import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin



@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  UpperCamelCase				( __lowercase ):

     '''simple docstring'''

     A_										=  42
     A_										=  None





def       __magic_name__(  __UpperCAmelCase ,						__UpperCAmelCase=0.9_9_9 ,						__UpperCAmelCase="cosine" ,						)						->    Optional[Any]:



      '''simple docstring'''

      if alpha_transform_type == "cosine":

            def alpha_bar_fn(__UpperCAmelCase		):
                  return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

      elif alpha_transform_type == "exp":

            def alpha_bar_fn(__UpperCAmelCase		):
                  return math.exp(t * -1_2.0		)

      else:
            raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}'		)

      _lowerCamelCase			    =				[]
      for i in range(__UpperCAmelCase		):
            _lowerCamelCase			    =				i / num_diffusion_timesteps
            _lowerCamelCase			    =				(i + 1) / num_diffusion_timesteps
            betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase		) / alpha_bar_fn(__UpperCAmelCase		) ,						__UpperCAmelCase		)		)
      return torch.tensor(__UpperCAmelCase ,						dtype=torch.floataa		)




class  UpperCamelCase				( __lowercase   ,  __lowercase ):

     '''simple docstring'''

     @register_to_config
     def __init__(					self				,				A_ = 10_00				,				A_ = "fixed_small_log"				,				A_ = True				,				A_ = 1.0				,				A_ = "epsilon"				,				A_ = "squaredcos_cap_v2"				,				) ->				str:






           """simple docstring"""
           if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''						)

           _lowerCamelCase			    =				betas_for_alpha_bar(A_						)

           _lowerCamelCase			    =				1.0 - self.betas
           _lowerCamelCase			    =				torch.cumprod(self.alphas				,				dim=0						)
           _lowerCamelCase			    =				torch.tensor(1.0						)

           # standard deviation of the initial noise distribution
           _lowerCamelCase			    =				1.0

           # setable values
           _lowerCamelCase			    =				None
           _lowerCamelCase			    =				torch.from_numpy(np.arange(0				,				A_						)[::-1].copy()						)

           _lowerCamelCase			    =				variance_type



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				torch.FloatTensor:






           """simple docstring"""
           return sample



     def 		UpperCamelCase_						(					self				,				A_				,				A_ = None						) ->				Union[str, Any]:






           """simple docstring"""
           _lowerCamelCase			    =				num_inference_steps
           _lowerCamelCase			    =				(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
           _lowerCamelCase			    =				(np.arange(0				,				A_						) * step_ratio).round()[::-1].copy().astype(np.intaa						)
           _lowerCamelCase			    =				torch.from_numpy(A_						).to(A_						)



     def 		UpperCamelCase_						(					self				,				A_				,				A_=None				,				A_=None				,				A_=None						) ->				List[Any]:






           """simple docstring"""
           if prev_timestep is None:
                 _lowerCamelCase			    =				t - 1

           _lowerCamelCase			    =				self.alphas_cumprod[t]
           _lowerCamelCase			    =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           _lowerCamelCase			    =				1 - alpha_prod_t
           _lowerCamelCase			    =				1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 _lowerCamelCase			    =				self.betas[t]
           else:
                 _lowerCamelCase			    =				1 - alpha_prod_t / alpha_prod_t_prev

           # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
           # and sample from it to get previous sample
           # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
           _lowerCamelCase			    =				beta_prod_t_prev / beta_prod_t * beta

           if variance_type is None:
                 _lowerCamelCase			    =				self.config.variance_type

           # hacks - were probably added for training stability
           if variance_type == "fixed_small_log":
                 _lowerCamelCase			    =				torch.log(torch.clamp(A_				,				min=1E-2_0						)						)
                 _lowerCamelCase			    =				torch.exp(0.5 * variance						)
           elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 _lowerCamelCase			    =				variance.log()
                 _lowerCamelCase			    =				beta.log()

                 _lowerCamelCase			    =				(predicted_variance + 1) / 2
                 _lowerCamelCase			    =				frac * max_log + (1 - frac) * min_log

           return variance



     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				A_ = None				,				A_=None				,				A_ = True				,				) ->				Union[UnCLIPSchedulerOutput, Tuple]:






           """simple docstring"""
           _lowerCamelCase			    =				timestep

           if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 _lowerCamelCase    ,					_lowerCamelCase			    =				torch.split(A_				,				sample.shape[1]				,				dim=1						)
           else:
                 _lowerCamelCase			    =				None

           # 1. compute alphas, betas
           if prev_timestep is None:
                 _lowerCamelCase			    =				t - 1

           _lowerCamelCase			    =				self.alphas_cumprod[t]
           _lowerCamelCase			    =				self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
           _lowerCamelCase			    =				1 - alpha_prod_t
           _lowerCamelCase			    =				1 - alpha_prod_t_prev

           if prev_timestep == t - 1:
                 _lowerCamelCase			    =				self.betas[t]
                 _lowerCamelCase			    =				self.alphas[t]
           else:
                 _lowerCamelCase			    =				1 - alpha_prod_t / alpha_prod_t_prev
                 _lowerCamelCase			    =				1 - beta

           # 2. compute predicted original sample from predicted noise also called
           # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
           if self.config.prediction_type == "epsilon":
                 _lowerCamelCase			    =				(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
           elif self.config.prediction_type == "sample":
                 _lowerCamelCase			    =				model_output
           else:
                 raise ValueError(
                     F'prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`'
                     ''' for the UnCLIPScheduler.'''						)

           # 3. Clip "predicted x_0"
           if self.config.clip_sample:
                 _lowerCamelCase			    =				torch.clamp(
                     A_				,				-self.config.clip_sample_range				,				self.config.clip_sample_range						)

           # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           _lowerCamelCase			    =				(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
           _lowerCamelCase			    =				alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

           # 5. Compute predicted previous sample µ_t
           # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
           _lowerCamelCase			    =				pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

           # 6. Add noise
           _lowerCamelCase			    =				0
           if t > 0:
                 _lowerCamelCase			    =				randn_tensor(
                     model_output.shape				,				dtype=model_output.dtype				,				generator=A_				,				device=model_output.device						)

                 _lowerCamelCase			    =				self._get_variance(
                     A_				,				predicted_variance=A_				,				prev_timestep=A_				,				)

                 if self.variance_type == "fixed_small_log":
                       _lowerCamelCase			    =				variance
                 elif self.variance_type == "learned_range":
                       _lowerCamelCase			    =				(0.5 * variance).exp()
                 else:
                       raise ValueError(
                           F'variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`'
                           ''' for the UnCLIPScheduler.'''						)

                 _lowerCamelCase			    =				variance * variance_noise

           _lowerCamelCase			    =				pred_prev_sample + variance

           if not return_dict:
                 return (pred_prev_sample,)

           return UnCLIPSchedulerOutput(prev_sample=A_				,				pred_original_sample=A_						)





     def 		UpperCamelCase_						(					self				,				A_				,				A_				,				A_				,				) ->				torch.FloatTensor:






           """simple docstring"""
           # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
           _lowerCamelCase			    =				self.alphas_cumprod.to(device=original_samples.device				,				dtype=original_samples.dtype						)
           _lowerCamelCase			    =				timesteps.to(original_samples.device						)

           _lowerCamelCase			    =				alphas_cumprod[timesteps] ** 0.5
           _lowerCamelCase			    =				sqrt_alpha_prod.flatten()
           while len(sqrt_alpha_prod.shape						) < len(original_samples.shape						):
                 _lowerCamelCase			    =				sqrt_alpha_prod.unsqueeze(-1						)

           _lowerCamelCase			    =				(1 - alphas_cumprod[timesteps]) ** 0.5
           _lowerCamelCase			    =				sqrt_one_minus_alpha_prod.flatten()
           while len(sqrt_one_minus_alpha_prod.shape						) < len(original_samples.shape						):
                 _lowerCamelCase			    =				sqrt_one_minus_alpha_prod.unsqueeze(-1						)

           _lowerCamelCase			    =				sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
           return noisy_samples
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  UpperCamelCase				( unittest.TestCase ):

     '''simple docstring'''

     def 		UpperCamelCase_						(					self						) ->				Optional[Any]:






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

           _lowerCamelCase			    =				{
               '''unk_token''': '''<unk>''',
               '''bos_token''': '''<s>''',
               '''eos_token''': '''</s>''',
           }
           _lowerCamelCase			    =				{
               '''feature_size''': 1,
               '''padding_value''': 0.0,
               '''sampling_rate''': 1_60_00,
               '''return_attention_mask''': False,
               '''do_normalize''': True,
           }

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



     def 		UpperCamelCase_						(					self				,				**A_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.add_kwargs_tokens_map.copy()
           kwargs.update(A_						)
           return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,				**A_						)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				Optional[Any]:






           """simple docstring"""
           return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,				**A_						)



     def 		UpperCamelCase_						(					self				,				**A_						) ->				int:






           """simple docstring"""
           return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name				,				**A_						)



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           shutil.rmtree(self.tmpdirname						)



     def 		UpperCamelCase_						(					self						) ->				Any:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           processor.save_pretrained(self.tmpdirname						)
           _lowerCamelCase			    =				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						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				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			    =				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						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				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						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				floats_list((3, 10_00)						)

           _lowerCamelCase			    =				feature_extractor(A_				,				return_tensors='''np'''						)
           _lowerCamelCase			    =				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						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				'''This is a test string'''

           _lowerCamelCase			    =				processor(text=A_						)

           _lowerCamelCase			    =				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						) ->				Optional[Any]:






           """simple docstring"""
           np.random.seed(A_						)
           return np.random.rand(*A_						)



     def 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

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

           _lowerCamelCase			    =				processor.decode(A_						)

           _lowerCamelCase			    =				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_						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				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			    =				processor.batch_decode(A_						)
           else:
                 with get_context(A_						).Pool() as pool:
                       _lowerCamelCase			    =				processor.batch_decode(A_				,				A_						)

           _lowerCamelCase			    =				list(A_						)

           with get_context('''fork'''						).Pool() as p:
                 _lowerCamelCase			    =				decoder.decode_beams_batch(A_				,				A_						)

           _lowerCamelCase    ,					_lowerCamelCase    ,					_lowerCamelCase			    =				[], [], []
           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						) ->				Optional[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				15
           _lowerCamelCase			    =				-20.0
           _lowerCamelCase			    =				-4.0

           _lowerCamelCase			    =				processor.batch_decode(
               A_				,				beam_width=A_				,				beam_prune_logp=A_				,				token_min_logp=A_				,				)
           _lowerCamelCase			    =				decoded_processor_out.text

           _lowerCamelCase			    =				list(A_						)

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

           _lowerCamelCase			    =				[d[0][0] for d in decoded_decoder_out]
           _lowerCamelCase			    =				[d[0][2] for d in decoded_decoder_out]
           _lowerCamelCase			    =				[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 		UpperCamelCase_						(					self						) ->				Optional[int]:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				WavaVecaProcessorWithLM(tokenizer=A_				,				feature_extractor=A_				,				decoder=A_						)

           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				2.0
           _lowerCamelCase			    =				5.0
           _lowerCamelCase			    =				-20.0
           _lowerCamelCase			    =				True

           _lowerCamelCase			    =				processor.batch_decode(
               A_				,				alpha=A_				,				beta=A_				,				unk_score_offset=A_				,				lm_score_boundary=A_				,				)
           _lowerCamelCase			    =				decoded_processor_out.text

           _lowerCamelCase			    =				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			    =				decoder.decode_beams_batch(
                     A_				,				A_				,				)

           _lowerCamelCase			    =				[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			    =				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						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

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

           _lowerCamelCase			    =				os.listdir(A_						)
           _lowerCamelCase			    =				['''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						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				snapshot_download('''hf-internal-testing/processor_with_lm'''						)

           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained(A_						)

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

           _lowerCamelCase			    =				os.listdir(A_						)
           _lowerCamelCase			    =				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						) ->				int:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				AutoProcessor.from_pretrained('''hf-internal-testing/processor_with_lm'''						)

           _lowerCamelCase			    =				floats_list((3, 10_00)						)

           _lowerCamelCase			    =				processor_wavaveca(A_				,				return_tensors='''np'''						)
           _lowerCamelCase			    =				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			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				processor_wavaveca.batch_decode(A_						)
           _lowerCamelCase			    =				processor_auto.batch_decode(A_						)

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



     def 		UpperCamelCase_						(					self						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				self.get_feature_extractor()
           _lowerCamelCase			    =				self.get_tokenizer()
           _lowerCamelCase			    =				self.get_decoder()

           _lowerCamelCase			    =				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_						) ->				str:






           """simple docstring"""
           _lowerCamelCase			    =				[d[key] for d in offsets]
           return retrieved_list



     def 		UpperCamelCase_						(					self						) ->				List[Any]:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				self._get_dummy_logits()[0]

           _lowerCamelCase			    =				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						) ->				Tuple:






           """simple docstring"""
           _lowerCamelCase			    =				WavaVecaProcessorWithLM.from_pretrained('''hf-internal-testing/processor_with_lm'''						)
           _lowerCamelCase			    =				self._get_dummy_logits()

           _lowerCamelCase			    =				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						) ->				List[Any]:






           """simple docstring"""
           import torch

           _lowerCamelCase			    =				load_dataset('''common_voice'''				,				'''en'''				,				split='''train'''				,				streaming=A_						)
           _lowerCamelCase			    =				ds.cast_column('''audio'''				,				datasets.Audio(sampling_rate=1_60_00						)						)
           _lowerCamelCase			    =				iter(A_						)
           _lowerCamelCase			    =				next(A_						)

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

           with torch.no_grad():
                 _lowerCamelCase			    =				model(A_						).logits.cpu().numpy()

           _lowerCamelCase			    =				processor.decode(logits[0]				,				output_word_offsets=A_						)

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

           _lowerCamelCase			    =				'''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			    =				torch.tensor(self.get_from_offsets(A_				,				'''start_time'''						)						)
           _lowerCamelCase			    =				torch.tensor(self.get_from_offsets(A_				,				'''end_time'''						)						)

           # fmt: off
           _lowerCamelCase			    =				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			    =				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						)						)