


import argparse

import runhouse as rh
import torch
from nlp_example import training_function

from accelerate.utils import PrepareForLaunch, patch_environment




def      a_				(   *SCREAMING_SNAKE_CASE__ :				Dict					):
       '''simple docstring'''





       _lowerCamelCase	:		Optional[Any]          =torch.cuda.device_count()
       print(F'''Device count: {num_processes}'''					)
       with patch_environment(
           world_size=SCREAMING_SNAKE_CASE__		,      master_addr='127.0.01'		,      master_port='29500'		,      mixed_precision=args[1].mixed_precision					):
              _lowerCamelCase	:		int          =PrepareForLaunch(SCREAMING_SNAKE_CASE__		,      distributed_type='MULTI_GPU'					)
              torch.multiprocessing.start_processes(SCREAMING_SNAKE_CASE__		,      args=SCREAMING_SNAKE_CASE__		,      nprocs=SCREAMING_SNAKE_CASE__		,      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
 lowerCamelCase						 =	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
 lowerCamelCase						 =	[
     'pip:./',
     'transformers',
     'datasets',
     'evaluate',
     'tqdm',
     'scipy',
     'scikit-learn',
     'tensorboard',
     'torch --upgrade --extra-index-url https://download.pytorch.org/whl/cu117',
 ]
 lowerCamelCase						 =	rh.function(fn=launch_train, system=gpu, reqs=reqs, name='train_bert_glue')

 # Define train args/config, run train function
 lowerCamelCase						 =	argparse.Namespace(cpu=False, mixed_precision='fp16')
 lowerCamelCase						 =	{'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'])






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


if is_torch_available():
 import torch

if is_vision_available():
 from PIL import Image

 from transformers import CLIPImageProcessor




class A  (       unittest.TestCase					):






      def __init__( self						:					Union[str, Any]      ,       lowercase_						:					Any      ,       lowercase_						:					Optional[Any]=7      ,       lowercase_						:					Union[str, Any]=3      ,       lowercase_						:					Any=18      ,       lowercase_						:					Optional[int]=30      ,       lowercase_						:					int=400      ,       lowercase_						:					List[Any]=True      ,       lowercase_						:					List[str]=None      ,       lowercase_						:					Optional[Any]=True      ,       lowercase_						:					Tuple=None      ,       lowercase_						:					str=True      ,       lowercase_						:					Any=[0.48145466, 0.4578275, 0.40821073]      ,       lowercase_						:					Optional[int]=[0.26862954, 0.26130258, 0.27577711]      ,       lowercase_						:					Dict=True      ,       ) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		List[str]          =size if size is not None else {'shortest_edge': 20}
             _lowerCamelCase	:		Optional[Any]          =crop_size if crop_size is not None else {'height': 18, 'width': 18}
             _lowerCamelCase	:		Any          =parent
             _lowerCamelCase	:		List[Any]          =batch_size
             _lowerCamelCase	:		Union[str, Any]          =num_channels
             _lowerCamelCase	:		str          =image_size
             _lowerCamelCase	:		str          =min_resolution
             _lowerCamelCase	:		Optional[int]          =max_resolution
             _lowerCamelCase	:		str          =do_resize
             _lowerCamelCase	:		Tuple          =size
             _lowerCamelCase	:		Any          =do_center_crop
             _lowerCamelCase	:		Any          =crop_size
             _lowerCamelCase	:		Optional[Any]          =do_normalize
             _lowerCamelCase	:		List[Any]          =image_mean
             _lowerCamelCase	:		int          =image_std
             _lowerCamelCase	:		Any          =do_convert_rgb






      def 	lowerCamelCase	( self						:					int			) ->						Tuple:
             """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,
                 "do_convert_rgb": self.do_convert_rgb,
             }






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					Any=False      ,       lowercase_						:					str=False      ,       lowercase_						:					Union[str, Any]=False			) ->						Tuple:
             """simple docstring"""




             assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

             if equal_resolution:
                    _lowerCamelCase	:		int          =[]
                    for i in range(self.batch_size			):
                           image_inputs.append(
                               np.random.randint(
                                   255      ,       size=(self.num_channels, self.max_resolution, self.max_resolution)      ,       dtype=np.uinta			)			)
             else:
                    _lowerCamelCase	:		Dict          =[]
                    for i in range(self.batch_size			):
                           _lowerCamelCase     ,					_lowerCamelCase	:		str          =np.random.choice(np.arange(self.min_resolution      ,       self.max_resolution			)      ,       2			)
                           image_inputs.append(np.random.randint(255      ,       size=(self.num_channels, width, height)      ,       dtype=np.uinta			)			)

             if not numpify and not torchify:
                    # PIL expects the channel dimension as last dimension
                    _lowerCamelCase	:		Optional[int]          =[Image.fromarray(np.moveaxis(lowercase_      ,       0      ,       -1			)			) for x in image_inputs]

             if torchify:
                    _lowerCamelCase	:		Tuple          =[torch.from_numpy(lowercase_			) for x in image_inputs]

             return image_inputs




@require_torch
@require_vision
class A  (       UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  Tuple				=CLIPImageProcessor if is_vision_available() else None






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Dict:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =CLIPImageProcessingTester(self			)






      @property
      def 	lowerCamelCase	( self						:					Tuple			) ->						Dict:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 	lowerCamelCase	( self						:					int			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_std'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_convert_rgb'			)			)






      def 	lowerCamelCase	( self						:					Any			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.image_processing_class.from_dict(self.image_processor_dict			)
             self.assertEqual(image_processor.size      ,       {'shortest_edge': 20}			)
             self.assertEqual(image_processor.crop_size      ,       {'height': 18, 'width': 18}			)

             _lowerCamelCase	:		Optional[Any]          =self.image_processing_class.from_dict(self.image_processor_dict      ,       size=42      ,       crop_size=84			)
             self.assertEqual(image_processor.size      ,       {'shortest_edge': 42}			)
             self.assertEqual(image_processor.crop_size      ,       {'height': 84, 'width': 84}			)






      def 	lowerCamelCase	( self						:					Tuple			) ->						Optional[Any]:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					List[Any]			) ->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase	:		str          =self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             _lowerCamelCase	:		Any          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       Image.Image			)

             # Test not batched input
             _lowerCamelCase	:		Optional[Any]          =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
             _lowerCamelCase	:		Optional[Any]          =image_processing(lowercase_      ,       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						:					List[Any]			) ->						Optional[int]:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.image_processing_class(**self.image_processor_dict			)
             # create random numpy tensors
             _lowerCamelCase	:		Tuple          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,       numpify=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       np.ndarray			)

             # Test not batched input
             _lowerCamelCase	:		Any          =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
             _lowerCamelCase	:		str          =image_processing(lowercase_      ,       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						:					List[str]			) ->						Tuple:
             """simple docstring"""




             _lowerCamelCase	:		int          =self.image_processing_class(**self.image_processor_dict			)
             # create random PyTorch tensors
             _lowerCamelCase	:		Dict          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_      ,       torchify=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       torch.Tensor			)

             # Test not batched input
             _lowerCamelCase	:		List[str]          =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
             _lowerCamelCase	:		str          =image_processing(lowercase_      ,       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'],
                 )      ,       )




@require_torch
@require_vision
class A  (       UpperCamelCase_   ,							unittest.TestCase					):
      UpperCamelCase__							:  List[str]				=CLIPImageProcessor if is_vision_available() else None






      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						str:
             """simple docstring"""




             _lowerCamelCase	:		int          =CLIPImageProcessingTester(self      ,       num_channels=4			)
             _lowerCamelCase	:		Any          =3






      @property
      def 	lowerCamelCase	( self						:					Union[str, Any]			) ->						Tuple:
             """simple docstring"""




             return self.image_processor_tester.prepare_image_processor_dict()






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Tuple          =self.image_processing_class(**self.image_processor_dict			)
             self.assertTrue(hasattr(lowercase_      ,       'do_resize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'size'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'center_crop'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_normalize'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_mean'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'image_std'			)			)
             self.assertTrue(hasattr(lowercase_      ,       'do_convert_rgb'			)			)






      def 	lowerCamelCase	( self						:					Optional[int]			) ->						Optional[int]:
             """simple docstring"""




             pass






      def 	lowerCamelCase	( self						:					Any			) ->						Any:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.image_processing_class(**self.image_processor_dict			)
             # create random PIL images
             _lowerCamelCase	:		Optional[Any]          =self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_			)
             for image in image_inputs:
                    self.assertIsInstance(lowercase_      ,       Image.Image			)

             # Test not batched input
             _lowerCamelCase	:		Any          =image_processing(image_inputs[0]      ,       return_tensors='pt'			).pixel_values
             self.assertEqual(
                 encoded_images.shape      ,       (
                     1,
                     self.expected_encoded_image_num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )      ,       )

             # Test batched
             _lowerCamelCase	:		Optional[Any]          =image_processing(lowercase_      ,       return_tensors='pt'			).pixel_values
             self.assertEqual(
                 encoded_images.shape      ,       (
                     self.image_processor_tester.batch_size,
                     self.expected_encoded_image_num_channels,
                     self.image_processor_tester.crop_size['height'],
                     self.image_processor_tester.crop_size['width'],
                 )      ,       )


