


import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import OPTConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, slow

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
				lowerCAmelCase__       :	int     				='platform'

				import jax
				import jax.numpy as jnp

				from transformers import FlaxOPTForCausalLM, FlaxOPTModel, GPTaTokenizer






def 		a__       (							A__,	A__,	A__=None,	A__=None							):


			if attention_mask is None:
						SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= np.where(input_ids != config.pad_token_id,	1,	0							)
			return {
			    "input_ids": input_ids,
			    "attention_mask": attention_mask,
			}







@require_flax
class      __lowercase  :





							"""simple docstring"""







							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__=1_3       ,		lowerCAmelCase__=7       ,		lowerCAmelCase__=True       ,		lowerCAmelCase__=False       ,		lowerCAmelCase__=9_9       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=4       ,		lowerCAmelCase__=4       ,		lowerCAmelCase__="gelu"       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=0.1       ,		lowerCAmelCase__=2_0       ,		lowerCAmelCase__=2       ,		lowerCAmelCase__=1       ,		lowerCAmelCase__=0       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=1_6       ,		lowerCAmelCase__=0.02       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Dict       						= parent
										SCREAMING_SNAKE_CASE_						:			Tuple       						= batch_size
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= seq_length
										SCREAMING_SNAKE_CASE_						:			Any       						= is_training
										SCREAMING_SNAKE_CASE_						:			List[str]       						= use_labels
										SCREAMING_SNAKE_CASE_						:			str       						= vocab_size
										SCREAMING_SNAKE_CASE_						:			int       						= hidden_size
										SCREAMING_SNAKE_CASE_						:			int       						= num_hidden_layers
										SCREAMING_SNAKE_CASE_						:			List[str]       						= num_attention_heads
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= intermediate_size
										SCREAMING_SNAKE_CASE_						:			Tuple       						= hidden_act
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= hidden_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= attention_probs_dropout_prob
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= max_position_embeddings
										SCREAMING_SNAKE_CASE_						:			Tuple       						= eos_token_id
										SCREAMING_SNAKE_CASE_						:			int       						= pad_token_id
										SCREAMING_SNAKE_CASE_						:			List[str]       						= bos_token_id
										SCREAMING_SNAKE_CASE_						:			Tuple       						= embed_dim
										SCREAMING_SNAKE_CASE_						:			Tuple       						= word_embed_proj_dim
										SCREAMING_SNAKE_CASE_						:			int       						= initializer_range
										SCREAMING_SNAKE_CASE_						:			str       						= False






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,		self.vocab_size    )       ,		3       ,		self.vocab_size    )
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,		dtype=np.intaa    ))       ,		-1    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= OPTConfig(
										    vocab_size=self.vocab_size       ,		hidden_size=self.hidden_size       ,		num_hidden_layers=self.num_hidden_layers       ,		num_attention_heads=self.num_attention_heads       ,		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       ,		embed_dim=self.embed_dim       ,		is_encoder_decoder=UpperCamelCase__       ,		word_embed_proj_dim=self.word_embed_proj_dim       ,		initializer_range=self.initializer_range       ,		use_cache=UpperCamelCase__       ,		)
										SCREAMING_SNAKE_CASE_						:			str       						= prepare_opt_inputs_dict(UpperCamelCase__       ,		UpperCamelCase__    )
										return config, inputs_dict






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= self.prepare_config_and_inputs()
										return config, inputs_dict






							def  UpperCamelCase__			(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= 2_0
										SCREAMING_SNAKE_CASE_						:			int       						= model_class_name(UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			int       						= inputs_dict['''input_ids''']
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= inputs_dict['''attention_mask''']

										SCREAMING_SNAKE_CASE_						:			Tuple       						= model.init_cache(input_ids.shape[0]       ,		UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Tuple       						= jnp.ones((input_ids.shape[0], max_length)       ,		dtype='i4'    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1    )[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)       ,		)
										SCREAMING_SNAKE_CASE_						:			Dict       						= model(
										    input_ids[:, :-1]       ,		attention_mask=UpperCamelCase__       ,		past_key_values=UpperCamelCase__       ,		position_ids=UpperCamelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			str       						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype='i4'    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(
										    input_ids[:, -1:]       ,		attention_mask=UpperCamelCase__       ,		past_key_values=outputs_cache.past_key_values       ,		position_ids=UpperCamelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 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       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= 2_0
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model_class_name(UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			Any       						= (
										    inputs_dict['''input_ids'''],
										    inputs_dict['''attention_mask'''],
										)

										SCREAMING_SNAKE_CASE_						:			str       						= jnp.concatenate(
										    [
										        attention_mask,
										        jnp.zeros((attention_mask.shape[0], max_length - attention_mask.shape[1])    ),
										    ]       ,		axis=-1       ,		)

										SCREAMING_SNAKE_CASE_						:			List[str]       						= model.init_cache(input_ids.shape[0]       ,		UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= jnp.broadcast_to(
										    jnp.arange(input_ids.shape[-1] - 1    )[None, :]       ,		(input_ids.shape[0], input_ids.shape[-1] - 1)       ,		)

										SCREAMING_SNAKE_CASE_						:			Dict       						= model(
										    input_ids[:, :-1]       ,		attention_mask=UpperCamelCase__       ,		past_key_values=UpperCamelCase__       ,		position_ids=UpperCamelCase__       ,		)
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]       ,		dtype='i4'    )
										SCREAMING_SNAKE_CASE_						:			str       						= model(
										    input_ids[:, -1:]       ,		past_key_values=outputs_cache.past_key_values       ,		attention_mask=UpperCamelCase__       ,		position_ids=UpperCamelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= model(UpperCamelCase__       ,		attention_mask=UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= 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      __lowercase  (__SCREAMING_SNAKE_CASE     ,    unittest.TestCase     ,    __SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							_UpperCAmelCase     =		(FlaxOPTModel, FlaxOPTForCausalLM) if is_flax_available() else ()
							_UpperCAmelCase     =		() if is_flax_available() else ()






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= FlaxOPTModelTester(self    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Tuple       						= 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"""




										SCREAMING_SNAKE_CASE_						:			str       						= 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__    )






							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										for model_class_name in self.all_model_classes:
													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= model_class_name.from_pretrained('facebook/opt-125m'    )
													SCREAMING_SNAKE_CASE_						:			List[str]       						= np.ones((1, 1)    ) * model.config.eos_token_id
													SCREAMING_SNAKE_CASE_						:			int       						= model(UpperCamelCase__    )
													self.assertIsNotNone(UpperCamelCase__    )







@require_sentencepiece
@require_flax
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@slow
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= FlaxOPTModel.from_pretrained('facebook/opt-350m'    )
										SCREAMING_SNAKE_CASE_						:			List[str]       						= jnp.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= model(input_ids=UpperCamelCase__    ).last_hidden_state
										SCREAMING_SNAKE_CASE_						:			Dict       						= (1, 1_1, 5_1_2)
										self.assertEqual(output.shape       ,		UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= jnp.array(
										    [[-0.2_867, -1.9_256, -0.3_062], [-1.2_711, -0.1_337, -0.1_897], [0.4_109, 0.1_187, -1.3_142]]    )
										self.assertTrue(jnp.allclose(output[:, :3, :3]       ,		UpperCamelCase__       ,		atol=4E-2    )    )







@require_flax
@slow
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										super().setUp()
										SCREAMING_SNAKE_CASE_						:			str       						= '''facebook/opt-350m'''






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= FlaxOPTForCausalLM.from_pretrained(self.path_model    )
										SCREAMING_SNAKE_CASE_						:			Any       						= GPTaTokenizer.from_pretrained(self.path_model    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= [
										    '''Today is a beautiful day and I want to''',
										    '''In the city of''',
										    '''Paris is the capital of France and''',
										    '''Computers and mobile phones have taken''',
										]
										# verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= tokenizer(UpperCamelCase__       ,		return_tensors='jax'       ,		padding=UpperCamelCase__       ,		add_special_tokens=UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= model(inputs.input_ids       ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
										SCREAMING_SNAKE_CASE_						:			str       						= jnp.array(
										    [
										        [1.3_851, -13.8_923, -10.5_229, -10.7_533, -0.2_309, -10.2_384, -0.5_365, -9.0_947, -5.1_670],
										        [-4.7_073, -10.6_276, -3.9_415, -21.5_242, -0.2_822, -0.2_822, -0.2_822, -0.2_822, -0.2_822],
										        [0.6_247, -3.4_229, -8.9_179, -1.4_297, -14.1_650, 1.4_146, -9.0_218, -0.2_703, -0.2_703],
										        [6.4_783, -1.9_913, -10.7_926, -2.3_336, 1.5_092, -0.9_974, -6.8_213, 1.3_477, 1.3_477],
										    ]    )
										self.assertTrue(jnp.allclose(UpperCamelCase__       ,		UpperCamelCase__       ,		atol=4E-2    )    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= jax.jit(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Dict       						= model(inputs.input_ids       ,		attention_mask=inputs.attention_mask    )[0].mean(axis=-1    )
										self.assertTrue(jnp.allclose(UpperCamelCase__       ,		UpperCamelCase__       ,		atol=4E-2    )    )







@require_flax
@slow
class      __lowercase  (unittest.TestCase   ):





							"""simple docstring"""







							@property
							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										return [
										    "Today is a beautiful day and I want",
										    "In the city of",
										    "Paris is the capital of France and",
										    "Computers and mobile phones have taken",
										]






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			Any       						= '''facebook/opt-125m'''

										SCREAMING_SNAKE_CASE_						:			Dict       						= [
										    '''Today is a beautiful day and I want to''',
										    '''In the city of New York, the city''',
										    '''Paris is the capital of France and the capital''',
										    '''Computers and mobile phones have taken over the''',
										]

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= []

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= GPTaTokenizer.from_pretrained(UpperCamelCase__    )

										for prompt in self.prompts:
													SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= tokenizer(UpperCamelCase__       ,		return_tensors='jax'    ).input_ids

													SCREAMING_SNAKE_CASE_						:			List[Any]       						= model.generate(UpperCamelCase__       ,		max_length=1_0    )
													SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= generated_ids[0]

													SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer.batch_decode(UpperCamelCase__       ,		skip_special_tokens=UpperCamelCase__    )
													predicted_outputs += generated_string

										self.assertListEqual(UpperCamelCase__       ,		UpperCamelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			str       						= '''facebook/opt-350m'''

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= [
										    '''Today is a beautiful day and I want to''',
										    '''In the city of San Francisco, the city''',
										    '''Paris is the capital of France and the capital''',
										    '''Computers and mobile phones have taken over the''',
										]

										SCREAMING_SNAKE_CASE_						:			str       						= []
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= GPTaTokenizer.from_pretrained(UpperCamelCase__    )

										for prompt in self.prompts:
													SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer(UpperCamelCase__       ,		return_tensors='jax'    ).input_ids

													SCREAMING_SNAKE_CASE_						:			Dict       						= model.generate(UpperCamelCase__       ,		max_length=1_0    )
													SCREAMING_SNAKE_CASE_						:			str       						= generated_ids[0]

													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= tokenizer.batch_decode(UpperCamelCase__       ,		skip_special_tokens=UpperCamelCase__    )
													predicted_outputs += generated_string

										self.assertListEqual(UpperCamelCase__       ,		UpperCamelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[str]       						= '''facebook/opt-125m'''
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= [
										    '''Today is a beautiful day and I want to thank''',
										    '''In the city of Rome Canaver Canaver Canaver Canaver''',
										]
										SCREAMING_SNAKE_CASE_						:			str       						= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= GPTaTokenizer.from_pretrained(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer(
										    [
										        'Today is a beautiful day and I want to',
										        'In the city of',
										    ]       ,		return_tensors='jax'       ,		padding=UpperCamelCase__       ,		)

										SCREAMING_SNAKE_CASE_						:			Tuple       						= jax.jit(model.generate    )

										SCREAMING_SNAKE_CASE_						:			Tuple       						= jit_generate(inputs['input_ids']       ,		attention_mask=inputs['attention_mask']    ).sequences

										SCREAMING_SNAKE_CASE_						:			List[str]       						= tokenizer.batch_decode(UpperCamelCase__       ,		skip_special_tokens=UpperCamelCase__    )

										self.assertIsNotNone(UpperCamelCase__       ,		UpperCamelCase__    )






							def  UpperCamelCase__			(	self    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			List[Any]       						= '''facebook/opt-350m'''

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= GPTaTokenizer.from_pretrained(UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			str       						= FlaxOPTForCausalLM.from_pretrained(UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			List[Any]       						= '''left'''

										# use different length sentences to test batching
										SCREAMING_SNAKE_CASE_						:			Any       						= [
										    '''Hello, my dog is a little''',
										    '''Today, I''',
										]

										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= tokenizer(UpperCamelCase__       ,		return_tensors='jax'       ,		padding=UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= inputs['''input_ids''']

										SCREAMING_SNAKE_CASE_						:			Dict       						= model.generate(input_ids=UpperCamelCase__       ,		attention_mask=inputs['attention_mask']       ,		trace=UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			List[str]       						= tokenizer(sentences[0]       ,		return_tensors='jax'    ).input_ids
										SCREAMING_SNAKE_CASE_						:			List[str]       						= model.generate(input_ids=UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			Dict       						= inputs_non_padded.shape[-1] - inputs['''attention_mask'''][-1].sum()
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= tokenizer(sentences[1]       ,		return_tensors='jax'    ).input_ids
										SCREAMING_SNAKE_CASE_						:			Any       						= model.generate(input_ids=UpperCamelCase__       ,		max_length=model.config.max_length - num_paddings    )

										SCREAMING_SNAKE_CASE_						:			Any       						= tokenizer.batch_decode(outputs[0]       ,		skip_special_tokens=UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			int       						= tokenizer.decode(output_non_padded[0][0]       ,		skip_special_tokens=UpperCamelCase__    )
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= tokenizer.decode(output_padded[0][0]       ,		skip_special_tokens=UpperCamelCase__    )

										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= [
										    '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
										    '''Today, I was in the middle of a conversation with a friend about the''',
										]
										self.assertListEqual(UpperCamelCase__       ,		UpperCamelCase__    )
										self.assertListEqual(UpperCamelCase__       ,		[non_padded_sentence, padded_sentence]    )







'''simple docstring'''
import argparse
import os
from dataclasses import dataclass

import torch
import torchaudio
from tqdm import tqdm

from transformers import AutoConfig, AutoModelForCTC, AutoProcessor






class      snake_case__    :







		"""simple docstring"""

		def __init__(	self							:       str   ,     UpperCamelCase__							:       List[str]   ,     UpperCamelCase__							:       Any   ,     UpperCamelCase__							:       Dict	)		->       List[Any]:






				"""simple docstring"""







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



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






				"""simple docstring"""







				snake_case       ,snake_case   :    Tuple			       =		torchaudio.load(UpperCamelCase__	)
				snake_case   :    Optional[Any]			       =		self.resampler(UpperCamelCase__	).squeeze().numpy()
				return speech



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






				"""simple docstring"""







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

				snake_case   :    List[Any]			       =		self.speech_file_to_array_fn(item['''wav_path''']	)
				snake_case   :    List[str]			       =		self.processor(UpperCamelCase__   ,     sampling_rate=1_6000   ,     return_tensors='''pt'''   ,     padding=UpperCamelCase__	)
				if self.cuda:
						snake_case   :    str			       =		inputs.to(device='''cuda'''	)

				with torch.no_grad():
						snake_case   :    List[Any]			       =		self.model(inputs.input_values	).logits

				# get the emission probability at frame level
				snake_case   :    str			       =		torch.log_softmax(UpperCamelCase__   ,     dim=-1	)
				snake_case   :    Optional[Any]			       =		emissions[0].cpu().detach()

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

				snake_case   :    List[str]			       =		{c: i for i, c in enumerate(UpperCamelCase__	)}
				snake_case   :    Any			       =		[]
				for c in transcript:
						if c in dictionary:
								tokens.append(dictionary[c]	)

				def get_trellis(UpperCamelCase__							:       str   ,     UpperCamelCase__							:       Dict   ,     UpperCamelCase__							:       str=0	):
						snake_case   :    List[str]			       =		emission.size(0	)
						snake_case   :    List[str]			       =		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.
						snake_case   :    List[Any]			       =		torch.full((num_frames + 1, num_tokens + 1)   ,     -float('''inf'''	)	)
						snake_case   :    Optional[int]			       =		0
						for t in range(UpperCamelCase__	):
								snake_case   :    Any			       =		torch.maximum(
								    # Score for staying at the same token
								    trellis[t, 1:] + emission[t, blank_id]   ,     trellis[t, :-1] + emission[t, tokens]   ,     )
						return trellis

				snake_case   :    Optional[Any]			       =		get_trellis(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42

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

						snake_case   :    Dict			       =		[]
						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.
								snake_case   :    Union[str, Any]			       =		trellis[t - 1, j] + emission[t - 1, blank_id]
								# Score for token changing from C-1 at T-1 to J at T.
								snake_case   :    Optional[int]			       =		trellis[t - 1, j - 1] + emission[t - 1, tokens[j - 1]]

								# 2. Store the path with frame-wise probability.
								snake_case   :    Optional[Any]			       =		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]

				snake_case   :    Any			       =		backtrack(UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__   ,     UpperCamelCase__	)






				@dataclass
				class      snake_case__    :







						"""simple docstring"""

						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42
						lowerCamelCase							=		42



						def __repr__(	self							:       Dict	)		->       int:






								"""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							:       List[Any]	)		->       Any:






								"""simple docstring"""







								return self.end - self.start

				def merge_repeats(UpperCamelCase__							:       str	):
						snake_case       ,snake_case   :    Union[str, Any]			       =		0, 0
						snake_case   :    List[Any]			       =		[]
						while ia < len(UpperCamelCase__	):
								while ia < len(UpperCamelCase__	) and path[ia].token_index == path[ia].token_index:
										ia += 1
								snake_case   :    Dict			       =		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__   ,     )	)
								snake_case   :    int			       =		ia
						return segments

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



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






				"""simple docstring"""







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

				# load text file
				snake_case   :    List[Any]			       =		open(UpperCamelCase__   ,     encoding='''utf8'''	).readlines()

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

						snake_case       ,snake_case   :    Dict			       =		line.strip().split('''\t'''	)
						snake_case   :    Tuple			       =		os.path.join(UpperCamelCase__   ,     wav_name + '''.wav'''	)
						snake_case   :    str			       =		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		(    )   ->				List[Any]:


		'''simple docstring'''


		snake_case   :    Optional[int]			       =		argparse.ArgumentParser()

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

		snake_case   :    Tuple			       =		parser.parse_args()

		snake_case   :    Tuple			       =		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()


