertIsNotNone(attentions.grad     )







   def 				snake_case__				(							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(snake_case     )
         model.to(snake_case     )
         model.eval()
         UpperCamelCase__					=       model(**snake_case     )

         self.assertEqual(len(result.feature_maps     )      ,						len(config.out_indices     )     )
         self.assertEqual(len(model.channels     )      ,						len(config.out_indices     )     )

         # Check output of last stage is taken if out_features=None, out_indices=None
         UpperCamelCase__					=       copy.deepcopy(snake_case     )
         UpperCamelCase__					=       None
         UpperCamelCase__					=       model_class(snake_case     )
         model.to(snake_case     )
         model.eval()
         UpperCamelCase__					=       model(**snake_case     )

         self.assertEqual(len(result.feature_maps     )      ,						1     )
         self.assertEqual(len(model.channels     )      ,						1     )

         # Check backbone can be initialized with fresh weights
         UpperCamelCase__					=       copy.deepcopy(snake_case     )
         UpperCamelCase__					=       False
         UpperCamelCase__					=       model_class(snake_case     )
         model.to(snake_case     )
         model.eval()
         UpperCamelCase__					=       model(**snake_case     )






def        UpperCamelCase_(				_A   :str  )->		str:
			UpperCamelCase__					=       ""
			for ch in key:
						if ch == " " or ch not in key_no_dups and ch.isalpha():
									key_no_dups += ch
			return key_no_dups
def        UpperCamelCase_(				_A   :str  )->		dict[str, str]:
			UpperCamelCase__					=       [chr(i + 65  ) for i in range(26  )]
			# Remove duplicate characters from key
			UpperCamelCase__					=       remove_duplicates(key.upper()  )
			UpperCamelCase__					=       len(_A  )
			# First fill cipher with key characters
			UpperCamelCase__					=       {alphabet[i]: char for i, char in enumerate(_A  )}
			# Then map remaining characters in alphabet to
			# the alphabet from the beginning
			for i in range(len(_A  )				,     26  ):
						UpperCamelCase__					=       alphabet[i - offset]
						# Ensure we are not mapping letters to letters previously mapped
						while char in key:
									offset -= 1
									UpperCamelCase__					=       alphabet[i - offset]
						UpperCamelCase__					=       char
			return cipher_alphabet
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			return "".join(cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				_A   :str				,     _A   :dict[str, str]  )->		str:
			UpperCamelCase__					=       {v: k for k, v in cipher_map.items()}
			return "".join(rev_cipher_map.get(_A				,     _A  ) for ch in message.upper()  )
def        UpperCamelCase_(				)->		None:
			UpperCamelCase__					=       input("Enter message to encode or decode: "  ).strip()
			UpperCamelCase__					=       input("Enter keyword: "  ).strip()
			UpperCamelCase__					=       input("Encipher or decipher? E/D:"  ).strip()[0].lower()
			try:
						UpperCamelCase__					=       {"e": encipher, "d": decipher}[option]
			except KeyError:
						raise KeyError("invalid input option"  )
			UpperCamelCase__					=       create_cipher_map(_A  )
			print(func(_A				,     _A  )  )


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()


