

"""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    lowerCamelCase						(unittest.TestCase			):

   def 					SCREAMING_SNAKE_CASE				(					self				:						Tuple       )   ->    Optional[int]:
      SCREAMING_SNAKE_CASE__								=      """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
      SCREAMING_SNAKE_CASE__								=      dict(zip(__UpperCAmelCase    ,				range(len(__UpperCAmelCase       )       )       )       )

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

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

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

      # load decoder from hub
      SCREAMING_SNAKE_CASE__								=      """hf-internal-testing/ngram-beam-search-decoder"""

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				**__UpperCAmelCase				:						Optional[int]       )   ->    Tuple:
      SCREAMING_SNAKE_CASE__								=      self.add_kwargs_tokens_map.copy()
      kwargs.update(__UpperCAmelCase       )
      return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname    ,				**__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						Any    ,				**__UpperCAmelCase				:						List[str]       )   ->    List[str]:
      return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname    ,				**__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				**__UpperCAmelCase				:						List[Any]       )   ->    List[str]:
      return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name    ,				**__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Optional[int]:
      shutil.rmtree(self.tmpdirname       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

      processor.save_pretrained(self.tmpdirname       )
      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname       )

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

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

      # 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    ,				__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Optional[Any]:
      SCREAMING_SNAKE_CASE__								=      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
      SCREAMING_SNAKE_CASE__								=      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 					SCREAMING_SNAKE_CASE				(					self				:						int       )   ->    Any:
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      # add token to trigger raise
      tokenizer.add_tokens(["""xx"""]       )
      with self.assertRaisesRegex(__UpperCAmelCase    ,				"""include"""       ):
         WavaVecaProcessorWithLM(
             tokenizer=__UpperCAmelCase    ,				feature_extractor=self.get_feature_extractor()    ,				decoder=self.get_decoder()       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    str:
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      floats_list((3, 1_0_0_0)       )

      SCREAMING_SNAKE_CASE__								=      feature_extractor(__UpperCAmelCase    ,				return_tensors="""np"""       )
      SCREAMING_SNAKE_CASE__								=      processor(__UpperCAmelCase    ,				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 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[Any]:
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      """This is a test string"""

      SCREAMING_SNAKE_CASE__								=      processor(text=__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      tokenizer(__UpperCAmelCase       )

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

   def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[Any]=(2, 1_0, 1_6)    ,				__UpperCAmelCase				:						int=7_7       )   ->    int:
      np.random.seed(__UpperCAmelCase       )
      return np.random.rand(*__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]       )   ->    Dict:
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      self._get_dummy_logits(shape=(1_0, 1_6)    ,				seed=1_3       )

      SCREAMING_SNAKE_CASE__								=      processor.decode(__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      decoder.decode_beams(__UpperCAmelCase       )[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 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						Union[str, Any]       )   ->    Optional[int]:
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      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:
         SCREAMING_SNAKE_CASE__								=      processor.batch_decode(__UpperCAmelCase       )
      else:
         with get_context(__UpperCAmelCase       ).Pool() as pool:
            SCREAMING_SNAKE_CASE__								=      processor.batch_decode(__UpperCAmelCase    ,				__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )

      with get_context("""fork"""       ).Pool() as p:
         SCREAMING_SNAKE_CASE__								=      decoder.decode_beams_batch(__UpperCAmelCase    ,				__UpperCAmelCase       )

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

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    int:
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      self._get_dummy_logits()

      SCREAMING_SNAKE_CASE__								=      1_5
      SCREAMING_SNAKE_CASE__								=      -20.0
      SCREAMING_SNAKE_CASE__								=      -4.0

      SCREAMING_SNAKE_CASE__								=      processor.batch_decode(
          __UpperCAmelCase    ,				beam_width=__UpperCAmelCase    ,				beam_prune_logp=__UpperCAmelCase    ,				token_min_logp=__UpperCAmelCase    ,				)
      SCREAMING_SNAKE_CASE__								=      decoded_processor_out.text

      SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )

      with get_context("""fork"""       ).Pool() as pool:
         SCREAMING_SNAKE_CASE__								=      decoder.decode_beams_batch(
             __UpperCAmelCase    ,				__UpperCAmelCase    ,				beam_width=__UpperCAmelCase    ,				beam_prune_logp=__UpperCAmelCase    ,				token_min_logp=__UpperCAmelCase    ,				)

      SCREAMING_SNAKE_CASE__								=      [d[0][0] for d in decoded_decoder_out]
      SCREAMING_SNAKE_CASE__								=      [d[0][2] for d in decoded_decoder_out]
      SCREAMING_SNAKE_CASE__								=      [d[0][3] for d in decoded_decoder_out]

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

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

      self.assertTrue(np.array_equal(__UpperCAmelCase    ,				decoded_processor_out.lm_score       )       )
      self.assertTrue(np.allclose([-15.554, -13.9_474]    ,				__UpperCAmelCase    ,				atol=1e-3       )       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    str:
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      self._get_dummy_logits()

      SCREAMING_SNAKE_CASE__								=      2.0
      SCREAMING_SNAKE_CASE__								=      5.0
      SCREAMING_SNAKE_CASE__								=      -20.0
      SCREAMING_SNAKE_CASE__								=      True

      SCREAMING_SNAKE_CASE__								=      processor.batch_decode(
          __UpperCAmelCase    ,				alpha=__UpperCAmelCase    ,				beta=__UpperCAmelCase    ,				unk_score_offset=__UpperCAmelCase    ,				lm_score_boundary=__UpperCAmelCase    ,				)
      SCREAMING_SNAKE_CASE__								=      decoded_processor_out.text

      SCREAMING_SNAKE_CASE__								=      list(__UpperCAmelCase       )
      decoder.reset_params(
          alpha=__UpperCAmelCase    ,				beta=__UpperCAmelCase    ,				unk_score_offset=__UpperCAmelCase    ,				lm_score_boundary=__UpperCAmelCase    ,				)

      with get_context("""fork"""       ).Pool() as pool:
         SCREAMING_SNAKE_CASE__								=      decoder.decode_beams_batch(
             __UpperCAmelCase    ,				__UpperCAmelCase    ,				)

      SCREAMING_SNAKE_CASE__								=      [d[0][0] for d in decoded_decoder_out]

      self.assertListEqual(__UpperCAmelCase    ,				__UpperCAmelCase       )
      self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""]    ,				__UpperCAmelCase       )
      SCREAMING_SNAKE_CASE__								=      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    ,				__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    int:
      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )

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

      SCREAMING_SNAKE_CASE__								=      os.listdir(__UpperCAmelCase       )
      SCREAMING_SNAKE_CASE__								=      ["""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(__UpperCAmelCase    ,				__UpperCAmelCase       )

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    Optional[Any]:
      SCREAMING_SNAKE_CASE__								=      snapshot_download("""hf-internal-testing/processor_with_lm"""       )

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM.from_pretrained(__UpperCAmelCase       )

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

      SCREAMING_SNAKE_CASE__								=      os.listdir(__UpperCAmelCase       )
      SCREAMING_SNAKE_CASE__								=      os.listdir(__UpperCAmelCase       )

      local_decoder_files.sort()
      expected_decoder_files.sort()

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

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[int]:
      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )
      SCREAMING_SNAKE_CASE__								=      AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""       )

      SCREAMING_SNAKE_CASE__								=      floats_list((3, 1_0_0_0)       )

      SCREAMING_SNAKE_CASE__								=      processor_wavaveca(__UpperCAmelCase    ,				return_tensors="""np"""       )
      SCREAMING_SNAKE_CASE__								=      processor_auto(__UpperCAmelCase    ,				return_tensors="""np"""       )

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

      SCREAMING_SNAKE_CASE__								=      self._get_dummy_logits()

      SCREAMING_SNAKE_CASE__								=      processor_wavaveca.batch_decode(__UpperCAmelCase       )
      SCREAMING_SNAKE_CASE__								=      processor_auto.batch_decode(__UpperCAmelCase       )

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

   def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    Dict:
      SCREAMING_SNAKE_CASE__								=      self.get_feature_extractor()
      SCREAMING_SNAKE_CASE__								=      self.get_tokenizer()
      SCREAMING_SNAKE_CASE__								=      self.get_decoder()

      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM(tokenizer=__UpperCAmelCase    ,				feature_extractor=__UpperCAmelCase    ,				decoder=__UpperCAmelCase       )

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

   @staticmethod
   def 					SCREAMING_SNAKE_CASE				(					__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Dict       )   ->    Optional[int]:
      SCREAMING_SNAKE_CASE__								=      [d[key] for d in offsets]
      return retrieved_list

   def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]       )   ->    int:
      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )
      SCREAMING_SNAKE_CASE__								=      self._get_dummy_logits()[0]

      SCREAMING_SNAKE_CASE__								=      processor.decode(__UpperCAmelCase    ,				output_word_offsets=__UpperCAmelCase       )
      # 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(__UpperCAmelCase    ,				__UpperCAmelCase       )       )

      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 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]       )   ->    List[Any]:
      SCREAMING_SNAKE_CASE__								=      WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""       )
      SCREAMING_SNAKE_CASE__								=      self._get_dummy_logits()

      SCREAMING_SNAKE_CASE__								=      processor.batch_decode(__UpperCAmelCase    ,				output_word_offsets=__UpperCAmelCase       )

      # 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(__UpperCAmelCase    ,				__UpperCAmelCase       )       )

      self.assertListEqual(
          [""" """.join(self.get_from_offsets(__UpperCAmelCase    ,				"""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 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]       )   ->    Optional[Any]:
      import torch

      SCREAMING_SNAKE_CASE__								=      load_dataset("""common_voice"""    ,				"""en"""    ,				split="""train"""    ,				streaming=__UpperCAmelCase       )
      SCREAMING_SNAKE_CASE__								=      ds.cast_column("""audio"""    ,				datasets.Audio(sampling_rate=1_6_0_0_0       )       )
      SCREAMING_SNAKE_CASE__								=      iter(__UpperCAmelCase       )
      SCREAMING_SNAKE_CASE__								=      next(__UpperCAmelCase       )

      SCREAMING_SNAKE_CASE__								=      AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""       )
      SCREAMING_SNAKE_CASE__								=      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
      SCREAMING_SNAKE_CASE__								=      processor(sample["""audio"""]["""array"""]    ,				return_tensors="""pt"""       ).input_values

      with torch.no_grad():
         SCREAMING_SNAKE_CASE__								=      model(__UpperCAmelCase       ).logits.cpu().numpy()

      SCREAMING_SNAKE_CASE__								=      processor.decode(logits[0]    ,				output_word_offsets=__UpperCAmelCase       )

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

      SCREAMING_SNAKE_CASE__								=      """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(__UpperCAmelCase    ,				"""word"""       )       )    ,				__UpperCAmelCase       )
      self.assertEqual(""" """.join(self.get_from_offsets(__UpperCAmelCase    ,				"""word"""       )       )    ,				output.text       )

      # output times
      SCREAMING_SNAKE_CASE__								=      torch.tensor(self.get_from_offsets(__UpperCAmelCase    ,				"""start_time"""       )       )
      SCREAMING_SNAKE_CASE__								=      torch.tensor(self.get_from_offsets(__UpperCAmelCase    ,				"""end_time"""       )       )

      # fmt: off
      SCREAMING_SNAKE_CASE__								=      torch.tensor([1.4_199, 1.6_599, 2.2_599, 3.0, 3.24, 3.5_999, 3.7_999, 4.0_999, 4.26, 4.94, 5.28, 5.6_599, 5.78, 5.94, 6.32, 6.5_399, 6.6_599]       )
      SCREAMING_SNAKE_CASE__								=      torch.tensor([1.5_399, 1.8_999, 2.9, 3.16, 3.5_399, 3.72, 4.0_199, 4.1_799, 4.76, 5.1_599, 5.5_599, 5.6_999, 5.86, 6.1_999, 6.38, 6.6_199, 6.94]       )
      # fmt: on

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





"""simple docstring"""




import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


A_							:			Tuple											= logging.get_logger(__name__)  # pylint: disable=invalid-name

def 	A       (     snake_case__    ):




			'''simple docstring'''


			warnings.warn(
			    """The preprocess method is deprecated and will be removed in a future version. Please"""
			    """ use VaeImageProcessor.preprocess instead"""		,				snake_case__		,				)
			if isinstance(snake_case__		,				torch.Tensor    ):
						return image
			elif isinstance(snake_case__		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__								=      [image]

			if isinstance(image[0]		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      image[0].size
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

						SCREAMING_SNAKE_CASE__								=      [np.array(i.resize((w, h)		,				resample=PIL_INTERPOLATION["""lanczos"""]    )    )[None, :] for i in image]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      np.array(snake_case__    ).astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      image.transpose(0		,				3		,				1		,				2    )
						SCREAMING_SNAKE_CASE__								=      2.0 * image - 1.0
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(image[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return image

def 	A       (     snake_case__    ):




			'''simple docstring'''


			if isinstance(snake_case__		,				torch.Tensor    ):
						return mask
			elif isinstance(snake_case__		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__								=      [mask]

			if isinstance(mask[0]		,				PIL.Image.Image    ):
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      mask[0].size
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      (x - x % 32 for x in (w, h))  # resize to integer multiple of 32
						SCREAMING_SNAKE_CASE__								=      [np.array(m.convert("""L"""    ).resize((w, h)		,				resample=PIL_INTERPOLATION["""nearest"""]    )    )[None, :] for m in mask]
						SCREAMING_SNAKE_CASE__								=      np.concatenate(snake_case__		,				axis=0    )
						SCREAMING_SNAKE_CASE__								=      mask.astype(np.floataa    ) / 2_55.0
						SCREAMING_SNAKE_CASE__								=      0
						SCREAMING_SNAKE_CASE__								=      1
						SCREAMING_SNAKE_CASE__								=      torch.from_numpy(snake_case__    )
			elif isinstance(mask[0]		,				torch.Tensor    ):
						SCREAMING_SNAKE_CASE__								=      torch.cat(snake_case__		,				dim=0    )
			return mask





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		UNetaDModel
			lowerCamelCase__   :		RePaintScheduler

			def __init__(					self				:						int    ,				__UpperCAmelCase				:						List[str]    ,				__UpperCAmelCase				:						Dict       )   ->    Tuple:
						super().__init__()
						self.register_modules(unet=__UpperCAmelCase    ,				scheduler=__UpperCAmelCase       )





			@torch.no_grad()
			def __call__(					self				:						List[str]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						Union[torch.Tensor, PIL.Image.Image]    ,				__UpperCAmelCase				:						int = 2_5_0    ,				__UpperCAmelCase				:						float = 0.0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						int = 1_0    ,				__UpperCAmelCase				:						Optional[Union[torch.Generator, List[torch.Generator]]] = None    ,				__UpperCAmelCase				:						Optional[str] = "pil"    ,				__UpperCAmelCase				:						bool = True    ,				)   ->    Union[ImagePipelineOutput, Tuple]:
						SCREAMING_SNAKE_CASE__								=      image

						SCREAMING_SNAKE_CASE__								=      _preprocess_image(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      original_image.to(device=self.device    ,				dtype=self.unet.dtype       )
						SCREAMING_SNAKE_CASE__								=      _preprocess_mask(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      mask_image.to(device=self.device    ,				dtype=self.unet.dtype       )

						SCREAMING_SNAKE_CASE__								=      original_image.shape[0]

						# sample gaussian noise to begin the loop
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) and len(__UpperCAmelCase       ) != batch_size:
									raise ValueError(
									    F"""You have passed a list of generators of length {len(__UpperCAmelCase       )}, but requested an effective batch"""
									    F""" size of {batch_size}. Make sure the batch size matches the length of the generators."""       )

						SCREAMING_SNAKE_CASE__								=      original_image.shape
						SCREAMING_SNAKE_CASE__								=      randn_tensor(__UpperCAmelCase    ,				generator=__UpperCAmelCase    ,				device=self.device    ,				dtype=self.unet.dtype       )

						# set step values
						self.scheduler.set_timesteps(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				self.device       )
						SCREAMING_SNAKE_CASE__								=      eta

						SCREAMING_SNAKE_CASE__								=      self.scheduler.timesteps[0] + 1
						SCREAMING_SNAKE_CASE__								=      generator[0] if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ) else generator
						for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
									if t < t_last:
												# predict the noise residual
												SCREAMING_SNAKE_CASE__								=      self.unet(__UpperCAmelCase    ,				__UpperCAmelCase       ).sample
												# compute previous image: x_t -> x_t-1
												SCREAMING_SNAKE_CASE__								=      self.scheduler.step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       ).prev_sample

									else:
												# compute the reverse: x_t-1 -> x_t
												SCREAMING_SNAKE_CASE__								=      self.scheduler.undo_step(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase       )
									SCREAMING_SNAKE_CASE__								=      t

						SCREAMING_SNAKE_CASE__								=      (image / 2 + 0.5).clamp(0    ,				1       )
						SCREAMING_SNAKE_CASE__								=      image.cpu().permute(0    ,				2    ,				3    ,				1       ).numpy()
						if output_type == "pil":
									SCREAMING_SNAKE_CASE__								=      self.numpy_to_pil(__UpperCAmelCase       )

						if not return_dict:
									return (image,)

						return ImagePipelineOutput(images=__UpperCAmelCase       )


