





'''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'):
				_lowerCamelCase					:						Optional[Any]					     =							True
				from torch.cuda.amp import autocast


_lowerCamelCase					:						List[Any]					     =							logging.getLogger(__name__)



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







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






def  __a			(  UpperCAmelCase							,      UpperCAmelCase     )   ->Union[str, 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					=   logging.WARNING
	if model_args.verbose_logging:
		A					=   logging.DEBUG
	elif trainer_utils.is_main_process(training_args.local_rank     ):
		A					=   logging.INFO
	logger.setLevel(UpperCAmelCase     )



@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







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






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     "|"
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     field(default_factory=A__    )
		__lowerCAmelCase								=     None







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







		def 							A  (self    :   Union[str, Any]   ,   _lowerCAmelCase    :   str   ):
			# TODO(elgeish) return a pipeline (e.g., from jiwer) instead? Or rely on branch predictor as is
			if len(self.translation_table   ) > 0:
				A					=   text.translate(self.translation_table   )
			if len(self.words_to_remove   ) == 0:
				A					=   """ """.join(text.split()   )  # clean up whitespaces
			else:
				A					=   """ """.join(w for w in text.split() if w not in self.words_to_remove   )  # and clean up whilespaces
			return text







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   ModelArguments   ):
			A					=   WavaVecaFeatureExtractor.from_pretrained(
			    model_args.model_name_or_path   ,   cache_dir=model_args.cache_dir   )
			if self.vocab_file:
				A					=   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					=   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(_lowerCAmelCase   ,   _lowerCAmelCase   )






@dataclass
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     42
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None
		__lowerCAmelCase								=     None







		def __call__(self    :   List[str]   ,   _lowerCAmelCase    :   List[Dict[str, Union[List[int], torch.Tensor]]]   ):
			# split inputs and labels since they have to be of different lenghts and need
			# different padding methods
			A					=   [{"""input_values""": feature["""input_values"""]} for feature in features]
			A					=   [{"""input_ids""": feature["""labels"""]} for feature in features]

			A					=   self.processor.pad(
			    _lowerCAmelCase   ,   padding=self.padding   ,   max_length=self.max_length   ,   pad_to_multiple_of=self.pad_to_multiple_of   ,   return_tensors="""pt"""   ,   )
			A					=   self.processor.pad(
			    labels=_lowerCAmelCase   ,   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					=   labels_batch["""input_ids"""].masked_fill(labels_batch.attention_mask.ne(1   )   ,   -100   )

			A					=   labels

			return batch






class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		def 							A  (self    :   str   ,   _lowerCAmelCase    :   nn.Module   ,   _lowerCAmelCase    :   Dict[str, Union[torch.Tensor, Any]]   ):
			model.train()
			A					=   self._prepare_inputs(_lowerCAmelCase   )

			if self.use_amp:
				with autocast():
					A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )
			else:
				A					=   self.compute_loss(_lowerCAmelCase   ,   _lowerCAmelCase   )

			if self.args.n_gpu > 1:
				if model.module.config.ctc_loss_reduction == "mean":
					A					=   loss.mean()
				elif model.module.config.ctc_loss_reduction == "sum":
					A					=   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					=   loss / self.args.gradient_accumulation_steps

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

			return loss.detach()






def  __a			(  )   ->Union[str, Any]:



	"""simple docstring"""







	A					=   HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)     )

	A	,     A	,     A					=   parser.parse_args_into_dataclasses()
	configure_logger(UpperCAmelCase							,      UpperCAmelCase     )

	A					=   Orthography.from_name(data_args.orthography.lower()     )
	A					=   orthography.create_processor(UpperCAmelCase     )
	A					=   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					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.train_split_name     )
	A					=   datasets.load_dataset(
	    data_args.dataset_name							,      data_args.dataset_config_name							,      split=data_args.validation_split_name     )

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

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

	A					=   train_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )
	A					=   val_dataset.map(UpperCAmelCase							,      remove_columns=[data_args.speech_file_column]     )

	if data_args.max_duration_in_seconds is not None:

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

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

	logger.warning(f"""Updated {len(UpperCAmelCase     )} 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					=   None

	def prepare_dataset(UpperCAmelCase     ):
		# 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					=   processor(
		    audio=batch["""speech"""]							,      text=batch[data_args.target_text_column]							,      sampling_rate=batch["""sampling_rate"""][0]     )
		batch.update(UpperCAmelCase     )
		return batch

	A					=   train_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )
	A					=   val_dataset.map(
	    UpperCAmelCase							,      batch_size=training_args.per_device_train_batch_size							,      batched=UpperCAmelCase							,      num_proc=data_args.preprocessing_num_workers							,      )

	A					=   DataCollatorCTCWithPadding(processor=UpperCAmelCase							,      padding=UpperCAmelCase     )

	def compute_metrics(UpperCAmelCase     ):
		A					=   pred.predictions
		A					=   np.argmax(UpperCAmelCase							,      axis=-1     )

		A					=   processor.tokenizer.pad_token_id

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

		A					=   wer_metric.compute(predictions=UpperCAmelCase							,      references=UpperCAmelCase     )

		return {"wer": wer}

	if model_args.freeze_feature_extractor:
		model.freeze_feature_extractor()

	A					=   CTCTrainer(
	    model=UpperCAmelCase							,      data_collator=UpperCAmelCase							,      args=UpperCAmelCase							,      compute_metrics=UpperCAmelCase							,      train_dataset=UpperCAmelCase							,      eval_dataset=UpperCAmelCase							,      tokenizer=processor.feature_extractor							,      )

	trainer.train()


if __name__ == "__main__":
				main()








'''simple docstring'''


from __future__ import annotations

import unittest
import warnings

from transformers import AutoTokenizer, MarianConfig, MarianTokenizer, TranslationPipeline, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
				import tensorflow as tf

				from transformers import TFAutoModelForSeqaSeqLM, TFMarianModel, TFMarianMTModel



@require_tf
class 				__UpperCAmelCase	:







		'''simple docstring'''







		__lowerCAmelCase								=     MarianConfig
		__lowerCAmelCase								=     {}
		__lowerCAmelCase								=     '''gelu'''







		def __init__(self    :   Tuple   ,   _lowerCAmelCase    :   Union[str, Any]   ,   _lowerCAmelCase    :   Dict=13   ,   _lowerCAmelCase    :   Optional[int]=7   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   Optional[int]=False   ,   _lowerCAmelCase    :   Tuple=99   ,   _lowerCAmelCase    :   str=32   ,   _lowerCAmelCase    :   str=2   ,   _lowerCAmelCase    :   Union[str, Any]=4   ,   _lowerCAmelCase    :   Dict=37   ,   _lowerCAmelCase    :   int=0.1   ,   _lowerCAmelCase    :   List[Any]=0.1   ,   _lowerCAmelCase    :   Tuple=20   ,   _lowerCAmelCase    :   List[str]=2   ,   _lowerCAmelCase    :   Any=1   ,   _lowerCAmelCase    :   int=0   ,   ):
			A					=   parent
			A					=   batch_size
			A					=   seq_length
			A					=   is_training
			A					=   use_labels
			A					=   vocab_size
			A					=   hidden_size
			A					=   num_hidden_layers
			A					=   num_attention_heads
			A					=   intermediate_size

			A					=   hidden_dropout_prob
			A					=   attention_probs_dropout_prob
			A					=   max_position_embeddings
			A					=   eos_token_id
			A					=   pad_token_id
			A					=   bos_token_id







		def 							A  (self    :   int   ):
			A					=   ids_tensor([self.batch_size, self.seq_length - 1]   ,   self.vocab_size   )
			A					=   tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )   ,   1   )
			A					=   tf.concat([input_ids, eos_tensor]   ,   axis=1   )

			A					=   ids_tensor([self.batch_size, self.seq_length]   ,   self.vocab_size   )

			A					=   self.config_cls(
			    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_ids=[2]   ,   bos_token_id=self.bos_token_id   ,   pad_token_id=self.pad_token_id   ,   decoder_start_token_id=self.pad_token_id   ,   **self.config_updates   ,   )
			A					=   prepare_marian_inputs_dict(_lowerCAmelCase   ,   _lowerCAmelCase   ,   _lowerCAmelCase   )
			return config, inputs_dict







		def 							A  (self    :   List[Any]   ,   _lowerCAmelCase    :   int   ,   _lowerCAmelCase    :   Any   ):
			A					=   TFMarianModel(config=_lowerCAmelCase   ).get_decoder()
			A					=   inputs_dict["""input_ids"""]

			A					=   input_ids[:1, :]
			A					=   inputs_dict["""attention_mask"""][:1, :]
			A					=   inputs_dict["""head_mask"""]
			A					=   1

			# first forward pass
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   head_mask=_lowerCAmelCase   ,   use_cache=_lowerCAmelCase   )

			A	,     A					=   outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			A					=   ids_tensor((self.batch_size, 3)   ,   config.vocab_size   )
			A					=   tf.cast(ids_tensor((self.batch_size, 3)   ,   2   )   ,   tf.inta   )

			# append to next input_ids and
			A					=   tf.concat([input_ids, next_tokens]   ,   axis=-1   )
			A					=   tf.concat([attention_mask, next_attn_mask]   ,   axis=-1   )

			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   )[0]
			A					=   model(_lowerCAmelCase   ,   attention_mask=_lowerCAmelCase   ,   past_key_values=_lowerCAmelCase   )[0]

			self.parent.assertEqual(next_tokens.shape[1]   ,   output_from_past.shape[1]   )

			# select random slice
			A					=   int(ids_tensor((1,)   ,   output_from_past.shape[-1]   )   )
			A					=   output_from_no_past[:, -3:, random_slice_idx]
			A					=   output_from_past[:, :, random_slice_idx]

			# test that outputs are equal for slice
			tf.debugging.assert_near(_lowerCAmelCase   ,   _lowerCAmelCase   ,   rtol=1e-3   )






def  __a			(  UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      UpperCAmelCase=None							,      )   ->List[str]:



	"""simple docstring"""







	if attention_mask is None:
		A					=   tf.cast(tf.math.not_equal(UpperCAmelCase							,      config.pad_token_id     )							,      tf.inta     )
	if decoder_attention_mask is None:
		A					=   tf.concat(
		    [
		        tf.ones(decoder_input_ids[:, :1].shape							,      dtype=tf.inta     ),
		        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]							,      config.pad_token_id     )							,      tf.inta     ),
		    ]							,      axis=-1							,      )
	if head_mask is None:
		A					=   tf.ones((config.encoder_layers, config.encoder_attention_heads)     )
	if decoder_head_mask is None:
		A					=   tf.ones((config.decoder_layers, config.decoder_attention_heads)     )
	if cross_attn_head_mask is None:
		A					=   tf.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,
	    "head_mask": head_mask,
	    "decoder_head_mask": decoder_head_mask,
	    "cross_attn_head_mask": cross_attn_head_mask,
	}



@require_tf
class 				__UpperCAmelCase	(							A__		,       A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     (TFMarianMTModel, TFMarianModel) if is_tf_available() else ()
		__lowerCAmelCase								=     (TFMarianMTModel,) if is_tf_available() else ()
		__lowerCAmelCase								=     (
		    {
		        '''conversational''': TFMarianMTModel,
		        '''feature-extraction''': TFMarianModel,
		        '''summarization''': TFMarianMTModel,
		        '''text2text-generation''': TFMarianMTModel,
		        '''translation''': TFMarianMTModel,
		    }
		    if is_tf_available()
		    else {}
		)
		__lowerCAmelCase								=     True
		__lowerCAmelCase								=     False
		__lowerCAmelCase								=     False







		def 							A  (self    :   Tuple   ):
			A					=   TFMarianModelTester(self   )
			A					=   ConfigTester(self   ,   config_class=_lowerCAmelCase   )







		def 							A  (self    :   List[str]   ):
			self.config_tester.run_common_tests()







		def 							A  (self    :   Any   ):
			A					=   self.model_tester.prepare_config_and_inputs_for_common()
			self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase   )






@require_tf
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     10_00  # show more chars for failing integration tests







		@classmethod
		def 							A  (cls    :   Union[str, Any]   ):
			A					=   F"""Helsinki-NLP/opus-mt-{cls.src}-{cls.tgt}"""
			return cls







		@cached_property
		def 							A  (self    :   str   ):
			return AutoTokenizer.from_pretrained(self.model_name   )







		@property
		def 							A  (self    :   Optional[int]   ):
			return self.tokenizer.eos_token_id







		@cached_property
		def 							A  (self    :   Optional[Any]   ):
			warnings.simplefilter("""error"""   )
			A					=   TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
			assert isinstance(_lowerCAmelCase   ,   _lowerCAmelCase   )
			A					=   model.config
			self.assertListEqual(c.bad_words_ids   ,   [[c.pad_token_id]]   )
			self.assertEqual(c.max_length   ,   512   )
			self.assertEqual(c.decoder_start_token_id   ,   c.pad_token_id   )
			return model







		def 							A  (self    :   Optional[int]   ,   **_lowerCAmelCase    :   Any   ):
			A					=   self.translate_src_text(**_lowerCAmelCase   )
			self.assertListEqual(self.expected_text   ,   _lowerCAmelCase   )







		def 							A  (self    :   Union[str, Any]   ,   **_lowerCAmelCase    :   Union[str, Any]   ):
			A					=   self.tokenizer(self.src_text   ,   **_lowerCAmelCase   ,   padding=_lowerCAmelCase   ,   return_tensors="""tf"""   )
			A					=   self.model.generate(
			    model_inputs.input_ids   ,   attention_mask=model_inputs.attention_mask   ,   num_beams=2   ,   max_length=128   )
			A					=   self.tokenizer.batch_decode(generated_ids.numpy()   ,   skip_special_tokens=_lowerCAmelCase   )
			return generated_words






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''mt'''
		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     ['''Billi messu b\'mod ġentili, Ġesù fejjaq raġel li kien milqut bil - marda kerha tal - ġdiem.''']
		__lowerCAmelCase								=     ['''Touching gently, Jesus healed a man who was affected by the sad disease of leprosy.''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   str   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''zh'''
		__lowerCAmelCase								=     ['''My name is Wolfgang and I live in Berlin''']
		__lowerCAmelCase								=     ['''我叫沃尔夫冈 我住在柏林''']







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Any   ):
			self._assert_generated_batch_equal_expected()






@require_sentencepiece
@require_tokenizers
@require_tf
class 				__UpperCAmelCase	(							A__    ):







		'''simple docstring'''







		__lowerCAmelCase								=     '''en'''
		__lowerCAmelCase								=     '''ROMANCE'''
		__lowerCAmelCase								=     [
		    '''>>fr<< Don\'t spend so much time watching TV.''',
		    '''>>pt<< Your message has been sent.''',
		    '''>>es<< He\'s two years older than me.''',
		]
		__lowerCAmelCase								=     [
		    '''Ne passez pas autant de temps à regarder la télé.''',
		    '''A sua mensagem foi enviada.''',
		    '''Es dos años más viejo que yo.''',
		]







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			self._assert_generated_batch_equal_expected()







		@unittest.skip("""Skipping until #12647 is resolved."""   )
		@slow
		def 							A  (self    :   Union[str, Any]   ):
			A					=   TranslationPipeline(self.model   ,   self.tokenizer   ,   framework="""tf"""   )
			A					=   pipeline(self.src_text   )
			self.assertEqual(self.expected_text   ,   [x["""translation_text"""] for x in output]   )

