




from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


__UpperCamelCase      : int					      =   logging.get_logger(__name__)

__UpperCamelCase      : Any					      =   {
    "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/config.json",
    # See all MBART models at https://huggingface.co/models?filter=mbart
}

class 	__lowerCAmelCase			(			__magic_name__				):
       UpperCamelCase__        =						'''mbart'''
       UpperCamelCase__        =						['''past_key_values''']
       UpperCamelCase__        =						{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}
       def __init__(		self		:int	,   __magic_name__		:List[str]=5_0265	,   __magic_name__		:List[Any]=1024	,   __magic_name__		:str=12	,   __magic_name__		:str=4096	,   __magic_name__		:List[str]=16	,   __magic_name__		:Optional[Any]=12	,   __magic_name__		:List[str]=4096	,   __magic_name__		:Dict=16	,   __magic_name__		:List[str]=0.0	,   __magic_name__		:Optional[int]=0.0	,   __magic_name__		:Any=True	,   __magic_name__		:Dict=True	,   __magic_name__		:int="gelu"	,   __magic_name__		:Tuple=1024	,   __magic_name__		:Any=0.1	,   __magic_name__		:Tuple=0.0	,   __magic_name__		:Any=0.0	,   __magic_name__		:Tuple=0.02	,   __magic_name__		:int=0.0	,   __magic_name__		:Optional[Any]=False	,   __magic_name__		:Optional[Any]=1	,   __magic_name__		:Dict=0	,   __magic_name__		:Optional[Any]=2	,   __magic_name__		:int=2	,   **__magic_name__		:int	,   ):







        '''simple docstring'''







        a		       =							vocab_size
        a		       =							max_position_embeddings
        a		       =							d_model
        a		       =							encoder_ffn_dim
        a		       =							encoder_layers
        a		       =							encoder_attention_heads
        a		       =							decoder_ffn_dim
        a		       =							decoder_layers
        a		       =							decoder_attention_heads
        a		       =							dropout
        a		       =							attention_dropout
        a		       =							activation_dropout
        a		       =							activation_function
        a		       =							init_std
        a		       =							encoder_layerdrop
        a		       =							decoder_layerdrop
        a		       =							classifier_dropout
        a		       =							use_cache
        a		       =							encoder_layers
        a		       =							scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=__magic_name__	,   bos_token_id=__magic_name__	,   eos_token_id=__magic_name__	,   is_encoder_decoder=__magic_name__	,   forced_eos_token_id=__magic_name__	,   **__magic_name__	,   )







class 	__lowerCAmelCase			(			__magic_name__				):
       @property
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
         a		       =							OrderedDict(
             [
                 ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                 ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
             ]       )

         if self.use_past:
          a		       =							{0: """batch"""}
          a		       =							{0: """batch""", 1: """past_decoder_sequence + sequence"""}
         else:
          a		       =							{0: """batch""", 1: """decoder_sequence"""}
          a		       =							{0: """batch""", 1: """decoder_sequence"""}

         if self.use_past:
          self.fill_with_past_key_values_(__magic_name__	,   direction="""inputs"""       )
        elif self.task == "causal-lm":
         # TODO: figure this case out.
         a		       =							OrderedDict(
             [
                 ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                 ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
             ]       )
         if self.use_past:
          a       ,				a		       =							self.num_layers
          for i in range(__magic_name__       ):
           a		       =							{0: """batch""", 2: """past_sequence + sequence"""}
           a		       =							{0: """batch""", 2: """past_sequence + sequence"""}
        else:
         a		       =							OrderedDict(
             [
                 ("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
                 ("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
                 ("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
                 ("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
             ]       )

        return common_inputs
       @property
       def    lowerCamelCase__							(		self		:Optional[Any]       ):







        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
         a		       =							super().outputs
        else:
         a		       =							super(__magic_name__	,   self       ).outputs
         if self.use_past:
          a       ,				a		       =							self.num_layers
          for i in range(__magic_name__       ):
           a		       =							{0: """batch""", 2: """past_sequence + sequence"""}
           a		       =							{0: """batch""", 2: """past_sequence + sequence"""}
        return common_outputs
       def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







        '''simple docstring'''







        a		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

        # Generate decoder inputs
        a		       =							seq_length if not self.use_past else 1
        a		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )
        a		       =							{F'decoder_{name}': tensor for name, tensor in decoder_inputs.items()}
        a		       =							dict(**__magic_name__	,   **__magic_name__       )

        if self.use_past:
         if not is_torch_available():
          raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
         else:
          import torch
         a       ,				a		       =							common_inputs["""input_ids"""].shape
         a		       =							common_inputs["""decoder_input_ids"""].shape[1]
         a       ,				a		       =							self.num_attention_heads
         a		       =							(
             batch,
             num_encoder_attention_heads,
             encoder_seq_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )
         a		       =							decoder_seq_length + 3
         a		       =							(
             batch,
             num_decoder_attention_heads,
             decoder_past_length,
             self._config.hidden_size // num_decoder_attention_heads,
         )

         a		       =							torch.cat(
             [common_inputs["""decoder_attention_mask"""], torch.ones(__magic_name__	,   __magic_name__       )]	,   dim=1       )

         a		       =							[]
         # If the number of encoder and decoder layers are present in the model configuration, both are considered
         a       ,				a		       =							self.num_layers
         a		       =							min(__magic_name__	,   __magic_name__       )
         a		       =							max(__magic_name__	,   __magic_name__       ) - min_num_layers
         a		       =							"""encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""

         for _ in range(__magic_name__       ):
          common_inputs["past_key_values"].append(
              (
                  torch.zeros(__magic_name__       ),
                  torch.zeros(__magic_name__       ),
                  torch.zeros(__magic_name__       ),
                  torch.zeros(__magic_name__       ),
              )       )
         # TODO: test this.
         a		       =							encoder_shape if remaining_side_name == """encoder""" else decoder_shape
         for _ in range(__magic_name__	,   __magic_name__       ):
          common_inputs["past_key_values"].append((torch.zeros(__magic_name__       ), torch.zeros(__magic_name__       ))       )
        return common_inputs
       def    lowerCamelCase__							(		self		:Any	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







        '''simple docstring'''







        a		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
            __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

        if self.use_past:
         if not is_torch_available():
          raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""       )
         else:
          import torch
         a       ,				a		       =							common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         a		       =							seqlen + 2
         a       ,				a		       =							self.num_layers
         a       ,				a		       =							self.num_attention_heads
         a		       =							(
             batch,
             num_encoder_attention_heads,
             past_key_values_length,
             self._config.hidden_size // num_encoder_attention_heads,
         )

         a		       =							common_inputs["""attention_mask"""].dtype
         a		       =							torch.cat(
             [common_inputs["""attention_mask"""], torch.ones(__magic_name__	,   __magic_name__	,   dtype=__magic_name__       )]	,   dim=1       )
         a		       =							[
             (torch.zeros(__magic_name__       ), torch.zeros(__magic_name__       )) for _ in range(__magic_name__       )
         ]
        return common_inputs
       def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







        '''simple docstring'''







        a		       =							compute_effective_axis_dimension(
            __magic_name__	,   fixed_dimension=OnnxConfig.default_fixed_batch	,   num_token_to_add=0       )

        # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
        a		       =							tokenizer.num_special_tokens_to_add(__magic_name__       )
        a		       =							compute_effective_axis_dimension(
            __magic_name__	,   fixed_dimension=OnnxConfig.default_fixed_sequence	,   num_token_to_add=__magic_name__       )

        # Generate dummy inputs according to compute batch and sequence
        a		       =							[""" """.join([tokenizer.unk_token]       ) * seq_length] * batch_size
        a		       =							dict(tokenizer(__magic_name__	,   return_tensors=__magic_name__       )       )
        return common_inputs
       def    lowerCamelCase__							(		self		:int	,   __magic_name__		:PreTrainedTokenizer	,   __magic_name__		:int = -1	,   __magic_name__		:int = -1	,   __magic_name__		:bool = False	,   __magic_name__		:Optional[TensorType] = None	,   ):







        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
         a		       =							self._generate_dummy_inputs_for_default_and_seqaseq_lm(
             __magic_name__	,   batch_size=__magic_name__	,   seq_length=__magic_name__	,   is_pair=__magic_name__	,   framework=__magic_name__       )

        elif self.task == "causal-lm":
         a		       =							self._generate_dummy_inputs_for_causal_lm(
             __magic_name__	,   batch_size=__magic_name__	,   seq_length=__magic_name__	,   is_pair=__magic_name__	,   framework=__magic_name__       )
        else:
         a		       =							self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
             __magic_name__	,   batch_size=__magic_name__	,   seq_length=__magic_name__	,   is_pair=__magic_name__	,   framework=__magic_name__       )

        return common_inputs





       def    lowerCamelCase__							(		self		:List[Any]	,   __magic_name__		:Optional[Any]	,   __magic_name__		:str	,   __magic_name__		:List[str]	,   __magic_name__		:Tuple       ):







        '''simple docstring'''







        if self.task in ["default", "seq2seq-lm"]:
         a		       =							super()._flatten_past_key_values_(__magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )
        else:
         a		       =							super(__magic_name__	,   self       )._flatten_past_key_values_(
             __magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )







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		:Dict	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:Union[str, Any]	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							cuda
								a		       =							AutoConfig.from_pretrained(__magic_name__       )
								a		       =							AutoModelForCTC.from_pretrained(__magic_name__       )
								self.model.eval()
								if self.cuda:
									self.model.to(device="""cuda"""       )
								a		       =							AutoProcessor.from_pretrained(__magic_name__       )
								a		       =							torchaudio.transforms.Resample(__magic_name__	,   1_6000       )
								a		       =							0
								a		       =							list(self.processor.tokenizer.get_vocab().keys()       )
								for i in range(len(__magic_name__       )       ):
									if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
										a		       =							i
								print("""Blank Token id [PAD]/<pad>"""	,   __magic_name__       )
								a		       =							blank_id
							def    lowerCamelCase__							(		self		:Optional[Any]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







								a       ,				a		       =							torchaudio.load(__magic_name__       )
								a		       =							self.resampler(__magic_name__       ).squeeze().numpy()
								return speech
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:List[str]       ):







								'''simple docstring'''







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

								a		       =							self.speech_file_to_array_fn(item["""wav_path"""]       )
								a		       =							self.processor(__magic_name__	,   sampling_rate=1_6000	,   return_tensors="""pt"""	,   padding=__magic_name__       )
								if self.cuda:
									a		       =							inputs.to(device="""cuda"""       )

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

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

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

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

								def get_trellis(__magic_name__		:Optional[Any]	,   __magic_name__		:Optional[int]	,   __magic_name__		:Any=0       ):
									a		       =							emission.size(0       )
									a		       =							len(__magic_name__       )

									# 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.
									a		       =							torch.full((num_frames + 1, num_tokens + 1)	,   -float("""inf"""       )       )
									a		       =							0
									for t in range(__magic_name__       ):
										a		       =							torch.maximum(
										    # Score for staying at the same token
										    trellis[t, 1:] + emission[t, blank_id]	,   trellis[t, :-1] + emission[t, tokens]	,   )
									return trellis

								a		       =							get_trellis(__magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42

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

									a		       =							[]
									for t in range(__magic_name__	,   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.
										a		       =							trellis[t - 1, j] + emission[t - 1, blank_id]
										# Score for token changing from C-1 at T-1 to J at T.
										a		       =							trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

										# 2. Store the path with frame-wise probability.
										a		       =							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	,   __magic_name__       )       )

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

								a		       =							backtrack(__magic_name__	,   __magic_name__	,   __magic_name__	,   __magic_name__       )

								@dataclass
								class 	__lowerCAmelCase			:
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															UpperCamelCase__        =						42
															def __repr__(		self		:Union[str, Any]       ):







																'''simple docstring'''







																return F'{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}'





															@property
															def    lowerCamelCase__							(		self		:int       ):







																'''simple docstring'''







																return self.end - self.start

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

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





							def    lowerCamelCase__							(		self		:int	,   __magic_name__		:str	,   __magic_name__		:List[Any]	,   __magic_name__		:Optional[int]       ):







								'''simple docstring'''







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

								# load text file
								a		       =							open(__magic_name__	,   encoding="""utf8"""       ).readlines()

								a		       =							[]
								for line in lines:
									if len(line.strip().split("""\t"""       )       ) != 2:
										print("""Script must be in format: 00001  this is my sentence"""       )
										exit()

									a       ,				a		       =							line.strip().split("""\t"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.wav"""       )
									a		       =							os.path.join(__magic_name__	,   wav_name + """.txt"""       )

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

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







def 	__A	(			)   -> Union[str, Any]:
	a		       =							argparse.ArgumentParser()

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

	a		       =							parser.parse_args()

	a		       =							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()

