
import concurrent.futures
import json
import os
import shutil
import tempfile
import unittest

from transformers import AutoTokenizer, PreTrainedTokenizerFast
from transformers.testing_utils import require_tokenizers

from ..test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class 						lowerCamelCase			(					__lowerCamelCase   ,		unittest.TestCase				):
	UpperCamelCase_     :				Dict								=  PreTrainedTokenizerFast
	UpperCamelCase_     :				List[Any]								=  False
	UpperCamelCase_     :				Any								=  True
	UpperCamelCase_     :				int								=  'tokenizer_file'







	def    snake_case__    (    self			:List[Any] )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   False  # because we don't have pretrained_vocab_files_map
				super().setUp()
				SCREAMING_SNAKE_CASE    	=   True

				SCREAMING_SNAKE_CASE    	=   ['''robot-test/dummy-tokenizer-fast''', '''robot-test/dummy-tokenizer-wordlevel''']
				SCREAMING_SNAKE_CASE    	=   '''SaulLu/dummy-tokenizer-bytelevel-bpe'''

				# Inclusion of 2 tokenizers to test different types of models (Unigram and WordLevel for the moment)
				SCREAMING_SNAKE_CASE    	=   [(PreTrainedTokenizerFast, model_path, {}) for model_path in model_paths]

				SCREAMING_SNAKE_CASE    	=   PreTrainedTokenizerFast.from_pretrained(model_paths[0] )
				tokenizer.save_pretrained(self.tmpdirname )







	def    snake_case__    (    self			:str )		->			Any:







				"""simple docstring"""






				pass







	def    snake_case__    (    self			:Dict )		->			str:







				"""simple docstring"""






				pass







	def    snake_case__    (    self			:List[str] )		->			Optional[Any]:







				"""simple docstring"""






				pass







	def    snake_case__    (    self			:List[str] )		->			Optional[Any]:







				"""simple docstring"""






				pass







	def    snake_case__    (    self			:Union[str, Any] )		->			int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.tmpdirname
				# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
										try:
													SCREAMING_SNAKE_CASE    	=   tempfile.mkdtemp()
													SCREAMING_SNAKE_CASE    	=   self.rust_tokenizer_class.from_pretrained(lowercase      ,    **lowercase )

													tokenizer.save_pretrained(self.tmpdirname )
													super().test_training_new_tokenizer()
										finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname )
													SCREAMING_SNAKE_CASE    	=   tmpdirname_orig







	def    snake_case__    (    self			:List[str] )		->			str:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.tmpdirname
				# Here we want to test the 2 available tokenizers that use 2 different types of models: Unigram and WordLevel.
				for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
										try:
													SCREAMING_SNAKE_CASE    	=   tempfile.mkdtemp()
													SCREAMING_SNAKE_CASE    	=   self.rust_tokenizer_class.from_pretrained(lowercase      ,    **lowercase )

													tokenizer.save_pretrained(self.tmpdirname )
													super().test_training_new_tokenizer_with_special_tokens_change()
										finally:
													# Even if the test fails, we must be sure that the folder is deleted and that the default tokenizer
													# is restored
													shutil.rmtree(self.tmpdirname )
													SCREAMING_SNAKE_CASE    	=   tmpdirname_orig







	def    snake_case__    (    self			:str )		->			Any:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.rust_tokenizer_class.from_pretrained(self.bytelevel_bpe_model_name )

				SCREAMING_SNAKE_CASE    	=   ('''a''' for _ in range(1_0_0_0 ))
				SCREAMING_SNAKE_CASE    	=   tokenizer.train_new_from_iterator(text_iterator=lowercase      ,    length=1_0_0_0      ,    vocab_size=5_0 )

				SCREAMING_SNAKE_CASE    	=   new_tokenizer.encode('''a🤗''' )
				self.assertEqual(lowercase      ,    [6_4, 1_7_2, 2_5_3, 9_7, 2_4_5] )





@require_tokenizers
class 						lowerCamelCase			(					unittest.TestCase				):







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   AutoTokenizer.from_pretrained('''bert-base-cased''' )
				SCREAMING_SNAKE_CASE    	=   json.loads(tokenizer._tokenizer.to_str() )
				SCREAMING_SNAKE_CASE    	=   len(lowercase )

				with tempfile.TemporaryDirectory() as tmp_dir:
							# Hack to save this in the tokenizer_config.json
							SCREAMING_SNAKE_CASE    	=   ['''tokenizer.4.0.0.json''']
							tokenizer.save_pretrained(lowercase )
							json.dump(lowercase      ,    open(os.path.join(lowercase      ,    '''tokenizer.4.0.0.json''' )      ,    '''w''' ) )

							# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
							SCREAMING_SNAKE_CASE    	=   AutoTokenizer.from_pretrained(lowercase )
							self.assertEqual(len(lowercase )      ,    len(lowercase ) + 1 )
							SCREAMING_SNAKE_CASE    	=   json.loads(new_tokenizer._tokenizer.to_str() )
							self.assertIn('''huggingface'''      ,    json_tokenizer['''model''']['''vocab'''] )

							# Will need to be adjusted if we reach v42 and this test is still here.
							# Should pick the old tokenizer file as the version of Transformers is < 4.0.0
							shutil.move(os.path.join(lowercase      ,    '''tokenizer.4.0.0.json''' )      ,    os.path.join(lowercase      ,    '''tokenizer.42.0.0.json''' ) )
							SCREAMING_SNAKE_CASE    	=   ['''tokenizer.42.0.0.json''']
							tokenizer.save_pretrained(lowercase )
							SCREAMING_SNAKE_CASE    	=   AutoTokenizer.from_pretrained(lowercase )
							self.assertEqual(len(lowercase )      ,    len(lowercase ) )
							SCREAMING_SNAKE_CASE    	=   json.loads(new_tokenizer._tokenizer.to_str() )
							self.assertNotIn('''huggingface'''      ,    json_tokenizer['''model''']['''vocab'''] )







	def    snake_case__    (    self			:Optional[Any] )		->			Tuple:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   '''hf-internal-testing/test-two-tokenizers'''

				# This should pick the new tokenizer file as the version of Transformers is > 4.0.0
				SCREAMING_SNAKE_CASE    	=   AutoTokenizer.from_pretrained(lowercase )
				self.assertEqual(len(lowercase )      ,    2_8_9_9_7 )
				SCREAMING_SNAKE_CASE    	=   json.loads(tokenizer._tokenizer.to_str() )
				self.assertIn('''huggingface'''      ,    json_tokenizer['''model''']['''vocab'''] )

				# Testing an older version by monkey-patching the version in the module it's used.
				import transformers as old_transformers

				SCREAMING_SNAKE_CASE    	=   '''3.0.0'''
				SCREAMING_SNAKE_CASE    	=   old_transformers.models.auto.AutoTokenizer.from_pretrained(lowercase )
				self.assertEqual(len(lowercase )      ,    2_8_9_9_6 )
				SCREAMING_SNAKE_CASE    	=   json.loads(old_tokenizer._tokenizer.to_str() )
				self.assertNotIn('''huggingface'''      ,    json_tokenizer['''model''']['''vocab'''] )





@require_tokenizers
class 						lowerCamelCase			(					unittest.TestCase				):







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







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   PreTrainedTokenizerFast.from_pretrained('''robot-test/dummy-tokenizer-wordlevel''' )
				SCREAMING_SNAKE_CASE    	=   '''The Matrix is a 1999 science fiction action film.'''

				with concurrent.futures.ThreadPoolExecutor() as executor:
							SCREAMING_SNAKE_CASE    	=   [executor.submit(self.fetch      ,    lowercase      ,    lowercase ) for i in range(1_0 )]
							SCREAMING_SNAKE_CASE    	=   [future.result() for future in futures]
							self.assertEqual(lowercase      ,    [[1, 1_0, 0, 8, 0, 1_8, 0, 0, 0, 2] for i in range(1_0 )] )







	def    snake_case__    (    self			:str      ,    lowercase			:int      ,    lowercase			:str )		->			Optional[int]:







				"""simple docstring"""






				return tokenizer.encode(lowercase      ,    truncation='''longest_first'''      ,    padding='''longest''' )

import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor





class 						lowerCamelCase			:







	def __init__(    self			:Optional[int]      ,    lowercase			:Optional[Any]      ,    lowercase			:Optional[int]      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   cuda
				SCREAMING_SNAKE_CASE    	=   AutoConfig.from_pretrained(lowercase )
				SCREAMING_SNAKE_CASE    	=   AutoModelForCTC.from_pretrained(lowercase )
				self.model.eval()
				if self.cuda:
							self.model.to(device='''cuda''' )
				SCREAMING_SNAKE_CASE    	=   AutoProcessor.from_pretrained(lowercase )
				SCREAMING_SNAKE_CASE    	=   torchaudio.transforms.Resample(lowercase      ,    1_6_0_0_0 )
				SCREAMING_SNAKE_CASE    	=   0
				SCREAMING_SNAKE_CASE    	=   list(self.processor.tokenizer.get_vocab().keys() )
				for i in range(len(lowercase ) ):
							if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										SCREAMING_SNAKE_CASE    	=   i
				print('''Blank Token id [PAD]/<pad>'''      ,    lowercase )
				SCREAMING_SNAKE_CASE    	=   blank_id







	def    snake_case__    (    self			:Any      ,    lowercase			:int )		->			Optional[int]:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   torchaudio.load(lowercase )
				SCREAMING_SNAKE_CASE    	=   self.resampler(lowercase ).squeeze().numpy()
				return speech







	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:Optional[Any] )		->			int:







				"""simple docstring"""






				SCREAMING_SNAKE_CASE    	=   self.blank_id
				SCREAMING_SNAKE_CASE    	=   '''|'''.join(item['''sent'''].split(''' ''' ) )
				if not os.path.isfile(item['''wav_path'''] ):
							print(item['''wav_path''']      ,    '''not found in wavs directory''' )

				SCREAMING_SNAKE_CASE    	=   self.speech_file_to_array_fn(item['''wav_path'''] )
				SCREAMING_SNAKE_CASE    	=   self.processor(lowercase      ,    sampling_rate=1_6_0_0_0      ,    return_tensors='''pt'''      ,    padding=lowercase )
				if self.cuda:
							SCREAMING_SNAKE_CASE    	=   inputs.to(device='''cuda''' )

				with torch.no_grad():
							SCREAMING_SNAKE_CASE    	=   self.model(inputs.input_values ).logits

				# get the emission probability at frame level
				SCREAMING_SNAKE_CASE    	=   torch.log_softmax(lowercase      ,    dim=-1 )
				SCREAMING_SNAKE_CASE    	=   emissions[0].cpu().detach()

				# get labels from vocab
				SCREAMING_SNAKE_CASE    	=   ([''''''] + list(self.processor.tokenizer.get_vocab().keys() ))[
				    :-1
				]  # logits don't align with the tokenizer's vocab

				SCREAMING_SNAKE_CASE    	=   {c: i for i, c in enumerate(lowercase )}
				SCREAMING_SNAKE_CASE    	=   []
				for c in transcript:
							if c in dictionary:
										tokens.append(dictionary[c] )

				def get_trellis(lowercase			:List[Any]      ,    lowercase			:Any      ,    lowercase			:int=0 ):
							SCREAMING_SNAKE_CASE    	=   emission.size(0 )
							SCREAMING_SNAKE_CASE    	=   len(lowercase )

							# Trellis has extra diemsions for both time axis and tokens.
							# The extra dim for tokens represents <SoS> (start-of-sentence)
							# The extra dim for time axis is for simplification of the code.
							SCREAMING_SNAKE_CASE    	=   torch.full((num_frames + 1, num_tokens + 1)      ,    -float('''inf''' ) )
							SCREAMING_SNAKE_CASE    	=   0
							for t in range(lowercase ):
										SCREAMING_SNAKE_CASE    	=   torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]      ,    trellis[t, :-1] + emission[t, tokens]      ,    )
							return trellis

				SCREAMING_SNAKE_CASE    	=   get_trellis(lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float

				def backtrack(lowercase			:Optional[int]      ,    lowercase			:List[str]      ,    lowercase			:Union[str, Any]      ,    lowercase			:Optional[Any]=0 ):
							SCREAMING_SNAKE_CASE    	=   trellis.size(1 ) - 1
							SCREAMING_SNAKE_CASE    	=   torch.argmax(trellis[:, j] ).item()

							SCREAMING_SNAKE_CASE    	=   []
							for t in range(lowercase      ,    0      ,    -1 ):
										# 1. Figure out if the current position was stay or change
										# Note (again):
										# `emission[J-1]` is the emission at time frame `J` of trellis dimension.
										# Score for token staying the same from time frame J-1 to T.
										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j] + emission[t - 1, blank_id]
										# Score for token changing from C-1 at T-1 to J at T.
										SCREAMING_SNAKE_CASE    	=   trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

										# 2. Store the path with frame-wise probability.
										SCREAMING_SNAKE_CASE    	=   emission[t - 1, tokens[j - 1] if changed > stayed else 0].exp().item()
										# Return token index and time index in non-trellis coordinate.
										path.append(Point(j - 1      ,    t - 1      ,    lowercase ) )

										# 3. Update the token
										if changed > stayed:
													j -= 1
													if j == 0:
																break
							else:
										raise ValueError('''Failed to align''' )
							return path[::-1]

				SCREAMING_SNAKE_CASE    	=   backtrack(lowercase      ,    lowercase      ,    lowercase      ,    lowercase )





				@dataclass
				class 						lowerCamelCase			:
					UpperCamelCase_     :				str
					UpperCamelCase_     :				int
					UpperCamelCase_     :				int
					UpperCamelCase_     :				float







					def __repr__(    self			:Optional[Any] )		->			str:







								"""simple docstring"""






								return f"""{self.label}\t{self.score:4.2f}\t{self.start*2_0:5d}\t{self.end*2_0:5d}"""







					@property
					def    snake_case__    (    self			:List[str] )		->			Any:







								"""simple docstring"""






								return self.end - self.start

				def merge_repeats(lowercase			:List[str] ):
							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   0, 0
							SCREAMING_SNAKE_CASE    	=   []
							while ia < len(lowercase ):
										while ia < len(lowercase ) and path[ia].token_index == path[ia].token_index:
													ia += 1
										SCREAMING_SNAKE_CASE    	=   sum(path[k].score for k in range(lowercase      ,    lowercase ) ) / (ia - ia)
										segments.append(
										    Segment(
										        transcript[path[ia].token_index]      ,    path[ia].time_index      ,    path[ia - 1].time_index + 1      ,    lowercase      ,    ) )
										SCREAMING_SNAKE_CASE    	=   ia
							return segments

				SCREAMING_SNAKE_CASE    	=   merge_repeats(lowercase )
				with open(item['''out_path''']      ,    '''w''' ) as out_align:
							for seg in segments:
										out_align.write(str(lowercase ) + '''\n''' )







	def    snake_case__    (    self			:List[Any]      ,    lowercase			:Dict      ,    lowercase			:Tuple      ,    lowercase			:str )		->			Dict:







				"""simple docstring"""






				if not os.path.exists(lowercase ):
							os.makedirs(lowercase )

				# load text file
				SCREAMING_SNAKE_CASE    	=   open(lowercase      ,    encoding='''utf8''' ).readlines()

				SCREAMING_SNAKE_CASE    	=   []
				for line in lines:
							if len(line.strip().split('''\t''' ) ) != 2:
										print('''Script must be in format: 00001  this is my sentence''' )
										exit()

							SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   line.strip().split('''\t''' )
							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.wav''' )
							SCREAMING_SNAKE_CASE    	=   os.path.join(lowercase      ,    wav_name + '''.txt''' )

							items.append({'''sent''': sentence, '''wav_path''': wav_path, '''out_path''': out_path} )
				print('''Number of samples found in script file'''      ,    len(lowercase ) )

				for item in tqdm(lowercase ):
							self.align_single_sample(lowercase )



def       a			(   )    ->Optional[int]:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   argparse.ArgumentParser()

			parser.add_argument(
			    '''--model_name'''		,	type=a		,	default='''arijitx/wav2vec2-xls-r-300m-bengali'''		,	help='''wav2vec model name'''  )
			parser.add_argument('''--wav_dir'''		,	type=a		,	default='''./wavs'''		,	help='''directory containing wavs'''  )
			parser.add_argument('''--text_file'''		,	type=a		,	default='''script.txt'''		,	help='''file containing text'''  )
			parser.add_argument('''--input_wavs_sr'''		,	type=a		,	default=1_6000		,	help='''sampling rate of input audios'''  )
			parser.add_argument(
			    '''--output_dir'''		,	type=a		,	default='''./out_alignment'''		,	help='''output directory containing the alignment files'''  )
			parser.add_argument('''--cuda'''		,	action='''store_true'''  )

			SCREAMING_SNAKE_CASE    	=   parser.parse_args()

			SCREAMING_SNAKE_CASE    	=   WavaVecaAligner(args.model_name		,	args.input_wavs_sr		,	args.cuda  )
			aligner.align_data(args.wav_dir		,	args.text_file		,	args.output_dir  )


if __name__ == "__main__":
							main()