




'''simple docstring'''





import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast




@require_vision
class      UpperCamelCase       (	unittest.TestCase       ):



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      tempfile.mkdtemp()

      _a				:							Any							       =      CLIPImageProcessor()
      _a				:							Any							       =      BertTokenizer.from_pretrained(
          """hf-internal-testing/tiny-random-BertModel"""		,     model_input_names=["""input_ids""", """attention_mask"""] )

      _a				:							Optional[int]							       =      GitProcessor(__lowerCAmelCase		,     __lowerCAmelCase )

      processor.save_pretrained(self.tmpdirname )



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCAmelCase ).tokenizer



  def     a_							(			self		,     **__lowerCAmelCase ):







      '''simple docstring'''


      return AutoProcessor.from_pretrained(self.tmpdirname		,     **__lowerCAmelCase ).image_processor



  def     a_							(			self ):







      '''simple docstring'''


      shutil.rmtree(self.tmpdirname )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      [np.random.randint(255		,     size=(3, 30, 400)		,     dtype=np.uinta )]

      _a				:							Union[str, Any]							       =      [Image.fromarray(np.moveaxis(__lowerCAmelCase		,     0		,     -1 ) ) for x in image_inputs]

      return image_inputs



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      GitProcessor(tokenizer=self.get_tokenizer()		,     image_processor=self.get_image_processor() )
      processor.save_pretrained(self.tmpdirname )

      _a				:							Optional[int]							       =      self.get_tokenizer(bos_token="""(BOS)"""		,     eos_token="""(EOS)""" )
      _a				:							Dict							       =      self.get_image_processor(do_normalize=__lowerCAmelCase		,     padding_value=1.0 )

      _a				:							Dict							       =      GitProcessor.from_pretrained(
          self.tmpdirname		,     bos_token="""(BOS)"""		,     eos_token="""(EOS)"""		,     do_normalize=__lowerCAmelCase		,     padding_value=1.0 )

      self.assertEqual(processor.tokenizer.get_vocab()		,     tokenizer_add_kwargs.get_vocab() )
      self.assertIsInstance(processor.tokenizer		,     __lowerCAmelCase )

      self.assertEqual(processor.image_processor.to_json_string()		,     image_processor_add_kwargs.to_json_string() )
      self.assertIsInstance(processor.image_processor		,     __lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.get_image_processor()
      _a				:							Tuple							       =      self.get_tokenizer()

      _a				:							int							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[Any]							       =      self.prepare_image_inputs()

      _a				:							List[str]							       =      image_processor(__lowerCAmelCase		,     return_tensors="""np""" )
      _a				:							Any							       =      processor(images=__lowerCAmelCase		,     return_tensors="""np""" )

      for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()		,     input_processor[key].sum()		,     delta=1e-2 )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      self.get_image_processor()
      _a				:							str							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[Any]							       =      """lower newer"""

      _a				:							str							       =      processor(text=__lowerCAmelCase )

      _a				:							List[str]							       =      tokenizer(__lowerCAmelCase		,     return_token_type_ids=__lowerCAmelCase )

      for key in encoded_tok.keys():
          self.assertListEqual(encoded_tok[key]		,     encoded_processor[key] )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							List[Any]							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Optional[int]							       =      """lower newer"""
      _a				:							List[Any]							       =      self.prepare_image_inputs()

      _a				:							Any							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

      self.assertListEqual(list(inputs.keys() )		,     ["""input_ids""", """attention_mask""", """pixel_values"""] )

      # test if it raises when no input is passed
      with pytest.raises(__lowerCAmelCase ):
          processor()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							str							       =      self.get_image_processor()
      _a				:							Optional[Any]							       =      self.get_tokenizer()

      _a				:							Dict							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							int							       =      [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      _a				:							Union[str, Any]							       =      processor.batch_decode(__lowerCAmelCase )
      _a				:							Optional[Any]							       =      tokenizer.batch_decode(__lowerCAmelCase )

      self.assertListEqual(__lowerCAmelCase		,     __lowerCAmelCase )



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.get_image_processor()
      _a				:							Union[str, Any]							       =      self.get_tokenizer()

      _a				:							str							       =      GitProcessor(tokenizer=__lowerCAmelCase		,     image_processor=__lowerCAmelCase )

      _a				:							Any							       =      """lower newer"""
      _a				:							int							       =      self.prepare_image_inputs()

      _a				:							int							       =      processor(text=__lowerCAmelCase		,     images=__lowerCAmelCase )

      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys() )		,     ["""input_ids""", """attention_mask""", """pixel_values"""] )




import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def 					lowerCamelCase__					(    *a__	:	Optional[Any]  )		->						Optional[Any]:
       UpperCamelCase_											=							torch.cuda.device_count()
       print(f'''Device count: {num_processes}'''  )
       with patch_environment(
           world_size=a__						,   master_addr="""127.0.01"""						,   master_port="""29500"""						,   mixed_precision=args[1].mixed_precision  ):
              UpperCamelCase_											=							PrepareForLaunch(a__						,   distributed_type="""MULTI_GPU"""  )
              torch.multiprocessing.start_processes(a__						,   args=a__						,   nprocs=a__						,   start_method="""spawn"""  )


if __name__ == "__main__":
      # Refer to https://runhouse-docs.readthedocs-hosted.com/en/main/rh_primitives/cluster.html#hardware-setup
      # for cloud access setup instructions (if using on-demand hardware), and for API specifications.

      # on-demand GPU
      # gpu = rh.cluster(name='rh-cluster', instance_type='V100:1', provider='cheapest', use_spot=False)  # single GPU
      _A							=       rh.cluster(name='''rh-cluster''', instance_type='''V100:4''', provider='''cheapest''', use_spot=False)  # multi GPU
      gpu.up_if_not()

      # on-prem GPU
      # gpu = rh.cluster(
      #           ips=["ip_addr"], ssh_creds={ssh_user:"<username>", ssh_private_key:"<key_path>"}, name="rh-cluster"
      #       )

      # Set up remote function
      _A							=       [
          '''pip:./''',
          '''transformers''',
          '''datasets''',
          '''evaluate''',
          '''tqdm''',
          '''scipy''',
          '''scikit-learn''',
          '''tensorboard''',
          '''torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117''',
      ]
      _A							=       rh.function(fn=launch_train, system=gpu, reqs=reqs, name='''train_bert_glue''')

      # Define train args/config, run train function
      _A							=       argparse.Namespace(cpu=False, mixed_precision='''fp16''')
      _A							=       {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
      launch_train_gpu(config, train_args, stream_logs=True)

      # Alternatively, we can just run as instructed in the README (but only because there's already a wrapper CLI):
      # gpu.install_packages(reqs)
      # gpu.run(['accelerate launch --multi_gpu accelerate/examples/nlp_example.py'])



