





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					):




     def 	lowerCAmelCase			(		self  )		->							List[str]:
          _snake_case									=				'| <pad> <unk> <s> </s> a b c d e f g h i j k'.split()
          _snake_case									=				dict(zip(lowerCAmelCase_				,						range(len(lowerCAmelCase_  )  )  )  )

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

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

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

          # load decoder from hub
          _snake_case									=				'hf-internal-testing/ngram-beam-search-decoder'




     def 	lowerCAmelCase			(		self				,						**lowerCAmelCase_  )		->							List[Any]:
          _snake_case									=				self.add_kwargs_tokens_map.copy()
          kwargs.update(lowerCAmelCase_  )
          return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname				,						**lowerCAmelCase_  )




     def 	lowerCAmelCase			(		self				,						**lowerCAmelCase_  )		->							Optional[Any]:
          return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname				,						**lowerCAmelCase_  )




     def 	lowerCAmelCase			(		self				,						**lowerCAmelCase_  )		->							Optional[int]:
          return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name				,						**lowerCAmelCase_  )




     def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
          shutil.rmtree(self.tmpdirname  )




     def 	lowerCAmelCase			(		self  )		->							Tuple:
          _snake_case									=				self.get_tokenizer()
          _snake_case									=				self.get_feature_extractor()
          _snake_case									=				self.get_decoder()

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

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

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

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

          # 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				,						lowerCAmelCase_  )




     def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
          _snake_case									=				WavaVecaProcessorWithLM(
              tokenizer=self.get_tokenizer()				,						feature_extractor=self.get_feature_extractor()				,						decoder=self.get_decoder()  )
          processor.save_pretrained(self.tmpdirname  )

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

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




     def 	lowerCAmelCase			(		self  )		->							List[str]:
          _snake_case									=				self.get_tokenizer()
          # add token to trigger raise
          tokenizer.add_tokens(['xx']  )
          with self.assertRaisesRegex(lowerCAmelCase_				,						'include'  ):
               WavaVecaProcessorWithLM(
                   tokenizer=lowerCAmelCase_				,						feature_extractor=self.get_feature_extractor()				,						decoder=self.get_decoder()  )




     def 	lowerCAmelCase			(		self  )		->							str:
          _snake_case									=				self.get_feature_extractor()
          _snake_case									=				self.get_tokenizer()
          _snake_case									=				self.get_decoder()

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

          _snake_case									=				floats_list((3, 1000)  )

          _snake_case									=				feature_extractor(lowerCAmelCase_				,						return_tensors='np'  )
          _snake_case									=				processor(lowerCAmelCase_				,						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  )		->							int:
          _snake_case									=				self.get_feature_extractor()
          _snake_case									=				self.get_tokenizer()
          _snake_case									=				self.get_decoder()

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

          _snake_case									=				'This is a test string'

          _snake_case									=				processor(text=lowerCAmelCase_  )

          _snake_case									=				tokenizer(lowerCAmelCase_  )

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




     def 	lowerCAmelCase			(		self				,						lowerCAmelCase_=(2, 10, 16)				,						lowerCAmelCase_=77  )		->							int:
          np.random.seed(lowerCAmelCase_  )
          return np.random.rand(*lowerCAmelCase_  )




     def 	lowerCAmelCase			(		self  )		->							Optional[int]:
          _snake_case									=				self.get_feature_extractor()
          _snake_case									=				self.get_tokenizer()
          _snake_case									=				self.get_decoder()

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

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

          _snake_case									=				processor.decode(lowerCAmelCase_  )

          _snake_case									=				decoder.decode_beams(lowerCAmelCase_  )[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				,						lowerCAmelCase_  )		->							str:
          _snake_case									=				self.get_feature_extractor()
          _snake_case									=				self.get_tokenizer()
          _snake_case									=				self.get_decoder()

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

          _snake_case									=				self._get_dummy_logits()

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

          _snake_case									=				list(lowerCAmelCase_  )

          with get_context('fork'  ).Pool() as p:
               _snake_case									=				decoder.decode_beams_batch(lowerCAmelCase_				,						lowerCAmelCase_  )

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

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




     def 	lowerCAmelCase			(		self  )		->							Dict:
          _snake_case									=				self.get_feature_extractor()
          _snake_case									=				self.get_tokenizer()
          _snake_case									=				self.get_decoder()

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

          _snake_case									=				self._get_dummy_logits()

          _snake_case									=				15
          _snake_case									=				-20.0
          _snake_case									=				-4.0

          _snake_case									=				processor.batch_decode(
              lowerCAmelCase_				,						beam_width=lowerCAmelCase_				,						beam_prune_logp=lowerCAmelCase_				,						token_min_logp=lowerCAmelCase_				,						)
          _snake_case									=				decoded_processor_out.text

          _snake_case									=				list(lowerCAmelCase_  )

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

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

          self.assertListEqual(lowerCAmelCase_				,						lowerCAmelCase_  )
          self.assertListEqual(['</s> <s> <s>', '<s> <s> <s>']				,						lowerCAmelCase_  )

          self.assertTrue(np.array_equal(lowerCAmelCase_				,						decoded_processor_out.logit_score  )  )
          self.assertTrue(np.allclose([-20.0_54, -18.4_47]				,						lowerCAmelCase_				,						atol=1E-3  )  )

          self.assertTrue(np.array_equal(lowerCAmelCase_				,						decoded_processor_out.lm_score  )  )
          self.assertTrue(np.allclose([-15.5_54, -13.94_74]				,						lowerCAmelCase_				,						atol=1E-3  )  )




     def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
          _snake_case									=				self.get_feature_extractor()
          _snake_case									=				self.get_tokenizer()
          _snake_case									=				self.get_decoder()

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

          _snake_case									=				self._get_dummy_logits()

          _snake_case									=				2.0
          _snake_case									=				5.0
          _snake_case									=				-20.0
          _snake_case									=				True

          _snake_case									=				processor.batch_decode(
              lowerCAmelCase_				,						alpha=lowerCAmelCase_				,						beta=lowerCAmelCase_				,						unk_score_offset=lowerCAmelCase_				,						lm_score_boundary=lowerCAmelCase_				,						)
          _snake_case									=				decoded_processor_out.text

          _snake_case									=				list(lowerCAmelCase_  )
          decoder.reset_params(
              alpha=lowerCAmelCase_				,						beta=lowerCAmelCase_				,						unk_score_offset=lowerCAmelCase_				,						lm_score_boundary=lowerCAmelCase_				,						)

          with get_context('fork'  ).Pool() as pool:
               _snake_case									=				decoder.decode_beams_batch(
                   lowerCAmelCase_				,						lowerCAmelCase_				,						)

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

          self.assertListEqual(lowerCAmelCase_				,						lowerCAmelCase_  )
          self.assertListEqual(['<s> </s> <s> </s> </s>', '</s> </s> <s> </s> </s>']				,						lowerCAmelCase_  )
          _snake_case									=				processor.decoder.model_container[processor.decoder._model_key]
          self.assertEqual(lm_model.alpha				,						2.0  )
          self.assertEqual(lm_model.beta				,						5.0  )
          self.assertEqual(lm_model.unk_score_offset				,						-20.0  )
          self.assertEqual(lm_model.score_boundary				,						lowerCAmelCase_  )




     def 	lowerCAmelCase			(		self  )		->							str:
          _snake_case									=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'  )

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

          _snake_case									=				os.listdir(lowerCAmelCase_  )
          _snake_case									=				['alphabet.json', 'language_model']

          downloaded_decoder_files.sort()
          expected_decoder_files.sort()

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




     def 	lowerCAmelCase			(		self  )		->							Optional[Any]:
          _snake_case									=				snapshot_download('hf-internal-testing/processor_with_lm'  )

          _snake_case									=				WavaVecaProcessorWithLM.from_pretrained(lowerCAmelCase_  )

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

          _snake_case									=				os.listdir(lowerCAmelCase_  )
          _snake_case									=				os.listdir(lowerCAmelCase_  )

          local_decoder_files.sort()
          expected_decoder_files.sort()

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




     def 	lowerCAmelCase			(		self  )		->							Union[str, Any]:
          _snake_case									=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'  )
          _snake_case									=				AutoProcessor.from_pretrained('hf-internal-testing/processor_with_lm'  )

          _snake_case									=				floats_list((3, 1000)  )

          _snake_case									=				processor_wavaveca(lowerCAmelCase_				,						return_tensors='np'  )
          _snake_case									=				processor_auto(lowerCAmelCase_				,						return_tensors='np'  )

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

          _snake_case									=				self._get_dummy_logits()

          _snake_case									=				processor_wavaveca.batch_decode(lowerCAmelCase_  )
          _snake_case									=				processor_auto.batch_decode(lowerCAmelCase_  )

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




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

          _snake_case									=				WavaVecaProcessorWithLM(tokenizer=lowerCAmelCase_				,						feature_extractor=lowerCAmelCase_				,						decoder=lowerCAmelCase_  )

          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			(		lowerCAmelCase_				,						lowerCAmelCase_  )		->							Union[str, Any]:
          _snake_case									=				[d[key] for d in offsets]
          return retrieved_list




     def 	lowerCAmelCase			(		self  )		->							Dict:
          _snake_case									=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'  )
          _snake_case									=				self._get_dummy_logits()[0]

          _snake_case									=				processor.decode(lowerCAmelCase_				,						output_word_offsets=lowerCAmelCase_  )
          # 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(lowerCAmelCase_				,						lowerCAmelCase_  )  )

          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  )		->							str:
          _snake_case									=				WavaVecaProcessorWithLM.from_pretrained('hf-internal-testing/processor_with_lm'  )
          _snake_case									=				self._get_dummy_logits()

          _snake_case									=				processor.batch_decode(lowerCAmelCase_				,						output_word_offsets=lowerCAmelCase_  )

          # 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(lowerCAmelCase_				,						lowerCAmelCase_  )  )

          self.assertListEqual(
              [' '.join(self.get_from_offsets(lowerCAmelCase_				,						'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  )		->							Union[str, Any]:
          import torch

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

          _snake_case									=				AutoProcessor.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'  )
          _snake_case									=				WavaVecaForCTC.from_pretrained('patrickvonplaten/wav2vec2-base-100h-with-lm'  )

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

          with torch.no_grad():
               _snake_case									=				model(lowerCAmelCase_  ).logits.cpu().numpy()

          _snake_case									=				processor.decode(logits[0]				,						output_word_offsets=lowerCAmelCase_  )

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

          _snake_case									=				'WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL'

          # output words
          self.assertEqual(' '.join(self.get_from_offsets(lowerCAmelCase_				,						'word'  )  )				,						lowerCAmelCase_  )
          self.assertEqual(' '.join(self.get_from_offsets(lowerCAmelCase_				,						'word'  )  )				,						output.text  )

          # output times
          _snake_case									=				torch.tensor(self.get_from_offsets(lowerCAmelCase_				,						'start_time'  )  )
          _snake_case									=				torch.tensor(self.get_from_offsets(lowerCAmelCase_				,						'end_time'  )  )

          # fmt: off
          _snake_case									=				torch.tensor([1.41_99, 1.65_99, 2.25_99, 3.0, 3.24, 3.59_99, 3.79_99, 4.09_99, 4.26, 4.94, 5.28, 5.65_99, 5.78, 5.94, 6.32, 6.53_99, 6.65_99]  )
          _snake_case									=				torch.tensor([1.53_99, 1.89_99, 2.9, 3.16, 3.53_99, 3.72, 4.01_99, 4.17_99, 4.76, 5.15_99, 5.55_99, 5.69_99, 5.86, 6.19_99, 6.38, 6.61_99, 6.94]  )
          # fmt: on

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










import argparse
import json
import os
import re
import shutil

import torch

from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import WEIGHTS_NAME, logging


logging.set_verbosity_warning()

UpperCAmelCase_      							=     2
class 			UpperCamelCase_		:




					def __init__(		self				,						*,  # begin keyword-only arguments
					    lowerCAmelCase_="<s>"				,						lowerCAmelCase_="<pad>"				,						lowerCAmelCase_="</s>"				,						lowerCAmelCase_="<unk>"				,						lowerCAmelCase_=None				,						)		->							Optional[int]:
										_snake_case							,     _snake_case							,     _snake_case							,     _snake_case									=				bos, unk, pad, eos
										_snake_case									=				[]
										_snake_case									=				[]
										_snake_case									=				{}
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				self.add_symbol(lowerCAmelCase_  )
										if extra_special_symbols:
															for s in extra_special_symbols:
																				self.add_symbol(lowerCAmelCase_  )
										_snake_case									=				len(self.symbols  )




					def __eq__(		self				,						lowerCAmelCase_  )		->							List[Any]:
										return self.indices == other.indices




					def __getitem__(		self				,						lowerCAmelCase_  )		->							Dict:
										if idx < len(self.symbols  ):
															return self.symbols[idx]
										return self.unk_word




					def __len__(		self  )		->							Optional[Any]:
										return len(self.symbols  )




					def __contains__(		self				,						lowerCAmelCase_  )		->							int:
										return sym in self.indices




					@classmethod
					def 	lowerCAmelCase			(		cls				,						lowerCAmelCase_  )		->							List[Any]:
										_snake_case									=				cls()
										d.add_from_file(lowerCAmelCase_  )
										return d




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=1				,						lowerCAmelCase_=False  )		->							str:
										if word in self.indices and not overwrite:
															_snake_case									=				self.indices[word]
															_snake_case									=				self.count[idx] + n
															return idx
										else:
															_snake_case									=				len(self.symbols  )
															_snake_case									=				idx
															self.symbols.append(lowerCAmelCase_  )
															self.count.append(lowerCAmelCase_  )
															return idx




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Optional[Any]:
										return 0






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_  )		->							Union[str, Any]:
										if isinstance(lowerCAmelCase_				,						lowerCAmelCase_  ):
															try:
																				with open(lowerCAmelCase_				,						'r'				,						encoding='utf-8'  ) as fd:
																									self.add_from_file(lowerCAmelCase_  )
															except FileNotFoundError as fnfe:
																				raise fnfe
															except UnicodeError:
																				raise Exception('Incorrect encoding detected in {}, please rebuild the dataset'.format(lowerCAmelCase_  )  )
															return

										_snake_case									=				f.readlines()
										_snake_case									=				self._load_meta(lowerCAmelCase_  )

										for line in lines[indices_start_line:]:
															try:
																				_snake_case							,     _snake_case									=				line.rstrip().rsplit(' '				,						1  )
																				if field == "#fairseq:overwrite":
																									_snake_case									=				True
																									_snake_case							,     _snake_case									=				line.rsplit(' '				,						1  )
																				else:
																									_snake_case									=				False
																				_snake_case									=				int(lowerCAmelCase_  )
																				_snake_case									=				line
																				if word in self and not overwrite:
																									raise RuntimeError(
																									    'Duplicate word found when loading Dictionary: \'{}\'. '
																									    'Duplicate words can overwrite earlier ones by adding the '
																									    '#fairseq:overwrite flag at the end of the corresponding row '
																									    'in the dictionary file. If using the Camembert model, please '
																									    'download an updated copy of the model file.'.format(lowerCAmelCase_  )  )
																				self.add_symbol(lowerCAmelCase_				,						n=lowerCAmelCase_				,						overwrite=lowerCAmelCase_  )
															except ValueError:
																				raise ValueError('Incorrect dictionary format, expected \'<token> <cnt> [flags]\''  )





def 						lowerCamelCase__     (     UpperCamelCase__			:       Union[str, Any]						)			-> Tuple:



					'''simple docstring'''




					_snake_case									=				dict((re.sub(R'@@$'       ,    ''       ,    UpperCamelCase__						), v) if k.endswith('@@'						) else (re.sub(R'$'       ,    '</w>'       ,    UpperCamelCase__						), v) for k, v in d.items()						)
					_snake_case									=				'<s> <pad> </s> <unk>'.split()
					# restore the special tokens
					for k in keep_keys:
										del da[F'''{k}</w>''']
										_snake_case									=				d[k]  # restore
					return da





def 						lowerCamelCase__     (     UpperCamelCase__			:       Optional[Any]       ,    UpperCamelCase__			:       Dict						)			-> List[str]:



					'''simple docstring'''




					if not os.path.exists(UpperCamelCase__						):
										raise ValueError(F'''path {biogpt_checkpoint_path} does not exist!'''						)
					os.makedirs(UpperCamelCase__       ,    exist_ok=UpperCamelCase__						)
					print(F'''Writing results to {pytorch_dump_folder_path}'''						)

					# handle various types of models

					_snake_case									=				os.path.join(UpperCamelCase__       ,    'checkpoint.pt'						)
					if not os.path.isfile(UpperCamelCase__						):
										raise ValueError(F'''path to the file {checkpoint_file} does not exist!'''						)
					_snake_case									=				torch.load(UpperCamelCase__       ,    map_location='cpu'						)

					_snake_case									=				chkpt['cfg']['model']

					# dicts
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'dict.txt'						)
					if not os.path.isfile(UpperCamelCase__						):
										raise ValueError(F'''path to the file {dict_file} does not exist!'''						)
					_snake_case									=				Dictionary.load(UpperCamelCase__						)
					_snake_case									=				rewrite_dict_keys(src_dict.indices						)
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				os.path.join(UpperCamelCase__       ,    VOCAB_FILES_NAMES['vocab_file']						)
					print(F'''Generating {src_vocab_file} of {src_vocab_size} records'''						)
					with open(UpperCamelCase__       ,    'w'       ,    encoding='utf-8'						) as f:
										f.write(json.dumps(UpperCamelCase__       ,    ensure_ascii=UpperCamelCase__       ,    indent=UpperCamelCase__						)						)

					# merges_file (bpecodes)
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'bpecodes'						)
					if not os.path.isfile(UpperCamelCase__						):
										raise ValueError(F'''path to the file {bpecodes_file} does not exist!'''						)

					_snake_case									=				os.path.join(UpperCamelCase__       ,    VOCAB_FILES_NAMES['merges_file']						)
					shutil.copyfile(UpperCamelCase__       ,    UpperCamelCase__						)

					# model config
					_snake_case									=				os.path.join(UpperCamelCase__       ,    'config.json'						)

					_snake_case									=				{
					    'activation_dropout': args['activation_dropout'],
					    'architectures': ['BioGptForCausalLM'],
					    'attention_probs_dropout_prob': args['attention_dropout'],
					    'bos_token_id': 0,
					    'eos_token_id': 2,
					    'hidden_act': args['activation_fn'],
					    'hidden_dropout_prob': args['dropout'],
					    'hidden_size': args['decoder_embed_dim'],
					    'initializer_range': 0.02,
					    'intermediate_size': args['decoder_ffn_embed_dim'],
					    'layer_norm_eps': 1e-12,
					    'layerdrop': args['decoder_layerdrop'],
					    'max_position_embeddings': args['max_target_positions'],
					    'model_type': 'biogpt',
					    'num_attention_heads': args['decoder_attention_heads'],
					    'num_hidden_layers': args['decoder_layers'],
					    'pad_token_id': 1,
					    'scale_embedding': not args['no_scale_embedding'],
					    'tie_word_embeddings': args['share_decoder_input_output_embed'],
					    'vocab_size': src_vocab_size,
					}

					# good hparam defaults to start with

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

					# tokenizer config
					_snake_case									=				os.path.join(UpperCamelCase__       ,    UpperCamelCase__						)

					_snake_case									=				{
					    'bos_token': '<s>',
					    'eos_token': '</s>',
					    'model_max_length': 1_024,
					    'pad_token': '<pad>',
					    'special_tokens_map_file': None,
					    'tokenizer_class': 'BioGptTokenizer',
					    'unk_token': '<unk>',
					}

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

					# model
					_snake_case									=				chkpt['model']

					# remove unneeded keys
					_snake_case									=				[
					    'decoder.version',
					]
					for k in ignore_keys:
										model_state_dict.pop(UpperCamelCase__       ,    UpperCamelCase__						)

					_snake_case									=				list(model_state_dict.keys()						)
					for layer_name in layer_names:
										if layer_name.endswith('output_projection.weight'						):
															_snake_case									=				model_state_dict.pop(UpperCamelCase__						)
										else:
															_snake_case									=				model_state_dict.pop(UpperCamelCase__						)

					_snake_case									=				BioGptConfig.from_pretrained(UpperCamelCase__						)
					_snake_case									=				BioGptForCausalLM(UpperCamelCase__						)

					# check that it loads ok
					model_new.load_state_dict(UpperCamelCase__						)

					# save
					_snake_case									=				os.path.join(UpperCamelCase__       ,    UpperCamelCase__						)
					print(F'''Generating {pytorch_weights_dump_path}'''						)
					torch.save(UpperCamelCase__       ,    UpperCamelCase__						)

					print('Conversion is done!'						)


if __name__ == "__main__":
					UpperCAmelCase_      							=     argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--biogpt_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."""
					)
					UpperCAmelCase_      							=     parser.parse_args()
					convert_biogpt_checkpoint_to_pytorch(args.biogpt_checkpoint_path, args.pytorch_dump_folder_path)



