





from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, 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, TFMarianModel, TFMarianMTModel




@require_tf
class 	_UpperCAmelCase						:
					__A        = MarianConfig
					__A        = {}
					__A        = "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     ,   )    ->	Optional[Any]:
							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 						snake_case_		(    self							)    ->	Optional[int]:
							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_marian_inputs_dict(__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)
							return config, inputs_dict




					def 						snake_case_		(    self     ,   __lowerCAmelCase     ,   __lowerCAmelCase							)    ->	Any:
							UpperCamelCase__								=						TFMarianModel(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()

							# create hypothetical next token and extent to next_input_ids
							UpperCamelCase__								=						ids_tensor((self.batch_size, 3)     ,   config.vocab_size							)
							UpperCamelCase__								=						tf.cast(ids_tensor((self.batch_size, 3)     ,   2							)     ,   tf.inta							)

							# append to next input_ids and
							UpperCamelCase__								=						tf.concat([input_ids, next_tokens]     ,   axis=-1							)
							UpperCamelCase__								=						tf.concat([attention_mask, next_attn_mask]     ,   axis=-1							)

							UpperCamelCase__								=						model(__lowerCAmelCase     ,   attention_mask=__lowerCAmelCase							)[0]
							UpperCamelCase__								=						model(__lowerCAmelCase     ,   attention_mask=__lowerCAmelCase     ,   past_key_values=__lowerCAmelCase							)[0]

							self.parent.assertEqual(next_tokens.shape[1]     ,   output_from_past.shape[1]							)

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

							# test that outputs are equal for slice
							tf.debugging.assert_near(__lowerCAmelCase     ,   __lowerCAmelCase     ,   rtol=1E-3							)



def      a    (   _lowercase :  Union[str, Any]			,    _lowercase :  Optional[int]			,    _lowercase :  int			,    _lowercase :  Tuple=None			,    _lowercase :  Union[str, Any]=None			,    _lowercase :  Tuple=None			,    _lowercase :  int=None			,    _lowercase :  Dict=None			,    )  ->Union[str, Any]:
		if attention_mask is None:
				UpperCamelCase__								=						tf.cast(tf.math.not_equal(_lowercase			,    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 	_UpperCAmelCase						( _lowercase			,							_lowercase			,							unittest.TestCase       ):
					__A        = (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
					__A        = (TFMarianMTModel,) if is_tf_available() else ()
					__A        = (
					    {
					        "conversational": TFMarianMTModel,
					        "feature-extraction": TFMarianModel,
					        "summarization": TFMarianMTModel,
					        "text2text-generation": TFMarianMTModel,
					        "translation": TFMarianMTModel,
					    }
					    if is_tf_available()
					    else {}
					)
					__A        = True
					__A        = False
					__A        = False




					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__								=						TFMarianModelTester(self							)
							UpperCamelCase__								=						ConfigTester(self     ,   config_class=__lowerCAmelCase							)




					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							self.config_tester.run_common_tests()




					def 						snake_case_		(    self							)    ->	List[Any]:
							UpperCamelCase__								=						self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*__lowerCAmelCase							)




@require_tf
class 	_UpperCAmelCase						( unittest.TestCase       ):
					__A        = 10_00  # show more chars for failing integration tests




					@classmethod
					def 						snake_case_		(    cls							)    ->	None:
							UpperCamelCase__								=						F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
							return cls




					@cached_property
					def 						snake_case_		(    self							)    ->	MarianTokenizer:
							return AutoTokenizer.from_pretrained(self.model_name							)




					@property
					def 						snake_case_		(    self							)    ->	int:
							return self.tokenizer.eos_token_id




					@cached_property
					def 						snake_case_		(    self							)    ->	List[Any]:
							warnings.simplefilter("""error"""							)
							UpperCamelCase__								=						TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name							)
							assert isinstance(__lowerCAmelCase     ,   __lowerCAmelCase							)
							UpperCamelCase__								=						model.config
							self.assertListEqual(c.bad_words_ids     ,   [[c.pad_token_id]]							)
							self.assertEqual(c.max_length     ,   512							)
							self.assertEqual(c.decoder_start_token_id     ,   c.pad_token_id							)
							return model




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	int:
							UpperCamelCase__								=						self.translate_src_text(**__lowerCAmelCase							)
							self.assertListEqual(self.expected_text     ,   __lowerCAmelCase							)




					def 						snake_case_		(    self     ,   **__lowerCAmelCase							)    ->	Union[str, Any]:
							UpperCamelCase__								=						self.tokenizer(self.src_text     ,   **__lowerCAmelCase     ,   padding=__lowerCAmelCase     ,   return_tensors="""tf"""							)
							UpperCamelCase__								=						self.model.generate(
							    model_inputs.input_ids     ,   attention_mask=model_inputs.attention_mask     ,   num_beams=2     ,   max_length=128							)
							UpperCamelCase__								=						self.tokenizer.batch_decode(generated_ids.numpy()     ,   skip_special_tokens=__lowerCAmelCase							)
							return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "mt"
					__A        = "en"
					__A        = ["Billi messu b'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem."]
					__A        = ["Touching gently, Jesus healed a man who was affected by the sad disease of leprosy."]




					@unittest.skip("""Skipping until #12647 is resolved."""							)
					@slow
					def 						snake_case_		(    self							)    ->	Any:
							self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "en"
					__A        = "zh"
					__A        = ["My name is Wolfgang and I live in Berlin"]
					__A        = ["我叫沃尔夫冈 我住在柏林"]




					@unittest.skip("""Skipping until #12647 is resolved."""							)
					@slow
					def 						snake_case_		(    self							)    ->	Union[str, Any]:
							self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class 	_UpperCAmelCase						( _lowercase       ):
					__A        = "en"
					__A        = "ROMANCE"
					__A        = [
					    ">>fr<< Don't spend so much time watching TV.",
					    ">>pt<< Your message has been sent.",
					    ">>es<< He's two years older than me.",
					]
					__A        = [
					    "Ne passez pas autant de temps à regarder la télé.",
					    "A sua mensagem foi enviada.",
					    "Es dos años más viejo que yo.",
					]




					@unittest.skip("""Skipping until #12647 is resolved."""							)
					@slow
					def 						snake_case_		(    self							)    ->	int:
							self._assert_generated_batch_equal_expected()




					@unittest.skip("""Skipping until #12647 is resolved."""							)
					@slow
					def 						snake_case_		(    self							)    ->	int:
							UpperCamelCase__								=						TranslationPipeline(self.model     ,   self.tokenizer     ,   framework="""tf"""							)
							UpperCamelCase__								=						pipeline(self.src_text							)
							self.assertEqual(self.expected_text     ,   [x["""translation_text"""] for x in output]							)












import itertools
from pathlib import Path

def      a    (   _lowercase :  list[str]							)  ->int:
		UpperCamelCase__								=						[tuple(_lowercase							) for login in logins]

		UpperCamelCase__								=						{char for login in split_logins for char in login}

		for permutation in itertools.permutations(_lowercase							):
				UpperCamelCase__								=						True
				for login in logins:
						if not (
						    permutation.index(login[0]							)
						    < permutation.index(login[1]							)
						    < permutation.index(login[2]							)
						):
								UpperCamelCase__								=						False
								break

				if satisfied:
						return int("""""".join(_lowercase							)							)

		raise Exception("""Unable to find the secret passcode"""							)

def      a    (   _lowercase :  str = "keylog.txt"							)  ->int:
		UpperCamelCase__								=						Path(_lowercase							).parent.joinpath(_lowercase							).read_text().splitlines()

		return find_secret_passcode(_lowercase							)


if __name__ == "__main__":
				print(f"""{solution() = }""")





