






import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
					import os

					# The slow tests are often failing with OOM error on GPU
					# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
					# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
					__snake_case     :     int       =  """platform"""

					import jax
					import jax.numpy as jnp

					from transformers import AutoTokenizer
					from transformers.models.mbart.modeling_flax_mbart import (
					    FlaxMBartForConditionalGeneration,
					    FlaxMBartForQuestionAnswering,
					    FlaxMBartForSequenceClassification,
					    FlaxMBartModel,
					    shift_tokens_right,
					)







def 						_UpperCamelCase (     UpperCamelCase_							:       Dict     ,							UpperCamelCase_							:       Optional[int]     ,							UpperCamelCase_							:       Any=None     ,							UpperCamelCase_							:       int=None     ,							UpperCamelCase_							:       str=None     ,							UpperCamelCase_							:       Tuple=None     ,							UpperCamelCase_							:       Union[str, Any]=None     ,							UpperCamelCase_							:       Union[str, Any]=None     ,							)	-> int:

						"""simple docstring"""



						if attention_mask is None:
												lowerCAmelCase__         =    np.where(input_ids != config.pad_token_id     ,							1     ,							0		)
						if decoder_attention_mask is None:
												lowerCAmelCase__         =    np.where(decoder_input_ids != config.pad_token_id     ,							1     ,							0		)
						if head_mask is None:
												lowerCAmelCase__         =    np.ones((config.encoder_layers, config.encoder_attention_heads)		)
						if decoder_head_mask is None:
												lowerCAmelCase__         =    np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						if cross_attn_head_mask is None:
												lowerCAmelCase__         =    np.ones((config.decoder_layers, config.decoder_attention_heads)		)
						return {
						    "input_ids": input_ids,
						    "decoder_input_ids": decoder_input_ids,
						    "attention_mask": attention_mask,
						    "decoder_attention_mask": decoder_attention_mask,
						}


class      __SCREAMING_SNAKE_CASE       ( unittest.TestCase):






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase=13  ,    _UpperCamelCase=7  ,    _UpperCamelCase=True  ,    _UpperCamelCase=False  ,    _UpperCamelCase=99  ,    _UpperCamelCase=16  ,    _UpperCamelCase=2  ,    _UpperCamelCase=4  ,    _UpperCamelCase=4  ,    _UpperCamelCase="gelu"  ,    _UpperCamelCase=0.1  ,    _UpperCamelCase=0.1  ,    _UpperCamelCase=32  ,    _UpperCamelCase=2  ,    _UpperCamelCase=1  ,    _UpperCamelCase=0  ,    _UpperCamelCase=2  ,    _UpperCamelCase=0.02  ,    ):






												"""simple docstring"""
												lowerCAmelCase__         =    parent
												lowerCAmelCase__         =    batch_size
												lowerCAmelCase__         =    seq_length
												lowerCAmelCase__         =    is_training
												lowerCAmelCase__         =    use_labels
												lowerCAmelCase__         =    vocab_size
												lowerCAmelCase__         =    hidden_size
												lowerCAmelCase__         =    num_hidden_layers
												lowerCAmelCase__         =    num_attention_heads
												lowerCAmelCase__         =    intermediate_size
												lowerCAmelCase__         =    hidden_act
												lowerCAmelCase__         =    hidden_dropout_prob
												lowerCAmelCase__         =    attention_probs_dropout_prob
												lowerCAmelCase__         =    max_position_embeddings
												lowerCAmelCase__         =    eos_token_id
												lowerCAmelCase__         =    pad_token_id
												lowerCAmelCase__         =    bos_token_id
												lowerCAmelCase__         =    decoder_start_token_id
												lowerCAmelCase__         =    initializer_range






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    np.clip(ids_tensor([self.batch_size, self.seq_length - 1]  ,    self.vocab_size )  ,    3  ,    self.vocab_size )
												lowerCAmelCase__         =    np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)  ,    dtype=np.intaa ))  ,    -1 )

												lowerCAmelCase__         =    shift_tokens_right(_UpperCamelCase  ,    1 )

												lowerCAmelCase__         =    MBartConfig(
												    vocab_size=self.vocab_size  ,    d_model=self.hidden_size  ,    encoder_layers=self.num_hidden_layers  ,    decoder_layers=self.num_hidden_layers  ,    encoder_attention_heads=self.num_attention_heads  ,    decoder_attention_heads=self.num_attention_heads  ,    encoder_ffn_dim=self.intermediate_size  ,    decoder_ffn_dim=self.intermediate_size  ,    dropout=self.hidden_dropout_prob  ,    attention_dropout=self.attention_probs_dropout_prob  ,    max_position_embeddings=self.max_position_embeddings  ,    eos_token_id=self.eos_token_id  ,    bos_token_id=self.bos_token_id  ,    pad_token_id=self.pad_token_id  ,    decoder_start_token_id=self.decoder_start_token_id  ,    initializer_range=self.initializer_range  ,    use_cache=_UpperCamelCase  ,    )
												lowerCAmelCase__         =    prepare_mbart_inputs_dict(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )
												return config, inputs_dict






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    self.prepare_config_and_inputs()
												return config, inputs_dict






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    20
												lowerCAmelCase__         =    model_class_name(_UpperCamelCase )

												lowerCAmelCase__         =    model.encode(inputs_dict['input_ids'] )

												lowerCAmelCase__    ,							lowerCAmelCase__         =    (
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

												lowerCAmelCase__         =    model.init_cache(decoder_input_ids.shape[0]  ,    _UpperCamelCase  ,    _UpperCamelCase )
												lowerCAmelCase__         =    jnp.ones((decoder_input_ids.shape[0], max_decoder_length)  ,    dtype='i4' )

												lowerCAmelCase__         =    jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )
												lowerCAmelCase__         =    model.decode(
												    decoder_input_ids[:, :-1]  ,    _UpperCamelCase  ,    decoder_attention_mask=_UpperCamelCase  ,    past_key_values=_UpperCamelCase  ,    decoder_position_ids=_UpperCamelCase  ,    )

												lowerCAmelCase__         =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,    dtype='i4' )
												lowerCAmelCase__         =    model.decode(
												    decoder_input_ids[:, -1:]  ,    _UpperCamelCase  ,    decoder_attention_mask=_UpperCamelCase  ,    past_key_values=outputs_cache.past_key_values  ,    decoder_position_ids=_UpperCamelCase  ,    )

												lowerCAmelCase__         =    model.decode(_UpperCamelCase  ,    _UpperCamelCase )

												lowerCAmelCase__         =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
												self.parent.assertTrue(diff < 1E-3  ,    msg=F"Max diff is {diff}" )







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    20
												lowerCAmelCase__         =    model_class_name(_UpperCamelCase )

												lowerCAmelCase__         =    model.encode(inputs_dict['input_ids'] )

												lowerCAmelCase__    ,							lowerCAmelCase__         =    (
												    inputs_dict['decoder_input_ids'],
												    inputs_dict['decoder_attention_mask'],
												)

												lowerCAmelCase__         =    jnp.concatenate(
												    [
												        decoder_attention_mask,
												        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
												    ]  ,    axis=-1  ,    )

												lowerCAmelCase__         =    model.init_cache(decoder_input_ids.shape[0]  ,    _UpperCamelCase  ,    _UpperCamelCase )
												lowerCAmelCase__         =    jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :]  ,    (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)  ,    )

												lowerCAmelCase__         =    model.decode(
												    decoder_input_ids[:, :-1]  ,    _UpperCamelCase  ,    decoder_attention_mask=_UpperCamelCase  ,    past_key_values=_UpperCamelCase  ,    decoder_position_ids=_UpperCamelCase  ,    )
												lowerCAmelCase__         =    jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]  ,    dtype='i4' )
												lowerCAmelCase__         =    model.decode(
												    decoder_input_ids[:, -1:]  ,    _UpperCamelCase  ,    past_key_values=outputs_cache.past_key_values  ,    decoder_attention_mask=_UpperCamelCase  ,    decoder_position_ids=_UpperCamelCase  ,    )

												lowerCAmelCase__         =    model.decode(_UpperCamelCase  ,    _UpperCamelCase  ,    decoder_attention_mask=_UpperCamelCase )

												lowerCAmelCase__         =    np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
												self.parent.assertTrue(diff < 1E-3  ,    msg=F"Max diff is {diff}" )




@require_flax
class      __SCREAMING_SNAKE_CASE       ( unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							Union[str, Any]				=       99






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    np.array(
												    [
												        [71, 82, 18, 33, 46, 91, 2],
												        [68, 34, 26, 58, 30, 82, 2],
												        [5, 97, 17, 39, 94, 40, 2],
												        [76, 83, 94, 25, 70, 78, 2],
												        [87, 59, 41, 35, 48, 66, 2],
												        [55, 13, 16, 58, 5, 2, 1],  # note padding
												        [64, 27, 31, 51, 12, 75, 2],
												        [52, 64, 86, 17, 83, 39, 2],
												        [48, 61, 9, 24, 71, 82, 2],
												        [26, 1, 60, 48, 22, 13, 2],
												        [21, 5, 62, 28, 14, 76, 2],
												        [45, 98, 37, 86, 59, 48, 2],
												        [70, 70, 50, 9, 28, 0, 2],
												    ]  ,    dtype=np.intaa  ,    )

												lowerCAmelCase__         =    input_ids.shape[0]
												lowerCAmelCase__         =    MBartConfig(
												    vocab_size=self.vocab_size  ,    d_model=24  ,    encoder_layers=2  ,    decoder_layers=2  ,    encoder_attention_heads=2  ,    decoder_attention_heads=2  ,    encoder_ffn_dim=32  ,    decoder_ffn_dim=32  ,    max_position_embeddings=48  ,    eos_token_id=2  ,    pad_token_id=1  ,    bos_token_id=0  ,    )
												return config, input_ids, batch_size






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    self._get_config_and_data()
												lowerCAmelCase__         =    FlaxMBartForSequenceClassification(_UpperCamelCase )
												lowerCAmelCase__         =    model(input_ids=_UpperCamelCase  ,    decoder_input_ids=_UpperCamelCase )
												lowerCAmelCase__         =    (batch_size, config.num_labels)
												self.assertEqual(outputs['logits'].shape  ,    _UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    self._get_config_and_data()
												lowerCAmelCase__         =    FlaxMBartForQuestionAnswering(_UpperCamelCase )
												lowerCAmelCase__         =    model(input_ids=_UpperCamelCase )

												self.assertEqual(outputs['start_logits'].shape  ,    input_ids.shape )
												self.assertEqual(outputs['end_logits'].shape  ,    input_ids.shape )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__    ,							lowerCAmelCase__         =    self._get_config_and_data()
												lowerCAmelCase__         =    FlaxMBartForConditionalGeneration(_UpperCamelCase )
												lowerCAmelCase__         =    lm_model(input_ids=_UpperCamelCase )
												lowerCAmelCase__         =    (batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs['logits'].shape  ,    _UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    MBartConfig(
												    vocab_size=self.vocab_size  ,    d_model=14  ,    encoder_layers=2  ,    decoder_layers=2  ,    encoder_attention_heads=2  ,    decoder_attention_heads=2  ,    encoder_ffn_dim=8  ,    decoder_ffn_dim=8  ,    max_position_embeddings=48  ,    )
												lowerCAmelCase__         =    FlaxMBartForConditionalGeneration(_UpperCamelCase )
												lowerCAmelCase__         =    np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]  ,    dtype=np.intaa )
												lowerCAmelCase__         =    np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]  ,    dtype=np.intaa )
												lowerCAmelCase__         =    lm_model(input_ids=_UpperCamelCase  ,    decoder_input_ids=_UpperCamelCase )
												lowerCAmelCase__         =    (*summary.shape, config.vocab_size)
												self.assertEqual(outputs['logits'].shape  ,    _UpperCamelCase )







						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]  ,    dtype=np.intaa )
												lowerCAmelCase__         =    shift_tokens_right(_UpperCamelCase  ,    1 )
												lowerCAmelCase__         =    np.equal(_UpperCamelCase  ,    1 ).astype(np.floataa ).sum()
												lowerCAmelCase__         =    np.equal(_UpperCamelCase  ,    1 ).astype(np.floataa ).sum()
												self.assertEqual(shifted.shape  ,    input_ids.shape )
												self.assertEqual(_UpperCamelCase  ,    n_pad_before - 1 )
												self.assertTrue(np.equal(shifted[:, 0]  ,    2 ).all() )




@require_flax
class      __SCREAMING_SNAKE_CASE       ( __lowercase		,       unittest.TestCase		,       __lowercase):
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       True
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       (
						    (
						        FlaxMBartModel,
						        FlaxMBartForConditionalGeneration,
						        FlaxMBartForSequenceClassification,
						        FlaxMBartForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)
						_SCREAMING_SNAKE_CASE						:							Optional[int]				=       (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    FlaxMBartModelTester(self )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																		self.model_tester.check_use_cache_forward_with_attn_mask(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__ ):
																								lowerCAmelCase__         =    self._prepare_for_class(_UpperCamelCase  ,    _UpperCamelCase )
																								lowerCAmelCase__         =    model_class(_UpperCamelCase )

																								@jax.jit
																								def encode_jitted(_UpperCamelCase  ,    _UpperCamelCase=None  ,    **_UpperCamelCase ):
																														return model.encode(input_ids=_UpperCamelCase  ,    attention_mask=_UpperCamelCase )

																								with self.subTest('JIT Enabled' ):
																														lowerCAmelCase__         =    encode_jitted(**_UpperCamelCase ).to_tuple()

																								with self.subTest('JIT Disabled' ):
																														with jax.disable_jit():
																																				lowerCAmelCase__         =    encode_jitted(**_UpperCamelCase ).to_tuple()

																								self.assertEqual(len(_UpperCamelCase )  ,    len(_UpperCamelCase ) )
																								for jitted_output, output in zip(_UpperCamelCase  ,    _UpperCamelCase ):
																														self.assertEqual(jitted_output.shape  ,    output.shape )






						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																		with self.subTest(model_class.__name__ ):
																								lowerCAmelCase__         =    model_class(_UpperCamelCase )
																								lowerCAmelCase__         =    model.encode(inputs_dict['input_ids']  ,    inputs_dict['attention_mask'] )

																								lowerCAmelCase__         =    {
																								    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																								    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																								    'encoder_outputs': encoder_outputs,
																								}

																								@jax.jit
																								def decode_jitted(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):
																														return model.decode(
																														    decoder_input_ids=_UpperCamelCase  ,    decoder_attention_mask=_UpperCamelCase  ,    encoder_outputs=_UpperCamelCase  ,    )

																								with self.subTest('JIT Enabled' ):
																														lowerCAmelCase__         =    decode_jitted(**_UpperCamelCase ).to_tuple()

																								with self.subTest('JIT Disabled' ):
																														with jax.disable_jit():
																																				lowerCAmelCase__         =    decode_jitted(**_UpperCamelCase ).to_tuple()

																								self.assertEqual(len(_UpperCamelCase )  ,    len(_UpperCamelCase ) )
																								for jitted_output, output in zip(_UpperCamelCase  ,    _UpperCamelCase ):
																														self.assertEqual(jitted_output.shape  ,    output.shape )







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												for model_class_name in self.all_model_classes:
																		lowerCAmelCase__         =    model_class_name.from_pretrained('facebook/mbart-large-cc25'  ,    from_pt=_UpperCamelCase )
																		# FlaxMBartForSequenceClassification expects eos token in input_ids
																		lowerCAmelCase__         =    np.ones((1, 1) ) * model.config.eos_token_id
																		lowerCAmelCase__         =    model(_UpperCamelCase )
																		self.assertIsNotNone(_UpperCamelCase )




@require_flax
@require_sentencepiece
@require_tokenizers
class      __SCREAMING_SNAKE_CASE       ( unittest.TestCase):
						_SCREAMING_SNAKE_CASE						:							List[str]				=       [
						    ''' UN Chief Says There Is No Military Solution in Syria''',
						]
						_SCREAMING_SNAKE_CASE						:							Dict				=       [
						    '''Şeful ONU declară că nu există o soluţie militară în Siria''',
						]
						_SCREAMING_SNAKE_CASE						:							List[Any]				=       '''facebook/mbart-large-en-ro'''






						@cached_property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												return AutoTokenizer.from_pretrained(self.model_name )






						@cached_property
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												lowerCAmelCase__         =    FlaxMBartForConditionalGeneration.from_pretrained(self.model_name  ,    from_pt=_UpperCamelCase )
												return model






						def    UpperCamelCase__   (						self  ,    **_UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.translate_src_text(**_UpperCamelCase )
												self.assertListEqual(self.expected_text  ,    _UpperCamelCase )






						def    UpperCamelCase__   (						self  ,    **_UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    self.tokenizer(self.src_text  ,    **_UpperCamelCase  ,    return_tensors='np' )
												lowerCAmelCase__         =    self.model.generate(
												    model_inputs.input_ids  ,    attention_mask=model_inputs.attention_mask  ,    decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']  ,    early_stopping=_UpperCamelCase  ,    num_beams=2  ,    ).sequences
												lowerCAmelCase__         =    self.tokenizer.batch_decode(_UpperCamelCase  ,    skip_special_tokens=_UpperCamelCase )
												return generated_words







						@slow
						def    UpperCamelCase__   (						self ):






												"""simple docstring"""
												self._assert_generated_batch_equal_expected()













import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor


class      __SCREAMING_SNAKE_CASE       :






						def __init__(						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__         =    cuda
												lowerCAmelCase__         =    AutoConfig.from_pretrained(_UpperCamelCase )
												lowerCAmelCase__         =    AutoModelForCTC.from_pretrained(_UpperCamelCase )
												self.model.eval()
												if self.cuda:
																		self.model.to(device='cuda' )
												lowerCAmelCase__         =    AutoProcessor.from_pretrained(_UpperCamelCase )
												lowerCAmelCase__         =    torchaudio.transforms.Resample(_UpperCamelCase  ,    1_60_00 )
												lowerCAmelCase__         =    0
												lowerCAmelCase__         =    list(self.processor.tokenizer.get_vocab().keys() )
												for i in range(len(_UpperCamelCase ) ):
																		if vocab[i] == "[PAD]" or vocab[i] == "<pad>":
																								lowerCAmelCase__         =    i
												print('Blank Token id [PAD]/<pad>'  ,    _UpperCamelCase )
												lowerCAmelCase__         =    blank_id






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






												"""simple docstring"""
												lowerCAmelCase__    ,							lowerCAmelCase__         =    torchaudio.load(_UpperCamelCase )
												lowerCAmelCase__         =    self.resampler(_UpperCamelCase ).squeeze().numpy()
												return speech






						def    UpperCamelCase__   (						self  ,    _UpperCamelCase ):






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

												lowerCAmelCase__         =    self.speech_file_to_array_fn(item['wav_path'] )
												lowerCAmelCase__         =    self.processor(_UpperCamelCase  ,    sampling_rate=1_60_00  ,    return_tensors='pt'  ,    padding=_UpperCamelCase )
												if self.cuda:
																		lowerCAmelCase__         =    inputs.to(device='cuda' )

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

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

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

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

												def get_trellis(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase=0 ):
																		lowerCAmelCase__         =    emission.size(0 )
																		lowerCAmelCase__         =    len(_UpperCamelCase )

																		# 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.
																		lowerCAmelCase__         =    torch.full((num_frames + 1, num_tokens + 1)  ,    -float('inf' ) )
																		lowerCAmelCase__         =    0
																		for t in range(_UpperCamelCase ):
																								lowerCAmelCase__         =    torch.maximum(
																								    # Score for staying at the same token
																								    trellis[t, 1:] + emission[t, blank_id]  ,    trellis[t, :-1] + emission[t, tokens]  ,    )
																		return trellis

												lowerCAmelCase__         =    get_trellis(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )


												@dataclass
												class      __SCREAMING_SNAKE_CASE       :
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							float

												def backtrack(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase=0 ):
																		lowerCAmelCase__         =    trellis.size(1 ) - 1
																		lowerCAmelCase__         =    torch.argmax(trellis[:, j] ).item()

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

																								# 2. Store the path with frame-wise probability.
																								lowerCAmelCase__         =    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  ,    _UpperCamelCase ) )

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

												lowerCAmelCase__         =    backtrack(_UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase )


												@dataclass
												class      __SCREAMING_SNAKE_CASE       :
																		_SCREAMING_SNAKE_CASE						:							str
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							int
																		_SCREAMING_SNAKE_CASE						:							float






																		def __repr__(						self ):






																								"""simple docstring"""
																								return F"{self.label}\t{self.score:4.2f}\t{self.start*20:5d}\t{self.end*20:5d}"







																		@property
																		def    UpperCamelCase__   (						self ):






																								"""simple docstring"""
																								return self.end - self.start

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

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







						def    UpperCamelCase__   (						self  ,    _UpperCamelCase  ,    _UpperCamelCase  ,    _UpperCamelCase ):






												"""simple docstring"""
												if not os.path.exists(_UpperCamelCase ):
																		os.makedirs(_UpperCamelCase )

												# load text file
												lowerCAmelCase__         =    open(_UpperCamelCase  ,    encoding='utf8' ).readlines()

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

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

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

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







def 						_UpperCamelCase (     )	-> Tuple:

						"""simple docstring"""



						lowerCAmelCase__         =    argparse.ArgumentParser()

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

						lowerCAmelCase__         =    parser.parse_args()

						lowerCAmelCase__         =    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()





