




import itertools
import os
import random
import tempfile
import unittest

import numpy as np

from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
  import torch

if is_datasets_available():
  from datasets import load_dataset

_snake_case            = random.Random()







def        lowerCAmelCase_			(  snake_case_,snake_case_=1.0,snake_case_=None,snake_case_=None  ):
      if rng is None:
            _A							:  str    =		global_rng

      _A							:  List[Any]    =		[]
      for batch_idx in range(shape[0]  ):
            values.append([]  )
            for _ in range(shape[1]  ):
                  values[-1].append(rng.random() * scale  )

      return values







class 		lowercase							(     unittest.TestCase	):

     def __init__(						self	,  _a	,  _a=7	,  _a=400	,  _a=2000	,  _a=2048	,  _a=128	,  _a=1	,  _a=512	,  _a=30	,  _a=4_4100	,  )		->							Tuple:
           _A							:  Any    =		parent
           _A							:  str    =		batch_size
           _A							:  Union[str, Any]    =		min_seq_length
           _A							:  int    =		max_seq_length
           _A							:  List[str]    =		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
           _A							:  Tuple    =		spectrogram_length
           _A							:  int    =		feature_size
           _A							:  str    =		num_audio_channels
           _A							:  Tuple    =		hop_length
           _A							:  List[str]    =		chunk_length
           _A							:  Union[str, Any]    =		sampling_rate

     def       a__			(						self       )		->							Tuple:
           return {
               "spectrogram_length": self.spectrogram_length,
               "feature_size": self.feature_size,
               "num_audio_channels": self.num_audio_channels,
               "hop_length": self.hop_length,
               "chunk_length": self.chunk_length,
               "sampling_rate": self.sampling_rate,
           }



     def       a__			(						self	,  _a=False	,  _a=False       )		->							Optional[int]:
           def _flatten(_a       ):
                 return list(itertools.chain(*_a       )       )

           if equal_length:
                 _A							:  List[str]    =		[floats_list((self.max_seq_length, self.feature_size)       ) for _ in range(self.batch_size       )]
           else:
                 # make sure that inputs increase in size
                 _A							:  Union[str, Any]    =		[
                     floats_list((x, self.feature_size)       )
                     for x in range(self.min_seq_length	,  self.max_seq_length	,  self.seq_length_diff       )
                 ]
           if numpify:
                 _A							:  List[Any]    =		[np.asarray(_a       ) for x in speech_inputs]
           return speech_inputs







@require_torch
@require_torchaudio
class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		TvltFeatureExtractor

     def       a__			(						self       )		->							Any:
           _A							:  int    =		TvltFeatureExtractionTester(self       )

     def       a__			(						self       )		->							List[Any]:
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )
           self.assertTrue(hasattr(_a	,  """spectrogram_length"""       )       )
           self.assertTrue(hasattr(_a	,  """feature_size"""       )       )
           self.assertTrue(hasattr(_a	,  """num_audio_channels"""       )       )
           self.assertTrue(hasattr(_a	,  """hop_length"""       )       )
           self.assertTrue(hasattr(_a	,  """chunk_length"""       )       )
           self.assertTrue(hasattr(_a	,  """sampling_rate"""       )       )

     def       a__			(						self       )		->							Optional[int]:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  Tuple    =		feat_extract_first.save_pretrained(_a       )[0]
                 check_json_file_has_correct_format(_a       )
                 _A							:  Optional[int]    =		self.feature_extraction_class.from_pretrained(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  int    =		feat_extract_second.to_dict()
           _A							:  int    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[int]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							int:
           _A							:  str    =		self.feature_extraction_class(**self.feat_extract_dict       )

           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:  List[str]    =		os.path.join(_a	,  """feat_extract.json"""       )
                 feat_extract_first.to_json_file(_a       )
                 _A							:  Union[str, Any]    =		self.feature_extraction_class.from_json_file(_a       )

           _A							:  Optional[Any]    =		feat_extract_first.to_dict()
           _A							:  Union[str, Any]    =		feat_extract_second.to_dict()
           _A							:  List[Any]    =		dict_first.pop("""mel_filters"""       )
           _A							:  Optional[Any]    =		dict_second.pop("""mel_filters"""       )
           self.assertTrue(np.allclose(_a	,  _a       )       )
           self.assertEqual(_a	,  _a       )

     def       a__			(						self       )		->							Optional[Any]:
           # Initialize feature_extractor
           _A							:  Tuple    =		self.feature_extraction_class(**self.feat_extract_dict       )

           # create three inputs of length 800, 1000, and 1200
           _A							:  Optional[int]    =		[floats_list((1, x)       )[0] for x in range(800	,  1400	,  200       )]
           _A							:  List[str]    =		[np.asarray(_a       ) for speech_input in speech_inputs]

           # Test not batched input
           _A							:  Optional[int]    =		feature_extractor(np_speech_inputs[0]	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test batched
           _A							:  str    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test audio masking
           _A							:  Optional[Any]    =		feature_extractor(
               _a	,  return_tensors="""np"""	,  sampling_rate=4_4100	,  mask_audio=_a       ).audio_values

           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

           # Test 2-D numpy arrays are batched.
           _A							:  Dict    =		[floats_list((1, x)       )[0] for x in (800, 800, 800)]
           _A							:  Optional[int]    =		np.asarray(_a       )
           _A							:  Optional[Any]    =		feature_extractor(_a	,  return_tensors="""np"""	,  sampling_rate=4_4100       ).audio_values
           self.assertTrue(encoded_audios.ndim == 4       )
           self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size       )
           self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length       )
           self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels       )

     def       a__			(						self	,  _a       )		->							str:
           _A							:  Optional[Any]    =		load_dataset("""hf-internal-testing/librispeech_asr_dummy"""	,  """clean"""	,  split="""validation"""       )
           # automatic decoding with librispeech
           _A							:  Dict    =		ds.sort("""id"""       ).select(range(_a       )       )[:num_samples]["""audio"""]

           return [x["array"] for x in speech_samples]



     def       a__			(						self       )		->							Optional[Any]:
           _A							:  List[str]    =		self._load_datasamples(1       )
           _A							:  List[str]    =		TvltFeatureExtractor()
           _A							:  List[Any]    =		feature_extractor(_a	,  return_tensors="""pt"""       ).audio_values

           self.assertEquals(audio_values.shape	,  (1, 1, 192, 128)       )

           _A							:  int    =		torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]]       )
           self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2]	,  _a	,  atol=1e-4       )       )







import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
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.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
  from transformers.models.rag.configuration_rag import RagConfig
  from transformers.models.rag.tokenization_rag import RagTokenizer







@require_faiss
@require_torch
class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							int:
           _A							:  int    =		tempfile.mkdtemp()
           _A							:  Union[str, Any]    =		8

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

           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )
           os.makedirs(_a	,  exist_ok=_a       )
           _A							:  str    =		os.path.join(_a	,  BART_VOCAB_FILES_NAMES["""vocab_file"""]       )
           _A							:  List[Any]    =		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       a__			(						self       )		->							DPRQuestionEncoderTokenizer:
           return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """dpr_tokenizer"""       )       )

     def       a__			(						self       )		->							BartTokenizer:
           return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,  """bart_tokenizer"""       )       )

     def       a__			(						self       )		->							Optional[Any]:
           shutil.rmtree(self.tmpdirname       )

     @require_tokenizers
     def       a__			(						self       )		->							str:
           _A							:  Optional[Any]    =		os.path.join(self.tmpdirname	,  """rag_tokenizer"""       )
           _A							:  int    =		RagConfig(question_encoder=DPRConfig().to_dict()	,  generator=BartConfig().to_dict()       )
           _A							:  Any    =		RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,  generator=self.get_bart_tokenizer()       )
           rag_config.save_pretrained(_a       )
           rag_tokenizer.save_pretrained(_a       )
           _A							:  Optional[Any]    =		RagTokenizer.from_pretrained(_a	,  config=_a       )
           self.assertIsInstance(new_rag_tokenizer.question_encoder	,  _a       )
           self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,  rag_tokenizer.question_encoder.get_vocab()       )
           self.assertIsInstance(new_rag_tokenizer.generator	,  _a       )
           self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,  rag_tokenizer.generator.get_vocab()       )

     @slow
     def       a__			(						self       )		->							str:
           _A							:  Union[str, Any]    =		RagTokenizer.from_pretrained("""facebook/rag-token-nq"""       )
           _A							:  Tuple    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Tuple    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )



     @slow
     def       a__			(						self       )		->							Dict:
           _A							:  Dict    =		RagTokenizer.from_pretrained("""facebook/rag-sequence-nq"""       )
           _A							:  str    =		[
               """who got the first nobel prize in physics""",
               """when is the next deadpool movie being released""",
               """which mode is used for short wave broadcast service""",
               """who is the owner of reading football club""",
               """when is the next scandal episode coming out""",
               """when is the last time the philadelphia won the superbowl""",
               """what is the most current adobe flash player version""",
               """how many episodes are there in dragon ball z""",
               """what is the first step in the evolution of the eye""",
               """where is gall bladder situated in human body""",
               """what is the main mineral in lithium batteries""",
               """who is the president of usa right now""",
               """where do the greasers live in the outsiders""",
               """panda is a national animal of which country""",
               """what is the name of manchester united stadium""",
           ]
           _A							:  Optional[Any]    =		tokenizer(_a       )
           self.assertIsNotNone(_a       )

