

'''simple docstring'''
import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
     lowerCamelCase				:   Union[str, Any]      				=  argparse.ArgumentParser()

     # Hyperparameters sent by the client are passed as command-line arguments to the script.
     parser.add_argument("--epochs", type=int, default=1)
     parser.add_argument("--per_device_train_batch_size", type=int, default=1_6)
     parser.add_argument("--per_device_eval_batch_size", type=int, default=8)
     parser.add_argument("--model_name_or_path", type=str)
     parser.add_argument("--learning_rate", type=str, default=5E-5)
     parser.add_argument("--do_train", type=bool, default=True)
     parser.add_argument("--do_eval", type=bool, default=True)
     parser.add_argument("--output_dir", type=str)

     lowerCamelCase				:   Any      				=  parser.parse_known_args()

     # overwrite batch size until we have tf_glue.py
     lowerCamelCase				:   Any      				=  1_6
     lowerCamelCase				:   List[str]      				=  1_6

     # Set up logging
     lowerCamelCase				:   Optional[Any]      				=  logging.getLogger(__name__)

     logging.basicConfig(
         level=logging.getLevelName("INFO"),
         handlers=[logging.StreamHandler(sys.stdout)],
         format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
     )

     # Load model and tokenizer
     lowerCamelCase				:   Union[str, Any]      				=  TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
     lowerCamelCase				:   Any      				=  AutoTokenizer.from_pretrained(args.model_name_or_path)

     # Load dataset
     lowerCamelCase				:   Dict      				=  load_dataset("imdb", split=["train", "test"])
     lowerCamelCase				:   Optional[Any]      				=  train_dataset.shuffle().select(range(5_0_0_0))  # smaller the size for train dataset to 5k
     lowerCamelCase				:   int      				=  test_dataset.shuffle().select(range(5_0_0))  # smaller the size for test dataset to 500

     # Preprocess train dataset
     lowerCamelCase				:   Dict      				=  train_dataset.map(
         lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
     )
     train_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

     lowerCamelCase				:   Union[str, Any]      				=  {
         x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
         for x in ["input_ids", "attention_mask"]
     }
     lowerCamelCase				:   int      				=  tf.data.Dataset.from_tensor_slices((train_features, train_dataset["label"])).batch(
         args.per_device_train_batch_size
     )

     # Preprocess test dataset
     lowerCamelCase				:   Union[str, Any]      				=  test_dataset.map(
         lambda e: tokenizer(e["text"], truncation=True, padding="max_length"), batched=True
     )
     test_dataset.set_format(type="tensorflow", columns=["input_ids", "attention_mask", "label"])

     lowerCamelCase				:   Dict      				=  {
         x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
         for x in ["input_ids", "attention_mask"]
     }
     lowerCamelCase				:   str      				=  tf.data.Dataset.from_tensor_slices((test_features, test_dataset["label"])).batch(
         args.per_device_eval_batch_size
     )

     # fine optimizer and loss
     lowerCamelCase				:   Optional[int]      				=  tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
     lowerCamelCase				:   Union[str, Any]      				=  tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
     lowerCamelCase				:   str      				=  [tf.keras.metrics.SparseCategoricalAccuracy()]
     model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

     lowerCamelCase				:   str      				=  time.time()
     lowerCamelCase				:   Optional[int]      				=  model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
     lowerCamelCase				:   Any      				=  time.time() - start_train_time

     logger.info("*** Train ***")
     logger.info(f'''train_runtime = {end_train_time}''')
     for key, value in train_results.history.items():
          logger.info(f'''  {key} = {value}''')









'''simple docstring'''
import unittest

import pytest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    MBartaaTokenizerFast,
    MBartConfig,
    MBartForConditionalGeneration,
    TranslationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch, slow

from .test_pipelines_common import ANY







@is_pipeline_test
class 							A__					( unittest.TestCase						):
  A__					=       MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
  A__					=       TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING





  def 		A						(	self   :			Optional[Any]							,      _a   :			Tuple							,      _a   :			Union[str, Any]							,      _a   :			str				)			->					List[Any]:





       '''simple docstring'''






       if isinstance(model.config							,      _a				):
            _SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE												=list(tokenizer.lang_code_to_id.keys()				)[:2]
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a							,      src_lang=_a							,      tgt_lang=_a				)
       else:
            _SCREAMING_SNAKE_CASE												=TranslationPipeline(model=_a							,      tokenizer=_a				)
       return translator, ["Some string", "Some other text"]





  def 		A						(	self   :			Union[str, Any]							,      _a   :			List[Any]							,      _a   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=translator('Some string'				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}]				)

       _SCREAMING_SNAKE_CASE												=translator(['Some string']				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}]				)

       _SCREAMING_SNAKE_CASE												=translator(['Some string', 'other string']				)
       self.assertEqual(_a							,      [{'translation_text': ANY(_a				)}, {'translation_text': ANY(_a				)}]				)





  @require_torch
  def 		A						(	self   :			Dict				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Tuple				)			->					Union[str, Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_ro'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide Beide'
                       ' Beide Beide'
                   )
               }
           ]							,      )





  @require_torch
  def 		A						(	self   :			Any				)			->					Optional[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='pt'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )





  @require_tf
  def 		A						(	self   :			Dict				)			->					Tuple:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=pipeline('translation_en_to_de'							,      model='patrickvonplaten/t5-tiny-random'							,      framework='tf'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test string'							,      max_length=20				)
       self.assertEqual(
           _a							,      [
               {
                   'translation_text': (
                       'monoton monoton monoton monoton monoton monoton monoton monoton monoton monoton urine urine'
                       ' urine urine urine urine urine urine urine'
                   )
               }
           ]							,      )







class 							A__					( unittest.TestCase						):





  @require_torch
  @slow
  def 		A						(	self   :			int				)			->					Optional[Any]:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation_cn_to_ar'				)

       # but we do for this one
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation_en_to_de'				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'en'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'de'				)





  @require_torch
  @slow
  def 		A						(	self   :			Optional[Any]				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												=MBartForConditionalGeneration.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)
       _SCREAMING_SNAKE_CASE												=MBartaaTokenizerFast.from_pretrained('facebook/mbart-large-50-many-to-many-mmt'				)

       _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a							,      tokenizer=_a				)
       # Missing src_lang, tgt_lang
       with self.assertRaises(_a				):
            translator('This is a test'				)

       _SCREAMING_SNAKE_CASE												=translator('This is a test'							,      src_lang='en_XX'							,      tgt_lang='ar_AR'				)
       self.assertEqual(_a							,      [{'translation_text': 'هذا إختبار'}]				)

       _SCREAMING_SNAKE_CASE												=translator('This is a test'							,      src_lang='en_XX'							,      tgt_lang='hi_IN'				)
       self.assertEqual(_a							,      [{'translation_text': 'यह एक परीक्षण है'}]				)

       # src_lang, tgt_lang can be defined at pipeline call time
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a							,      tokenizer=_a							,      src_lang='en_XX'							,      tgt_lang='ar_AR'				)
       _SCREAMING_SNAKE_CASE												=translator('This is a test'				)
       self.assertEqual(_a							,      [{'translation_text': 'هذا إختبار'}]				)





  @require_torch
  def 		A						(	self   :			int				)			->					int:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       _SCREAMING_SNAKE_CASE												=pipeline(task='translation_cn_to_ar'							,      model=_a				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'cn'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'ar'				)





  @require_torch
  def 		A						(	self   :			str				)			->					List[Any]:





       '''simple docstring'''






       _SCREAMING_SNAKE_CASE												='patrickvonplaten/t5-tiny-random'
       with pytest.warns(_a							,      match=r'.*translation_en_to_de.*'				):
            _SCREAMING_SNAKE_CASE												=pipeline(task='translation'							,      model=_a				)
       self.assertEqual(translator.task							,      'translation_en_to_de'				)
       self.assertEqual(translator._preprocess_params['src_lang']							,      'en'				)
       self.assertEqual(translator._preprocess_params['tgt_lang']							,      'de'				)





  @require_torch
  def 		A						(	self   :			Any				)			->					Dict:





       '''simple docstring'''






       with self.assertRaises(_a				):
            pipeline(task='translation'				)
