






import unittest

from parameterized import parameterized

from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaTokenizer


class    __lowercase   :







     '''simple docstring'''




     def __init__(					self							:      str			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Union[str, Any]=13			,						UpperCamelCase_							:      List[Any]=7			,						UpperCamelCase_							:      Optional[int]=True			,						UpperCamelCase_							:      int=True			,						UpperCamelCase_							:      Any=False			,						UpperCamelCase_							:      List[Any]=True			,						UpperCamelCase_							:      Optional[Any]=99			,						UpperCamelCase_							:      List[Any]=32			,						UpperCamelCase_							:      Optional[Any]=5			,						UpperCamelCase_							:      List[str]=4			,						UpperCamelCase_							:      Any=37			,						UpperCamelCase_							:      Any="gelu"			,						UpperCamelCase_							:      int=0.1			,						UpperCamelCase_							:      int=0.1			,						UpperCamelCase_							:      List[Any]=512			,						UpperCamelCase_							:      Optional[Any]=16			,						UpperCamelCase_							:      List[Any]=2			,						UpperCamelCase_							:      Any=0.02			,						UpperCamelCase_							:      str=3			,						UpperCamelCase_							:      str=4			,						UpperCamelCase_							:      Tuple=None			,						):


         """simple docstring"""



         __A       =						parent
         __A       =						batch_size
         __A       =						seq_length
         __A       =						is_training
         __A       =						use_input_mask
         __A       =						use_token_type_ids
         __A       =						use_labels
         __A       =						vocab_size
         __A       =						hidden_size
         __A       =						num_hidden_layers
         __A       =						num_attention_heads
         __A       =						intermediate_size
         __A       =						hidden_act
         __A       =						hidden_dropout_prob
         __A       =						attention_probs_dropout_prob
         __A       =						max_position_embeddings
         __A       =						type_vocab_size
         __A       =						type_sequence_label_size
         __A       =						initializer_range
         __A       =						num_labels
         __A       =						num_choices
         __A       =						scope




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.vocab_size					)

         __A       =						None
         if self.use_input_mask:
             __A       =						random_attention_mask([self.batch_size, self.seq_length]					)

         __A       =						None
         if self.use_token_type_ids:
             __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.type_vocab_size					)

         __A       =						None
         __A       =						None
         __A       =						None
         if self.use_labels:
             __A       =						ids_tensor([self.batch_size]			,						self.type_sequence_label_size					)
             __A       =						ids_tensor([self.batch_size, self.seq_length]			,						self.num_labels					)
             __A       =						ids_tensor([self.batch_size]			,						self.num_choices					)

         __A       =						self.get_config()

         return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         return LlamaConfig(
             vocab_size=self.vocab_size			,						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			,						max_position_embeddings=self.max_position_embeddings			,						type_vocab_size=self.type_vocab_size			,						is_decoder=UpperCamelCase_			,						initializer_range=self.initializer_range			,						)




     def 		lowerCAmelCase_  (					self							:      int			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Optional[Any]					):


         """simple docstring"""



         __A       =						LlamaModel(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_					)
         __A       =						model(UpperCamelCase_					)
         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)					)




     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Dict			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Tuple			,						):


         """simple docstring"""



         __A       =						True
         __A       =						LlamaModel(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						)
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						)
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_					)
         self.parent.assertEqual(result.last_hidden_state.shape			,						(self.batch_size, self.seq_length, self.hidden_size)					)




     def 		lowerCAmelCase_  (					self							:      Dict			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Tuple			,						UpperCamelCase_							:      Optional[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      int			,						UpperCamelCase_							:      Any			,						):


         """simple docstring"""



         __A       =						LlamaForCausalLM(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.parent.assertEqual(result.logits.shape			,						(self.batch_size, self.seq_length, self.vocab_size)					)




     def 		lowerCAmelCase_  (					self							:      Union[str, Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      str			,						UpperCamelCase_							:      Any			,						UpperCamelCase_							:      List[Any]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Optional[int]			,						UpperCamelCase_							:      List[str]			,						UpperCamelCase_							:      Union[str, Any]			,						UpperCamelCase_							:      Union[str, Any]			,						):


         """simple docstring"""



         __A       =						True
         __A       =						True
         __A       =						LlamaForCausalLM(config=UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()

         # first forward pass
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						use_cache=UpperCamelCase_			,						)
         __A       =						outputs.past_key_values

         # create hypothetical multiple next token and extent to next_input_ids
         __A       =						ids_tensor((self.batch_size, 3)			,						config.vocab_size					)
         __A       =						ids_tensor((self.batch_size, 3)			,						vocab_size=2					)

         # append to next input_ids and
         __A       =						torch.cat([input_ids, next_tokens]			,						dim=-1					)
         __A       =						torch.cat([input_mask, next_mask]			,						dim=-1					)

         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						output_hidden_states=UpperCamelCase_			,						)["""hidden_states"""][0]
         __A       =						model(
             UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						encoder_hidden_states=UpperCamelCase_			,						encoder_attention_mask=UpperCamelCase_			,						past_key_values=UpperCamelCase_			,						output_hidden_states=UpperCamelCase_			,						)["""hidden_states"""][0]

         # select random slice
         __A       =						ids_tensor((1,)			,						output_from_past.shape[-1]					).item()
         __A       =						output_from_no_past[:, -3:, random_slice_idx].detach()
         __A       =						output_from_past[:, :, random_slice_idx].detach()

         self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]					)

         # test that outputs are equal for slice
         self.parent.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-3					)					)





     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						self.prepare_config_and_inputs()
         (
             (
             __A
         )     ,						(
             __A
         )     ,						(
             __A
         )     ,						(
             __A
         )     ,						(
             __A
         )     ,						(
             __A
         )     ,						(
             __A
         )     ,						
         )       =						config_and_inputs
         __A       =						{"""input_ids""": input_ids, """attention_mask""": input_mask}
         return config, inputs_dict


@require_torch
class    __lowercase   (						lowercase_      ,   lowercase_      ,   lowercase_      ,   unittest.TestCase	):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE     =					(LlamaModel, LlamaForCausalLM, LlamaForSequenceClassification) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE     =					(LlamaForCausalLM,) if is_torch_available() else ()
     SCREAMING_SNAKE_CASE     =					(
         {
             "feature-extraction": LlamaModel,
             "text-classification": LlamaForSequenceClassification,
             "text-generation": LlamaForCausalLM,
             "zero-shot": LlamaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     SCREAMING_SNAKE_CASE     =					False
     SCREAMING_SNAKE_CASE     =					False




     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         __A       =						LlamaModelTester(self					)
         __A       =						ConfigTester(self			,						config_class=UpperCamelCase_			,						hidden_size=37					)




     def 		lowerCAmelCase_  (					self							:      Dict					):


         """simple docstring"""



         self.config_tester.run_common_tests()




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



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




     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             __A       =						type
             self.model_tester.create_and_check_model(*UpperCamelCase_					)




     def 		lowerCAmelCase_  (					self							:      List[str]					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()
         __A       =						3
         __A       =						input_dict["""input_ids"""]
         __A       =						input_ids.ne(1					).to(UpperCamelCase_					)
         __A       =						ids_tensor([self.model_tester.batch_size]			,						self.model_tester.type_sequence_label_size					)
         __A       =						LlamaForSequenceClassification(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()
         __A       =						3
         __A       =						"""single_label_classification"""
         __A       =						input_dict["""input_ids"""]
         __A       =						input_ids.ne(1					).to(UpperCamelCase_					)
         __A       =						ids_tensor([self.model_tester.batch_size]			,						self.model_tester.type_sequence_label_size					)
         __A       =						LlamaForSequenceClassification(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels)					)




     def 		lowerCAmelCase_  (					self							:      Tuple					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()
         __A       =						3
         __A       =						"""multi_label_classification"""
         __A       =						input_dict["""input_ids"""]
         __A       =						input_ids.ne(1					).to(UpperCamelCase_					)
         __A       =						ids_tensor(
             [self.model_tester.batch_size, config.num_labels]			,						self.model_tester.type_sequence_label_size					).to(torch.float					)
         __A       =						LlamaForSequenceClassification(UpperCamelCase_					)
         model.to(UpperCamelCase_					)
         model.eval()
         __A       =						model(UpperCamelCase_			,						attention_mask=UpperCamelCase_			,						labels=UpperCamelCase_					)
         self.assertEqual(result.logits.shape			,						(self.model_tester.batch_size, self.model_tester.num_labels)					)




     @unittest.skip("""LLaMA buffers include complex numbers, which breaks this test"""					)
     def 		lowerCAmelCase_  (					self							:      int					):


         """simple docstring"""



         pass





     @parameterized.expand([("""linear""",), ("""dynamic""",)]					)
     def 		lowerCAmelCase_  (					self							:      List[str]			,						UpperCamelCase_							:      Any					):


         """simple docstring"""



         __A     ,						__A       =						self.model_tester.prepare_config_and_inputs_for_common()
         __A       =						ids_tensor([1, 10]			,						config.vocab_size					)
         __A       =						ids_tensor([1, int(config.max_position_embeddings * 1.5					)]			,						config.vocab_size					)

         set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
         __A       =						LlamaModel(UpperCamelCase_					)
         original_model.to(UpperCamelCase_					)
         original_model.eval()
         __A       =						original_model(UpperCamelCase_					).last_hidden_state
         __A       =						original_model(UpperCamelCase_					).last_hidden_state

         set_seed(42					)  # Fixed seed at init time so the two models get the same random weights
         __A       =						{"""type""": scaling_type, """factor""": 10.0}
         __A       =						LlamaModel(UpperCamelCase_					)
         scaled_model.to(UpperCamelCase_					)
         scaled_model.eval()
         __A       =						scaled_model(UpperCamelCase_					).last_hidden_state
         __A       =						scaled_model(UpperCamelCase_					).last_hidden_state

         # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
         # maximum sequence length, so the outputs for the short input should match.
         if scaling_type == "dynamic":
             self.assertTrue(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-5					)					)
         else:
             self.assertFalse(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-5					)					)

         # The output should be different for long inputs
         self.assertFalse(torch.allclose(UpperCamelCase_			,						UpperCamelCase_			,						atol=1e-5					)					)


@require_torch
class    __lowercase   (						unittest.TestCase	):







     '''simple docstring'''




     @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""					)
     @slow
     def 		lowerCAmelCase_  (					self							:      str					):


         """simple docstring"""



         __A       =						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A       =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-7b-hf"""			,						device_map="""auto"""					)
         __A       =						model(torch.tensor([input_ids]					)					)
         # Expected mean on dim = -1
         __A       =						torch.tensor([[-6.6550, -4.1227, -4.9859, -3.2406, 0.8262, -3.0033, 1.2964, -3.3699]]					)
         torch.testing.assert_close(out.mean(-1					)			,						UpperCamelCase_			,						atol=1e-2			,						rtol=1e-2					)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A       =						torch.tensor([-12.8281, -7.4453, -0.4639, -8.0625, -7.2500, -8.0000, -6.4883, -7.7695, -7.8438, -7.0312, -6.2188, -7.1328, -1.8496, 1.9961, -8.6250, -6.7227, -12.8281, -6.9492, -7.0742, -7.7852, -7.5820, -7.9062, -6.9375, -7.9805, -8.3438, -8.1562, -8.0469, -7.6250, -7.7422, -7.3398,]					)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]			,						UpperCamelCase_			,						atol=1e-5			,						rtol=1e-5					)




     @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""					)
     @slow
     def 		lowerCAmelCase_  (					self							:      Union[str, Any]					):


         """simple docstring"""



         __A       =						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A       =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-hf"""			,						device_map="""auto"""					)
         __A       =						model(torch.tensor(UpperCamelCase_					)					)
         # Expected mean on dim = -1
         __A       =						torch.tensor([[-2.0622, -1.2794, -1.1638, -0.9788, -1.4603, -1.0238, -1.7893, -1.4411]]					)
         torch.testing.assert_close(out.mean(-1					)			,						UpperCamelCase_			,						atol=1e-2			,						rtol=1e-2					)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A       =						torch.tensor([-8.1406, -8.0547, 2.7461, -1.2344, -0.1448, -1.8262, -1.0020, -1.8154, -1.6895, -1.8516, -2.3574, -0.9277, 3.7598, 6.5742, -1.2998, -0.1177, -8.1406, -2.9688, -2.9199, -3.1699, -3.5254, -2.3555, -2.7988, -3.4141, -2.8262, -4.5195, -3.3379, -3.3164, -2.7832, -3.0273]					)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]			,						UpperCamelCase_			,						atol=1e-5			,						rtol=1e-5					)




     @unittest.skip("""Logits are not exactly the same, once we fix the instabalities somehow, will update!"""					)
     @slow
     def 		lowerCAmelCase_  (					self							:      Any					):


         """simple docstring"""



         __A       =						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A       =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""			,						device_map="""auto"""					)
         __A       =						model(torch.tensor(UpperCamelCase_					)					)
         # Expected mean on dim = -1
         __A       =						torch.tensor([[-0.8562, -1.8520, -0.7551, -0.4162, -1.5161, -1.2038, -2.4823, -2.3254]]					)
         torch.testing.assert_close(out.mean(-1					)			,						UpperCamelCase_			,						atol=1e-2			,						rtol=1e-2					)
         # slicing logits[0, 0, 0:30]
         # fmt: off
         __A       =						torch.tensor([-2.2227, 4.8828, 0.9023, -0.4578, -0.7871, -0.1033, -0.6221, -0.5786, -0.7803, -1.0674, -1.2920, -0.1570, 0.8008, 2.0723, -0.9497, 0.2771, -2.2227, -0.7612, -1.4346, -1.2061, -1.6426, -0.3000, -0.7139, -1.1934, -1.8691, -1.6973, -1.5947, -1.2705, -0.3523, -0.5513]					)
         # fmt: on
         torch.testing.assert_close(out.mean(-1					)			,						UpperCamelCase_			,						atol=1e-2			,						rtol=1e-2					)




     @unittest.skip(
         """Logits are not exactly the same, once we fix the instabalities somehow, will update! Also it is gonna be a `too_slow` test"""					)
     @slow
     def 		lowerCAmelCase_  (					self							:      List[Any]					):


         """simple docstring"""



         __A       =						[1, 306, 4_658, 278, 6_593, 310, 2_834, 338]
         __A       =						LlamaForCausalLM.from_pretrained("""meta-llama/Llama-2-70b-hf"""			,						device_map="""auto"""					)
         __A       =						model(torch.tensor(UpperCamelCase_					)					)

         __A       =						torch.tensor(
             [[-4.2327, -3.3360, -4.6665, -4.7631, -1.8180, -3.4170, -1.4211, -3.1810]]			,						dtype=torch.floataa					)
         torch.testing.assert_close(out.mean(-1					)			,						UpperCamelCase_			,						atol=1e-2			,						rtol=1e-2					)
         # fmt: off
         __A       =						torch.tensor([-9.4922, -3.9551, 1.7998, -5.6758, -5.1055, -5.8984, -4.8320, -6.8086, -6.5391, -5.6172, -5.5820, -5.5352, 1.7881, 3.6289, -6.5117, -3.4785, -9.5000, -6.0352, -6.8125, -6.0195, -6.6836, -5.4727, -6.2812, -6.0391, -7.3398, -7.4297, -7.4844, -6.5820, -5.8789, -5.5312]					)
         # fmt: on
         torch.testing.assert_close(out[0, 0, :30]			,						UpperCamelCase_			,						atol=1e-5			,						rtol=1e-5					)





     @unittest.skip("""Model is curently gated"""					)
     @slow
     def 		lowerCAmelCase_  (					self							:      Optional[Any]					):


         """simple docstring"""



         __A       =						"""Simply put, the theory of relativity states that 1) the laws of physics are the same everywhere in the universe and 2) the passage of time and the length of objects can vary depending on the observer\'s frame of reference.\n\nThe first part of the theory, that the laws of physics are the same everywhere, is known as the \"princi"""
         __A       =						"""Simply put, the theory of relativity states that """
         __A       =						LlamaTokenizer.from_pretrained("""meta-llama/Llama-2-13b-chat-hf"""					)
         __A       =						tokenizer.encode(UpperCamelCase_			,						return_tensors="""pt"""					)
         __A       =						LlamaForCausalLM.from_pretrained(
             """meta-llama/Llama-2-13b-chat-hf"""			,						device_map="""sequential"""			,						use_safetensors=UpperCamelCase_					)

         # greedy generation outputs
         __A       =						model.generate(UpperCamelCase_			,						max_new_tokens=64			,						top_p=UpperCamelCase_			,						temperature=1			,						do_sample=UpperCamelCase_					)
         __A       =						tokenizer.decode(generated_ids[0]			,						skip_special_tokens=UpperCamelCase_					)
         self.assertEqual(UpperCamelCase_			,						UpperCamelCase_					)












def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   str:





    """simple docstring"""

    __A       =						""""""
    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       _SCREAMING_SNAKE_CASE    (			__lowercase :      str     )    ->   dict[str, str]:





    """simple docstring"""

    __A       =						[chr(i + 6_5     ) for i in range(2_6     )]
    # Remove duplicate characters from key
    __A       =						remove_duplicates(key.upper()     )
    __A       =						len(__lowercase     )
    # First fill cipher with key characters
    __A       =						{alphabet[i]: char for i, char in enumerate(__lowercase     )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(__lowercase     )      ,					2_6     ):
        __A       =						alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __A       =						alphabet[i - offset]
        __A       =						char
    return cipher_alphabet



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    return "".join(cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      dict[str, str]     )    ->   str:





    """simple docstring"""

    __A       =						{v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(__lowercase      ,					__lowercase     ) for ch in message.upper()     )



def       _SCREAMING_SNAKE_CASE    (			)    ->   None:





    """simple docstring"""

    __A       =						input("""Enter message to encode or decode: """     ).strip()
    __A       =						input("""Enter keyword: """     ).strip()
    __A       =						input("""Encipher or decipher? E/D:"""     ).strip()[0].lower()
    try:
        __A       =						{"""e""": encipher, """d""": decipher}[option]
    except KeyError:
        raise KeyError("""invalid input option"""     )
    __A       =						create_cipher_map(__lowercase     )
    print(func(__lowercase      ,					__lowercase     )     )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




