






"""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 MobileNetVaImageProcessor


class      UpperCamelCase		(unittest.TestCase     ):






						def __init__(		self  :Union[str, Any]			, __magic_name__  :List[str]			, __magic_name__  :Dict=7			, __magic_name__  :Union[str, Any]=3			, __magic_name__  :Any=18			, __magic_name__  :int=30			, __magic_name__  :Tuple=400			, __magic_name__  :List[str]=True			, __magic_name__  :str=None			, __magic_name__  :List[str]=True			, __magic_name__  :Optional[Any]=None			, )      ->int:
										lowercase    :      Tuple       = size if size is not None else {"""shortest_edge""": 20}
										lowercase    :      str       = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowercase    :      Union[str, Any]       = parent
										lowercase    :      int       = batch_size
										lowercase    :      List[str]       = num_channels
										lowercase    :      Union[str, Any]       = image_size
										lowercase    :      int       = min_resolution
										lowercase    :      Optional[int]       = max_resolution
										lowercase    :      Tuple       = do_resize
										lowercase    :      Dict       = size
										lowercase    :      str       = do_center_crop
										lowercase    :      str       = crop_size






						def      __snake_case      (		self  :Optional[int]				)      ->Tuple:
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										}


@require_torch
@require_vision
class      UpperCamelCase		(__snake_case						,						unittest.TestCase     ):
						_SCREAMING_SNAKE_CASE  : Optional[Any]												=		MobileNetVaImageProcessor if is_vision_available() else None






						def      __snake_case      (		self  :List[Any]				)      ->Optional[Any]:
										lowercase    :      int       = MobileNetVaImageProcessingTester(self				)






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






						def      __snake_case      (		self  :List[str]				)      ->Union[str, Any]:
										lowercase    :      Optional[int]       = self.image_processing_class(**self.image_processor_dict				)
										self.assertTrue(hasattr(__magic_name__			, """do_resize"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """size"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """do_center_crop"""				)				)
										self.assertTrue(hasattr(__magic_name__			, """crop_size"""				)				)






						def      __snake_case      (		self  :Optional[int]				)      ->Union[str, Any]:
										lowercase    :      List[str]       = 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}				)

										lowercase    :      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      __snake_case      (		self  :Optional[int]				)      ->Dict:
										pass






						def      __snake_case      (		self  :Any				)      ->int:
										# Initialize image_processing
										lowercase    :      Optional[Any]       = self.image_processing_class(**self.image_processor_dict				)
										# create random PIL images
										lowercase    :      Optional[int]       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, Image.Image				)

										# Test not batched input
										lowercase    :      Dict       = 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
										lowercase    :      Any       = image_processing(__magic_name__			, 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      __snake_case      (		self  :Union[str, Any]				)      ->Optional[int]:
										# Initialize image_processing
										lowercase    :      str       = self.image_processing_class(**self.image_processor_dict				)
										# create random numpy tensors
										lowercase    :      Any       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__			, numpify=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, np.ndarray				)

										# Test not batched input
										lowercase    :      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
										lowercase    :      Any       = image_processing(__magic_name__			, 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      __snake_case      (		self  :str				)      ->str:
										# Initialize image_processing
										lowercase    :      Tuple       = self.image_processing_class(**self.image_processor_dict				)
										# create random PyTorch tensors
										lowercase    :      Any       = prepare_image_inputs(self.image_processor_tester			, equal_resolution=__magic_name__			, torchify=__magic_name__				)
										for image in image_inputs:
														self.assertIsInstance(__magic_name__			, torch.Tensor				)

										# Test not batched input
										lowercase    :      Optional[int]       = 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
										lowercase    :      int       = image_processing(__magic_name__			, 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
)
_lowerCAmelCase            =	logging.getLogger(__name__)

if __name__ == "__main__":
				_lowerCAmelCase            =	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)
				_lowerCAmelCase            =	parser.parse_args()

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

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