'''simple docstring'''





import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor






class 				UpperCAmelCase (       unittest.TestCase ):




    '''simple docstring'''







    def __init__(       self    ,							lowercase__    ,							lowercase__=7    ,							lowercase__=3    ,							lowercase__=30    ,							lowercase__=400    ,							lowercase__=True    ,							lowercase__=None    ,							lowercase__=True    ,							lowercase__=[0.5, 0.5, 0.5]    ,							lowercase__=[0.5, 0.5, 0.5]    ,							lowercase__=True    ,							lowercase__=1 / 255    ,							lowercase__=True    ,							)							-> Any:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       parent
         SCREAMING_SNAKE_CASE					:			str    	=       batch_size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       num_channels
         SCREAMING_SNAKE_CASE					:			Any    	=       min_resolution
         SCREAMING_SNAKE_CASE					:			Any    	=       max_resolution
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_resize
         SCREAMING_SNAKE_CASE					:			Dict    	=       size
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       do_normalize
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image_mean
         SCREAMING_SNAKE_CASE					:			Dict    	=       image_std
         SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       do_rescale
         SCREAMING_SNAKE_CASE					:			List[str]    	=       rescale_factor
         SCREAMING_SNAKE_CASE					:			List[str]    	=       do_pad






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






    def       _UpperCamelCase    (       self    ,							lowercase__    ,							lowercase__=False )							-> Any:
         if not batched:
              SCREAMING_SNAKE_CASE					:			int    	=       image_inputs[0]
              if isinstance(lowercase__    ,							Image.Image ):
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       image.size
              else:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Any    	=       image.shape[1], image.shape[2]
              if w < h:
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       int(self.size['shortest_edge'] * h / w )
                   SCREAMING_SNAKE_CASE					:			int    	=       self.size['shortest_edge']
              elif w > h:
                   SCREAMING_SNAKE_CASE					:			List[str]    	=       self.size['shortest_edge']
                   SCREAMING_SNAKE_CASE					:			Optional[int]    	=       int(self.size['shortest_edge'] * w / h )
              else:
                   SCREAMING_SNAKE_CASE					:			int    	=       self.size['shortest_edge']
                   SCREAMING_SNAKE_CASE					:			str    	=       self.size['shortest_edge']

         else:
              SCREAMING_SNAKE_CASE					:			List[str]    	=       []
              for image in image_inputs:
                   SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.get_expected_values([image] )
                   expected_values.append((expected_height, expected_width) )
              SCREAMING_SNAKE_CASE					:			Tuple    	=       max(lowercase__    ,							key=lambda lowercase__ : item[0] )[0]
              SCREAMING_SNAKE_CASE					:			str    	=       max(lowercase__    ,							key=lambda lowercase__ : item[1] )[1]

         return expected_height, expected_width






@require_torch
@require_vision
class 				UpperCAmelCase (       _SCREAMING_SNAKE_CASE   ,    unittest.TestCase ):




    '''simple docstring'''







    snake_case__							:   Union[str, Any]							=						ConditionalDetrImageProcessor if is_vision_available() else None






    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         SCREAMING_SNAKE_CASE					:			Dict    	=       ConditionalDetrImageProcessingTester(self )






    @property
    def       _UpperCamelCase    (       self )							-> Optional[Any]:
         return self.image_processor_tester.prepare_image_processor_dict()






    def       _UpperCamelCase    (       self )							-> Optional[int]:
         SCREAMING_SNAKE_CASE					:			Any    	=       self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(lowercase__    ,							'image_mean' ) )
         self.assertTrue(hasattr(lowercase__    ,							'image_std' ) )
         self.assertTrue(hasattr(lowercase__    ,							'do_normalize' ) )
         self.assertTrue(hasattr(lowercase__    ,							'do_resize' ) )
         self.assertTrue(hasattr(lowercase__    ,							'size' ) )






    def       _UpperCamelCase    (       self )							-> List[Any]:
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.image_processing_class.from_dict(self.image_processor_dict )
         self.assertEqual(image_processor.size    ,							{'shortest_edge': 18, 'longest_edge': 1_333} )
         self.assertEqual(image_processor.do_pad    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       self.image_processing_class.from_dict(
             self.image_processor_dict    ,							size=42    ,							max_size=84    ,							pad_and_return_pixel_mask=lowercase__ )
         self.assertEqual(image_processor.size    ,							{'shortest_edge': 42, 'longest_edge': 84} )
         self.assertEqual(image_processor.do_pad    ,							lowercase__ )






    def       _UpperCamelCase    (       self )							-> Tuple:
         pass






    def       _UpperCamelCase    (       self )							-> str:
         # Initialize image_processing
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         SCREAMING_SNAKE_CASE					:			Dict    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__ )
         for image in image_inputs:
              self.assertIsInstance(lowercase__    ,							Image.Image )

         # Test not batched input
         SCREAMING_SNAKE_CASE					:			List[str]    	=       image_processing(image_inputs[0]    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       self.image_processor_tester.get_expected_values(lowercase__ )

         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Any    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[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,
                 expected_height,
                 expected_width,
             )    ,							)






    def       _UpperCamelCase    (       self )							-> Any:
         # Initialize image_processing
         SCREAMING_SNAKE_CASE					:			str    	=       self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         SCREAMING_SNAKE_CASE					:			Any    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__    ,							numpify=lowercase__ )
         for image in image_inputs:
              self.assertIsInstance(lowercase__    ,							np.ndarray )

         # Test not batched input
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processing(image_inputs[0]    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.image_processor_tester.get_expected_values(lowercase__ )

         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         SCREAMING_SNAKE_CASE					:			int    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			Tuple    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )

         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)






    def       _UpperCamelCase    (       self )							-> Any:
         # Initialize image_processing
         SCREAMING_SNAKE_CASE					:			str    	=       self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       prepare_image_inputs(self.image_processor_tester    ,							equal_resolution=lowercase__    ,							torchify=lowercase__ )
         for image in image_inputs:
              self.assertIsInstance(lowercase__    ,							torch.Tensor )

         # Test not batched input
         SCREAMING_SNAKE_CASE					:			int    	=       image_processing(image_inputs[0]    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			str    	=       self.image_processor_tester.get_expected_values(lowercase__ )

         self.assertEqual(
             encoded_images.shape    ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,							)

         # Test batched
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       image_processing(lowercase__    ,							return_tensors='pt' ).pixel_values

         SCREAMING_SNAKE_CASE	,   SCREAMING_SNAKE_CASE					:			int    	=       self.image_processor_tester.get_expected_values(lowercase__    ,							batched=lowercase__ )

         self.assertEqual(
             encoded_images.shape    ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )    ,							)






    @slow
    def       _UpperCamelCase    (       self )							-> int:
         # prepare image and target
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'    ,							'r' ) as f:
              SCREAMING_SNAKE_CASE					:			Tuple    	=       json.loads(f.read() )

         SCREAMING_SNAKE_CASE					:			List[str]    	=       {'image_id': 39_769, 'annotations': target}

         # encode them
         SCREAMING_SNAKE_CASE					:			Tuple    	=       ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50' )
         SCREAMING_SNAKE_CASE					:			Tuple    	=       image_processing(images=lowercase__    ,							annotations=lowercase__    ,							return_tensors='pt' )

         # verify pixel values
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.Size([1, 3, 800, 1_066] )
         self.assertEqual(encoding['pixel_values'].shape    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,							lowercase__    ,							atol=1E-4 ) )

         # verify area
         SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,							lowercase__ ) )
         # verify boxes
         SCREAMING_SNAKE_CASE					:			int    	=       torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,							lowercase__    ,							atol=1E-3 ) )
         # verify image_id
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor([39_769] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,							lowercase__ ) )
         # verify is_crowd
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,							lowercase__ ) )
         # verify class_labels
         SCREAMING_SNAKE_CASE					:			Any    	=       torch.tensor([75, 75, 63, 65, 17, 17] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,							lowercase__ ) )
         # verify orig_size
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([480, 640] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,							lowercase__ ) )
         # verify size
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor([800, 1_066] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,							lowercase__ ) )






    @slow
    def       _UpperCamelCase    (       self )							-> Any:
         # prepare image, target and masks_path
         SCREAMING_SNAKE_CASE					:			Tuple    	=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'    ,							'r' ) as f:
              SCREAMING_SNAKE_CASE					:			Optional[int]    	=       json.loads(f.read() )

         SCREAMING_SNAKE_CASE					:			Any    	=       {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}

         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )

         # encode them
         SCREAMING_SNAKE_CASE					:			str    	=       ConditionalDetrImageProcessor(format='coco_panoptic' )
         SCREAMING_SNAKE_CASE					:			str    	=       image_processing(images=lowercase__    ,							annotations=lowercase__    ,							masks_path=lowercase__    ,							return_tensors='pt' )

         # verify pixel values
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.Size([1, 3, 800, 1_066] )
         self.assertEqual(encoding['pixel_values'].shape    ,							lowercase__ )

         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]    ,							lowercase__    ,							atol=1E-4 ) )

         # verify area
         SCREAMING_SNAKE_CASE					:			Optional[int]    	=       torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']    ,							lowercase__ ) )
         # verify boxes
         SCREAMING_SNAKE_CASE					:			Dict    	=       torch.Size([6, 4] )
         self.assertEqual(encoding['labels'][0]['boxes'].shape    ,							lowercase__ )
         SCREAMING_SNAKE_CASE					:			int    	=       torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]    ,							lowercase__    ,							atol=1E-3 ) )
         # verify image_id
         SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       torch.tensor([39_769] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']    ,							lowercase__ ) )
         # verify is_crowd
         SCREAMING_SNAKE_CASE					:			Tuple    	=       torch.tensor([0, 0, 0, 0, 0, 0] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']    ,							lowercase__ ) )
         # verify class_labels
         SCREAMING_SNAKE_CASE					:			List[Any]    	=       torch.tensor([17, 17, 63, 75, 75, 93] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']    ,							lowercase__ ) )
         # verify masks
         SCREAMING_SNAKE_CASE					:			Dict    	=       822_873
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()    ,							lowercase__ )
         # verify orig_size
         SCREAMING_SNAKE_CASE					:			str    	=       torch.tensor([480, 640] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']    ,							lowercase__ ) )
         # verify size
         SCREAMING_SNAKE_CASE					:			List[str]    	=       torch.tensor([800, 1_066] )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']    ,							lowercase__ ) )




'''simple docstring'''





import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline


def     __lowerCAmelCase						(       a_       ,      a_		)      ->	Tuple:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       args.log_outputs
     SCREAMING_SNAKE_CASE					:			List[Any]    	=       '_'.join(args.dataset.split('/'		) + [args.config, args.split]		)

     # load metric
     SCREAMING_SNAKE_CASE					:			int    	=       load_metric('wer'		)
     SCREAMING_SNAKE_CASE					:			Any    	=       load_metric('cer'		)

     # compute metrics
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       wer.compute(references=result['target']       ,      predictions=result['prediction']		)
     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       cer.compute(references=result['target']       ,      predictions=result['prediction']		)

     # print & log results
     SCREAMING_SNAKE_CASE					:			int    	=       f"""WER: {wer_result}\nCER: {cer_result}"""
     print(a_		)

     with open(f"""{dataset_id}_eval_results.txt"""       ,      'w'		) as f:
          f.write(a_		)

     # log all results in text file. Possibly interesting for analysis
     if log_outputs is not None:
          SCREAMING_SNAKE_CASE					:			Any    	=       f"""log_{dataset_id}_predictions.txt"""
          SCREAMING_SNAKE_CASE					:			int    	=       f"""log_{dataset_id}_targets.txt"""

          with open(a_       ,      'w'		) as p, open(a_       ,      'w'		) as t:
               # mapping function to write output
               def write_to_file(a_       ,      a_		):
                    p.write(f"""{i}""" + '\n'		)
                    p.write(batch['prediction'] + '\n'		)
                    t.write(f"""{i}""" + '\n'		)
                    t.write(batch['target'] + '\n'		)

               result.map(a_       ,      with_indices=a_		)


def     __lowerCAmelCase						(       a_		)      ->	str:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Any    	=       '[,?.!\-\;\:"“%‘”�—’…–]'  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

     SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       re.sub(a_       ,      ''       ,      text.lower()		)

     # In addition, we can normalize the target text, e.g. removing new lines characters etc...
     # note that order is important here!
     SCREAMING_SNAKE_CASE					:			str    	=       ['\n\n', '\n', '   ', '  ']

     for t in token_sequences_to_ignore:
          SCREAMING_SNAKE_CASE					:			List[str]    	=       ' '.join(text.split(a_		)		)

     return text


def     __lowerCAmelCase						(       a_		)      ->	Union[str, Any]:





     '''simple docstring'''





     SCREAMING_SNAKE_CASE					:			Dict    	=       load_dataset(args.dataset       ,      args.config       ,      split=args.split       ,      use_auth_token=a_		)

     # for testing: only process the first two examples as a test
     # dataset = dataset.select(range(10))

     # load processor
     SCREAMING_SNAKE_CASE					:			str    	=       AutoFeatureExtractor.from_pretrained(args.model_id		)
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       feature_extractor.sampling_rate

     # resample audio
     SCREAMING_SNAKE_CASE					:			Union[str, Any]    	=       dataset.cast_column('audio'       ,      Audio(sampling_rate=a_		)		)

     # load eval pipeline
     if args.device is None:
          SCREAMING_SNAKE_CASE					:			Tuple    	=       0 if torch.cuda.is_available() else -1
     SCREAMING_SNAKE_CASE					:			int    	=       pipeline('automatic-speech-recognition'       ,      model=args.model_id       ,      device=args.device		)

     # map function to decode audio
     def map_to_pred(a_		):
          SCREAMING_SNAKE_CASE					:			List[Any]    	=       asr(
              batch['audio']['array']       ,      chunk_length_s=args.chunk_length_s       ,      stride_length_s=args.stride_length_s		)

          SCREAMING_SNAKE_CASE					:			str    	=       prediction['text']
          SCREAMING_SNAKE_CASE					:			Optional[Any]    	=       normalize_text(batch['sentence']		)
          return batch

     # run inference on all examples
     SCREAMING_SNAKE_CASE					:			Optional[int]    	=       dataset.map(a_       ,      remove_columns=dataset.column_names		)

     # compute and log_results
     # do not change function below
     log_results(a_       ,      a_		)


if __name__ == "__main__":
 _lowerCAmelCase  :Optional[Any]  				=    argparse.ArgumentParser()

 parser.add_argument(
     """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
 )
 parser.add_argument(
     """--dataset""",
     type=str,
     required=True,
     help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
 )
 parser.add_argument(
     """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
 )
 parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
 parser.add_argument(
     """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
 )
 parser.add_argument(
     """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
 )
 parser.add_argument(
     """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
 )
 parser.add_argument(
     """--device""",
     type=int,
     default=None,
     help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
 )
 _lowerCAmelCase  :List[str]  				=    parser.parse_args()

 main(args)



