






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      __SCREAMING_SNAKE_CASE       :
						_SCREAMING_SNAKE_CASE						:							int				=       MarianConfig
						_SCREAMING_SNAKE_CASE						:							int				=       {}
						_SCREAMING_SNAKE_CASE						:							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  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    parent
												lowerCAmelCase__         =    batch_size
												lowerCAmelCase__         =    seq_length
												lowerCAmelCase__         =    is_training
												lowerCAmelCase__         =    use_labels
												lowerCAmelCase__         =    vocab_size
												lowerCAmelCase__         =    hidden_size
												lowerCAmelCase__         =    num_hidden_layers
												lowerCAmelCase__         =    num_attention_heads
												lowerCAmelCase__         =    intermediate_size

												lowerCAmelCase__         =    hidden_dropout_prob
												lowerCAmelCase__         =    attention_probs_dropout_prob
												lowerCAmelCase__         =    max_position_embeddings
												lowerCAmelCase__         =    eos_token_id
												lowerCAmelCase__         =    pad_token_id
												lowerCAmelCase__         =    bos_token_id






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    ids_tensor([self.batch_size, self.seq_length - 1]  ,    self.vocab_size )
												lowerCAmelCase__         =    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size )  ,    1 )
												lowerCAmelCase__         =    tf.concat([input_ids, eos_tensor]  ,    axis=1 )

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

												lowerCAmelCase__         =    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  ,    )
												lowerCAmelCase__         =    prepare_marian_inputs_dict(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )
												return config, inputs_dict







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    TFMarianModel(config=_UpperCamelCase ).get_decoder()
												lowerCAmelCase__         =    inputs_dict['input_ids']

												lowerCAmelCase__         =    input_ids[:1, :]
												lowerCAmelCase__         =    inputs_dict['attention_mask'][:1, :]
												lowerCAmelCase__         =    inputs_dict['head_mask']
												lowerCAmelCase__         =    1

												# first forward pass
												lowerCAmelCase__         =    model(_UpperCamelCase  ,    attention_mask=_UpperCamelCase  ,    head_mask=_UpperCamelCase  ,    use_cache=_UpperCamelCase )

												lowerCAmelCase__    ,							lowerCAmelCase__         =    outputs.to_tuple()

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

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

												lowerCAmelCase__         =    model(_UpperCamelCase  ,    attention_mask=_UpperCamelCase )[0]
												lowerCAmelCase__         =    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
												lowerCAmelCase__         =    int(ids_tensor((1,)  ,    output_from_past.shape[-1] ) )
												lowerCAmelCase__         =    output_from_no_past[:, -3:, random_slice_idx]
												lowerCAmelCase__         =    output_from_past[:, :, random_slice_idx]

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







def 						_UpperCamelCase (     UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       str     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       List[str]=None     ,							UpperCamelCase_							:       Dict=None     ,							UpperCamelCase_							:       List[Any]=None     ,							UpperCamelCase_							:       Optional[Any]=None     ,							)	-> Tuple:

						"""simple docstring"""



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






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    TFMarianModelTester(self )
												lowerCAmelCase__         =    ConfigTester(self  ,    config_class=_UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self.config_tester.run_common_tests()







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()
												self.model_tester.check_decoder_model_past_large_inputs(*_UpperCamelCase )




@require_tf
class      __SCREAMING_SNAKE_CASE       ( unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							Dict				=       1000  # show more chars for failing integration tests






						@classmethod
						def    UpperCamelCase__   (						cls ):






												"""simple docstring"""
												lowerCAmelCase__         =    F"Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"
												return cls






						@cached_property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return AutoTokenizer.from_pretrained(self.model_name )






						@property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return self.tokenizer.eos_token_id






						@cached_property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												warnings.simplefilter('error' )
												lowerCAmelCase__         =    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
												assert isinstance(_UpperCamelCase  ,    _UpperCamelCase )
												lowerCAmelCase__         =    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    UpperCamelCase__   (						self  ,    **_UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.translate_src_text(**_UpperCamelCase )
												self.assertListEqual(self.expected_text  ,    _UpperCamelCase )







						def    UpperCamelCase__   (						self  ,    **_UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer(self.src_text  ,    **_UpperCamelCase  ,    padding=_UpperCamelCase  ,    return_tensors='tf' )
												lowerCAmelCase__         =    self.model.generate(
												    model_inputs.input_ids  ,    attention_mask=model_inputs.attention_mask  ,    num_beams=2  ,    max_length=1_28 )
												lowerCAmelCase__         =    self.tokenizer.batch_decode(generated_ids.numpy()  ,    skip_special_tokens=_UpperCamelCase )
												return generated_words




@require_sentencepiece
@require_tokenizers
@require_tf
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							str				=       '''mt'''
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       '''en'''
						_SCREAMING_SNAKE_CASE						:							Optional[Any]				=       ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
						_SCREAMING_SNAKE_CASE						:							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    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self._assert_generated_batch_equal_expected()




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






						@unittest.skip('Skipping until #12647 is resolved.' )
						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self._assert_generated_batch_equal_expected()




@require_sentencepiece
@require_tokenizers
@require_tf
class      __SCREAMING_SNAKE_CASE       ( __lowercase):
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       '''en'''
						_SCREAMING_SNAKE_CASE						:							List[str]				=       '''ROMANCE'''
						_SCREAMING_SNAKE_CASE						:							str				=       [
						    '''>>fr<< Don\'t spend so much time watching TV.''',
						    '''>>pt<< Your message has been sent.''',
						    '''>>es<< He\'s two years older than me.''',
						]
						_SCREAMING_SNAKE_CASE						:							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    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self._assert_generated_batch_equal_expected()







						@unittest.skip('Skipping until #12647 is resolved.' )
						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    TranslationPipeline(self.model  ,    self.tokenizer  ,    framework='tf' )
												lowerCAmelCase__         =    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





