



"""simple docstring"""




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   a__		:
   _lowerCamelCase												=				MarianConfig
   _lowerCamelCase												=				{}
   _lowerCamelCase												=				'gelu'


   def __init__(							self      :							Optional[int],     lowerCAmelCase      :							Any,     lowerCAmelCase      :							str=13,     lowerCAmelCase      :							Union[str, Any]=7,     lowerCAmelCase      :							Optional[int]=True,     lowerCAmelCase      :							Optional[Any]=False,     lowerCAmelCase      :							int=99,     lowerCAmelCase      :							Dict=32,     lowerCAmelCase      :							str=2,     lowerCAmelCase      :							int=4,     lowerCAmelCase      :							List[str]=37,     lowerCAmelCase      :							List[Any]=0.1,     lowerCAmelCase      :							Union[str, Any]=0.1,     lowerCAmelCase      :							int=20,     lowerCAmelCase      :							Union[str, Any]=2,     lowerCAmelCase      :							Dict=1,     lowerCAmelCase      :							List[Any]=0,     )    ->       str:
         lowercase  :      Any					=   parent
         lowercase  :      List[Any]					=   batch_size
         lowercase  :      int					=   seq_length
         lowercase  :      Any					=   is_training
         lowercase  :      Dict					=   use_labels
         lowercase  :      List[Any]					=   vocab_size
         lowercase  :      int					=   hidden_size
         lowercase  :      List[Any]					=   num_hidden_layers
         lowercase  :      List[str]					=   num_attention_heads
         lowercase  :      int					=   intermediate_size

         lowercase  :      str					=   hidden_dropout_prob
         lowercase  :      str					=   attention_probs_dropout_prob
         lowercase  :      Any					=   max_position_embeddings
         lowercase  :      Union[str, Any]					=   eos_token_id
         lowercase  :      Optional[int]					=   pad_token_id
         lowercase  :      Any					=   bos_token_id


   def        lowercase				(							self      :							Any			)    ->       Optional[int]:
         lowercase  :      Tuple					=   ids_tensor([self.batch_size, self.seq_length - 1],     self.vocab_size			)
         lowercase  :      List[Any]					=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size			),     1			)
         lowercase  :      List[str]					=   tf.concat([input_ids, eos_tensor],     axis=1			)

         lowercase  :      Tuple					=   ids_tensor([self.batch_size, self.seq_length],     self.vocab_size			)

         lowercase  :      int					=   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,     )
         lowercase  :      List[str]					=   prepare_marian_inputs_dict(lowerCAmelCase,     lowerCAmelCase,     lowerCAmelCase			)
         return config, inputs_dict






   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							int,     lowerCAmelCase      :							int			)    ->       str:
         lowercase  :      List[str]					=   TFMarianModel(config=lowerCAmelCase			).get_decoder()
         lowercase  :      Optional[Any]					=   inputs_dict['input_ids']

         lowercase  :      Tuple					=   input_ids[:1, :]
         lowercase  :      int					=   inputs_dict['attention_mask'][:1, :]
         lowercase  :      List[str]					=   inputs_dict['head_mask']
         lowercase  :      Union[str, Any]					=   1

         # first forward pass
         lowercase  :      Tuple					=   model(lowerCAmelCase,     attention_mask=lowerCAmelCase,     head_mask=lowerCAmelCase,     use_cache=lowerCAmelCase			)

         lowercase	,    lowercase  :      List[Any]					=   outputs.to_tuple()

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

         # append to next input_ids and
         lowercase  :      Union[str, Any]					=   tf.concat([input_ids, next_tokens],     axis=-1			)
         lowercase  :      List[Any]					=   tf.concat([attention_mask, next_attn_mask],     axis=-1			)

         lowercase  :      Optional[Any]					=   model(lowerCAmelCase,     attention_mask=lowerCAmelCase			)[0]
         lowercase  :      List[str]					=   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
         lowercase  :      int					=   int(ids_tensor((1,),     output_from_past.shape[-1]			)			)
         lowercase  :      List[Any]					=   output_from_no_past[:, -3:, random_slice_idx]
         lowercase  :      Optional[int]					=   output_from_past[:, :, random_slice_idx]

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




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase=None	,      _UpperCAmelCase=None	,      _UpperCAmelCase=None	,      _UpperCAmelCase=None	,      _UpperCAmelCase=None	,      )     ->			Optional[Any]:






      '''simple docstring'''


      if attention_mask is None:
            lowercase  :      Union[str, Any]					=   tf.cast(tf.math.not_equal(_UpperCAmelCase	,      config.pad_token_id				)	,      tf.inta				)
      if decoder_attention_mask is None:
            lowercase  :      Optional[Any]					=   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:
            lowercase  :      List[str]					=   tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
      if decoder_head_mask is None:
            lowercase  :      Tuple					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
      if cross_attn_head_mask is None:
            lowercase  :      Optional[int]					=   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   a__		(						SCREAMING_SNAKE_CASE__,  SCREAMING_SNAKE_CASE__,  unittest.TestCase     ):
   _lowerCamelCase												=				(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
   _lowerCamelCase												=				(TFMarianMTModel,) if is_tf_available() else ()
   _lowerCamelCase												=				(
       {
           'conversational': TFMarianMTModel,
           'feature-extraction': TFMarianModel,
           'summarization': TFMarianMTModel,
           'text2text-generation': TFMarianMTModel,
           'translation': TFMarianMTModel,
       }
       if is_tf_available()
       else {}
   )
   _lowerCamelCase												=				True
   _lowerCamelCase												=				False
   _lowerCamelCase												=				False


   def        lowercase				(							self      :							int			)    ->       Tuple:
         lowercase  :      List[Any]					=   TFMarianModelTester(self			)
         lowercase  :      str					=   ConfigTester(self,     config_class=lowerCAmelCase			)


   def        lowercase				(							self      :							str			)    ->       Optional[Any]:
         self.config_tester.run_common_tests()






   def        lowercase				(							self      :							List[str]			)    ->       List[str]:
         lowercase  :      List[Any]					=   self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase			)




@require_tf
class   a__		(						unittest.TestCase     ):
   _lowerCamelCase												=				1_000  # show more chars for failing integration tests


   @classmethod
   def        lowercase				(							cls      :							Optional[int]			)    ->       None:
         lowercase  :      int					=   f'''Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'''
         return cls


   @cached_property
   def        lowercase				(							self      :							Optional[Any]			)    ->       MarianTokenizer:
         return AutoTokenizer.from_pretrained(self.model_name			)


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


   @cached_property
   def        lowercase				(							self      :							str			)    ->       str:
         warnings.simplefilter('error'			)
         lowercase  :      TFMarianMTModel					=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name			)
         assert isinstance(lowerCAmelCase,     lowerCAmelCase			)
         lowercase  :      Dict					=   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        lowercase				(							self      :							Dict,     **lowerCAmelCase      :							Tuple			)    ->       List[str]:
         lowercase  :      Dict					=   self.translate_src_text(**lowerCAmelCase			)
         self.assertListEqual(self.expected_text,     lowerCAmelCase			)






   def        lowercase				(							self      :							Optional[int],     **lowerCAmelCase      :							Optional[Any]			)    ->       List[str]:
         lowercase  :      Tuple					=   self.tokenizer(self.src_text,     **lowerCAmelCase,     padding=lowerCAmelCase,     return_tensors='tf'			)
         lowercase  :      List[Any]					=   self.model.generate(
             model_inputs.input_ids,     attention_mask=model_inputs.attention_mask,     num_beams=2,     max_length=128			)
         lowercase  :      List[Any]					=   self.tokenizer.batch_decode(generated_ids.numpy(),     skip_special_tokens=lowerCAmelCase			)
         return generated_words




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


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




@require_sentencepiece
@require_tokenizers
@require_tf
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'en'
   _lowerCamelCase												=				'zh'
   _lowerCamelCase												=				['My name is Wolfgang and I live in Berlin']
   _lowerCamelCase												=				['我叫沃尔夫冈 我住在柏林']


   @unittest.skip('Skipping until #12647 is resolved.'			)
   @slow
   def        lowercase				(							self      :							Optional[Any]			)    ->       Optional[int]:
         self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class   a__		(						SCREAMING_SNAKE_CASE__     ):
   _lowerCamelCase												=				'en'
   _lowerCamelCase												=				'ROMANCE'
   _lowerCamelCase												=				[
       '>>fr<< Don\'t spend so much time watching TV.',
       '>>pt<< Your message has been sent.',
       '>>es<< He\'s two years older than me.',
   ]
   _lowerCamelCase												=				[
       '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        lowercase				(							self      :							Tuple			)    ->       List[str]:
         self._assert_generated_batch_equal_expected()






   @unittest.skip('Skipping until #12647 is resolved.'			)
   @slow
   def        lowercase				(							self      :							Dict			)    ->       Union[str, Any]:
         lowercase  :      Optional[int]					=   TranslationPipeline(self.model,     self.tokenizer,     framework='tf'			)
         lowercase  :      Optional[int]					=   pipeline(self.src_text			)
         self.assertEqual(self.expected_text,     [x['translation_text'] for x in output]			)





"""simple docstring"""




from arguments import TokenizerTrainingArguments
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, HfArgumentParser
from transformers.models.gpta.tokenization_gpta import bytes_to_unicode


def   lowercase__     (			_UpperCAmelCase=10				)     ->			Any:






      '''simple docstring'''


      for _ in tqdm(range(0	,      args.n_examples	,      _UpperCAmelCase				)				):
            yield [next(_UpperCAmelCase				)[args.text_column] for _ in range(_UpperCAmelCase				)]


# Configuration
_UpperCamelCase:				Union[str, Any]    							=  HfArgumentParser(TokenizerTrainingArguments)
_UpperCamelCase:				Dict    							=  parser.parse_args()

# Base tokenizer
_UpperCamelCase:				List[Any]    							=  AutoTokenizer.from_pretrained(args.base_tokenizer)
_UpperCamelCase:				int    							=  list(bytes_to_unicode().values())

# Load dataset
_UpperCamelCase:				Union[str, Any]    							=  load_dataset(args.dataset_name, split='train', streaming=True)
_UpperCamelCase:				Union[str, Any]    							=  iter(dataset)


# Training and saving
_UpperCamelCase:				Union[str, Any]    							=  tokenizer.train_new_from_iterator(
    batch_iterator(), vocab_size=args.vocab_size, initial_alphabet=base_vocab
)





new_tokenizer.save_pretrained(args.tokenizer_name, push_to_hub=args.push_to_hub)
