
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor

class 		lowercase_							(							unittest.TestCase					):

 def __init__(   self  ,						__UpperCamelCase  ,						__UpperCamelCase=7  ,						__UpperCamelCase=3  ,						__UpperCamelCase=1_8  ,						__UpperCamelCase=3_0  ,						__UpperCamelCase=4_0_0  ,						__UpperCamelCase=True  ,						__UpperCamelCase=None  ,						__UpperCamelCase=True  ,						__UpperCamelCase=None  ,						__UpperCamelCase=True  ,						__UpperCamelCase=[0.5, 0.5, 0.5]  ,						__UpperCamelCase=[0.5, 0.5, 0.5]  ,						):




        """simple docstring"""


        UpperCamelCase_											=							size if size is not None else {"""height""": 2_0, """width""": 2_0}
        UpperCamelCase_											=							crop_size if crop_size is not None else {"""height""": 1_8, """width""": 1_8}

        UpperCamelCase_											=							parent
        UpperCamelCase_											=							batch_size
        UpperCamelCase_											=							num_channels
        UpperCamelCase_											=							image_size
        UpperCamelCase_											=							min_resolution
        UpperCamelCase_											=							max_resolution
        UpperCamelCase_											=							do_resize
        UpperCamelCase_											=							size
        UpperCamelCase_											=							do_center_crop
        UpperCamelCase_											=							crop_size
        UpperCamelCase_											=							do_normalize
        UpperCamelCase_											=							image_mean
        UpperCamelCase_											=							image_std

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return {
            "do_resize": self.do_resize,
            "size": self.size,
            "do_center_crop": self.do_center_crop,
            "crop_size": self.crop_size,
            "do_normalize": self.do_normalize,
            "image_mean": self.image_mean,
            "image_std": self.image_std,
        }



@require_torch
@require_vision
class 		lowercase_							(							__SCREAMING_SNAKE_CASE				,						unittest.TestCase					):
 A__    :					Optional[int]    =     DeiTImageProcessor if is_vision_available() else None
 A__    :					Union[str, Any]    =     True

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							DeiTImageProcessingTester(self  )

 @property
 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        self.assertTrue(hasattr(__UpperCamelCase  ,						"""do_resize"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,						"""size"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,						"""do_center_crop"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,						"""center_crop"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,						"""do_normalize"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,						"""image_mean"""  )  )
        self.assertTrue(hasattr(__UpperCamelCase  ,						"""image_std"""  )  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class.from_dict(self.image_processor_dict  )
        self.assertEqual(image_processor.size  ,						{"""height""": 2_0, """width""": 2_0}  )
        self.assertEqual(image_processor.crop_size  ,						{"""height""": 1_8, """width""": 1_8}  )

        UpperCamelCase_											=							self.image_processing_class.from_dict(self.image_processor_dict  ,						size=4_2  ,						crop_size=8_4  )
        self.assertEqual(image_processor.size  ,						{"""height""": 4_2, """width""": 4_2}  )
        self.assertEqual(image_processor.crop_size  ,						{"""height""": 8_4, """width""": 8_4}  )

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        pass

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCamelCase_											=							prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__UpperCamelCase  )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase  ,						Image.Image  )

        # Test not batched input
        UpperCamelCase_											=							image_processing(image_inputs[0]  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )  ,						)

        # Test batched
        UpperCamelCase_											=							image_processing(__UpperCamelCase  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCamelCase_											=							prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__UpperCamelCase  ,						numpify=__UpperCamelCase  )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase  ,						np.ndarray  )

        # Test not batched input
        UpperCamelCase_											=							image_processing(image_inputs[0]  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )  ,						)

        # Test batched
        UpperCamelCase_											=							image_processing(__UpperCamelCase  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )  ,						)

 def lowerCamelCase_					(   self  ):




        """simple docstring"""


        UpperCamelCase_											=							self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCamelCase_											=							prepare_image_inputs(self.image_processor_tester  ,						equal_resolution=__UpperCamelCase  ,						torchify=__UpperCamelCase  )
        for image in image_inputs:
               self.assertIsInstance(__UpperCamelCase  ,						torch.Tensor  )

        # Test not batched input
        UpperCamelCase_											=							image_processing(image_inputs[0]  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )  ,						)

        # Test batched
        UpperCamelCase_											=							image_processing(__UpperCamelCase  ,						return_tensors="""pt"""  ).pixel_values
        self.assertEqual(
            encoded_images.shape  ,						(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.crop_size["""height"""],
                self.image_processor_tester.crop_size["""width"""],
            )  ,						)





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'])



