
import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


a_											=						logging.getLogger(__name__)






class    UpperCAmelCase_				:





 def __init__(    self):
     snake_case_ :    Union[str, Any]     			=						False





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_):
     if not self.initialized:
         snake_case_ :    Tuple     			=						RagRetriever(
             UpperCamelCase_ ,       question_encoder_tokenizer=UpperCamelCase_ ,       generator_tokenizer=UpperCamelCase_ ,       index=UpperCamelCase_ ,       init_retrieval=UpperCamelCase_ ,       )
         snake_case_ :    Optional[Any]     			=						True





 def        snake_case__		(    self):
     self.retriever.index.init_index()





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     snake_case_			, snake_case_ :    Union[str, Any]     			=						self.retriever._main_retrieve(UpperCamelCase_ ,       UpperCamelCase_)
     return doc_ids, retrieved_doc_embeds






class    UpperCAmelCase_				(	__lowerCamelCase					):





 def __init__(    self ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_ ,       lowercase_=None):
     if index is not None and index.is_initialized() and len(UpperCamelCase_) > 0:
         raise ValueError(
             "When using Ray for distributed fine-tuning, "
             "you'll need to provide the paths instead, "
             "as the dataset and the index are loaded "
             "separately. More info in examples/rag/use_own_knowledge_dataset.py ")
     super().__init__(
         UpperCamelCase_ ,       question_encoder_tokenizer=UpperCamelCase_ ,       generator_tokenizer=UpperCamelCase_ ,       index=UpperCamelCase_ ,       init_retrieval=UpperCamelCase_ ,       )
     snake_case_ :    Optional[Any]     			=						retrieval_workers
     if len(self.retrieval_workers) > 0:
         ray.get(
             [
                 worker.create_rag_retriever.remote(UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_ ,       UpperCamelCase_)
                 for worker in self.retrieval_workers
             ])





 def        snake_case__		(    self):
     logger.info("initializing retrieval")

     if len(self.retrieval_workers) > 0:
         ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers])
     else:
         # Non-distributed training. Load index into this same process.
         self.index.init_index()





 def        snake_case__		(    self ,       lowercase_ ,       lowercase_):
     if len(self.retrieval_workers) > 0:
         # Select a random retrieval actor.
         snake_case_ :    Dict     			=						self.retrieval_workers[random.randint(0 ,       len(self.retrieval_workers) - 1)]
         snake_case_			, snake_case_ :    List[Any]     			=						ray.get(random_worker.retrieve.remote(UpperCamelCase_ ,       UpperCamelCase_))
     else:
         snake_case_			, snake_case_ :    List[Any]     			=						self._main_retrieve(UpperCamelCase_ ,       UpperCamelCase_)
     return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase_)





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ ,       lowercase_=None ,       **lowercase_):
     return super(UpperCamelCase_ ,       cls).get_tokenizers(UpperCamelCase_ ,       UpperCamelCase_ ,       **UpperCamelCase_)





 @classmethod
 def        snake_case__		(    cls ,       lowercase_ ,       lowercase_ ,       lowercase_=None ,       **lowercase_):
     snake_case_ :    List[str]     			=						kwargs.pop("config" ,       UpperCamelCase_) or RagConfig.from_pretrained(UpperCamelCase_ ,       **UpperCamelCase_)
     snake_case_ :    Dict     			=						RagTokenizer.from_pretrained(UpperCamelCase_ ,       config=UpperCamelCase_)
     snake_case_ :    Optional[int]     			=						rag_tokenizer.question_encoder
     snake_case_ :    Any     			=						rag_tokenizer.generator
     if indexed_dataset is not None:
         snake_case_ :    int     			=						"custom"
         snake_case_ :    Any     			=						CustomHFIndex(config.retrieval_vector_size ,       UpperCamelCase_)
     else:
         snake_case_ :    List[str]     			=						cls._build_index(UpperCamelCase_)
     return cls(
         UpperCamelCase_ ,       question_encoder_tokenizer=UpperCamelCase_ ,       generator_tokenizer=UpperCamelCase_ ,       retrieval_workers=UpperCamelCase_ ,       index=UpperCamelCase_ ,       )






'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
  import tensorflow as tf

if is_tensorflow_text_available():
  from transformers.models.bert import TFBertTokenizer


a_											=						["bert-base-uncased", "bert-base-cased"]
a_											=						"hf-internal-testing/tiny-bert-tf-only"

if is_tf_available():






  class    UpperCAmelCase_				(	tf.keras.Model					):





   def __init__(    self ,       lowercase_):
       super().__init__()
       snake_case_ :    Union[str, Any]     			=						tokenizer
       snake_case_ :    List[str]     			=						AutoConfig.from_pretrained(lowercase_)
       snake_case_ :    List[str]     			=						TFAutoModel.from_config(lowercase_)





   def        snake_case__		(    self ,       lowercase_):
       snake_case_ :    List[str]     			=						self.tokenizer(lowercase_)
       snake_case_ :    str     			=						self.bert(**lowercase_)
       return out["pooler_output"]






@require_tf
@require_tensorflow_text
class    UpperCAmelCase_				(	unittest.TestCase					):





 def        snake_case__		(    self):
     super().setUp()

     snake_case_ :    Dict     			=						[
         BertTokenizer.from_pretrained(lowercase_) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
     ]  # repeat for when fast_bert_tokenizer=false
     snake_case_ :    Tuple     			=						[TFBertTokenizer.from_pretrained(lowercase_) for checkpoint in TOKENIZER_CHECKPOINTS] + [
         TFBertTokenizer.from_pretrained(lowercase_ ,       use_fast_bert_tokenizer=lowercase_)
         for checkpoint in TOKENIZER_CHECKPOINTS
     ]
     assert len(self.tokenizers) == len(self.tf_tokenizers)

     snake_case_ :    Dict     			=						[
         "This is a straightforward English test sentence.",
         "This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.",
         "Now we're going to add some Chinese: 一 二 三 一二三",
         "And some much more rare Chinese: 齉 堃 齉堃",
         "Je vais aussi écrire en français pour tester les accents",
         "Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
     ]
     snake_case_ :    str     			=						list(zip(self.test_sentences ,       self.test_sentences[::-1]))





 def        snake_case__		(    self):
     for tokenizer, tf_tokenizer in zip(self.tokenizers ,       self.tf_tokenizers):
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Any     			=						tokenizer(lowercase_ ,       return_tensors="tf" ,       padding="longest")
             snake_case_ :    Union[str, Any]     			=						tf_tokenizer(lowercase_)

             for key in python_outputs.keys():
                 self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
                 self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] ,       tf.intaa) == tf_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    Union[str, Any]     			=						tf_tokenizer(self.paired_sentences)
         snake_case_ :    str     			=						tf_tokenizer(
             text=[sentence[0] for sentence in self.paired_sentences] ,       text_pair=[sentence[1] for sentence in self.paired_sentences] ,       )
         for key in merged_outputs.keys():
             self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] ,       tf.intaa) == separated_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    int     			=						tf.function(lowercase_)
         for test_inputs in (self.test_sentences, self.paired_sentences):
             snake_case_ :    Optional[int]     			=						tf.constant(lowercase_)
             snake_case_ :    Tuple     			=						compiled_tokenizer(lowercase_)
             snake_case_ :    Any     			=						tf_tokenizer(lowercase_)

             for key in eager_outputs.keys():
                 self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))





 @slow
 def        snake_case__		(    self):
     for tf_tokenizer in self.tf_tokenizers:
         snake_case_ :    List[Any]     			=						ModelToSave(tokenizer=lowercase_)
         snake_case_ :    Any     			=						tf.convert_to_tensor(self.test_sentences)
         snake_case_ :    List[Any]     			=						model(lowercase_)  # Build model with some sample inputs
         with TemporaryDirectory() as tempdir:
             snake_case_ :    Optional[Any]     			=						Path(lowercase_) / "saved.model"
             model.save(lowercase_)
             snake_case_ :    Tuple     			=						tf.keras.models.load_model(lowercase_)
         snake_case_ :    str     			=						loaded_model(lowercase_)
         # We may see small differences because the loaded model is compiled, so we need an epsilon for the test
         self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output)) ,       1E-5)




