
"""simple docstring"""
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
			lowercase		:						Optional[Any] =					'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     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				lowerCamelCase_=None	,				lowerCamelCase_=None	,				lowerCamelCase_=None	,				lowerCamelCase_=None	,				lowerCamelCase_=None	,				lowerCamelCase_=None	,				)     ->			Union[str, Any]:


					'''simple docstring'''
					if attention_mask is None:
										__a:						Any		    =					np.where(input_ids != config.pad_token_id	,				1	,				0 )
					if decoder_attention_mask is None:
										__a:						str		    =					np.where(decoder_input_ids != config.pad_token_id	,				1	,				0 )
					if head_mask is None:
										__a:						Any		    =					np.ones((config.encoder_layers, config.encoder_attention_heads) )
					if decoder_head_mask is None:
										__a:						int		    =					np.ones((config.decoder_layers, config.decoder_attention_heads) )
					if cross_attn_head_mask is None:
										__a:						Optional[Any]		    =					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    _snake_case  (						unittest.TestCase       ):






							def __init__(					self						:			List[Any]						, snake_case__						:			Dict						, snake_case__						:			int=1_3						, snake_case__						:			List[Any]=7						, snake_case__						:			List[str]=True						, snake_case__						:			int=False						, snake_case__						:			Optional[Any]=9_9						, snake_case__						:			Union[str, Any]=1_6						, snake_case__						:			Any=2						, snake_case__						:			int=4						, snake_case__						:			Dict=4						, snake_case__						:			Tuple="gelu"						, snake_case__						:			Optional[Any]=0.1						, snake_case__						:			List[Any]=0.1						, snake_case__						:			Any=3_2						, snake_case__						:			Optional[int]=2						, snake_case__						:			Dict=1						, snake_case__						:			Tuple=0						, snake_case__						:			str=2						, snake_case__						:			List[Any]=0.02						, ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



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

												__a:						int		    =					shift_tokens_right(snake_case__						, 1    )

												__a:						Optional[Any]		    =					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=snake_case__						, )
												__a:						Dict		    =					prepare_mbart_inputs_dict(snake_case__						, snake_case__						, snake_case__    )
												return config, inputs_dict






							def 							__lowercase  (					self						:			Union[str, Any]    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Optional[int]						, snake_case__						:			List[str]						, snake_case__						:			Union[str, Any]						, snake_case__						:			Optional[Any]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					2_0
												__a:						Dict		    =					model_class_name(snake_case__    )

												__a:						Optional[Any]		    =					model.encode(inputs_dict["""input_ids"""]    )

												__a    ,				__a:						List[str]		    =					(
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

												__a:						int		    =					model.init_cache(decoder_input_ids.shape[0]						, snake_case__						, snake_case__    )
												__a:						Any		    =					jnp.ones((decoder_input_ids.shape[0], max_decoder_length)						, dtype="""i4"""    )

												__a:						List[str]		    =					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )
												__a:						Dict		    =					model.decode(
												    decoder_input_ids[:, :-1]						, snake_case__						, decoder_attention_mask=snake_case__						, past_key_values=snake_case__						, decoder_position_ids=snake_case__						, )

												__a:						int		    =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						, dtype="""i4"""    )
												__a:						Tuple		    =					model.decode(
												    decoder_input_ids[:, -1:]						, snake_case__						, decoder_attention_mask=snake_case__						, past_key_values=outputs_cache.past_key_values						, decoder_position_ids=snake_case__						, )

												__a:						Any		    =					model.decode(snake_case__						, snake_case__    )

												__a:						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 							__lowercase  (					self						:			List[str]						, snake_case__						:			List[Any]						, snake_case__						:			Optional[Any]						, snake_case__						:			Optional[int]    ):






												"""simple docstring"""



												__a:						List[str]		    =					2_0
												__a:						List[str]		    =					model_class_name(snake_case__    )

												__a:						List[Any]		    =					model.encode(inputs_dict["""input_ids"""]    )

												__a    ,				__a:						Optional[Any]		    =					(
												    inputs_dict["""decoder_input_ids"""],
												    inputs_dict["""decoder_attention_mask"""],
												)

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

												__a:						Any		    =					model.init_cache(decoder_input_ids.shape[0]						, snake_case__						, snake_case__    )
												__a:						int		    =					jnp.broadcast_to(
												    jnp.arange(decoder_input_ids.shape[-1] - 1    )[None, :]						, (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)						, )

												__a:						int		    =					model.decode(
												    decoder_input_ids[:, :-1]						, snake_case__						, decoder_attention_mask=snake_case__						, past_key_values=snake_case__						, decoder_position_ids=snake_case__						, )
												__a:						Tuple		    =					jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]						, dtype="""i4"""    )
												__a:						List[Any]		    =					model.decode(
												    decoder_input_ids[:, -1:]						, snake_case__						, past_key_values=outputs_cache.past_key_values						, decoder_attention_mask=snake_case__						, decoder_position_ids=snake_case__						, )

												__a:						int		    =					model.decode(snake_case__						, snake_case__						, decoder_attention_mask=snake_case__    )

												__a:						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    _snake_case  (						unittest.TestCase       ):
							SCREAMING_SNAKE_CASE		     =						99






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					np.array(
												    [
												        [7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2],
												        [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2],
												        [5, 9_7, 1_7, 3_9, 9_4, 4_0, 2],
												        [7_6, 8_3, 9_4, 2_5, 7_0, 7_8, 2],
												        [8_7, 5_9, 4_1, 3_5, 4_8, 6_6, 2],
												        [5_5, 1_3, 1_6, 5_8, 5, 2, 1],  # note padding
												        [6_4, 2_7, 3_1, 5_1, 1_2, 7_5, 2],
												        [5_2, 6_4, 8_6, 1_7, 8_3, 3_9, 2],
												        [4_8, 6_1, 9, 2_4, 7_1, 8_2, 2],
												        [2_6, 1, 6_0, 4_8, 2_2, 1_3, 2],
												        [2_1, 5, 6_2, 2_8, 1_4, 7_6, 2],
												        [4_5, 9_8, 3_7, 8_6, 5_9, 4_8, 2],
												        [7_0, 7_0, 5_0, 9, 2_8, 0, 2],
												    ]						, dtype=np.intaa						, )

												__a:						Union[str, Any]		    =					input_ids.shape[0]
												__a:						List[str]		    =					MBartConfig(
												    vocab_size=self.vocab_size						, d_model=2_4						, encoder_layers=2						, decoder_layers=2						, encoder_attention_heads=2						, decoder_attention_heads=2						, encoder_ffn_dim=3_2						, decoder_ffn_dim=3_2						, max_position_embeddings=4_8						, eos_token_id=2						, pad_token_id=1						, bos_token_id=0						, )
												return config, input_ids, batch_size






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Dict		    =					self._get_config_and_data()
												__a:						Union[str, Any]		    =					FlaxMBartForSequenceClassification(snake_case__    )
												__a:						Any		    =					model(input_ids=snake_case__						, decoder_input_ids=snake_case__    )
												__a:						Optional[Any]		    =					(batch_size, config.num_labels)
												self.assertEqual(outputs["""logits"""].shape						, snake_case__    )






							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Any		    =					self._get_config_and_data()
												__a:						Optional[Any]		    =					FlaxMBartForQuestionAnswering(snake_case__    )
												__a:						Tuple		    =					model(input_ids=snake_case__    )

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






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Dict		    =					self._get_config_and_data()
												__a:						List[str]		    =					FlaxMBartForConditionalGeneration(snake_case__    )
												__a:						Optional[int]		    =					lm_model(input_ids=snake_case__    )
												__a:						Dict		    =					(batch_size, input_ids.shape[1], config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape						, snake_case__    )






							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						List[Any]		    =					MBartConfig(
												    vocab_size=self.vocab_size						, d_model=1_4						, 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=4_8						, )
												__a:						Any		    =					FlaxMBartForConditionalGeneration(snake_case__    )
												__a:						Any		    =					np.array([[7_1, 8_2, 1_8, 3_3, 4_6, 9_1, 2], [6_8, 3_4, 2_6, 5_8, 3_0, 2, 1]]						, dtype=np.intaa    )
												__a:						Union[str, Any]		    =					np.array([[8_2, 7_1, 8_2, 1_8, 2], [5_8, 6_8, 2, 1, 1]]						, dtype=np.intaa    )
												__a:						Optional[Any]		    =					lm_model(input_ids=snake_case__						, decoder_input_ids=snake_case__    )
												__a:						Any		    =					(*summary.shape, config.vocab_size)
												self.assertEqual(outputs["""logits"""].shape						, snake_case__    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a:						Dict		    =					np.array([[7_1, 8_2, 1_8, 3_3, 2, 1, 1], [6_8, 3_4, 2_6, 5_8, 3_0, 8_2, 2]]						, dtype=np.intaa    )
												__a:						Optional[int]		    =					shift_tokens_right(snake_case__						, 1    )
												__a:						Optional[Any]		    =					np.equal(snake_case__						, 1    ).astype(np.floataa    ).sum()
												__a:						int		    =					np.equal(snake_case__						, 1    ).astype(np.floataa    ).sum()
												self.assertEqual(shifted.shape						, input_ids.shape    )
												self.assertEqual(snake_case__						, n_pad_before - 1    )
												self.assertTrue(np.equal(shifted[:, 0]						, 2    ).all()    )






@require_flax
class    _snake_case  (						_lowercase ,				unittest.TestCase ,				_lowercase       ):
							SCREAMING_SNAKE_CASE		     =						True
							SCREAMING_SNAKE_CASE		     =						(
							    (
							        FlaxMBartModel,
							        FlaxMBartForConditionalGeneration,
							        FlaxMBartForSequenceClassification,
							        FlaxMBartForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)
							SCREAMING_SNAKE_CASE		     =						(FlaxMBartForConditionalGeneration,) if is_flax_available() else ()






							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						str		    =					FlaxMBartModelTester(self    )






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												__a    ,				__a:						Any		    =					self.model_tester.prepare_config_and_inputs()
												for model_class in self.all_model_classes:
																	self.model_tester.check_use_cache_forward(snake_case__						, snake_case__						, snake_case__    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a    ,				__a:						Any		    =					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(snake_case__						, snake_case__						, snake_case__    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a    ,				__a:						Optional[int]		    =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__    ):
																						__a:						Optional[int]		    =					self._prepare_for_class(snake_case__						, snake_case__    )
																						__a:						Union[str, Any]		    =					model_class(snake_case__    )

																						@jax.jit
																						def encode_jitted(snake_case__						:			Dict						, snake_case__						:			Tuple=None						, **snake_case__						:			Any    ):
																											return model.encode(input_ids=snake_case__						, attention_mask=snake_case__    )

																						with self.subTest("""JIT Enabled"""    ):
																											__a:						Tuple		    =					encode_jitted(**snake_case__    ).to_tuple()

																						with self.subTest("""JIT Disabled"""    ):
																											with jax.disable_jit():
																																__a:						List[Any]		    =					encode_jitted(**snake_case__    ).to_tuple()

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






							def 							__lowercase  (					self						:			Union[str, Any]    ):






												"""simple docstring"""



												__a    ,				__a:						Any		    =					self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	with self.subTest(model_class.__name__    ):
																						__a:						Any		    =					model_class(snake_case__    )
																						__a:						Tuple		    =					model.encode(inputs_dict["""input_ids"""]						, inputs_dict["""attention_mask"""]    )

																						__a:						Any		    =					{
																						    """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(snake_case__						:			int						, snake_case__						:			Dict						, snake_case__						:			Optional[Any]    ):
																											return model.decode(
																											    decoder_input_ids=snake_case__						, decoder_attention_mask=snake_case__						, encoder_outputs=snake_case__						, )

																						with self.subTest("""JIT Enabled"""    ):
																											__a:						Union[str, Any]		    =					decode_jitted(**snake_case__    ).to_tuple()

																						with self.subTest("""JIT Disabled"""    ):
																											with jax.disable_jit():
																																__a:						Optional[int]		    =					decode_jitted(**snake_case__    ).to_tuple()

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






							@slow
							def 							__lowercase  (					self						:			Optional[int]    ):






												"""simple docstring"""



												for model_class_name in self.all_model_classes:
																	__a:						Any		    =					model_class_name.from_pretrained("""facebook/mbart-large-cc25"""						, from_pt=snake_case__    )
																	# FlaxMBartForSequenceClassification expects eos token in input_ids
																	__a:						Dict		    =					np.ones((1, 1)    ) * model.config.eos_token_id
																	__a:						List[str]		    =					model(snake_case__    )
																	self.assertIsNotNone(snake_case__    )






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






							@cached_property
							def 							__lowercase  (					self						:			Optional[Any]    ):






												"""simple docstring"""



												return AutoTokenizer.from_pretrained(self.model_name    )






							@cached_property
							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						str		    =					FlaxMBartForConditionalGeneration.from_pretrained(self.model_name						, from_pt=snake_case__    )
												return model






							def 							__lowercase  (					self						:			Tuple						, **snake_case__						:			Optional[int]    ):






												"""simple docstring"""



												__a:						Union[str, Any]		    =					self.translate_src_text(**snake_case__    )
												self.assertListEqual(self.expected_text						, snake_case__    )






							def 							__lowercase  (					self						:			List[str]						, **snake_case__						:			int    ):






												"""simple docstring"""



												__a:						Tuple		    =					self.tokenizer(self.src_text						, **snake_case__						, return_tensors="""np"""    )
												__a:						List[Any]		    =					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=snake_case__						, num_beams=2						, ).sequences
												__a:						int		    =					self.tokenizer.batch_decode(snake_case__						, skip_special_tokens=snake_case__    )
												return generated_words






							@slow
							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												self._assert_generated_batch_equal_expected()


"""simple docstring"""
import logging
import pathlib
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Callable, Dict, List, Optional, Set, Union

import datasets
import librosa
import numpy as np
import torch
from lang_trans import arabic
from packaging import version
from torch import nn

from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaProcessor,
    is_apex_available,
    trainer_utils,
)


if is_apex_available():
			from apex import amp

if version.parse(version.parse(torch.__version__).base_version) >= version.parse('1.6'):
			lowercase		:						str =					True
			from torch.cuda.amp import autocast


lowercase		:						int =					logging.getLogger(__name__)




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Whether to log verbose messages or not.'''} ,				)






def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ )     ->			List[Any]:


					'''simple docstring'''
					logging.basicConfig(
					    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""	,				datefmt="""%m/%d/%Y %H:%M:%S"""	,				handlers=[logging.StreamHandler(sys.stdout )]	,				)
					__a:						List[Any]		    =					logging.WARNING
					if model_args.verbose_logging:
										__a:						Dict		    =					logging.DEBUG
					elif trainer_utils.is_main_process(training_args.local_rank ):
										__a:						Optional[int]		    =					logging.INFO
					logger.setLevel(lowerCamelCase_ )




@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''train''' ,				metadata={
							        '''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''validation''' ,				metadata={
							        '''help''': (
							            '''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
							        )
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''text''' ,				metadata={'''help''': '''Column in the dataset that contains label (target text). Defaults to \'text\''''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''file''' ,				metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Resample loaded audio to target feature extractor\'s sampling rate or not.'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Filters out examples longer than specified. Defaults to no filtering.'''} ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default='''librispeech''' ,				metadata={
							        '''help''': (
							            '''Orthography used for normalization and tokenization: \'librispeech\' (default), \'timit\', or'''
							            ''' \'buckwalter\'.'''
							        )
							    } ,				)
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''}       )
							SCREAMING_SNAKE_CASE		     =						field(
							    default=_lowercase ,				metadata={'''help''': '''The number of processes to use for the preprocessing.'''} ,				)






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						False
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						"|"
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						field(default_factory=_lowercase       )
							SCREAMING_SNAKE_CASE		     =						None






							@classmethod
							def 							__lowercase  (					cls						:			List[Any]						, snake_case__						:			str    ):






												"""simple docstring"""



												if name == "librispeech":
																	return cls()
												if name == "timit":
																	return cls(
																	    do_lower_case=snake_case__						, translation_table=str.maketrans({"""-""": """ """}    )						, )
												if name == "buckwalter":
																	__a:						List[Any]		    =					{
																	    """-""": """ """,  # sometimes used to represent pauses
																	    """^""": """v""",  # fixing "tha" in arabic_speech_corpus dataset
																	}
																	return cls(
																	    vocab_file=pathlib.Path(snake_case__    ).parent.joinpath("""vocab/buckwalter.json"""    )						, word_delimiter_token="""/"""						, translation_table=str.maketrans(snake_case__    )						, words_to_remove={"""sil"""}						, untransliterator=arabic.buckwalter.untransliterate						, )
												raise ValueError(f"""Unsupported orthography: '{name}'."""    )






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			str    ):






												"""simple docstring"""



												if len(self.translation_table    ) > 0:
																	__a:						Optional[int]		    =					text.translate(self.translation_table    )
												if len(self.words_to_remove    ) == 0:
																	__a:						Tuple		    =					""" """.join(text.split()    )  # clean up whitespaces
												else:
																	__a:						int		    =					""" """.join(w for w in text.split() if w not in self.words_to_remove    )  # and clean up whilespaces
												return text






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			ModelArguments    ):






												"""simple docstring"""



												__a:						Dict		    =					WavaVecaFeatureExtractor.from_pretrained(
												    model_args.model_name_or_path						, cache_dir=model_args.cache_dir    )
												if self.vocab_file:
																	__a:						Dict		    =					WavaVecaCTCTokenizer(
																	    self.vocab_file						, cache_dir=model_args.cache_dir						, do_lower_case=self.do_lower_case						, word_delimiter_token=self.word_delimiter_token						, )
												else:
																	__a:						Dict		    =					WavaVecaCTCTokenizer.from_pretrained(
																	    model_args.model_name_or_path						, cache_dir=model_args.cache_dir						, do_lower_case=self.do_lower_case						, word_delimiter_token=self.word_delimiter_token						, )
												return WavaVecaProcessor(snake_case__						, snake_case__    )






@dataclass
class    _snake_case  :
							SCREAMING_SNAKE_CASE		     =						42
							SCREAMING_SNAKE_CASE		     =						True
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None
							SCREAMING_SNAKE_CASE		     =						None






							def __call__(					self						:			Optional[Any]						, snake_case__						:			List[Dict[str, Union[List[int], torch.Tensor]]]    ):






												"""simple docstring"""



												__a:						Optional[int]		    =					[{"""input_values""": feature["""input_values"""]} for feature in features]
												__a:						Optional[Any]		    =					[{"""input_ids""": feature["""labels"""]} for feature in features]

												__a:						Optional[Any]		    =					self.processor.pad(
												    snake_case__						, padding=self.padding						, max_length=self.max_length						, pad_to_multiple_of=self.pad_to_multiple_of						, return_tensors="""pt"""						, )
												__a:						str		    =					self.processor.pad(
												    labels=snake_case__						, padding=self.padding						, max_length=self.max_length_labels						, pad_to_multiple_of=self.pad_to_multiple_of_labels						, return_tensors="""pt"""						, )

												# replace padding with -100 to ignore loss correctly
												__a:						Any		    =					labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1    )						, -1_0_0    )

												__a:						List[Any]		    =					labels

												return batch






class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Union[str, Any]						, snake_case__						:			nn.Module						, snake_case__						:			Dict[str, Union[torch.Tensor, Any]]    ):






												"""simple docstring"""



												model.train()
												__a:						int		    =					self._prepare_inputs(snake_case__    )

												if self.use_amp:
																	with autocast():
																						__a:						List[str]		    =					self.compute_loss(snake_case__						, snake_case__    )
												else:
																	__a:						Tuple		    =					self.compute_loss(snake_case__						, snake_case__    )

												if self.args.n_gpu > 1:
																	if model.module.config.ctc_loss_reduction == "mean":
																						__a:						Tuple		    =					loss.mean()
																	elif model.module.config.ctc_loss_reduction == "sum":
																						__a:						List[str]		    =					loss.sum() / (inputs["""labels"""] >= 0).sum()
																	else:
																						raise ValueError(f"""{model.config.ctc_loss_reduction} is not valid. Choose one of ['mean', 'sum']"""    )

												if self.args.gradient_accumulation_steps > 1:
																	__a:						Any		    =					loss / self.args.gradient_accumulation_steps

												if self.use_amp:
																	self.scaler.scale(snake_case__    ).backward()
												elif self.use_apex:
																	with amp.scale_loss(snake_case__						, self.optimizer    ) as scaled_loss:
																						scaled_loss.backward()
												elif self.deepspeed:
																	self.deepspeed.backward(snake_case__    )
												else:
																	loss.backward()

												return loss.detach()






def     lowerCamelCase_    (	)     ->			List[Any]:


					'''simple docstring'''
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

					__a:						int		    =					HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )

					__a    ,				__a    ,				__a:						List[str]		    =					parser.parse_args_into_dataclasses()
					configure_logger(lowerCamelCase_	,				lowerCamelCase_ )

					__a:						Union[str, Any]		    =					Orthography.from_name(data_args.orthography.lower() )
					__a:						List[str]		    =					orthography.create_processor(lowerCamelCase_ )
					__a:						int		    =					WavaVecaForCTC.from_pretrained(
					    model_args.model_name_or_path	,				cache_dir=model_args.cache_dir	,				gradient_checkpointing=training_args.gradient_checkpointing	,				vocab_size=len(processor.tokenizer )	,				)

					__a:						int		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.train_split_name )
					__a:						Tuple		    =					datasets.load_dataset(
					    data_args.dataset_name	,				data_args.dataset_config_name	,				split=data_args.validation_split_name )

					__a:						List[Any]		    =					datasets.load_metric("""wer""" )
					__a:						Optional[Any]		    =					processor.feature_extractor.sampling_rate if data_args.target_feature_extractor_sampling_rate else None
					__a:						int		    =					"""""".join(t for t in processor.tokenizer.get_vocab().keys() if len(lowerCamelCase_ ) == 1 )
					__a:						int		    =					re.compile(  # remove characters not in vocabulary
					    rf"""[^\s{re.escape(lowerCamelCase_ )}]"""	,				flags=re.IGNORECASE if processor.tokenizer.do_lower_case else 0	,				)
					__a:						Optional[Any]		    =					[]

					def prepare_example(lowerCamelCase_ ):  # TODO(elgeish) make use of multiprocessing?
										__a    ,				__a:						Tuple		    =					librosa.load(example[data_args.speech_file_column]	,				sr=lowerCamelCase_ )
										if data_args.max_duration_in_seconds is not None:
															__a:						List[Any]		    =					len(example["""speech"""] ) / example["""sampling_rate"""]
										# Normalize and clean up text; order matters!
										__a:						Tuple		    =					orthography.preprocess_for_training(example[data_args.target_text_column] )
										__a:						Any		    =					vocabulary_text_cleaner.sub(""""""	,				lowerCamelCase_ )
										if updated_text != example[data_args.target_text_column]:
															text_updates.append((example[data_args.target_text_column], updated_text) )
															__a:						List[Any]		    =					updated_text
										return example

					__a:						str		    =					train_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )
					__a:						Union[str, Any]		    =					val_dataset.map(lowerCamelCase_	,				remove_columns=[data_args.speech_file_column] )

					if data_args.max_duration_in_seconds is not None:

										def filter_by_max_duration(lowerCamelCase_ ):
															return example["duration_in_seconds"] <= data_args.max_duration_in_seconds

										__a:						List[Any]		    =					len(lowerCamelCase_ )
										__a:						List[str]		    =					len(lowerCamelCase_ )
										__a:						int		    =					train_dataset.filter(lowerCamelCase_	,				remove_columns=["""duration_in_seconds"""] )
										__a:						Optional[int]		    =					val_dataset.filter(lowerCamelCase_	,				remove_columns=["""duration_in_seconds"""] )
										if len(lowerCamelCase_ ) > old_train_size:
															logger.warning(
															    f"""Filtered out {len(lowerCamelCase_ ) - old_train_size} train example(s) longer than"""
															    f""" {data_args.max_duration_in_seconds} second(s).""" )
										if len(lowerCamelCase_ ) > old_val_size:
															logger.warning(
															    f"""Filtered out {len(lowerCamelCase_ ) - old_val_size} validation example(s) longer than"""
															    f""" {data_args.max_duration_in_seconds} second(s).""" )
					logger.info(f"""Split sizes: {len(lowerCamelCase_ )} train and {len(lowerCamelCase_ )} validation.""" )

					logger.warning(f"""Updated {len(lowerCamelCase_ )} transcript(s) using '{data_args.orthography}' orthography rules.""" )
					if logger.isEnabledFor(logging.DEBUG ):
										for original_text, updated_text in text_updates:
															logger.debug(f"""Updated text: \"{original_text}\" -> \"{updated_text}\"""" )
					__a:						List[str]		    =					None

					def prepare_dataset(lowerCamelCase_ ):
										# check that all files have the correct sampling rate
										assert (
										    len(set(batch["""sampling_rate"""] ) ) == 1
										), f"""Make sure all inputs have the same sampling rate of {processor.feature_extractor.sampling_rate}."""

										__a:						Tuple		    =					processor(
										    audio=batch["""speech"""]	,				text=batch[data_args.target_text_column]	,				sampling_rate=batch["""sampling_rate"""][0] )
										batch.update(lowerCamelCase_ )
										return batch

					__a:						Union[str, Any]		    =					train_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)
					__a:						Tuple		    =					val_dataset.map(
					    lowerCamelCase_	,				batch_size=training_args.per_device_train_batch_size	,				batched=lowerCamelCase_	,				num_proc=data_args.preprocessing_num_workers	,				)

					__a:						Union[str, Any]		    =					DataCollatorCTCWithPadding(processor=lowerCamelCase_	,				padding=lowerCamelCase_ )

					def compute_metrics(lowerCamelCase_ ):
										__a:						Any		    =					pred.predictions
										__a:						List[Any]		    =					np.argmax(lowerCamelCase_	,				axis=-1 )

										__a:						int		    =					processor.tokenizer.pad_token_id

										__a:						List[Any]		    =					processor.batch_decode(lowerCamelCase_ )
										# we do not want to group tokens when computing the metrics
										__a:						str		    =					processor.batch_decode(pred.label_ids	,				group_tokens=lowerCamelCase_ )
										if logger.isEnabledFor(logging.DEBUG ):
															for reference, predicted in zip(lowerCamelCase_	,				lowerCamelCase_ ):
																				logger.debug(f"""reference: \"{reference}\"""" )
																				logger.debug(f"""predicted: \"{predicted}\"""" )
																				if orthography.untransliterator is not None:
																									logger.debug(f"""reference (untransliterated): \"{orthography.untransliterator(lowerCamelCase_ )}\"""" )
																									logger.debug(f"""predicted (untransliterated): \"{orthography.untransliterator(lowerCamelCase_ )}\"""" )

										__a:						Optional[int]		    =					wer_metric.compute(predictions=lowerCamelCase_	,				references=lowerCamelCase_ )

										return {"wer": wer}

					if model_args.freeze_feature_extractor:
										model.freeze_feature_extractor()

					__a:						Any		    =					CTCTrainer(
					    model=lowerCamelCase_	,				data_collator=lowerCamelCase_	,				args=lowerCamelCase_	,				compute_metrics=lowerCamelCase_	,				train_dataset=lowerCamelCase_	,				eval_dataset=lowerCamelCase_	,				tokenizer=processor.feature_extractor	,				)

					trainer.train()


if __name__ == "__main__":
			main()
