





from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class        __SCREAMING_SNAKE_CASE					:
							snake_case     :      Optional[Any]						     =       MBartConfig
							snake_case     :      Any						     =       {}
							snake_case     :      List[str]						     =       """gelu"""






							def __init__(						self  , __lowerCAmelCase  , __lowerCAmelCase=13  , __lowerCAmelCase=7  , __lowerCAmelCase=True  , __lowerCAmelCase=False  , __lowerCAmelCase=99  , __lowerCAmelCase=32  , __lowerCAmelCase=2  , __lowerCAmelCase=4  , __lowerCAmelCase=37  , __lowerCAmelCase=0.1  , __lowerCAmelCase=0.1  , __lowerCAmelCase=20  , __lowerCAmelCase=2  , __lowerCAmelCase=1  , __lowerCAmelCase=0  , ):
														UpperCamelCase__				     =			parent
														UpperCamelCase__				     =			batch_size
														UpperCamelCase__				     =			seq_length
														UpperCamelCase__				     =			is_training
														UpperCamelCase__				     =			use_labels
														UpperCamelCase__				     =			vocab_size
														UpperCamelCase__				     =			hidden_size
														UpperCamelCase__				     =			num_hidden_layers
														UpperCamelCase__				     =			num_attention_heads
														UpperCamelCase__				     =			intermediate_size
														UpperCamelCase__				     =			hidden_dropout_prob
														UpperCamelCase__				     =			attention_probs_dropout_prob
														UpperCamelCase__				     =			max_position_embeddings
														UpperCamelCase__				     =			eos_token_id
														UpperCamelCase__				     =			pad_token_id
														UpperCamelCase__				     =			bos_token_id






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			ids_tensor([self.batch_size, self.seq_length - 1]  , self.vocab_size  )
														UpperCamelCase__				     =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size  )  , 1  )
														UpperCamelCase__				     =			tf.concat([input_ids, eos_tensor]  , axis=1  )

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

														UpperCamelCase__				     =			self.config_cls(
														    vocab_size=self.vocab_size  , d_model=self.hidden_size  , encoder_layers=self.num_hidden_layers  , decoder_layers=self.num_hidden_layers  , encoder_attention_heads=self.num_attention_heads  , decoder_attention_heads=self.num_attention_heads  , encoder_ffn_dim=self.intermediate_size  , decoder_ffn_dim=self.intermediate_size  , dropout=self.hidden_dropout_prob  , attention_dropout=self.attention_probs_dropout_prob  , max_position_embeddings=self.max_position_embeddings  , eos_token_ids=[2]  , bos_token_id=self.bos_token_id  , pad_token_id=self.pad_token_id  , decoder_start_token_id=self.pad_token_id  , **self.config_updates  , )
														UpperCamelCase__				     =			prepare_mbart_inputs_dict(__lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  )
														return config, inputs_dict







							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  ):
														UpperCamelCase__				     =			TFMBartModel(config=__lowerCAmelCase  ).get_decoder()
														UpperCamelCase__				     =			inputs_dict["""input_ids"""]

														UpperCamelCase__				     =			input_ids[:1, :]
														UpperCamelCase__				     =			inputs_dict["""attention_mask"""][:1, :]
														UpperCamelCase__				     =			inputs_dict["""head_mask"""]
														UpperCamelCase__				     =			1

														# first forward pass
														UpperCamelCase__				     =			model(__lowerCAmelCase  , attention_mask=__lowerCAmelCase  , head_mask=__lowerCAmelCase  , use_cache=__lowerCAmelCase  )

														UpperCamelCase__     ,     UpperCamelCase__				     =			outputs.to_tuple()
														UpperCamelCase__				     =			past_key_values[1]






def     _UpperCamelCase     (a__   :Optional[int]   ,			a__   :int   ,			a__   :Optional[Any]   ,			a__   :Tuple=None   ,			a__   :List[Any]=None   ,			a__   :int=None   ,			a__   :List[Any]=None   ,			a__   :List[str]=None   ,			):






							"""simple docstring"""





							if attention_mask is None:
														UpperCamelCase__				     =			tf.cast(tf.math.not_equal(a__   ,			config.pad_token_id       )   ,			tf.inta       )
							if decoder_attention_mask is None:
														UpperCamelCase__				     =			tf.concat(
														    [
														        tf.ones(decoder_input_ids[:, :1].shape   ,			dtype=tf.inta       ),
														        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]   ,			config.pad_token_id       )   ,			tf.inta       ),
														    ]   ,			axis=-1   ,			)
							if head_mask is None:
														UpperCamelCase__				     =			tf.ones((config.encoder_layers, config.encoder_attention_heads)       )
							if decoder_head_mask is None:
														UpperCamelCase__				     =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							if cross_attn_head_mask is None:
														UpperCamelCase__				     =			tf.ones((config.decoder_layers, config.decoder_attention_heads)       )
							return {
							    "input_ids": input_ids,
							    "decoder_input_ids": decoder_input_ids,
							    "attention_mask": attention_mask,
							    "decoder_attention_mask": decoder_attention_mask,
							    "head_mask": head_mask,
							    "decoder_head_mask": decoder_head_mask,
							    "cross_attn_head_mask": cross_attn_head_mask,
							}

@require_tf
class        __SCREAMING_SNAKE_CASE					(     _a   ,		_a   ,		unittest.TestCase       ):
							snake_case     :      Optional[Any]						     =       (TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
							snake_case     :      List[Any]						     =       (TFMBartForConditionalGeneration,) if is_tf_available() else ()
							snake_case     :      Dict						     =       (
							    {
							        """conversational""": TFMBartForConditionalGeneration,
							        """feature-extraction""": TFMBartModel,
							        """summarization""": TFMBartForConditionalGeneration,
							        """text2text-generation""": TFMBartForConditionalGeneration,
							        """translation""": TFMBartForConditionalGeneration,
							    }
							    if is_tf_available()
							    else {}
							)
							snake_case     :      List[Any]						     =       True
							snake_case     :      List[Any]						     =       False
							snake_case     :      str						     =       False






							def 				_lowerCamelCase		(						self  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  , __lowerCAmelCase  ):
														if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
																					# Exception encountered when calling layer '...'
																					return True

														return False






							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			TFMBartModelTester(self  )
														UpperCamelCase__				     =			ConfigTester(self  , config_class=__lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  ):
														self.config_tester.run_common_tests()







							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			self.model_tester.prepare_config_and_inputs_for_common()
														self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase  )






@require_sentencepiece
@require_tokenizers
@require_tf
class        __SCREAMING_SNAKE_CASE					(     unittest.TestCase       ):
							snake_case     :      List[Any]						     =       [
							    """ UN Chief Says There Is No Military Solution in Syria""",
							]
							snake_case     :      int						     =       [
							    """Şeful ONU declară că nu există o soluţie militară în Siria""",
							]
							snake_case     :      str						     =       """facebook/mbart-large-en-ro"""






							@cached_property
							def 				_lowerCamelCase		(						self  ):
														return AutoTokenizer.from_pretrained(self.model_name  )






							@cached_property
							def 				_lowerCamelCase		(						self  ):
														UpperCamelCase__				     =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name  )
														return model






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			self.translate_src_text(**__lowerCAmelCase  )
														self.assertListEqual(self.expected_text  , __lowerCAmelCase  )






							def 				_lowerCamelCase		(						self  , **__lowerCAmelCase  ):
														UpperCamelCase__				     =			self.tokenizer(self.src_text  , **__lowerCAmelCase  , return_tensors="""tf"""  )
														UpperCamelCase__				     =			self.model.generate(
														    model_inputs.input_ids  , attention_mask=model_inputs.attention_mask  , num_beams=2  )
														UpperCamelCase__				     =			self.tokenizer.batch_decode(__lowerCAmelCase  , skip_special_tokens=__lowerCAmelCase  )
														return generated_words







							@slow
							def 				_lowerCamelCase		(						self  ):
														self._assert_generated_batch_equal_expected()











UpperCamelCase__											= "ABCDEFGHIJKLMNOPQRSTUVWXYZ"





def     _UpperCamelCase     ():






							"""simple docstring"""





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

							if mode.lower().startswith("""e"""       ):
														UpperCamelCase__				     =			"""encrypt"""
														UpperCamelCase__				     =			encrypt_message(a__   ,			a__       )
							elif mode.lower().startswith("""d"""       ):
														UpperCamelCase__				     =			"""decrypt"""
														UpperCamelCase__				     =			decrypt_message(a__   ,			a__       )

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





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""encrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							return translate_message(a__   ,			a__   ,			"""decrypt"""       )





def     _UpperCamelCase     (a__   :str   ,			a__   :str   ,			a__   :str       ):






							"""simple docstring"""





							UpperCamelCase__				     =			[]
							UpperCamelCase__				     =			0
							UpperCamelCase__				     =			key.upper()

							for symbol in message:
														UpperCamelCase__				     =			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(a__       )

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

																					key_index += 1
																					if key_index == len(a__       ):
																												UpperCamelCase__				     =			0
														else:
																					translated.append(a__       )
							return "".join(a__       )


if __name__ == "__main__":
					main()




