






# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the config
# - generate model_cards - useful if you have multiple models from the same paper

import argparse
import json
import os
import re
from collections import OrderedDict
from os.path import basename, dirname

import fairseq
import torch
from fairseq import hub_utils
from fairseq.data.dictionary import Dictionary

from transformers import FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

lowerCamelCase : Dict						   =       2

# based on the results of a search on a range of `num_beams`, `length_penalty` and `early_stopping`
# values against wmt19 test data to obtain the best BLEU scores, we will use the following defaults:
#
# * `num_beams`: 5 (higher scores better, but requires more memory/is slower, can be adjusted by users)
# * `early_stopping`: `False` consistently scored better
# * `length_penalty` varied, so will assign the best one depending on the model
lowerCamelCase : str						   =       {
    # fairseq:
    '''wmt19-ru-en''': {'''length_penalty''': 1.1},
    '''wmt19-en-ru''': {'''length_penalty''': 1.15},
    '''wmt19-en-de''': {'''length_penalty''': 1.0},
    '''wmt19-de-en''': {'''length_penalty''': 1.1},
    # allenai:
    '''wmt16-en-de-dist-12-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-dist-6-1''': {'''length_penalty''': 0.6},
    '''wmt16-en-de-12-1''': {'''length_penalty''': 0.8},
    '''wmt19-de-en-6-6-base''': {'''length_penalty''': 0.6},
    '''wmt19-de-en-6-6-big''': {'''length_penalty''': 0.6},
}

# this remaps the different models to their organization names
lowerCamelCase : Tuple						   =       {}
for m in ["wmt19-ru-en", "wmt19-en-ru", "wmt19-en-de", "wmt19-de-en"]:
	lowerCamelCase : str						   =       '''facebook'''
for m in [
    "wmt16-en-de-dist-12-1",
    "wmt16-en-de-dist-6-1",
    "wmt16-en-de-12-1",
    "wmt19-de-en-6-6-base",
    "wmt19-de-en-6-6-big",
]:
	lowerCamelCase : Optional[Any]						   =       '''allenai'''
def       snake_case_		(     lowerCAmelCase_       :						int      ):
							# (1) remove word breaking symbol, (2) add word ending symbol where the word is not broken up,
							# e.g.: d = {'le@@': 5, 'tt@@': 6, 'er': 7} => {'le': 5, 'tt': 6, 'er</w>': 7}
							__lowercase      :							Optional[Any]				=     dict((re.sub(r"""@@$"""       ,						""""""       ,						lowerCAmelCase_      ), v) if k.endswith("""@@"""      ) else (re.sub(r"""$"""       ,						"""</w>"""       ,						lowerCAmelCase_      ), v) for k, v in d.items()      )
							__lowercase      :							Any				=     """<s> <pad> </s> <unk>""".split()
							# restore the special tokens
							for k in keep_keys:
														del da[F"{k}</w>"]
														__lowercase      :							Optional[int]				=     d[k]  # restore
							return da
def       snake_case_		(     lowerCAmelCase_       :						int       ,						lowerCAmelCase_       :						Any      ):
							# prep
							assert os.path.exists(lowerCAmelCase_      )
							os.makedirs(lowerCAmelCase_       ,						exist_ok=lowerCAmelCase_      )
							print(F"Writing results to {pytorch_dump_folder_path}"      )

							# handle various types of models

							__lowercase      :							Tuple				=     basename(lowerCAmelCase_      )
							__lowercase      :							str				=     dirname(lowerCAmelCase_      )

							__lowercase      :							Any				=     fairseq.model_parallel.models.transformer.ModelParallelTransformerModel
							__lowercase      :							Optional[Any]				=     cls.hub_models()
							__lowercase      :							Any				=     {"""bpe""": """fastbpe""", """tokenizer""": """moses"""}
							__lowercase      :							List[str]				=     """."""
							# note: since the model dump is old, fairseq has upgraded its model some
							# time later, and it does a whole lot of rewrites and splits on the saved
							# weights, therefore we can't use torch.load() directly on the model file.
							# see: upgrade_state_dict(state_dict) in fairseq_model.py
							print(F"using checkpoint {checkpoint_file}"      )
							__lowercase      :							List[Any]				=     hub_utils.from_pretrained(
							    lowerCAmelCase_       ,						lowerCAmelCase_       ,						lowerCAmelCase_       ,						archive_map=lowerCAmelCase_       ,						**lowerCAmelCase_      )

							__lowercase      :							Union[str, Any]				=     vars(chkpt["""args"""]["""model"""]      )

							__lowercase      :							Optional[int]				=     args["""source_lang"""]
							__lowercase      :							Tuple				=     args["""target_lang"""]

							__lowercase      :							Optional[int]				=     dirname(lowerCAmelCase_      )
							__lowercase      :							List[str]				=     basename(lowerCAmelCase_      )

							# dicts
							__lowercase      :							List[Any]				=     os.path.join(lowerCAmelCase_       ,						F"dict.{src_lang}.txt"      )
							__lowercase      :							Optional[Any]				=     os.path.join(lowerCAmelCase_       ,						F"dict.{tgt_lang}.txt"      )

							__lowercase      :							List[str]				=     Dictionary.load(lowerCAmelCase_      )
							__lowercase      :							Tuple				=     rewrite_dict_keys(src_dict.indices      )
							__lowercase      :							int				=     len(lowerCAmelCase_      )
							__lowercase      :							List[Any]				=     os.path.join(lowerCAmelCase_       ,						"""vocab-src.json"""      )
							print(F"Generating {src_vocab_file} of {src_vocab_size} of {src_lang} records"      )
							with open(lowerCAmelCase_       ,						"""w"""       ,						encoding="""utf-8"""      ) as f:
														f.write(json.dumps(lowerCAmelCase_       ,						ensure_ascii=lowerCAmelCase_       ,						indent=lowerCAmelCase_      )      )

							# detect whether this is a do_lower_case situation, which can be derived by checking whether we
							# have at least one uppercase letter in the source vocab
							__lowercase      :							Optional[Any]				=     True
							for k in src_vocab.keys():
														if not k.islower():
																					__lowercase      :							Any				=     False
																					break

							__lowercase      :							str				=     Dictionary.load(lowerCAmelCase_      )
							__lowercase      :							List[Any]				=     rewrite_dict_keys(tgt_dict.indices      )
							__lowercase      :							Dict				=     len(lowerCAmelCase_      )
							__lowercase      :							Optional[int]				=     os.path.join(lowerCAmelCase_       ,						"""vocab-tgt.json"""      )
							print(F"Generating {tgt_vocab_file} of {tgt_vocab_size} of {tgt_lang} records"      )
							with open(lowerCAmelCase_       ,						"""w"""       ,						encoding="""utf-8"""      ) as f:
														f.write(json.dumps(lowerCAmelCase_       ,						ensure_ascii=lowerCAmelCase_       ,						indent=lowerCAmelCase_      )      )

							# merges_file (bpecodes)
							__lowercase      :							List[str]				=     os.path.join(lowerCAmelCase_       ,						VOCAB_FILES_NAMES["""merges_file"""]      )
							for fn in ["bpecodes", "code"]:  # older fairseq called the merges file "code"
														__lowercase      :							Tuple				=     os.path.join(lowerCAmelCase_       ,						lowerCAmelCase_      )
														if os.path.exists(lowerCAmelCase_      ):
																					break
							with open(lowerCAmelCase_       ,						encoding="""utf-8"""      ) as fin:
														__lowercase      :							Optional[Any]				=     fin.read()
							__lowercase      :							str				=     re.sub(r""" \d+$"""       ,						""""""       ,						lowerCAmelCase_       ,						0       ,						re.M      )  # remove frequency number
							print(F"Generating {merges_file}"      )
							with open(lowerCAmelCase_       ,						"""w"""       ,						encoding="""utf-8"""      ) as fout:
														fout.write(lowerCAmelCase_      )

							# model config
							__lowercase      :							int				=     os.path.join(lowerCAmelCase_       ,						"""config.json"""      )

							# validate bpe/tokenizer config, as currently it's hardcoded to moses+fastbpe -
							# may have to modify the tokenizer if a different type is used by a future model
							assert args["bpe"] == "fastbpe", F"need to extend tokenizer to support bpe={args['bpe']}"
							assert args["tokenizer"] == "moses", F"need to extend tokenizer to support bpe={args['tokenizer']}"

							__lowercase      :							Any				=     {
							    """architectures""": ["""FSMTForConditionalGeneration"""],
							    """model_type""": """fsmt""",
							    """activation_dropout""": args["""activation_dropout"""],
							    """activation_function""": """relu""",
							    """attention_dropout""": args["""attention_dropout"""],
							    """d_model""": args["""decoder_embed_dim"""],
							    """dropout""": args["""dropout"""],
							    """init_std""": 0.02,
							    """max_position_embeddings""": args["""max_source_positions"""],
							    """num_hidden_layers""": args["""encoder_layers"""],
							    """src_vocab_size""": src_vocab_size,
							    """tgt_vocab_size""": tgt_vocab_size,
							    """langs""": [src_lang, tgt_lang],
							    """encoder_attention_heads""": args["""encoder_attention_heads"""],
							    """encoder_ffn_dim""": args["""encoder_ffn_embed_dim"""],
							    """encoder_layerdrop""": args["""encoder_layerdrop"""],
							    """encoder_layers""": args["""encoder_layers"""],
							    """decoder_attention_heads""": args["""decoder_attention_heads"""],
							    """decoder_ffn_dim""": args["""decoder_ffn_embed_dim"""],
							    """decoder_layerdrop""": args["""decoder_layerdrop"""],
							    """decoder_layers""": args["""decoder_layers"""],
							    """bos_token_id""": 0,
							    """pad_token_id""": 1,
							    """eos_token_id""": 2,
							    """is_encoder_decoder""": True,
							    """scale_embedding""": not args["""no_scale_embedding"""],
							    """tie_word_embeddings""": args["""share_all_embeddings"""],
							}

							# good hparam defaults to start with
							__lowercase      :							Any				=     5
							__lowercase      :							List[str]				=     False
							if model_dir in best_score_hparams and "length_penalty" in best_score_hparams[model_dir]:
														__lowercase      :							List[Any]				=     best_score_hparams[model_dir]["""length_penalty"""]
							else:
														__lowercase      :							Optional[Any]				=     1.0

							print(F"Generating {fsmt_model_config_file}"      )
							with open(lowerCAmelCase_       ,						"""w"""       ,						encoding="""utf-8"""      ) as f:
														f.write(json.dumps(lowerCAmelCase_       ,						ensure_ascii=lowerCAmelCase_       ,						indent=lowerCAmelCase_      )      )

							# tokenizer config
							__lowercase      :							Dict				=     os.path.join(lowerCAmelCase_       ,						lowerCAmelCase_      )

							__lowercase      :							List[str]				=     {
							    """langs""": [src_lang, tgt_lang],
							    """model_max_length""": 1024,
							    """do_lower_case""": do_lower_case,
							}

							print(F"Generating {fsmt_tokenizer_config_file}"      )
							with open(lowerCAmelCase_       ,						"""w"""       ,						encoding="""utf-8"""      ) as f:
														f.write(json.dumps(lowerCAmelCase_       ,						ensure_ascii=lowerCAmelCase_       ,						indent=lowerCAmelCase_      )      )

							# model
							__lowercase      :							List[Any]				=     chkpt["""models"""][0]
							__lowercase      :							Optional[Any]				=     model.state_dict()

							# rename keys to start with 'model.'
							__lowercase      :							int				=     OrderedDict(("""model.""" + k, v) for k, v in model_state_dict.items()      )

							# remove unneeded keys
							__lowercase      :							Union[str, Any]				=     [
							    """model.model""",
							    """model.encoder.version""",
							    """model.decoder.version""",
							    """model.encoder_embed_tokens.weight""",
							    """model.decoder_embed_tokens.weight""",
							    """model.encoder.embed_positions._float_tensor""",
							    """model.decoder.embed_positions._float_tensor""",
							]
							for k in ignore_keys:
														model_state_dict.pop(lowerCAmelCase_       ,						lowerCAmelCase_      )

							__lowercase      :							Optional[int]				=     FSMTConfig.from_pretrained(lowerCAmelCase_      )
							__lowercase      :							List[Any]				=     FSMTForConditionalGeneration(lowerCAmelCase_      )

							# check that it loads ok
							model_new.load_state_dict(lowerCAmelCase_       ,						strict=lowerCAmelCase_      )

							# save
							__lowercase      :							int				=     os.path.join(lowerCAmelCase_       ,						lowerCAmelCase_      )
							print(F"Generating {pytorch_weights_dump_path}"      )
							torch.save(lowerCAmelCase_       ,						lowerCAmelCase_      )

							print("""Conversion is done!"""      )
							print("""\nLast step is to upload the files to s3"""      )
							print(F"cd {data_root}"      )
							print(F"transformers-cli upload {model_dir}"      )


if __name__ == "__main__":
	lowerCamelCase : List[str]						   =       argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--fsmt_checkpoint_path''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''Path to the official PyTorch checkpoint file which is expected to reside in the dump dir with dicts,'''
	        ''' bpecodes, etc.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	lowerCamelCase : Dict						   =       parser.parse_args()
	convert_fsmt_checkpoint_to_pytorch(args.fsmt_checkpoint_path, args.pytorch_dump_folder_path)







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			):

					'''simple docstring'''

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


												"""simple docstring"""

												__lowercase      :							Optional[int]				=     """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
												__lowercase      :							List[str]				=     dict(zip(__a		,     range(len(__a							)							)							)							)

												__lowercase      :							Dict				=     {
												    """unk_token""": """<unk>""",
												    """bos_token""": """<s>""",
												    """eos_token""": """</s>""",
												}
												__lowercase      :							List[str]				=     {
												    """feature_size""": 1,
												    """padding_value""": 0.0,
												    """sampling_rate""": 16000,
												    """return_attention_mask""": False,
												    """do_normalize""": True,
												}

												__lowercase      :							Tuple				=     tempfile.mkdtemp()
												__lowercase      :							List[Any]				=     os.path.join(self.tmpdirname		,     VOCAB_FILES_NAMES["""vocab_file"""]							)
												__lowercase      :							str				=     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
												__lowercase      :							Optional[int]				=     """hf-internal-testing/ngram-beam-search-decoder"""



					def       lowerCAmelCase				(  self				:	Optional[Any]		,     **__a				:	Dict							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							Union[str, Any]				=     self.add_kwargs_tokens_map.copy()
												kwargs.update(__a							)
												return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname		,     **__a							)



					def       lowerCAmelCase				(  self				:	str		,     **__a				:	int							)			->  Tuple:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]		,     **__a				:	Union[str, Any]							)			->  List[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	int							)			->  Tuple:


												"""simple docstring"""

												shutil.rmtree(self.tmpdirname							)



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


												"""simple docstring"""

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

												__lowercase      :							Tuple				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												processor.save_pretrained(self.tmpdirname							)
												__lowercase      :							Tuple				=     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       lowerCAmelCase				(  self				:	Optional[int]							)			->  Union[str, Any]:


												"""simple docstring"""

												__lowercase      :							Any				=     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
												__lowercase      :							str				=     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       lowerCAmelCase				(  self				:	Any							)			->  List[str]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     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       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Any:


												"""simple docstring"""

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

												__lowercase      :							int				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Optional[int]				=     floats_list((3, 1000)							)

												__lowercase      :							List[Any]				=     feature_extractor(__a		,     return_tensors="""np"""							)
												__lowercase      :							List[str]				=     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       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  Optional[Any]:


												"""simple docstring"""

												__lowercase      :							List[Any]				=     self.get_feature_extractor()
												__lowercase      :							int				=     self.get_tokenizer()
												__lowercase      :							Dict				=     self.get_decoder()

												__lowercase      :							Union[str, Any]				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Dict				=     """This is a test string"""

												__lowercase      :							Any				=     processor(text=__a							)

												__lowercase      :							Dict				=     tokenizer(__a							)

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



					def       lowerCAmelCase				(  self				:	str		,     __a				:	Tuple=(2, 10, 16)		,     __a				:	int=77							)			->  Optional[Any]:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Union[str, Any]							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							List[str]				=     self.get_feature_extractor()
												__lowercase      :							Dict				=     self.get_tokenizer()
												__lowercase      :							str				=     self.get_decoder()

												__lowercase      :							int				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							List[str]				=     self._get_dummy_logits(shape=(10, 16)		,     seed=13							)

												__lowercase      :							Optional[Any]				=     processor.decode(__a							)

												__lowercase      :							Any				=     decoder.decode_beams(__a							)[0]

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



					@parameterized.expand([[None], ["""fork"""], ["""spawn"""]]							)
					def       lowerCAmelCase				(  self				:	List[str]		,     __a				:	Dict							)			->  List[Any]:


												"""simple docstring"""

												__lowercase      :							str				=     self.get_feature_extractor()
												__lowercase      :							Dict				=     self.get_tokenizer()
												__lowercase      :							Optional[int]				=     self.get_decoder()

												__lowercase      :							Any				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Optional[Any]				=     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:
																			__lowercase      :							Union[str, Any]				=     processor.batch_decode(__a							)
												else:
																			with get_context(__a							).Pool() as pool:
																										__lowercase      :							Optional[Any]				=     processor.batch_decode(__a		,     __a							)

												__lowercase      :							Union[str, Any]				=     list(__a							)

												with get_context("""fork"""							).Pool() as p:
																			__lowercase      :							Optional[Any]				=     decoder.decode_beams_batch(__a		,     __a							)

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

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



					def       lowerCAmelCase				(  self				:	Any							)			->  Optional[int]:


												"""simple docstring"""

												__lowercase      :							int				=     self.get_feature_extractor()
												__lowercase      :							Dict				=     self.get_tokenizer()
												__lowercase      :							List[str]				=     self.get_decoder()

												__lowercase      :							int				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

												__lowercase      :							Dict				=     self._get_dummy_logits()

												__lowercase      :							Tuple				=     15
												__lowercase      :							Tuple				=     -20.0
												__lowercase      :							Dict				=     -4.0

												__lowercase      :							Dict				=     processor.batch_decode(
												    __a		,     beam_width=__a		,     beam_prune_logp=__a		,     token_min_logp=__a		,     )
												__lowercase      :							Tuple				=     decoded_processor_out.text

												__lowercase      :							List[Any]				=     list(__a							)

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

												__lowercase      :							Optional[Any]				=     [d[0][0] for d in decoded_decoder_out]
												__lowercase      :							Optional[int]				=     [d[0][2] for d in decoded_decoder_out]
												__lowercase      :							Optional[int]				=     [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       lowerCAmelCase				(  self				:	List[str]							)			->  List[Any]:


												"""simple docstring"""

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

												__lowercase      :							Dict				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

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

												__lowercase      :							Optional[int]				=     2.0
												__lowercase      :							Tuple				=     5.0
												__lowercase      :							Optional[Any]				=     -20.0
												__lowercase      :							Tuple				=     True

												__lowercase      :							Union[str, Any]				=     processor.batch_decode(
												    __a		,     alpha=__a		,     beta=__a		,     unk_score_offset=__a		,     lm_score_boundary=__a		,     )
												__lowercase      :							Any				=     decoded_processor_out.text

												__lowercase      :							List[Any]				=     list(__a							)
												decoder.reset_params(
												    alpha=__a		,     beta=__a		,     unk_score_offset=__a		,     lm_score_boundary=__a		,     )

												with get_context("""fork"""							).Pool() as pool:
																			__lowercase      :							Tuple				=     decoder.decode_beams_batch(
																			    __a		,     __a		,     )

												__lowercase      :							int				=     [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							)
												__lowercase      :							str				=     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       lowerCAmelCase				(  self				:	Any							)			->  List[Any]:


												"""simple docstring"""

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

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

												__lowercase      :							int				=     os.listdir(__a							)
												__lowercase      :							Optional[Any]				=     ["""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       lowerCAmelCase				(  self				:	str							)			->  Optional[int]:


												"""simple docstring"""

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

												__lowercase      :							Union[str, Any]				=     WavaVecaProcessorWithLM.from_pretrained(__a							)

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

												__lowercase      :							Dict				=     os.listdir(__a							)
												__lowercase      :							List[Any]				=     os.listdir(__a							)

												local_decoder_files.sort()
												expected_decoder_files.sort()

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



					def       lowerCAmelCase				(  self				:	Tuple							)			->  int:


												"""simple docstring"""

												__lowercase      :							Optional[Any]				=     WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
												__lowercase      :							Dict				=     AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm"""							)

												__lowercase      :							Any				=     floats_list((3, 1000)							)

												__lowercase      :							List[str]				=     processor_wavaveca(__a		,     return_tensors="""np"""							)
												__lowercase      :							List[Any]				=     processor_auto(__a		,     return_tensors="""np"""							)

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

												__lowercase      :							List[str]				=     self._get_dummy_logits()

												__lowercase      :							List[str]				=     processor_wavaveca.batch_decode(__a							)
												__lowercase      :							Optional[int]				=     processor_auto.batch_decode(__a							)

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



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


												"""simple docstring"""

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

												__lowercase      :							Optional[Any]				=     WavaVecaProcessorWithLM(tokenizer=__a		,     feature_extractor=__a		,     decoder=__a							)

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



					@staticmethod
					def       lowerCAmelCase				(  __a				:	Union[str, Any]		,     __a				:	List[Any]							)			->  Dict:


												"""simple docstring"""

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



					def       lowerCAmelCase				(  self				:	Optional[int]							)			->  str:


												"""simple docstring"""

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

												__lowercase      :							Dict				=     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       lowerCAmelCase				(  self				:	List[str]							)			->  Tuple:


												"""simple docstring"""

												__lowercase      :							List[str]				=     WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm"""							)
												__lowercase      :							Any				=     self._get_dummy_logits()

												__lowercase      :							Dict				=     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       lowerCAmelCase				(  self				:	Dict							)			->  int:


												"""simple docstring"""

												import torch

												__lowercase      :							Any				=     load_dataset("""common_voice"""		,     """en"""		,     split="""train"""		,     streaming=__a							)
												__lowercase      :							str				=     ds.cast_column("""audio"""		,     datasets.Audio(sampling_rate=16000							)							)
												__lowercase      :							Tuple				=     iter(__a							)
												__lowercase      :							Union[str, Any]				=     next(__a							)

												__lowercase      :							int				=     AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm"""							)
												__lowercase      :							int				=     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
												__lowercase      :							Union[str, Any]				=     processor(sample["""audio"""]["""array"""]		,     return_tensors="""pt"""							).input_values

												with torch.no_grad():
																			__lowercase      :							List[Any]				=     model(__a							).logits.cpu().numpy()

												__lowercase      :							Tuple				=     processor.decode(logits[0]		,     output_word_offsets=__a							)

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

												__lowercase      :							str				=     """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
												__lowercase      :							Tuple				=     torch.tensor(self.get_from_offsets(__a		,     """start_time"""							)							)
												__lowercase      :							Dict				=     torch.tensor(self.get_from_offsets(__a		,     """end_time"""							)							)

												# fmt: off
												__lowercase      :							List[Any]				=     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]							)
												__lowercase      :							Optional[int]				=     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							)							)