





from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available

@dataclass
class  __A(						__UpperCAmelCase ):
     __A	  =       42
     __A	  =       42


if is_transformers_available() and is_torch_available():
       from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline












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 ):

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						'''| <pad> <unk> <s> </s> a b c d e f g h i j k'''.split()
            _UpperCamelCase              =						dict(zip(A,       range(len(A      )      )      )      )

            _UpperCamelCase              =						{
                '''unk_token''': '''<unk>''',
                '''bos_token''': '''<s>''',
                '''eos_token''': '''</s>''',
            }
            _UpperCamelCase              =						{
                '''feature_size''': 1,
                '''padding_value''': 0.0,
                '''sampling_rate''': 1_6000,
                '''return_attention_mask''': False,
                '''do_normalize''': True,
            }

            _UpperCamelCase              =						tempfile.mkdtemp()
            _UpperCamelCase              =						os.path.join(self.tmpdirname,       VOCAB_FILES_NAMES['''vocab_file''']      )
            _UpperCamelCase              =						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
            _UpperCamelCase              =						'''hf-internal-testing/ngram-beam-search-decoder'''

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.add_kwargs_tokens_map.copy()
            kwargs.update(A      )
            return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname,       **A      )

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self,       **A      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            shutil.rmtree(self.tmpdirname      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						WavaVecaProcessorWithLM(tokenizer=A,       feature_extractor=A,       decoder=A      )

            processor.save_pretrained(self.tmpdirname      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						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
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						WavaVecaProcessorWithLM(tokenizer=A,       feature_extractor=A,       decoder=A      )

            _UpperCamelCase              =						floats_list((3, 1000)      )

            _UpperCamelCase              =						feature_extractor(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						WavaVecaProcessorWithLM(tokenizer=A,       feature_extractor=A,       decoder=A      )

            _UpperCamelCase              =						'''This is a test string'''

            _UpperCamelCase              =						processor(text=A      )

            _UpperCamelCase              =						tokenizer(A      )

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

     def 	_UpperCamelCase     (      self,       A=(2, 10, 16),       A=77      ):





            """simple docstring"""

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						WavaVecaProcessorWithLM(tokenizer=A,       feature_extractor=A,       decoder=A      )

            _UpperCamelCase              =						self._get_dummy_logits(shape=(10, 16),       seed=13      )

            _UpperCamelCase              =						processor.decode(A      )

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						WavaVecaProcessorWithLM(tokenizer=A,       feature_extractor=A,       decoder=A      )

            _UpperCamelCase              =						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:
                   _UpperCamelCase              =						processor.batch_decode(A      )
            else:
                   with get_context(A      ).Pool() as pool:
                          _UpperCamelCase              =						processor.batch_decode(A,       A      )

            _UpperCamelCase              =						list(A      )

            with get_context('''fork'''      ).Pool() as p:
                   _UpperCamelCase              =						decoder.decode_beams_batch(A,       A      )

            _UpperCamelCase       ,		_UpperCamelCase       ,		_UpperCamelCase              =						[], [], []
            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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						WavaVecaProcessorWithLM(tokenizer=A,       feature_extractor=A,       decoder=A      )

            _UpperCamelCase              =						self._get_dummy_logits()

            _UpperCamelCase              =						15
            _UpperCamelCase              =						-20.0
            _UpperCamelCase              =						-4.0

            _UpperCamelCase              =						processor.batch_decode(
                A,       beam_width=A,       beam_prune_logp=A,       token_min_logp=A,       )
            _UpperCamelCase              =						decoded_processor_out.text

            _UpperCamelCase              =						list(A      )

            with get_context('''fork'''      ).Pool() as pool:
                   _UpperCamelCase              =						decoder.decode_beams_batch(
                       A,       A,       beam_width=A,       beam_prune_logp=A,       token_min_logp=A,       )

            _UpperCamelCase              =						[d[0][0] for d in decoded_decoder_out]
            _UpperCamelCase              =						[d[0][2] for d in decoded_decoder_out]
            _UpperCamelCase              =						[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, -13.9_474],       A,       atol=1E-3      )      )

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						WavaVecaProcessorWithLM(tokenizer=A,       feature_extractor=A,       decoder=A      )

            _UpperCamelCase              =						self._get_dummy_logits()

            _UpperCamelCase              =						2.0
            _UpperCamelCase              =						5.0
            _UpperCamelCase              =						-20.0
            _UpperCamelCase              =						True

            _UpperCamelCase              =						processor.batch_decode(
                A,       alpha=A,       beta=A,       unk_score_offset=A,       lm_score_boundary=A,       )
            _UpperCamelCase              =						decoded_processor_out.text

            _UpperCamelCase              =						list(A      )
            decoder.reset_params(
                alpha=A,       beta=A,       unk_score_offset=A,       lm_score_boundary=A,       )

            with get_context('''fork'''      ).Pool() as pool:
                   _UpperCamelCase              =						decoder.decode_beams_batch(
                       A,       A,       )

            _UpperCamelCase              =						[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      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )

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

            _UpperCamelCase              =						os.listdir(A      )
            _UpperCamelCase              =						['''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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						snapshot_download('''hf-internal-testing/processor_with_lm'''      )

            _UpperCamelCase              =						WavaVecaProcessorWithLM.from_pretrained(A      )

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

            _UpperCamelCase              =						os.listdir(A      )
            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )
            _UpperCamelCase              =						AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''      )

            _UpperCamelCase              =						floats_list((3, 1000)      )

            _UpperCamelCase              =						processor_wavaveca(A,       return_tensors='''np'''      )
            _UpperCamelCase              =						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      )

            _UpperCamelCase              =						self._get_dummy_logits()

            _UpperCamelCase              =						processor_wavaveca.batch_decode(A      )
            _UpperCamelCase              =						processor_auto.batch_decode(A      )

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

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						self.get_feature_extractor()
            _UpperCamelCase              =						self.get_tokenizer()
            _UpperCamelCase              =						self.get_decoder()

            _UpperCamelCase              =						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 	_UpperCamelCase     (      A,       A      ):





            """simple docstring"""

            _UpperCamelCase              =						[d[key] for d in offsets]
            return retrieved_list

     def 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )
            _UpperCamelCase              =						self._get_dummy_logits()[0]

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            _UpperCamelCase              =						WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''      )
            _UpperCamelCase              =						self._get_dummy_logits()

            _UpperCamelCase              =						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 	_UpperCamelCase     (      self      ):





            """simple docstring"""

            import torch

            _UpperCamelCase              =						load_dataset('''common_voice''',       '''en''',       split='''train''',       streaming=A      )
            _UpperCamelCase              =						ds.cast_column('''audio''',       datasets.Audio(sampling_rate=1_6000      )      )
            _UpperCamelCase              =						iter(A      )
            _UpperCamelCase              =						next(A      )

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

            with torch.no_grad():
                   _UpperCamelCase              =						model(A      ).logits.cpu().numpy()

            _UpperCamelCase              =						processor.decode(logits[0],       output_word_offsets=A      )

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

            _UpperCamelCase              =						'''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
            _UpperCamelCase              =						torch.tensor(self.get_from_offsets(A,       '''start_time'''      )      )
            _UpperCamelCase              =						torch.tensor(self.get_from_offsets(A,       '''end_time'''      )      )

            # fmt: off
            _UpperCamelCase              =						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]      )
            _UpperCamelCase              =						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(A,       A,       atol=0.01      )      )
            self.assertTrue(torch.allclose(A,       A,       atol=0.01      )      )






