

'''simple docstring'''





import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path

import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized

from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available

from ..wavaveca.test_feature_extraction_wavaveca import floats_list


if is_pyctcdecode_available():
  from huggingface_hub import snapshot_download
  from pyctcdecode import BeamSearchDecoderCTC

  from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
  from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput

if is_torch_available():
  from transformers import WavaVecaForCTC





@require_pyctcdecode
class A__    ( unittest.TestCase ):



 """simple docstring"""







 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Dict        =    "| <pad> <unk> <s> </s> a b c d e f g h i j k".split()
      _lowerCAmelCase   :					int        =    dict(zip(A_     ,						range(len(A_       )       )       )       )

      _lowerCAmelCase   :					int        =    {
          "unk_token": "<unk>",
          "bos_token": "<s>",
          "eos_token": "</s>",
      }
      _lowerCAmelCase   :					Any        =    {
          "feature_size": 1,
          "padding_value": 0.0,
          "sampling_rate": 1_6_0_0_0,
          "return_attention_mask": False,
          "do_normalize": True,
      }

      _lowerCAmelCase   :					int        =    tempfile.mkdtemp()
      _lowerCAmelCase   :					int        =    os.path.join(self.tmpdirname     ,						VOCAB_FILES_NAMES["vocab_file"]       )
      _lowerCAmelCase   :					Tuple        =    os.path.join(self.tmpdirname     ,						A_       )
      with open(self.vocab_file     ,						"w"     ,						encoding="utf-8"       ) as fp:
           fp.write(json.dumps(A_       ) + "\n"       )

      with open(self.feature_extraction_file     ,						"w"     ,						encoding="utf-8"       ) as fp:
           fp.write(json.dumps(A_       ) + "\n"       )

      # load decoder from hub
      _lowerCAmelCase   :					Union[str, Any]        =    "hf-internal-testing/ngram-beam-search-decoder"

 def    __magic_name__       (		self :	Tuple     ,						**A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.add_kwargs_tokens_map.copy()
      kwargs.update(A_       )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname     ,						**A_       )

 def    __magic_name__       (		self :	Tuple     ,						**A_ :	Tuple       ):






      '''simple docstring'''




      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname     ,						**A_       )

 def    __magic_name__       (		self :	str     ,						**A_ :	Tuple       ):






      '''simple docstring'''




      return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name     ,						**A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      shutil.rmtree(self.tmpdirname       )

 def    __magic_name__       (		self :	Dict       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.get_tokenizer()
      _lowerCAmelCase   :					str        =    self.get_feature_extractor()
      _lowerCAmelCase   :					Optional[Any]        =    self.get_decoder()

      _lowerCAmelCase   :					List[str]        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      processor.save_pretrained(self.tmpdirname       )
      _lowerCAmelCase   :					int        =    WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname       )

      # tokenizer
      self.assertEqual(processor.tokenizer.get_vocab()     ,						tokenizer.get_vocab()       )
      self.assertIsInstance(processor.tokenizer     ,						A_       )

      # feature extractor
      self.assertEqual(processor.feature_extractor.to_json_string()     ,						feature_extractor.to_json_string()       )
      self.assertIsInstance(processor.feature_extractor     ,						A_       )

      # decoder
      self.assertEqual(processor.decoder._alphabet.labels     ,						decoder._alphabet.labels       )
      self.assertEqual(
          processor.decoder.model_container[decoder._model_key]._unigram_set     ,						decoder.model_container[decoder._model_key]._unigram_set     ,						)
      self.assertIsInstance(processor.decoder     ,						A_       )

 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    WavaVecaProcessorWithLM(
          tokenizer=self.get_tokenizer()     ,						feature_extractor=self.get_feature_extractor()     ,						decoder=self.get_decoder()       )
      processor.save_pretrained(self.tmpdirname       )

      # make sure that error is thrown when decoder alphabet doesn't match
      _lowerCAmelCase   :					Union[str, Any]        =    WavaVecaProcessorWithLM.from_pretrained(
          self.tmpdirname     ,						alpha=5.0     ,						beta=3.0     ,						score_boundary=-7.0     ,						unk_score_offset=3       )

      # decoder
      self.assertEqual(processor.language_model.alpha     ,						5.0       )
      self.assertEqual(processor.language_model.beta     ,						3.0       )
      self.assertEqual(processor.language_model.score_boundary     ,						-7.0       )
      self.assertEqual(processor.language_model.unk_score_offset     ,						3       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.get_tokenizer()
      # add token to trigger raise
      tokenizer.add_tokens(["xx"]       )
      with self.assertRaisesRegex(A_     ,						"include"       ):
           WavaVecaProcessorWithLM(
               tokenizer=A_     ,						feature_extractor=self.get_feature_extractor()     ,						decoder=self.get_decoder()       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.get_feature_extractor()
      _lowerCAmelCase   :					Dict        =    self.get_tokenizer()
      _lowerCAmelCase   :					Tuple        =    self.get_decoder()

      _lowerCAmelCase   :					Optional[Any]        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      _lowerCAmelCase   :					int        =    floats_list((3, 1_0_0_0)       )

      _lowerCAmelCase   :					Tuple        =    feature_extractor(A_     ,						return_tensors="np"       )
      _lowerCAmelCase   :					Any        =    processor(A_     ,						return_tensors="np"       )

      for key in input_feat_extract.keys():
           self.assertAlmostEqual(input_feat_extract[key].sum()     ,						input_processor[key].sum()     ,						delta=1E-2       )

 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Tuple        =    self.get_feature_extractor()
      _lowerCAmelCase   :					Optional[int]        =    self.get_tokenizer()
      _lowerCAmelCase   :					List[str]        =    self.get_decoder()

      _lowerCAmelCase   :					List[str]        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      _lowerCAmelCase   :					int        =    "This is a test string"

      _lowerCAmelCase   :					List[str]        =    processor(text=A_       )

      _lowerCAmelCase   :					int        =    tokenizer(A_       )

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

 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Tuple=(2, 1_0, 1_6)     ,						A_ :	str=7_7       ):






      '''simple docstring'''




      np.random.seed(A_       )
      return np.random.rand(*A_       )

 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Union[str, Any]        =    self.get_feature_extractor()
      _lowerCAmelCase   :					Dict        =    self.get_tokenizer()
      _lowerCAmelCase   :					List[str]        =    self.get_decoder()

      _lowerCAmelCase   :					List[str]        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      _lowerCAmelCase   :					Tuple        =    self._get_dummy_logits(shape=(1_0, 1_6)     ,						seed=1_3       )

      _lowerCAmelCase   :					List[Any]        =    processor.decode(A_       )

      _lowerCAmelCase   :					Any        =    decoder.decode_beams(A_       )[0]

      self.assertEqual(decoded_decoder[0]     ,						decoded_processor.text       )
      self.assertEqual("</s> <s> </s>"     ,						decoded_processor.text       )
      self.assertEqual(decoded_decoder[-2]     ,						decoded_processor.logit_score       )
      self.assertEqual(decoded_decoder[-1]     ,						decoded_processor.lm_score       )

 @parameterized.expand([[None], ["fork"], ["spawn"]]       )
 def    __magic_name__       (		self :	Optional[int]     ,						A_ :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					str        =    self.get_feature_extractor()
      _lowerCAmelCase   :					Optional[int]        =    self.get_tokenizer()
      _lowerCAmelCase   :					Any        =    self.get_decoder()

      _lowerCAmelCase   :					Tuple        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      _lowerCAmelCase   :					int        =    self._get_dummy_logits()

      # note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
      #       otherwise, the LM won't be available to the pool's sub-processes.
      # manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
      if pool_context is None:
           _lowerCAmelCase   :					int        =    processor.batch_decode(A_       )
      else:
           with get_context(A_       ).Pool() as pool:
                _lowerCAmelCase   :					List[Any]        =    processor.batch_decode(A_     ,						A_       )

      _lowerCAmelCase   :					List[Any]        =    list(A_       )

      with get_context("fork"       ).Pool() as p:
           _lowerCAmelCase   :					Any        =    decoder.decode_beams_batch(A_     ,						A_       )

      _lowerCAmelCase   :					Optional[Any]        =    [], [], []
      for beams in decoded_beams:
           texts_decoder.append(beams[0][0]       )
           logit_scores_decoder.append(beams[0][-2]       )
           lm_scores_decoder.append(beams[0][-1]       )

      self.assertListEqual(A_     ,						decoded_processor.text       )
      self.assertListEqual(["<s> <s> </s>", "<s> <s> <s>"]     ,						decoded_processor.text       )
      self.assertListEqual(A_     ,						decoded_processor.logit_score       )
      self.assertListEqual(A_     ,						decoded_processor.lm_score       )

 def    __magic_name__       (		self :	Union[str, Any]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    self.get_feature_extractor()
      _lowerCAmelCase   :					str        =    self.get_tokenizer()
      _lowerCAmelCase   :					Tuple        =    self.get_decoder()

      _lowerCAmelCase   :					int        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      _lowerCAmelCase   :					List[Any]        =    self._get_dummy_logits()

      _lowerCAmelCase   :					Optional[Any]        =    1_5
      _lowerCAmelCase   :					str        =    -20.0
      _lowerCAmelCase   :					Any        =    -4.0

      _lowerCAmelCase   :					int        =    processor.batch_decode(
          A_     ,						beam_width=A_     ,						beam_prune_logp=A_     ,						token_min_logp=A_     ,						)
      _lowerCAmelCase   :					Tuple        =    decoded_processor_out.text

      _lowerCAmelCase   :					Optional[Any]        =    list(A_       )

      with get_context("fork"       ).Pool() as pool:
           _lowerCAmelCase   :					Any        =    decoder.decode_beams_batch(
               A_     ,						A_     ,						beam_width=A_     ,						beam_prune_logp=A_     ,						token_min_logp=A_     ,						)

      _lowerCAmelCase   :					List[Any]        =    [d[0][0] for d in decoded_decoder_out]
      _lowerCAmelCase   :					Tuple        =    [d[0][2] for d in decoded_decoder_out]
      _lowerCAmelCase   :					str        =    [d[0][3] for d in decoded_decoder_out]

      self.assertListEqual(A_     ,						A_       )
      self.assertListEqual(["</s> <s> <s>", "<s> <s> <s>"]     ,						A_       )

      self.assertTrue(np.array_equal(A_     ,						decoded_processor_out.logit_score       )       )
      self.assertTrue(np.allclose([-20.054, -18.447]     ,						A_     ,						atol=1E-3       )       )

      self.assertTrue(np.array_equal(A_     ,						decoded_processor_out.lm_score       )       )
      self.assertTrue(np.allclose([-15.554, -13.9474]     ,						A_     ,						atol=1E-3       )       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    self.get_feature_extractor()
      _lowerCAmelCase   :					str        =    self.get_tokenizer()
      _lowerCAmelCase   :					int        =    self.get_decoder()

      _lowerCAmelCase   :					Union[str, Any]        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      _lowerCAmelCase   :					Any        =    self._get_dummy_logits()

      _lowerCAmelCase   :					int        =    2.0
      _lowerCAmelCase   :					int        =    5.0
      _lowerCAmelCase   :					Tuple        =    -20.0
      _lowerCAmelCase   :					List[str]        =    True

      _lowerCAmelCase   :					List[str]        =    processor.batch_decode(
          A_     ,						alpha=A_     ,						beta=A_     ,						unk_score_offset=A_     ,						lm_score_boundary=A_     ,						)
      _lowerCAmelCase   :					List[Any]        =    decoded_processor_out.text

      _lowerCAmelCase   :					Optional[int]        =    list(A_       )
      decoder.reset_params(
          alpha=A_     ,						beta=A_     ,						unk_score_offset=A_     ,						lm_score_boundary=A_     ,						)

      with get_context("fork"       ).Pool() as pool:
           _lowerCAmelCase   :					List[Any]        =    decoder.decode_beams_batch(
               A_     ,						A_     ,						)

      _lowerCAmelCase   :					str        =    [d[0][0] for d in decoded_decoder_out]

      self.assertListEqual(A_     ,						A_       )
      self.assertListEqual(["<s> </s> <s> </s> </s>", "</s> </s> <s> </s> </s>"]     ,						A_       )
      _lowerCAmelCase   :					Any        =    processor.decoder.model_container[processor.decoder._model_key]
      self.assertEqual(lm_model.alpha     ,						2.0       )
      self.assertEqual(lm_model.beta     ,						5.0       )
      self.assertEqual(lm_model.unk_score_offset     ,						-20.0       )
      self.assertEqual(lm_model.score_boundary     ,						A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"       )

      _lowerCAmelCase   :					Dict        =    processor.decoder.model_container[processor.decoder._model_key]
      _lowerCAmelCase   :					str        =    Path(language_model._kenlm_model.path.decode("utf-8"       )       ).parent.parent.absolute()

      _lowerCAmelCase   :					Any        =    os.listdir(A_       )
      _lowerCAmelCase   :					List[str]        =    ["alphabet.json", "language_model"]

      downloaded_decoder_files.sort()
      expected_decoder_files.sort()

      # test that only decoder relevant files from
      # https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
      # are downloaded and none of the rest (e.g. README.md, ...)
      self.assertListEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[str]        =    snapshot_download("hf-internal-testing/processor_with_lm"       )

      _lowerCAmelCase   :					str        =    WavaVecaProcessorWithLM.from_pretrained(A_       )

      _lowerCAmelCase   :					List[Any]        =    processor.decoder.model_container[processor.decoder._model_key]
      _lowerCAmelCase   :					int        =    Path(language_model._kenlm_model.path.decode("utf-8"       )       ).parent.parent.absolute()

      _lowerCAmelCase   :					Optional[Any]        =    os.listdir(A_       )
      _lowerCAmelCase   :					Optional[Any]        =    os.listdir(A_       )

      local_decoder_files.sort()
      expected_decoder_files.sort()

      # test that both decoder form hub and local files in cache are the same
      self.assertListEqual(A_     ,						A_       )

 def    __magic_name__       (		self :	Any       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[int]        =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"       )
      _lowerCAmelCase   :					Optional[int]        =    AutoProcessor.from_pretrained("hf-internal-testing/processor_with_lm"       )

      _lowerCAmelCase   :					Any        =    floats_list((3, 1_0_0_0)       )

      _lowerCAmelCase   :					Optional[int]        =    processor_wavaveca(A_     ,						return_tensors="np"       )
      _lowerCAmelCase   :					Optional[Any]        =    processor_auto(A_     ,						return_tensors="np"       )

      for key in input_wavaveca.keys():
           self.assertAlmostEqual(input_wavaveca[key].sum()     ,						input_auto[key].sum()     ,						delta=1E-2       )

      _lowerCAmelCase   :					Any        =    self._get_dummy_logits()

      _lowerCAmelCase   :					Any        =    processor_wavaveca.batch_decode(A_       )
      _lowerCAmelCase   :					Union[str, Any]        =    processor_auto.batch_decode(A_       )

      self.assertListEqual(decoded_wavaveca.text     ,						decoded_auto.text       )

 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      _lowerCAmelCase   :					List[Any]        =    self.get_feature_extractor()
      _lowerCAmelCase   :					Tuple        =    self.get_tokenizer()
      _lowerCAmelCase   :					str        =    self.get_decoder()

      _lowerCAmelCase   :					Any        =    WavaVecaProcessorWithLM(tokenizer=A_     ,						feature_extractor=A_     ,						decoder=A_       )

      self.assertListEqual(
          processor.model_input_names     ,						feature_extractor.model_input_names     ,						msg="`processor` and `feature_extractor` model input names do not match"     ,						)

 @staticmethod
 def    __magic_name__       (		A_ :	Union[str, Any]     ,						A_ :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    [d[key] for d in offsets]
      return retrieved_list

 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Any        =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"       )
      _lowerCAmelCase   :					Dict        =    self._get_dummy_logits()[0]

      _lowerCAmelCase   :					Union[str, Any]        =    processor.decode(A_     ,						output_word_offsets=A_       )
      # check Wav2Vec2CTCTokenizerOutput keys for word
      self.assertEqual(len(outputs.keys()       )     ,						4       )
      self.assertTrue("text" in outputs       )
      self.assertTrue("word_offsets" in outputs       )
      self.assertTrue(isinstance(A_     ,						A_       )       )

      self.assertEqual(" ".join(self.get_from_offsets(outputs["word_offsets"]     ,						"word"       )       )     ,						outputs.text       )
      self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]     ,						"word"       )     ,						["<s>", "<s>", "</s>"]       )
      self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]     ,						"start_offset"       )     ,						[0, 2, 4]       )
      self.assertListEqual(self.get_from_offsets(outputs["word_offsets"]     ,						"end_offset"       )     ,						[1, 3, 5]       )

 def    __magic_name__       (		self :	str       ):






      '''simple docstring'''




      _lowerCAmelCase   :					Optional[Any]        =    WavaVecaProcessorWithLM.from_pretrained("hf-internal-testing/processor_with_lm"       )
      _lowerCAmelCase   :					Union[str, Any]        =    self._get_dummy_logits()

      _lowerCAmelCase   :					Union[str, Any]        =    processor.batch_decode(A_     ,						output_word_offsets=A_       )

      # check Wav2Vec2CTCTokenizerOutput keys for word
      self.assertEqual(len(outputs.keys()       )     ,						4       )
      self.assertTrue("text" in outputs       )
      self.assertTrue("word_offsets" in outputs       )
      self.assertTrue(isinstance(A_     ,						A_       )       )

      self.assertListEqual(
          [" ".join(self.get_from_offsets(A_     ,						"word"       )       ) for o in outputs["word_offsets"]]     ,						outputs.text       )
      self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]     ,						"word"       )     ,						["<s>", "<s>", "</s>"]       )
      self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]     ,						"start_offset"       )     ,						[0, 2, 4]       )
      self.assertListEqual(self.get_from_offsets(outputs["word_offsets"][0]     ,						"end_offset"       )     ,						[1, 3, 5]       )


 @slow
 @require_torch
 @require_torchaudio
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      import torch

      _lowerCAmelCase   :					Any        =    load_dataset("common_voice"     ,						"en"     ,						split="train"     ,						streaming=A_       )
      _lowerCAmelCase   :					Optional[int]        =    ds.cast_column("audio"     ,						datasets.Audio(sampling_rate=1_6_0_0_0       )       )
      _lowerCAmelCase   :					Union[str, Any]        =    iter(A_       )
      _lowerCAmelCase   :					Optional[int]        =    next(A_       )

      _lowerCAmelCase   :					str        =    AutoProcessor.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"       )
      _lowerCAmelCase   :					List[str]        =    WavaVecaForCTC.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm"       )

      # compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
      _lowerCAmelCase   :					List[Any]        =    processor(sample["audio"]["array"]     ,						return_tensors="pt"       ).input_values

      with torch.no_grad():
           _lowerCAmelCase   :					List[str]        =    model(A_       ).logits.cpu().numpy()

      _lowerCAmelCase   :					List[Any]        =    processor.decode(logits[0]     ,						output_word_offsets=A_       )

      _lowerCAmelCase   :					Union[str, Any]        =    model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
      _lowerCAmelCase   :					Optional[Any]        =    [
          {
              "start_time": d["start_offset"] * time_offset,
              "end_time": d["end_offset"] * time_offset,
              "word": d["word"],
          }
          for d in output["word_offsets"]
      ]

      _lowerCAmelCase   :					Optional[int]        =    "WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"

      # output words
      self.assertEqual(" ".join(self.get_from_offsets(A_     ,						"word"       )       )     ,						A_       )
      self.assertEqual(" ".join(self.get_from_offsets(A_     ,						"word"       )       )     ,						output.text       )

      # output times
      _lowerCAmelCase   :					Dict        =    torch.tensor(self.get_from_offsets(A_     ,						"start_time"       )       )
      _lowerCAmelCase   :					Tuple        =    torch.tensor(self.get_from_offsets(A_     ,						"end_time"       )       )

      # fmt: off
      _lowerCAmelCase   :					List[str]        =    torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599]       )
      _lowerCAmelCase   :					Optional[Any]        =    torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94]       )
      # fmt: on

      self.assertTrue(torch.allclose(A_     ,						A_     ,						atol=0.01       )       )
      self.assertTrue(torch.allclose(A_     ,						A_     ,						atol=0.01       )       )









from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_tf_available():
  import tensorflow as tf


__UpperCAmelCase    				=     logging.get_logger(__name__)





@dataclass
class A__    ( A ):



 """simple docstring"""







 _lowercase     :					List[Any]      =						[
     '''no_inference''',
     '''no_cuda''',
     '''no_tpu''',
     '''no_speed''',
     '''no_memory''',
     '''no_env_print''',
     '''no_multi_process''',
 ]

 def __init__(		self :	List[Any]     ,						**A_ :	str       ):






      '''simple docstring'''




      for deprecated_arg in self.deprecated_args:
           if deprecated_arg in kwargs:
                _lowerCAmelCase   :					str        =    deprecated_arg[3:]
                _lowerCAmelCase   :					List[Any]        =    not kwargs.pop(A_       )
                logger.warning(
                    F'''{deprecated_arg} is depreciated. Please use --no-{positive_arg} or'''
                    F''' {positive_arg}={kwargs[positive_arg]}'''       )
      _lowerCAmelCase   :					Any        =    kwargs.pop("tpu_name"     ,						self.tpu_name       )
      _lowerCAmelCase   :					str        =    kwargs.pop("device_idx"     ,						self.device_idx       )
      _lowerCAmelCase   :					str        =    kwargs.pop("eager_mode"     ,						self.eager_mode       )
      _lowerCAmelCase   :					Tuple        =    kwargs.pop("use_xla"     ,						self.use_xla       )
      super().__init__(**A_       )

 _lowercase     :					str      =						field(
     default=A  ,		metadata={'''help''': '''Name of TPU'''}  ,		)
 _lowercase     :					int      =						field(
     default=0  ,		metadata={'''help''': '''CPU / GPU device index. Defaults to 0.'''}  ,		)
 _lowercase     :					bool      =						field(default=A  ,		metadata={'''help''': '''Benchmark models in eager model.'''} )
 _lowercase     :					bool      =						field(
     default=A  ,		metadata={
         '''help''': '''Benchmark models using XLA JIT compilation. Note that `eager_model` has to be set to `False`.'''
     }  ,		)

 @cached_property
 def    __magic_name__       (		self :	Tuple       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      _lowerCAmelCase   :					Any        =    None
      if self.tpu:
           try:
                if self.tpu_name:
                     _lowerCAmelCase   :					Union[str, Any]        =    tf.distribute.cluster_resolver.TPUClusterResolver(self.tpu_name       )
                else:
                     _lowerCAmelCase   :					List[str]        =    tf.distribute.cluster_resolver.TPUClusterResolver()
           except ValueError:
                _lowerCAmelCase   :					Optional[int]        =    None
      return tpu

 @cached_property
 def    __magic_name__       (		self :	Optional[int]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.is_tpu:
           tf.config.experimental_connect_to_cluster(self._setup_tpu       )
           tf.tpu.experimental.initialize_tpu_system(self._setup_tpu       )

           _lowerCAmelCase   :					List[Any]        =    tf.distribute.TPUStrategy(self._setup_tpu       )
      else:
           # currently no multi gpu is allowed
           if self.is_gpu:
                # TODO: Currently only single GPU is supported
                tf.config.set_visible_devices(self.gpu_list[self.device_idx]     ,						"GPU"       )
                _lowerCAmelCase   :					Dict        =    tf.distribute.OneDeviceStrategy(device=F'''/gpu:{self.device_idx}'''       )
           else:
                tf.config.set_visible_devices([]     ,						"GPU"       )  # disable GPU
                _lowerCAmelCase   :					Optional[Any]        =    tf.distribute.OneDeviceStrategy(device=F'''/cpu:{self.device_idx}'''       )

      return strategy

 @property
 def    __magic_name__       (		self :	int       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_tpu is not None

 @property
 def    __magic_name__       (		self :	List[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return self._setup_strategy

 @property
 def    __magic_name__       (		self :	List[str]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      return tf.config.list_physical_devices("GPU"       )

 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      requires_backends(self     ,						["tf"]       )
      if self.cuda:
           return len(self.gpu_list       )
      return 0


 @property
 def    __magic_name__       (		self :	Optional[Any]       ):






      '''simple docstring'''




      return self.n_gpu > 0






