


"""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_       :





  """simple docstring"""

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

     a   :	Union[str, Any]						=    hidden_dropout_prob
     a   :	List[Any]						=    attention_probs_dropout_prob
     a   :	Tuple						=    max_position_embeddings
     a   :	Dict						=    eos_token_id
     a   :	int						=    pad_token_id
     a   :	Any						=    bos_token_id
  def 							lowercase_     (						self						)				->					Optional[int]:
     a   :	Optional[int]						=    ids_tensor([self.batch_size, self.seq_length - 1]  ,					self.vocab_size						)
     a   :	List[str]						=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size						)  ,					1						)
     a   :	List[str]						=    tf.concat([input_ids, eos_tensor]  ,					axis=1						)

     a   :	int						=    ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size						)

     a   :	List[str]						=    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  ,					)
     a   :	Optional[int]						=    prepare_marian_inputs_dict(_UpperCamelCase  ,					_UpperCamelCase  ,					_UpperCamelCase						)
     return config, inputs_dict






  def 							lowercase_     (						self  ,					__UpperCAmelCase  ,					__UpperCAmelCase						)				->					Any:
     a   :	int						=    TFMarianModel(config=_UpperCamelCase						).get_decoder()
     a   :	List[Any]						=    inputs_dict['input_ids']

     a   :	List[Any]						=    input_ids[:1, :]
     a   :	int						=    inputs_dict['attention_mask'][:1, :]
     a   :	Optional[Any]						=    inputs_dict['head_mask']
     a   :	List[Any]						=    1

     # first forward pass
     a   :	Dict						=    model(_UpperCamelCase  ,					attention_mask=_UpperCamelCase  ,					head_mask=_UpperCamelCase  ,					use_cache=_UpperCamelCase						)

     a	, a   :	Dict						=    outputs.to_tuple()

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

     # append to next input_ids and
     a   :	Optional[int]						=    tf.concat([input_ids, next_tokens]  ,					axis=-1						)
     a   :	Tuple						=    tf.concat([attention_mask, next_attn_mask]  ,					axis=-1						)

     a   :	List[str]						=    model(_UpperCamelCase  ,					attention_mask=_UpperCamelCase						)[0]
     a   :	Any						=    model(_UpperCamelCase  ,					attention_mask=_UpperCamelCase  ,					past_key_values=_UpperCamelCase						)[0]

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

     # select random slice
     a   :	Optional[Any]						=    int(ids_tensor((1,)  ,					output_from_past.shape[-1]						)						)
     a   :	str						=    output_from_no_past[:, -3:, random_slice_idx]
     a   :	Tuple						=    output_from_past[:, :, random_slice_idx]

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




def        A_					(					UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=None       ,   UpperCAmelCase__=None       ,   )     ->     Tuple:



   if attention_mask is None:
      a   :	Dict						=    tf.cast(tf.math.not_equal(UpperCamelCase_       ,   config.pad_token_id				)       ,   tf.inta				)
   if decoder_attention_mask is None:
      a   :	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:
      a   :	List[str]						=    tf.ones((config.encoder_layers, config.encoder_attention_heads)				)
   if decoder_head_mask is None:
      a   :	Optional[int]						=    tf.ones((config.decoder_layers, config.decoder_attention_heads)				)
   if cross_attn_head_mask is None:
      a   :	List[str]						=    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_       (     __lowercase      ,  __lowercase      ,  unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Optional[Any]   =						(TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
  lowercase		:	List[str]   =						(TFMarianMTModel,) if is_tf_available() else ()
  lowercase		:	Dict   =						(
      {
          '''conversational''': TFMarianMTModel,
          '''feature-extraction''': TFMarianModel,
          '''summarization''': TFMarianMTModel,
          '''text2text-generation''': TFMarianMTModel,
          '''translation''': TFMarianMTModel,
      }
      if is_tf_available()
      else {}
  )
  lowercase		:	Tuple   =						True
  lowercase		:	Optional[int]   =						False
  lowercase		:	Dict   =						False
  def 							lowercase_     (						self						)				->					List[str]:
     a   :	Any						=    TFMarianModelTester(self						)
     a   :	List[str]						=    ConfigTester(self  ,					config_class=_UpperCamelCase						)
  def 							lowercase_     (						self						)				->					Optional[Any]:
     self.config_tester.run_common_tests()






  def 							lowercase_     (						self						)				->					Any:
     a   :	int						=    self.model_tester.prepare_config_and_inputs_for_common()
     self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase						)



@require_tf
class 	A_       (     unittest.TestCase   ):





  """simple docstring"""

  lowercase		:	Dict   =						10_00  # show more chars for failing integration tests
  @classmethod
  def 							lowercase_     (						cls						)				->					int:
     a   :	List[Any]						=    f'Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}'
     return cls
  @cached_property
  def 							lowercase_     (						self						)				->					Any:
     return AutoTokenizer.from_pretrained(self.model_name						)
  @property
  def 							lowercase_     (						self						)				->					Optional[int]:
     return self.tokenizer.eos_token_id
  @cached_property
  def 							lowercase_     (						self						)				->					List[Any]:
     warnings.simplefilter('error'						)
     a   :	List[Any]						=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name						)
     assert isinstance(_UpperCamelCase  ,					_UpperCamelCase						)
     a   :	Any						=    model.config
     self.assertListEqual(c.bad_words_ids  ,					[[c.pad_token_id]]						)
     self.assertEqual(c.max_length  ,					5_12						)
     self.assertEqual(c.decoder_start_token_id  ,					c.pad_token_id						)
     return model
  def 							lowercase_     (						self  ,					**__UpperCAmelCase						)				->					List[str]:
     a   :	str						=    self.translate_src_text(**_UpperCamelCase						)
     self.assertListEqual(self.expected_text  ,					_UpperCamelCase						)






  def 							lowercase_     (						self  ,					**__UpperCAmelCase						)				->					Any:
     a   :	Union[str, Any]						=    self.tokenizer(self.src_text  ,					**_UpperCamelCase  ,					padding=_UpperCamelCase  ,					return_tensors='tf'						)
     a   :	Union[str, Any]						=    self.model.generate(
         model_inputs.input_ids  ,					attention_mask=model_inputs.attention_mask  ,					num_beams=2  ,					max_length=1_28						)
     a   :	List[Any]						=    self.tokenizer.batch_decode(generated_ids.numpy()  ,					skip_special_tokens=_UpperCamelCase						)
     return generated_words



@require_sentencepiece
@require_tokenizers
@require_tf
class 	A_       (     __lowercase   ):





  """simple docstring"""

  lowercase		:	str   =						'''mt'''
  lowercase		:	Optional[Any]   =						'''en'''
  lowercase		:	Optional[Any]   =						['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
  lowercase		:	Dict   =						['''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:
     self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class 	A_       (     __lowercase   ):





  """simple docstring"""

  lowercase		:	Tuple   =						'''en'''
  lowercase		:	Any   =						'''zh'''
  lowercase		:	Tuple   =						['''My name is Wolfgang and I live in Berlin''']
  lowercase		:	Any   =						['''我叫沃尔夫冈 我住在柏林''']
  @unittest.skip('Skipping until #12647 is resolved.'						)
  @slow
  def 							lowercase_     (						self						)				->					Any:
     self._assert_generated_batch_equal_expected()



@require_sentencepiece
@require_tokenizers
@require_tf
class 	A_       (     __lowercase   ):





  """simple docstring"""

  lowercase		:	Optional[int]   =						'''en'''
  lowercase		:	List[str]   =						'''ROMANCE'''
  lowercase		:	str   =						[
      '''>>fr<< Don\'t spend so much time watching TV.''',
      '''>>pt<< Your message has been sent.''',
      '''>>es<< He\'s two years older than me.''',
  ]
  lowercase		:	List[str]   =						[
      '''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						)				->					int:
     self._assert_generated_batch_equal_expected()






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













import os
from functools import partial
from glob import glob

import faiss
from datasets import Features, Sequence, Value, concatenate_datasets, load_dataset, load_from_disk

from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Union[str, Any]=100     ,							UpperCamelCase_							:       Optional[int]=" "		)	-> int:

						"""simple docstring"""



						lowerCAmelCase__         =    text.split(UpperCamelCase_		)
						return [character.join(text[i : i + n]		).strip() for i in range(0     ,							len(UpperCamelCase_		)     ,							UpperCamelCase_		)]







def 						_UpperCamelCase (     UpperCamelCase_							:       List[str]		)	-> Any:

						"""simple docstring"""



						lowerCAmelCase__    ,							lowerCAmelCase__         =    [], []
						for title, text in zip(documents['title']     ,							documents['text']		):
												if text is not None:
																		for passage in split_text(UpperCamelCase_		):
																								titles.append(title if title is not None else ''		)
																								texts.append(UpperCamelCase_		)
						return {"title": titles, "text": texts}







def 						_UpperCamelCase (     UpperCamelCase_							:       Union[str, Any]     ,							UpperCamelCase_							:       int     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Tuple     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Tuple		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    load_dataset(
						    'csv'     ,							data_files=[csv_path]     ,							split='train'     ,							delimiter='\t'     ,							column_names=['title', 'text']		)
						lowerCAmelCase__         =    kb_dataset.map(
						    UpperCamelCase_     ,							batched=UpperCamelCase_     ,							num_proc=1		)  # if you want you can load already splitted csv.
						lowerCAmelCase__         =    [kb_dataset.shard(UpperCamelCase_     ,							UpperCamelCase_     ,							contiguous=UpperCamelCase_		) for i in range(UpperCamelCase_		)]
						lowerCAmelCase__         =    kb_list[process_num]

						lowerCAmelCase__         =    'data_' + str(UpperCamelCase_		)
						lowerCAmelCase__         =    os.path.join(UpperCamelCase_     ,							UpperCamelCase_		)

						lowerCAmelCase__         =    DPRContextEncoderTokenizerFast.from_pretrained('facebook/dpr-ctx_encoder-multiset-base'		)
						lowerCAmelCase__         =    ctx_encoder.to(device=UpperCamelCase_		)

						def embed(
						    UpperCamelCase_							:       dict     ,							UpperCamelCase_							:       DPRContextEncoder     ,							UpperCamelCase_							:       DPRContextEncoderTokenizerFast     ,							UpperCamelCase_							:       Tuple		) -> dict:
												lowerCAmelCase__         =    ctx_tokenizer(
												    documents['title']     ,							documents['text']     ,							truncation=UpperCamelCase_     ,							padding='longest'     ,							return_tensors='pt'		)['input_ids']
												lowerCAmelCase__         =    ctx_encoder(input_ids.to(device=UpperCamelCase_		)     ,							return_dict=UpperCamelCase_		).pooler_output
												return {"embeddings": embeddings.detach().cpu().numpy()}

						lowerCAmelCase__         =    Features(
						    {'text': Value('string'		), 'title': Value('string'		), 'embeddings': Sequence(Value('float32'		)		)}		)  # optional, save as float32 instead of float64 to save space

						lowerCAmelCase__         =    data_shrad.map(
						    partial(UpperCamelCase_     ,							ctx_encoder=UpperCamelCase_     ,							ctx_tokenizer=UpperCamelCase_     ,							device=UpperCamelCase_		)     ,							batched=UpperCamelCase_     ,							batch_size=16     ,							features=UpperCamelCase_     ,							)
						dataset.save_to_disk(UpperCamelCase_		)







def 						_UpperCamelCase (     UpperCamelCase_							:       List[Any]     ,							UpperCamelCase_							:       Dict		)	-> Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__         =    []

						for shard_address in glob(str(UpperCamelCase_		) + '/*/'		):
												data_shard_list.append(load_from_disk(UpperCamelCase_		)		)

						lowerCAmelCase__         =    concatenate_datasets(UpperCamelCase_		)
						faiss.omp_set_num_threads(96		)

						lowerCAmelCase__         =    faiss.IndexHNSWFlat(768     ,							128     ,							faiss.METRIC_INNER_PRODUCT		)
						concat.add_faiss_index('embeddings'     ,							custom_index=UpperCamelCase_		)
						concat.get_index('embeddings'		).save(
						    UpperCamelCase_		)  # since we load the index in to memory,we can directly update the index in the disk





