





import unittest

from transformers import load_tool
from transformers.tools.agent_types import AGENT_TYPE_MAPPING

from .test_tools_common import ToolTesterMixin, output_types


class   lowerCamelCase__  (					unittest.TestCase       ,     _A):



 """simple docstring"""

 def 						snake_case_			(						self				:				List[str]			)      ->  Union[str, Any]:
    _A						     =					load_tool('''translation'''			)
    self.tool.setup()
    _A						     =					load_tool('''translation''' ,	remote=__lowerCAmelCase			)

 def 						snake_case_			(						self				:				Dict			)      ->  Union[str, Any]:
    _A						     =					self.tool('''Hey, what\'s up?''' ,	src_lang='''English''' ,	tgt_lang='''French'''			)
    self.assertEqual(__lowerCAmelCase ,	'''- Hé, comment ça va?'''			)

 def 						snake_case_			(						self				:				List[Any]			)      ->  Dict:
    _A						     =					self.remote_tool('''Hey, what\'s up?''' ,	src_lang='''English''' ,	tgt_lang='''French'''			)
    self.assertEqual(__lowerCAmelCase ,	'''- Hé, comment ça va?'''			)

 def 						snake_case_			(						self				:				List[Any]			)      ->  Optional[int]:
    _A						     =					self.tool(text='''Hey, what\'s up?''' ,	src_lang='''English''' ,	tgt_lang='''French'''			)
    self.assertEqual(__lowerCAmelCase ,	'''- Hé, comment ça va?'''			)

 def 						snake_case_			(						self				:				Optional[int]			)      ->  str:
    _A						     =					self.remote_tool(text='''Hey, what\'s up?''' ,	src_lang='''English''' ,	tgt_lang='''French'''			)
    self.assertEqual(__lowerCAmelCase ,	'''- Hé, comment ça va?'''			)

 def 						snake_case_			(						self				:				Optional[Any]			)      ->  List[Any]:
    _A						     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']
    _A						     =					self.tool(*__lowerCAmelCase			)

    # There is a single output
    if len(self.tool.outputs			) == 1:
       _A						     =					[outputs]

    self.assertListEqual(output_types(__lowerCAmelCase			) ,	self.tool.outputs			)

 def 						snake_case_			(						self				:				List[str]			)      ->  Optional[int]:
    _A						     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']
    _A						     =					self.tool(*__lowerCAmelCase			)

    if not isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
       _A						     =					[outputs]

    self.assertEqual(len(__lowerCAmelCase			) ,	len(self.tool.outputs			)			)

    for output, output_type in zip(__lowerCAmelCase ,	self.tool.outputs			):
       _A						     =					AGENT_TYPE_MAPPING[output_type]
       self.assertTrue(isinstance(__lowerCAmelCase ,	__lowerCAmelCase			)			)

 def 						snake_case_			(						self				:				List[str]			)      ->  List[Any]:
    _A						     =					['''Hey, what\'s up?''', '''English''', '''Spanish''']

    _A						     =					[]

    for _input, input_type in zip(__lowerCAmelCase ,	self.tool.inputs			):
       if isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
          _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input			) for _input_type in input_type]			)
       else:
          _inputs.append(AGENT_TYPE_MAPPING[input_type](_input			)			)

        # Should not raise an error
    _A						     =					self.tool(*__lowerCAmelCase			)

    if not isinstance(__lowerCAmelCase ,	__lowerCAmelCase			):
       _A						     =					[outputs]

    self.assertEqual(len(__lowerCAmelCase			) ,	len(self.tool.outputs			)			)








import argparse
import logging
import os
import sys
import time

import tensorflow as tf
from datasets import load_dataset
from tqdm import tqdm

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
from transformers.utils import is_sagemaker_dp_enabled


if os.environ.get("""SDP_ENABLED""") or is_sagemaker_dp_enabled():
   UpperCAmelCase_		   =				True
   UpperCAmelCase_		   =				"""p3dn.24xlarge"""
   import smdistributed.dataparallel.tensorflow as sdp
else:
   UpperCAmelCase_		   =				False

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :Optional[Any]  ,      _snake_case  :Union[str, Any]  ,      _snake_case  :List[str]  ,      _snake_case  :int  ,      _snake_case  :Any  ,      _snake_case  :List[Any]  ,      _snake_case  :Optional[int]=None							)     ->    Union[str, Any]:
   _A						     =					tqdm(_snake_case							)
   for i, batch in enumerate(_snake_case							):
      with tf.GradientTape() as tape:
         _A     ,						_A						     =					batch
         _A						     =					model(_snake_case							)
         _A						     =					loss(_snake_case  ,      outputs.logits							)

      if SDP_ENABLED:
         _A						     =					sdp.DistributedGradientTape(_snake_case  ,      sparse_as_dense=_snake_case							)

      _A						     =					tape.gradient(_snake_case  ,      model.trainable_variables							)
      opt.apply_gradients(zip(_snake_case  ,      model.trainable_variables							)							)

      pbar.set_description(F'''Loss: {loss_value:.4f}'''							)

      if SDP_ENABLED and i == 0:
         sdp.broadcast_variables(model.variables  ,      root_rank=0							)
         sdp.broadcast_variables(opt.variables()  ,      root_rank=0							)

      if max_steps and i >= max_steps:
         break

   _A						     =					{'''loss''': loss_value.numpy()}
   return train_results

def   SCREAMING_SNAKE_CASE_       (  _snake_case  :List[Any]  ,      _snake_case  :Optional[Any]  ,      _snake_case  :List[str]							)     ->    Dict:
   # Load dataset
   _A     ,						_A						     =					load_dataset('''imdb'''  ,      split=['''train''', '''test''']							)

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

   _A						     =					{
       x: train_dataset[x].to_tensor(default_value=0  ,      shape=[None, tokenizer.model_max_length]							)
       for x in ['''input_ids''', '''attention_mask''']
   }
   _A						     =					tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])							)

   # Preprocess test dataset
   _A						     =					test_dataset.map(
       lambda _snake_case							: tokenizer(e['''text''']  ,      truncation=_snake_case  ,      padding='''max_length'''							)  ,      batched=_snake_case							)
   test_dataset.set_format(type='''tensorflow'''  ,      columns=['''input_ids''', '''attention_mask''', '''label''']							)

   _A						     =					{
       x: test_dataset[x].to_tensor(default_value=0  ,      shape=[None, tokenizer.model_max_length]							)
       for x in ['''input_ids''', '''attention_mask''']
   }
   _A						     =					tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])							)

   if SDP_ENABLED:
      _A						     =					tf_train_dataset.shard(sdp.size()  ,      sdp.rank()							)
      _A						     =					tf_test_dataset.shard(sdp.size()  ,      sdp.rank()							)
   _A						     =					tf_train_dataset.batch(_snake_case  ,      drop_remainder=_snake_case							)
   _A						     =					tf_test_dataset.batch(_snake_case  ,      drop_remainder=_snake_case							)

   return tf_train_dataset, tf_test_dataset


if __name__ == "__main__":
   UpperCAmelCase_		   =				argparse.ArgumentParser()

   # Hyperparameters sent by the client are passed as command-line arguments to the script.
   parser.add_argument("""--epochs""", type=int, default=3)
   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)
   parser.add_argument("""--max_steps""", type=int, default=None)

   # Data, model, and output directories
   parser.add_argument("""--output_data_dir""", type=str, default=os.environ["""SM_OUTPUT_DATA_DIR"""])
   parser.add_argument("""--model_dir""", type=str, default=os.environ["""SM_MODEL_DIR"""])
   parser.add_argument("""--n_gpus""", type=str, default=os.environ["""SM_NUM_GPUS"""])

   UpperCAmelCase_   ,UpperCAmelCase_		   =				parser.parse_known_args()

   # Set up logging
   UpperCAmelCase_		   =				logging.getLogger(__name__)

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

   if SDP_ENABLED:
      sdp.init()

      UpperCAmelCase_		   =				tf.config.experimental.list_physical_devices("""GPU""")
      for gpu in gpus:
         tf.config.experimental.set_memory_growth(gpu, True)
      if gpus:
         tf.config.experimental.set_visible_devices(gpus[sdp.local_rank()], """GPU""")

    # Load model and tokenizer
   UpperCAmelCase_		   =				TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
   UpperCAmelCase_		   =				AutoTokenizer.from_pretrained(args.model_name_or_path)

   # get datasets
   UpperCAmelCase_   ,UpperCAmelCase_		   =				get_datasets(
       tokenizer=tokenizer,
       train_batch_size=args.per_device_train_batch_size,
       eval_batch_size=args.per_device_eval_batch_size,
   )

   # fine optimizer and loss
   UpperCAmelCase_		   =				tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
   UpperCAmelCase_		   =				tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
   UpperCAmelCase_		   =				[tf.keras.metrics.SparseCategoricalAccuracy()]
   model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

   # Training
   if args.do_train:
      # train_results = model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.train_batch_size)
      UpperCAmelCase_		   =				time.time()
      UpperCAmelCase_		   =				fit(
          model,
          loss,
          optimizer,
          tf_train_dataset,
          args.epochs,
          args.per_device_train_batch_size,
          max_steps=args.max_steps,
      )
      UpperCAmelCase_		   =				time.time() - start_train_time
      logger.info("""*** Train ***""")
      logger.info(f'train_runtime = {end_train_time}')

      UpperCAmelCase_		   =				os.path.join(args.output_dir, """train_results.txt""")

      if not SDP_ENABLED or sdp.rank() == 0:
         with open(output_eval_file, """w""") as writer:
            logger.info("""***** Train results *****""")
            logger.info(train_results)
            for key, value in train_results.items():
               logger.info(f'  {key} = {value}')
               writer.write(f'{key} = {value}\n')

    # Evaluation
   if args.do_eval and (not SDP_ENABLED or sdp.rank() == 0):
      UpperCAmelCase_		   =				model.evaluate(tf_test_dataset, batch_size=args.per_device_eval_batch_size, return_dict=True)
      logger.info("""*** Evaluate ***""")

      UpperCAmelCase_		   =				os.path.join(args.output_dir, """eval_results.txt""")

      with open(output_eval_file, """w""") as writer:
         logger.info("""***** Eval results *****""")
         logger.info(result)
         for key, value in result.items():
            logger.info(f'  {key} = {value}')
            writer.write(f'{key} = {value}\n')

    # Save result
   if SDP_ENABLED:
      if sdp.rank() == 0:
         model.save_pretrained(args.output_dir)
         tokenizer.save_pretrained(args.output_dir)
   else:
      model.save_pretrained(args.output_dir)
      tokenizer.save_pretrained(args.output_dir)



