



"""simple docstring"""





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 MobileViTImageProcessor




class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=7	,       snake_case=3	,       snake_case=18	,       snake_case=30	,       snake_case=400	,       snake_case=True	,       snake_case=None	,       snake_case=True	,       snake_case=None	,       snake_case=True	,       )			->							Dict:
									_UpperCAmelCase							 =		size if size is not None else {'shortest_edge': 20}
									_UpperCAmelCase							 =		crop_size if crop_size is not None else {'height': 18, 'width': 18}
									_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_flip_channel_order






							def        lowerCamelCase_   (  self						)			->							Any:
									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_flip_channel_order": self.do_flip_channel_order,
									}







@require_torch
@require_vision
class 			lowercase__			(      A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					MobileViTImageProcessor if is_vision_available() else None






							def        lowerCamelCase_   (  self						)			->							List[Any]:
									_UpperCAmelCase							 =		MobileViTImageProcessingTester(self						)






							@property
							def        lowerCamelCase_   (  self						)			->							Dict:
									return self.image_processor_tester.prepare_image_processor_dict()






							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									self.assertTrue(hasattr(snake_case	,       'do_resize'						)						)
									self.assertTrue(hasattr(snake_case	,       'size'						)						)
									self.assertTrue(hasattr(snake_case	,       'do_center_crop'						)						)
									self.assertTrue(hasattr(snake_case	,       'center_crop'						)						)
									self.assertTrue(hasattr(snake_case	,       'do_flip_channel_order'						)						)






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		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}						)

									_UpperCAmelCase							 =		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						)			->							Union[str, Any]:
									pass






							def        lowerCamelCase_   (  self						)			->							Tuple:
									# Initialize image_processing
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random PIL images
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       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(snake_case	,       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]:
									# Initialize image_processing
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random numpy tensors
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case	,       numpify=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       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(snake_case	,       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						)			->							Dict:
									# Initialize image_processing
									_UpperCAmelCase							 =		self.image_processing_class(**self.image_processor_dict						)
									# create random PyTorch tensors
									_UpperCAmelCase							 =		prepare_image_inputs(self.image_processor_tester	,       equal_resolution=snake_case	,       torchify=snake_case						)
									for image in image_inputs:
											self.assertIsInstance(snake_case	,       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(snake_case	,       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'],
									    )	,       )







"""simple docstring"""





import argparse
import logging
import pickle
from collections import Counter


logging.basicConfig(
    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO
)
lowercase  							=					logging.getLogger(__name__)

if __name__ == "__main__":
					lowercase  							=					argparse.ArgumentParser(
					    description='''Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)'''
					)
					parser.add_argument(
					    '''--data_file''', type=str, default='''data/dump.bert-base-uncased.pickle''', help='''The binarized dataset.'''
					)
					parser.add_argument(
					    '''--token_counts_dump''', type=str, default='''data/token_counts.bert-base-uncased.pickle''', help='''The dump file.'''
					)
					parser.add_argument('''--vocab_size''', default=3_05_22, type=int)
					lowercase  							=					parser.parse_args()

					logger.info(F'''Loading data from {args.data_file}''')
					with open(args.data_file, '''rb''') as fp:
										lowercase  							=					pickle.load(fp)

					logger.info('''Counting occurrences for MLM.''')
					lowercase  							=					Counter()
					for tk_ids in data:
										counter.update(tk_ids)
					lowercase  							=					[0] * args.vocab_size
					for k, v in counter.items():
										lowercase  							=					v

					logger.info(F'''Dump to {args.token_counts_dump}''')
					with open(args.token_counts_dump, '''wb''') as handle:
										pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)


