




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 a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    tempfile.mkdtemp()
													__lowercase      =    8

													# DPR tok
													__lowercase      =    [
													    '''[UNK]''',
													    '''[CLS]''',
													    '''[SEP]''',
													    '''[PAD]''',
													    '''[MASK]''',
													    '''want''',
													    '''##want''',
													    '''##ed''',
													    '''wa''',
													    '''un''',
													    '''runn''',
													    '''##ing''',
													    ''',''',
													    '''low''',
													    '''lowest''',
													]
													__lowercase      =    os.path.join(self.tmpdirname				,							'''dpr_tokenizer'''				)
													os.makedirs(lowerCamelCase__				,							exist_ok=lowerCamelCase__				)
													__lowercase      =    os.path.join(lowerCamelCase__				,							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
													__lowercase      =    [
													    '''l''',
													    '''o''',
													    '''w''',
													    '''e''',
													    '''r''',
													    '''s''',
													    '''t''',
													    '''i''',
													    '''d''',
													    '''n''',
													    '''\u0120''',
													    '''\u0120l''',
													    '''\u0120n''',
													    '''\u0120lo''',
													    '''\u0120low''',
													    '''er''',
													    '''\u0120lowest''',
													    '''\u0120newer''',
													    '''\u0120wider''',
													    '''<unk>''',
													]
													__lowercase      =    dict(zip(lowerCamelCase__				,							range(len(lowerCamelCase__				)				)				)				)
													__lowercase      =    ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
													__lowercase      =    {'''unk_token''': '''<unk>'''}

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






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						DPRQuestionEncoderTokenizer:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						DPRContextEncoderTokenizer:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						BartTokenizer:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Optional[Any]:
													"""simple docstring"""


													shutil.rmtree(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    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     UpperCAmelCase_      ( self :     str				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_dataset()
													__lowercase      =    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:
																				__lowercase      =    dataset
																				__lowercase      =    RagRetriever(
																				    lowerCamelCase__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							)
													return retriever






						def     UpperCAmelCase_      ( self :     Optional[int]				,							lowerCamelCase__ :     bool				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_dataset()
													__lowercase      =    RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							index_name='''custom'''				,							)
													if from_disk:
																				__lowercase      =    os.path.join(self.tmpdirname				,							'''dataset'''				)
																				__lowercase      =    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
																				__lowercase      =    RagRetriever(
																				    lowerCamelCase__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							)
													else:
																				__lowercase      =    RagRetriever(
																				    lowerCamelCase__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				,							index=CustomHFIndex(config.retrieval_vector_size				,							lowerCamelCase__				)				,							)
													return retriever






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    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				)

													__lowercase      =    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'''				)				)

													__lowercase      =    os.path.join(self.tmpdirname				,							'''psgs_w100.tsv.pkl'''				)
													__lowercase      =    {sample['''id''']: [sample['''text'''], sample['''title''']] for sample in dataset}
													pickle.dump(lowerCamelCase__				,							open(lowerCamelCase__				,							'''wb'''				)				)

													__lowercase      =    RagConfig(
													    retrieval_vector_size=self.retrieval_vector_size				,							question_encoder=DPRConfig().to_dict()				,							generator=BartConfig().to_dict()				,							index_name='''legacy'''				,							index_path=self.tmpdirname				,							)
													__lowercase      =    RagRetriever(
													    lowerCamelCase__				,							question_encoder_tokenizer=self.get_dpr_tokenizer()				,							generator_tokenizer=self.get_bart_tokenizer()				)
													return retriever






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    1
													__lowercase      =    self.get_dummy_canonical_hf_index_retriever()
													__lowercase      =    np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
													__lowercase     ,     __lowercase     ,     __lowercase      =    retriever.retrieve(lowerCamelCase__				,							n_docs=lowerCamelCase__				)
													self.assertEqual(retrieved_doc_embeds.shape				,							(2, n_docs, self.retrieval_vector_size)				)
													self.assertEqual(len(lowerCamelCase__				)				,							2				)
													self.assertEqual(sorted(doc_dicts[0]				)				,							['''embeddings''', '''id''', '''text''', '''title''']				)
													self.assertEqual(len(doc_dicts[0]['''id''']				)				,							lowerCamelCase__				)
													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     UpperCAmelCase_      ( self :     List[str]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    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:
																											__lowercase      =    self.get_dummy_dataset()
																											retriever.save_pretrained(lowerCamelCase__				)
																											__lowercase      =    RagRetriever.from_pretrained(lowerCamelCase__				)
																											self.assertIsInstance(lowerCamelCase__				,							lowerCamelCase__				)
																											__lowercase      =    np.array(
																											    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
																											__lowercase      =    retriever.retrieve(lowerCamelCase__				,							n_docs=1				)
																											self.assertTrue(out is not None				)






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    1
													__lowercase      =    self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__				)
													__lowercase      =    np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
													__lowercase     ,     __lowercase     ,     __lowercase      =    retriever.retrieve(lowerCamelCase__				,							n_docs=lowerCamelCase__				)
													self.assertEqual(retrieved_doc_embeds.shape				,							(2, n_docs, self.retrieval_vector_size)				)
													self.assertEqual(len(lowerCamelCase__				)				,							2				)
													self.assertEqual(sorted(doc_dicts[0]				)				,							['''embeddings''', '''id''', '''text''', '''title''']				)
													self.assertEqual(len(doc_dicts[0]['''id''']				)				,							lowerCamelCase__				)
													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     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						str:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     int				)    ->						int:
													"""simple docstring"""


													__lowercase      =    1
													__lowercase      =    self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__				)
													__lowercase      =    np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
													__lowercase     ,     __lowercase     ,     __lowercase      =    retriever.retrieve(lowerCamelCase__				,							n_docs=lowerCamelCase__				)
													self.assertEqual(retrieved_doc_embeds.shape				,							(2, n_docs, self.retrieval_vector_size)				)
													self.assertEqual(len(lowerCamelCase__				)				,							2				)
													self.assertEqual(sorted(doc_dicts[0]				)				,							['''embeddings''', '''id''', '''text''', '''title''']				)
													self.assertEqual(len(doc_dicts[0]['''id''']				)				,							lowerCamelCase__				)
													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     UpperCAmelCase_      ( self :     List[str]				)    ->						Tuple:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Optional[Any]:
													"""simple docstring"""


													__lowercase      =    1
													__lowercase      =    self.get_dummy_legacy_index_retriever()
													__lowercase      =    np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
													__lowercase     ,     __lowercase     ,     __lowercase      =    retriever.retrieve(lowerCamelCase__				,							n_docs=lowerCamelCase__				)
													self.assertEqual(retrieved_doc_embeds.shape				,							(2, n_docs, self.retrieval_vector_size)				)
													self.assertEqual(len(lowerCamelCase__				)				,							2				)
													self.assertEqual(sorted(doc_dicts[0]				)				,							['''text''', '''title''']				)
													self.assertEqual(len(doc_dicts[0]['''text''']				)				,							lowerCamelCase__				)
													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     UpperCAmelCase_      ( self :     Dict				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.get_dummy_legacy_index_retriever()
													with tempfile.TemporaryDirectory() as tmp_dirname:
																				retriever.save_pretrained(lowerCamelCase__				)
																				__lowercase      =    RagRetriever.from_pretrained(lowerCamelCase__				)
																				self.assertIsInstance(lowerCamelCase__				,							lowerCamelCase__				)
																				__lowercase      =    np.array(
																				    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
																				__lowercase      =    retriever.retrieve(lowerCamelCase__				,							n_docs=1				)
																				self.assertTrue(out is not None				)






						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						int:
													"""simple docstring"""


													import torch

													__lowercase      =    1
													__lowercase      =    self.get_dummy_canonical_hf_index_retriever()
													__lowercase      =    [[5, 7], [10, 11]]
													__lowercase      =    np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
													__lowercase      =    retriever(lowerCamelCase__				,							lowerCamelCase__				,							prefix=retriever.config.generator.prefix				,							n_docs=lowerCamelCase__				)
													__lowercase     ,     __lowercase     ,     __lowercase      =    (
													    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(lowerCamelCase__				,							lowerCamelCase__				)
													self.assertIsInstance(lowerCamelCase__				,							lowerCamelCase__				)
													self.assertIsInstance(lowerCamelCase__				,							np.ndarray				)

													__lowercase      =    retriever(
													    lowerCamelCase__				,							lowerCamelCase__				,							prefix=retriever.config.generator.prefix				,							n_docs=lowerCamelCase__				,							return_tensors='''pt'''				,							)
													__lowercase     ,     __lowercase     ,     __lowercase     ,     __lowercase      =    (  # 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(lowerCamelCase__				,							torch.Tensor				)
													self.assertIsInstance(lowerCamelCase__				,							torch.Tensor				)
													self.assertIsInstance(lowerCamelCase__				,							torch.Tensor				)






						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def     UpperCAmelCase_      ( self :     Any				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.get_dpr_ctx_encoder_tokenizer()
													__lowercase      =    1
													__lowercase      =    self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__				)
													retriever.set_ctx_encoder_tokenizer(lowerCamelCase__				)

													__lowercase      =    [[5, 7], [10, 11]]
													__lowercase      =    np.array(
													    [np.ones(self.retrieval_vector_size				), -np.ones(self.retrieval_vector_size				)]				,							dtype=np.floataa				)
													__lowercase      =    retriever(lowerCamelCase__				,							lowerCamelCase__				,							prefix=retriever.config.generator.prefix				,							n_docs=lowerCamelCase__				)

													self.assertEqual(
													    len(lowerCamelCase__				)				,							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''')				)				,							lowerCamelCase__				)  # check for doc token related keys in dictionary.












from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     List[str]				,							lowerCamelCase__ :     float				)    ->						float:
													"""simple docstring"""


													return 0.0







def 		_A(       UpperCamelCase__							:			np.ndarray				,       UpperCamelCase__							:			int				)    -> tuple[int | float, int | float]:
							'''simple docstring'''


							__lowercase      =    min([-20, np.min(fft_results[1 : samplerate // 2 - 1]				)]				)
							__lowercase      =    max([20, np.max(fft_results[1 : samplerate // 2 - 1]				)]				)
							return lowest, highest







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.abs(np.fft.fft(UpperCamelCase__				)				)
							__lowercase      =    20 * np.logaa(UpperCamelCase__				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							# Display within reasonable bounds
							__lowercase      =    get_bounds(UpperCamelCase__				,       UpperCamelCase__				)
							plt.ylim(max([-80, bounds[0]]				)				,       min([80, bounds[1]]				)				)
							plt.ylabel('''Gain (dB)'''				)

							plt.plot(UpperCamelCase__				)
							plt.show()







def 		_A(       UpperCamelCase__							:			FilterType				,       UpperCamelCase__							:			int				)    -> None:
							'''simple docstring'''


							__lowercase      =    512
							__lowercase      =    [1] + [0] * (size - 1)
							__lowercase      =    [filter_type.process(UpperCamelCase__				) for item in inputs]

							__lowercase      =    [0] * (samplerate - size)  # zero-padding
							outputs += filler
							__lowercase      =    np.angle(np.fft.fft(UpperCamelCase__				)				)

							# Frequencies on log scale from 24 to nyquist frequency
							plt.xlim(24				,       samplerate / 2 - 1				)
							plt.xlabel('''Frequency (Hz)'''				)
							plt.xscale('''log'''				)

							plt.ylim(-2 * pi				,       2 * pi				)
							plt.ylabel('''Phase shift (Radians)'''				)
							plt.plot(np.unwrap(UpperCamelCase__				,       -2 * pi				)				)
							plt.show()






