
'''simple docstring'''





import math
from typing import Optional

import torch
import torch.utils.checkpoint as checkpoint
from torch import nn

from ....modeling_utils import PreTrainedModel
from ....utils import add_start_docstrings, logging
from ...bert.modeling_bert import BertModel
from .configuration_retribert import RetriBertConfig


UpperCAmelCase            = logging.get_logger(__name__)

UpperCAmelCase            = [
    "yjernite/retribert-base-uncased",
    # See all RetriBert models at https://huggingface.co/models?filter=retribert
]

class     a (					__magic_name__     ):
				_snake_case		     =			RetriBertConfig
				_snake_case		     =			None
				_snake_case		     =			'''retribert'''




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					List[Any]   ):
										if isinstance(SCREAMING_SNAKE_CASE_,     nn.Linear   ):
																module.weight.data.normal_(mean=0.0,     std=self.config.initializer_range   )
																if module.bias is not None:
																						module.bias.data.zero_()
										elif isinstance(SCREAMING_SNAKE_CASE_,     nn.Embedding   ):
																module.weight.data.normal_(mean=0.0,     std=self.config.initializer_range   )
																if module.padding_idx is not None:
																						module.weight.data[module.padding_idx].zero_()
										elif isinstance(SCREAMING_SNAKE_CASE_,     nn.LayerNorm   ):
																module.bias.data.zero_()
																module.weight.data.fill_(1.0   )




UpperCAmelCase            = r"\n\n    This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the\n    library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads\n    etc.)\n\n    This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.\n    Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage\n    and behavior.\n\n    Parameters:\n        config ([`RetriBertConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

@add_start_docstrings(
    '''Bert Based model to embed queries or document for document retrieval.'''					,__magic_name__					,)
class     a (					__magic_name__     ):




				def __init__(				self						:					Dict,     SCREAMING_SNAKE_CASE_						:					RetriBertConfig   ):
										super().__init__(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     config.projection_dim

										snake_case							:  Optional[int]			=     BertModel(SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     None if config.share_encoders else BertModel(SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[Any]			=     nn.Dropout(config.hidden_dropout_prob   )
										snake_case							:  str			=     nn.Linear(config.hidden_size,     config.projection_dim,     bias=SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     nn.Linear(config.hidden_size,     config.projection_dim,     bias=SCREAMING_SNAKE_CASE_   )

										snake_case							:  Tuple			=     nn.CrossEntropyLoss(reduction='''mean'''   )

										# Initialize weights and apply final processing
										self.post_init()




				def 						__snake_case				(				self						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Dict,     SCREAMING_SNAKE_CASE_						:					Tuple=-1,     ):
										# reproduces BERT forward pass with checkpointing
										if checkpoint_batch_size < 0 or input_ids.shape[0] < checkpoint_batch_size:
																return sent_encoder(SCREAMING_SNAKE_CASE_,     attention_mask=SCREAMING_SNAKE_CASE_   )[1]
										else:
																# prepare implicit variables
																snake_case							:  Optional[Any]			=     input_ids.device
																snake_case							:  int			=     input_ids.size()
																snake_case							:  Any			=     torch.zeros(SCREAMING_SNAKE_CASE_,     dtype=torch.long,     device=SCREAMING_SNAKE_CASE_   )
																snake_case							:  Optional[Any]			=     [None] * sent_encoder.config.num_hidden_layers
																snake_case							:  torch.Tensor			=     sent_encoder.get_extended_attention_mask(
																    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )

																# define function for checkpointing
																def partial_encode(*SCREAMING_SNAKE_CASE_						:					str   ):
																						snake_case							:  Any			=     sent_encoder.encoder(
																						    inputs[0],     attention_mask=inputs[1],     head_mask=SCREAMING_SNAKE_CASE_,     )
																						snake_case							:  str			=     encoder_outputs[0]
																						snake_case							:  Optional[int]			=     sent_encoder.pooler(SCREAMING_SNAKE_CASE_   )
																						return pooled_output

																# run embedding layer on everything at once
																snake_case							:  Union[str, Any]			=     sent_encoder.embeddings(
																    input_ids=SCREAMING_SNAKE_CASE_,     position_ids=SCREAMING_SNAKE_CASE_,     token_type_ids=SCREAMING_SNAKE_CASE_,     inputs_embeds=SCREAMING_SNAKE_CASE_   )
																# run encoding and pooling on one mini-batch at a time
																snake_case							:  List[str]			=     []
																for b in range(math.ceil(input_ids.shape[0] / checkpoint_batch_size   )   ):
																						snake_case							:  Any			=     embedding_output[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																						snake_case							:  str			=     extended_attention_mask[b * checkpoint_batch_size : (b + 1) * checkpoint_batch_size]
																						snake_case							:  int			=     checkpoint.checkpoint(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																						pooled_output_list.append(SCREAMING_SNAKE_CASE_   )
																return torch.cat(SCREAMING_SNAKE_CASE_,     dim=0   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					List[Any]=-1,     ):
										snake_case							:  Optional[Any]			=     self.embed_sentences_checkpointed(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     self.bert_query,     SCREAMING_SNAKE_CASE_,     )
										return self.project_query(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					List[str]=None,     SCREAMING_SNAKE_CASE_						:					int=-1,     ):
										snake_case							:  int			=     self.embed_sentences_checkpointed(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     self.bert_query if self.bert_doc is None else self.bert_doc,     SCREAMING_SNAKE_CASE_,     )
										return self.project_doc(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					str,     SCREAMING_SNAKE_CASE_						:					torch.LongTensor,     SCREAMING_SNAKE_CASE_						:					Optional[torch.FloatTensor],     SCREAMING_SNAKE_CASE_						:					torch.LongTensor,     SCREAMING_SNAKE_CASE_						:					Optional[torch.FloatTensor],     SCREAMING_SNAKE_CASE_						:					int = -1,     ):
										snake_case							:  Optional[int]			=     input_ids_query.device
										snake_case							:  List[str]			=     self.embed_questions(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Optional[int]			=     self.embed_answers(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Union[str, Any]			=     torch.mm(SCREAMING_SNAKE_CASE_,     a_reps.t()   )
										snake_case							:  Optional[Any]			=     self.ce_loss(SCREAMING_SNAKE_CASE_,     torch.arange(compare_scores.shape[1]   ).to(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  Any			=     self.ce_loss(compare_scores.t(),     torch.arange(compare_scores.shape[0]   ).to(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  List[str]			=     (loss_qa + loss_aq) / 2
										return loss






"""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   UpperCAmelCase__		:
		lowerCAmelCase_							:						Optional[Any]							=  MarianConfig
		lowerCAmelCase_							:						Dict							=  {}
		lowerCAmelCase_							:						List[str]							=  """gelu"""



		def __init__(			self	:     Optional[Any]   ,				snake_case	:     Any   ,				snake_case	:     int=13   ,				snake_case	:     Dict=7   ,				snake_case	:     Dict=True   ,				snake_case	:     List[str]=False   ,				snake_case	:     int=99   ,				snake_case	:     Optional[Any]=32   ,				snake_case	:     Optional[Any]=2   ,				snake_case	:     int=4   ,				snake_case	:     Optional[int]=37   ,				snake_case	:     Optional[int]=0.1   ,				snake_case	:     str=0.1   ,				snake_case	:     Any=20   ,				snake_case	:     Dict=2   ,				snake_case	:     List[Any]=1   ,				snake_case	:     str=0   ,				)      ->    str:






									'''simple docstring'''

									A    =       parent
									A    =       batch_size
									A    =       seq_length
									A    =       is_training
									A    =       use_labels
									A    =       vocab_size
									A    =       hidden_size
									A    =       num_hidden_layers
									A    =       num_attention_heads
									A    =       intermediate_size

									A    =       hidden_dropout_prob
									A    =       attention_probs_dropout_prob
									A    =       max_position_embeddings
									A    =       eos_token_id
									A    =       pad_token_id
									A    =       bos_token_id



		def        A_		(			self	:     List[Any]					)      ->    Tuple:






									'''simple docstring'''

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

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

									A    =       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    =       prepare_marian_inputs_dict(snake_case   ,				snake_case   ,				snake_case					)
									return config, inputs_dict



		def        A_		(			self	:     Tuple   ,				snake_case	:     Optional[int]   ,				snake_case	:     Optional[int]					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       TFMarianModel(config=snake_case					).get_decoder()
									A    =       inputs_dict['input_ids']

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

									# first forward pass
									A    =       model(snake_case   ,				attention_mask=snake_case   ,				head_mask=snake_case   ,				use_cache=snake_case					)

									A   ,	A    =       outputs.to_tuple()

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

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

									A    =       model(snake_case   ,				attention_mask=snake_case					)[0]
									A    =       model(snake_case   ,				attention_mask=snake_case   ,				past_key_values=snake_case					)[0]

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

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

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



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



		def        A_		(			self	:     str					)      ->    Optional[int]:






									'''simple docstring'''

									A    =       TFMarianModelTester(self					)
									A    =       ConfigTester(self   ,				config_class=snake_case					)



		def        A_		(			self	:     int					)      ->    Optional[int]:






									'''simple docstring'''

									self.config_tester.run_common_tests()



		def        A_		(			self	:     Dict					)      ->    int:






									'''simple docstring'''

									A    =       self.model_tester.prepare_config_and_inputs_for_common()
									self.model_tester.check_decoder_model_past_large_inputs(*snake_case					)






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



		@classmethod
		def        A_		(			cls	:     str					)      ->    None:






									'''simple docstring'''

									A    =       f"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
									return cls



		@cached_property
		def        A_		(			self	:     Tuple					)      ->    MarianTokenizer:






									'''simple docstring'''

									return AutoTokenizer.from_pretrained(self.model_name					)



		@property
		def        A_		(			self	:     str					)      ->    int:






									'''simple docstring'''

									return self.tokenizer.eos_token_id



		@cached_property
		def        A_		(			self	:     List[Any]					)      ->    Optional[int]:






									'''simple docstring'''

									warnings.simplefilter('error'					)
									A    =       TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name					)
									assert isinstance(snake_case   ,				snake_case					)
									A    =       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        A_		(			self	:     List[Any]   ,				**snake_case	:     Optional[int]					)      ->    Any:






									'''simple docstring'''

									A    =       self.translate_src_text(**snake_case					)
									self.assertListEqual(self.expected_text   ,				snake_case					)



		def        A_		(			self	:     Optional[Any]   ,				**snake_case	:     int					)      ->    str:






									'''simple docstring'''

									A    =       self.tokenizer(self.src_text   ,				**snake_case   ,				padding=snake_case   ,				return_tensors='tf'					)
									A    =       self.model.generate(
									    model_inputs.input_ids   ,				attention_mask=model_inputs.attention_mask   ,				num_beams=2   ,				max_length=128					)
									A    =       self.tokenizer.batch_decode(generated_ids.numpy()   ,				skip_special_tokens=snake_case					)
									return generated_words






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



		@unittest.skip('Skipping until #12647 is resolved.'					)
		@slow
		def        A_		(			self	:     List[str]					)      ->    List[Any]:






									'''simple docstring'''

									self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						List[str]							=  """en"""
		lowerCAmelCase_							:						Dict							=  """zh"""
		lowerCAmelCase_							:						Any							=  ["""My name is Wolfgang and I live in Berlin"""]
		lowerCAmelCase_							:						int							=  ["""我叫沃尔夫冈 我住在柏林"""]



		@unittest.skip('Skipping until #12647 is resolved.'					)
		@slow
		def        A_		(			self	:     Tuple					)      ->    List[str]:






									'''simple docstring'''

									self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class   UpperCAmelCase__		(   UpperCamelCase  ):
		lowerCAmelCase_							:						str							=  """en"""
		lowerCAmelCase_							:						Dict							=  """ROMANCE"""
		lowerCAmelCase_							:						Optional[int]							=  [
		    """>>fr<< Don't spend so much time watching TV.""",
		    """>>pt<< Your message has been sent.""",
		    """>>es<< He's two years older than me.""",
		]
		lowerCAmelCase_							:						Union[str, Any]							=  [
		    """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        A_		(			self	:     Optional[int]					)      ->    List[Any]:






									'''simple docstring'''

									self._assert_generated_batch_equal_expected()



		@unittest.skip('Skipping until #12647 is resolved.'					)
		@slow
		def        A_		(			self	:     Tuple					)      ->    List[str]:






									'''simple docstring'''

									A    =       TranslationPipeline(self.model   ,				self.tokenizer   ,				framework='tf'					)
									A    =       pipeline(self.src_text					)
									self.assertEqual(self.expected_text   ,				[x['translation_text'] for x in output]					)



