





"""simple docstring"""






import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
		import faiss




@require_faiss
class  __lowercase	(	SCREAMING_SNAKE_CASE_							):







					'''simple docstring'''




					def        _lowerCamelCase						(  self   ):
									__a       :  Union[str, Any]					=     tempfile.mkdtemp()
									__a       :  List[Any]					=     8

									# DPR tok
									__a       :  str					=     [
									    """[UNK]""",
									    """[CLS]""",
									    """[SEP]""",
									    """[PAD]""",
									    """[MASK]""",
									    """want""",
									    """##want""",
									    """##ed""",
									    """wa""",
									    """un""",
									    """runn""",
									    """##ing""",
									    """,""",
									    """low""",
									    """lowest""",
									]
									__a       :  List[str]					=     os.path.join(self.tmpdirname		,	'''dpr_tokenizer'''   )
									os.makedirs(a__		,	exist_ok=a__   )
									__a       :  int					=     os.path.join(a__		,	DPR_VOCAB_FILES_NAMES['''vocab_file''']   )
									with open(self.vocab_file		,	'''w'''		,	encoding='''utf-8'''   ) as vocab_writer:
													vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]   )   )

									# BART tok
									__a       :  int					=     [
									    """l""",
									    """o""",
									    """w""",
									    """e""",
									    """r""",
									    """s""",
									    """t""",
									    """i""",
									    """d""",
									    """n""",
									    """\u0120""",
									    """\u0120l""",
									    """\u0120n""",
									    """\u0120lo""",
									    """\u0120low""",
									    """er""",
									    """\u0120lowest""",
									    """\u0120newer""",
									    """\u0120wider""",
									    """<unk>""",
									]
									__a       :  str					=     dict(zip(a__		,	range(len(a__   )   )   )   )
									__a       :  Optional[Any]					=     ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
									__a       :  List[str]					=     {"""unk_token""": """<unk>"""}

									__a       :  Dict					=     os.path.join(self.tmpdirname		,	'''bart_tokenizer'''   )
									os.makedirs(a__		,	exist_ok=a__   )
									__a       :  Dict					=     os.path.join(a__		,	BART_VOCAB_FILES_NAMES['''vocab_file''']   )
									__a       :  Tuple					=     os.path.join(a__		,	BART_VOCAB_FILES_NAMES['''merges_file''']   )
									with open(self.vocab_file		,	'''w'''		,	encoding='''utf-8'''   ) as fp:
													fp.write(json.dumps(a__   ) + '''\n'''   )
									with open(self.merges_file		,	'''w'''		,	encoding='''utf-8'''   ) as fp:
													fp.write('''\n'''.join(a__   )   )







					def        _lowerCamelCase						(  self   ):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,	'''dpr_tokenizer'''   )   )







					def        _lowerCamelCase						(  self   ):
									return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,	'''dpr_tokenizer'''   )   )







					def        _lowerCamelCase						(  self   ):
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,	'''bart_tokenizer'''   )   )







					def        _lowerCamelCase						(  self   ):
									shutil.rmtree(self.tmpdirname   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[Any]					=     Dataset.from_dict(
									    {
									        '''id''': ['''0''', '''1'''],
									        '''text''': ['''foo''', '''bar'''],
									        '''title''': ['''Foo''', '''Bar'''],
									        '''embeddings''': [np.ones(self.retrieval_vector_size   ), 2 * np.ones(self.retrieval_vector_size   )],
									    }   )
									dataset.add_faiss_index('''embeddings'''		,	string_factory='''Flat'''		,	metric_type=faiss.METRIC_INNER_PRODUCT   )
									return dataset







					def        _lowerCamelCase						(  self   ):
									__a       :  Union[str, Any]					=     self.get_dummy_dataset()
									__a       :  Optional[Any]					=     RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size		,	question_encoder=DPRConfig().to_dict()		,	generator=BartConfig().to_dict()		,	)
									with patch('''transformers.models.rag.retrieval_rag.load_dataset'''   ) as mock_load_dataset:
													__a       :  List[Any]					=     dataset
													__a       :  Optional[Any]					=     RagRetriever(
													    a__		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()		,	)
									return retriever







					def        _lowerCamelCase						(  self		,	_UpperCAmelCase   ):
									__a       :  Optional[Any]					=     self.get_dummy_dataset()
									__a       :  Union[str, Any]					=     RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size		,	question_encoder=DPRConfig().to_dict()		,	generator=BartConfig().to_dict()		,	index_name='''custom'''		,	)
									if from_disk:
													__a       :  Optional[int]					=     os.path.join(self.tmpdirname		,	'''dataset'''   )
													__a       :  List[Any]					=     os.path.join(self.tmpdirname		,	'''index.faiss'''   )
													dataset.get_index('''embeddings'''   ).save(os.path.join(self.tmpdirname		,	'''index.faiss'''   )   )
													dataset.drop_index('''embeddings'''   )
													dataset.save_to_disk(os.path.join(self.tmpdirname		,	'''dataset'''   )   )
													del dataset
													__a       :  Optional[int]					=     RagRetriever(
													    a__		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()		,	)
									else:
													__a       :  int					=     RagRetriever(
													    a__		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()		,	index=CustomHFIndex(config.retrieval_vector_size		,	a__   )		,	)
									return retriever







					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[Any]					=     Dataset.from_dict(
									    {
									        '''id''': ['''0''', '''1'''],
									        '''text''': ['''foo''', '''bar'''],
									        '''title''': ['''Foo''', '''Bar'''],
									        '''embeddings''': [np.ones(self.retrieval_vector_size + 1   ), 2 * np.ones(self.retrieval_vector_size + 1   )],
									    }   )
									dataset.add_faiss_index('''embeddings'''		,	string_factory='''Flat'''		,	metric_type=faiss.METRIC_INNER_PRODUCT   )

									__a       :  Optional[Any]					=     os.path.join(self.tmpdirname		,	'''hf_bert_base.hnswSQ8_correct_phi_128.c_index'''   )
									dataset.save_faiss_index('''embeddings'''		,	index_file_name + '''.index.dpr'''   )
									pickle.dump(dataset['''id''']		,	open(index_file_name + '''.index_meta.dpr'''		,	'''wb'''   )   )

									__a       :  Union[str, Any]					=     os.path.join(self.tmpdirname		,	'''psgs_w100.tsv.pkl'''   )
									__a       :  Union[str, Any]					=     {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
									pickle.dump(a__		,	open(a__		,	'''wb'''   )   )

									__a       :  List[str]					=     RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size		,	question_encoder=DPRConfig().to_dict()		,	generator=BartConfig().to_dict()		,	index_name='''legacy'''		,	index_path=self.tmpdirname		,	)
									__a       :  str					=     RagRetriever(
									    a__		,	question_encoder_tokenizer=self.get_dpr_tokenizer()		,	generator_tokenizer=self.get_bart_tokenizer()   )
									return retriever







					def        _lowerCamelCase						(  self   ):
									__a       :  Tuple					=     1
									__a       :  Dict					=     self.get_dummy_canonical_hf_index_retriever()
									__a       :  int					=     np.array(
									    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
									__a       :  Union[str, Any]					=     retriever.retrieve(a__		,	n_docs=a__   )
									self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)   )
									self.assertEqual(len(a__   )		,	2   )
									self.assertEqual(sorted(doc_dicts[0]   )		,	['''embeddings''', '''id''', '''text''', '''title''']   )
									self.assertEqual(len(doc_dicts[0]['''id''']   )		,	a__   )
									self.assertEqual(doc_dicts[0]['''id'''][0]		,	'''1'''   )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''id'''][0]		,	'''0'''   )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[Any]					=     self.get_dummy_canonical_hf_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
													with patch('''transformers.models.rag.retrieval_rag.load_dataset'''   ) as mock_load_dataset:
																	__a       :  Optional[int]					=     self.get_dummy_dataset()
																	retriever.save_pretrained(a__   )
																	__a       :  Dict					=     RagRetriever.from_pretrained(a__   )
																	self.assertIsInstance(a__		,	a__   )
																	__a       :  Any					=     np.array(
																	    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
																	__a       :  int					=     retriever.retrieve(a__		,	n_docs=1   )
																	self.assertTrue(out is not None   )







					def        _lowerCamelCase						(  self   ):
									__a       :  int					=     1
									__a       :  List[Any]					=     self.get_dummy_custom_hf_index_retriever(from_disk=a__   )
									__a       :  str					=     np.array(
									    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
									__a       :  List[Any]					=     retriever.retrieve(a__		,	n_docs=a__   )
									self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)   )
									self.assertEqual(len(a__   )		,	2   )
									self.assertEqual(sorted(doc_dicts[0]   )		,	['''embeddings''', '''id''', '''text''', '''title''']   )
									self.assertEqual(len(doc_dicts[0]['''id''']   )		,	a__   )
									self.assertEqual(doc_dicts[0]['''id'''][0]		,	'''1'''   )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''id'''][0]		,	'''0'''   )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Any					=     self.get_dummy_custom_hf_index_retriever(from_disk=a__   )
									with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(a__   )
													__a       :  Tuple					=     RagRetriever.from_pretrained(a__   )
													self.assertIsInstance(a__		,	a__   )
													__a       :  Tuple					=     np.array(
													    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
													__a       :  Any					=     retriever.retrieve(a__		,	n_docs=1   )
													self.assertTrue(out is not None   )







					def        _lowerCamelCase						(  self   ):
									__a       :  Any					=     1
									__a       :  List[str]					=     self.get_dummy_custom_hf_index_retriever(from_disk=a__   )
									__a       :  Optional[Any]					=     np.array(
									    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
									__a       :  List[str]					=     retriever.retrieve(a__		,	n_docs=a__   )
									self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)   )
									self.assertEqual(len(a__   )		,	2   )
									self.assertEqual(sorted(doc_dicts[0]   )		,	['''embeddings''', '''id''', '''text''', '''title''']   )
									self.assertEqual(len(doc_dicts[0]['''id''']   )		,	a__   )
									self.assertEqual(doc_dicts[0]['''id'''][0]		,	'''1'''   )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''id'''][0]		,	'''0'''   )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]   )







					def        _lowerCamelCase						(  self   ):
									__a       :  List[Any]					=     self.get_dummy_custom_hf_index_retriever(from_disk=a__   )
									with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(a__   )
													__a       :  List[Any]					=     RagRetriever.from_pretrained(a__   )
													self.assertIsInstance(a__		,	a__   )
													__a       :  List[str]					=     np.array(
													    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
													__a       :  List[Any]					=     retriever.retrieve(a__		,	n_docs=1   )
													self.assertTrue(out is not None   )







					def        _lowerCamelCase						(  self   ):
									__a       :  int					=     1
									__a       :  Tuple					=     self.get_dummy_legacy_index_retriever()
									__a       :  Optional[Any]					=     np.array(
									    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
									__a       :  List[Any]					=     retriever.retrieve(a__		,	n_docs=a__   )
									self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)   )
									self.assertEqual(len(a__   )		,	2   )
									self.assertEqual(sorted(doc_dicts[0]   )		,	['''text''', '''title''']   )
									self.assertEqual(len(doc_dicts[0]['''text''']   )		,	a__   )
									self.assertEqual(doc_dicts[0]['''text'''][0]		,	'''bar'''   )  # max inner product is reached with second doc
									self.assertEqual(doc_dicts[1]['''text'''][0]		,	'''foo'''   )  # max inner product is reached with first doc
									self.assertListEqual(doc_ids.tolist()		,	[[1], [0]]   )







					def        _lowerCamelCase						(  self   ):
									__a       :  str					=     self.get_dummy_legacy_index_retriever()
									with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(a__   )
													__a       :  Tuple					=     RagRetriever.from_pretrained(a__   )
													self.assertIsInstance(a__		,	a__   )
													__a       :  str					=     np.array(
													    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
													__a       :  List[str]					=     retriever.retrieve(a__		,	n_docs=1   )
													self.assertTrue(out is not None   )







					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def        _lowerCamelCase						(  self   ):
									import torch

									__a       :  Tuple					=     1
									__a       :  int					=     self.get_dummy_canonical_hf_index_retriever()
									__a       :  Optional[Any]					=     [[5, 7], [10, 11]]
									__a       :  str					=     np.array(
									    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
									__a       :  List[str]					=     retriever(a__		,	a__		,	prefix=retriever.config.generator.prefix		,	n_docs=a__   )
									__a       :  Optional[int]					=     (
									    out["""context_input_ids"""],
									    out["""context_attention_mask"""],
									    out["""retrieved_doc_embeds"""],
									)
									self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)   )
									self.assertIsInstance(a__		,	a__   )
									self.assertIsInstance(a__		,	a__   )
									self.assertIsInstance(a__		,	np.ndarray   )

									__a       :  int					=     retriever(
									    a__		,	a__		,	prefix=retriever.config.generator.prefix		,	n_docs=a__		,	return_tensors='''pt'''		,	)
									__a       :  Union[str, Any]					=     (  # noqa: F841
									    out["""context_input_ids"""],
									    out["""context_attention_mask"""],
									    out["""retrieved_doc_embeds"""],
									    out["""doc_ids"""],
									)
									self.assertEqual(retrieved_doc_embeds.shape		,	(2, n_docs, self.retrieval_vector_size)   )
									self.assertIsInstance(a__		,	torch.Tensor   )
									self.assertIsInstance(a__		,	torch.Tensor   )
									self.assertIsInstance(a__		,	torch.Tensor   )







					@require_torch
					@require_tokenizers
					@require_sentencepiece
					def        _lowerCamelCase						(  self   ):
									__a       :  Optional[int]					=     self.get_dpr_ctx_encoder_tokenizer()
									__a       :  Optional[int]					=     1
									__a       :  int					=     self.get_dummy_custom_hf_index_retriever(from_disk=a__   )
									retriever.set_ctx_encoder_tokenizer(a__   )

									__a       :  Any					=     [[5, 7], [10, 11]]
									__a       :  Any					=     np.array(
									    [np.ones(self.retrieval_vector_size   ), -np.ones(self.retrieval_vector_size   )]		,	dtype=np.floataa   )
									__a       :  List[Any]					=     retriever(a__		,	a__		,	prefix=retriever.config.generator.prefix		,	n_docs=a__   )

									self.assertEqual(
									    len(a__   )		,	6   )  # check whether the retriever output consist of 6 attributes including tokenized docs
									self.assertEqual(
									    all(k in out for k in ('''tokenized_doc_ids''', '''tokenized_doc_attention_mask''')   )		,	a__   )  # check for doc token related keys in dictionary.
"""simple docstring"""




import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class 						__A							( unittest.TestCase		):






     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     """ylacombe/bark-small"""
       _lowerCAmelCase						:       Optional[Any]  			=     tempfile.mkdtemp()
       _lowerCAmelCase						:       int  			=     """en_speaker_1"""
       _lowerCAmelCase						:       List[Any]  			=     """This is a test string"""
       _lowerCAmelCase						:       Any  			=     """speaker_embeddings_path.json"""
       _lowerCAmelCase						:       List[Any]  			=     """speaker_embeddings"""






     def     __A  (		self				,     **a__  ):
       return AutoTokenizer.from_pretrained(self.checkpoint				,     **a__  )






     def     __A  (		self  ):
       shutil.rmtree(self.tmpdirname  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[Any]  			=     self.get_tokenizer()

       _lowerCAmelCase						:       int  			=     BarkProcessor(tokenizer=a__  )

       processor.save_pretrained(self.tmpdirname  )
       _lowerCAmelCase						:       str  			=     BarkProcessor.from_pretrained(self.tmpdirname  )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer.get_vocab()  )






     @slow
     def     __A  (		self  ):
       _lowerCAmelCase						:       Optional[int]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )
       processor.save_pretrained(
           self.tmpdirname				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     speaker_embeddings_directory=self.speaker_embeddings_directory				,     )

       _lowerCAmelCase						:       Tuple  			=     self.get_tokenizer(bos_token="""(BOS)"""				,     eos_token="""(EOS)"""  )

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor.from_pretrained(
           self.tmpdirname				,     self.speaker_embeddings_dict_path				,     bos_token="""(BOS)"""				,     eos_token="""(EOS)"""				,     )

       self.assertEqual(processor.tokenizer.get_vocab()				,     tokenizer_add_kwargs.get_vocab()  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       List[str]  			=     BarkProcessor.from_pretrained(
           pretrained_processor_name_or_path=self.checkpoint				,     speaker_embeddings_dict_path=self.speaker_embeddings_dict_path				,     )

       _lowerCAmelCase						:       Union[str, Any]  			=     35
       _lowerCAmelCase						:       Union[str, Any]  			=     2
       _lowerCAmelCase						:       Optional[int]  			=     8

       _lowerCAmelCase						:       Dict  			=     {
           """semantic_prompt""": np.ones(a__  ),
           """coarse_prompt""": np.ones((nb_codebooks_coarse, seq_len)  ),
           """fine_prompt""": np.ones((nb_codebooks_total, seq_len)  ),
       }

       # test providing already loaded voice_preset
       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string				,     voice_preset=a__  )

       _lowerCAmelCase						:       Tuple  			=     inputs["""history_prompt"""]
       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from npz file
       _lowerCAmelCase						:       List[Any]  			=     os.path.join(self.tmpdirname				,     """file.npz"""  )
       np.savez(a__				,     **a__  )
       _lowerCAmelCase						:       List[Any]  			=     processor(text=self.input_string				,     voice_preset=a__  )
       _lowerCAmelCase						:       Optional[int]  			=     inputs["""history_prompt"""]

       for key in voice_preset:
         self.assertListEqual(voice_preset[key].tolist()				,     processed_voice_preset.get(a__				,     np.array([]  )  ).tolist()  )

       # test loading voice preset from the hub
       _lowerCAmelCase						:       str  			=     processor(text=self.input_string				,     voice_preset=self.voice_preset  )






     def     __A  (		self  ):
       _lowerCAmelCase						:       int  			=     self.get_tokenizer()

       _lowerCAmelCase						:       List[Any]  			=     BarkProcessor(tokenizer=a__  )

       _lowerCAmelCase						:       Dict  			=     processor(text=self.input_string  )

       _lowerCAmelCase						:       Tuple  			=     tokenizer(
           self.input_string				,     padding="""max_length"""				,     max_length=256				,     add_special_tokens=a__				,     return_attention_mask=a__				,     return_token_type_ids=a__				,     )

       for key in encoded_tok.keys():
         self.assertListEqual(encoded_tok[key]				,     encoded_processor[key].squeeze().tolist()  )




