


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      __snake_case				(   unittest.TestCase):
					def __init__(						self       :						Optional[int]	,      _lowerCAmelCase       :						Union[str, Any]	,      _lowerCAmelCase       :						str=7	,      _lowerCAmelCase       :						str=3	,      _lowerCAmelCase       :						Optional[Any]=1_8	,      _lowerCAmelCase       :						Any=3_0	,      _lowerCAmelCase       :						str=4_0_0	,      _lowerCAmelCase       :						Optional[Any]=True	,      _lowerCAmelCase       :						str=None	,      _lowerCAmelCase       :						List[Any]=True	,      _lowerCAmelCase       :						Optional[Any]=None	,      _lowerCAmelCase       :						List[Any]=True	,      _lowerCAmelCase       :						Dict=[0.5, 0.5, 0.5]	,      _lowerCAmelCase       :						str=[0.5, 0.5, 0.5]	,      )							->					int:



											'''simple docstring'''





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

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







					def 	a			(						self       :						List[str]  )							->					Optional[Any]:



											'''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      __snake_case				(   _UpperCAmelCase	,      unittest.TestCase):
					SCREAMING_SNAKE_CASE_		:       Dict								=			DeiTImageProcessor if is_vision_available() else None
					SCREAMING_SNAKE_CASE_		:       int								=			True
					def 	a			(						self       :						Dict  )							->					Dict:



											'''simple docstring'''





											__magic_name__					=      DeiTImageProcessingTester(self  )
					@property
					def 	a			(						self       :						str  )							->					Tuple:



											'''simple docstring'''





											return self.image_processor_tester.prepare_image_processor_dict()
					def 	a			(						self       :						List[Any]  )							->					Optional[int]:



											'''simple docstring'''





											__magic_name__					=      self.image_processing_class(**self.image_processor_dict  )
											self.assertTrue(hasattr(_lowerCAmelCase	,      'do_resize'  )  )
											self.assertTrue(hasattr(_lowerCAmelCase	,      'size'  )  )
											self.assertTrue(hasattr(_lowerCAmelCase	,      'do_center_crop'  )  )
											self.assertTrue(hasattr(_lowerCAmelCase	,      'center_crop'  )  )
											self.assertTrue(hasattr(_lowerCAmelCase	,      'do_normalize'  )  )
											self.assertTrue(hasattr(_lowerCAmelCase	,      'image_mean'  )  )
											self.assertTrue(hasattr(_lowerCAmelCase	,      'image_std'  )  )
					def 	a			(						self       :						Optional[Any]  )							->					Optional[Any]:



											'''simple docstring'''





											__magic_name__					=      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}  )

											__magic_name__					=      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 	a			(						self       :						List[Any]  )							->					int:



											'''simple docstring'''





											pass
					def 	a			(						self       :						Dict  )							->					List[Any]:



											'''simple docstring'''





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

											# Test not batched input
											__magic_name__					=      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
											__magic_name__					=      image_processing(_lowerCAmelCase	,      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 	a			(						self       :						int  )							->					Optional[int]:



											'''simple docstring'''





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

											# Test not batched input
											__magic_name__					=      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
											__magic_name__					=      image_processing(_lowerCAmelCase	,      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 	a			(						self       :						Any  )							->					Optional[int]:



											'''simple docstring'''





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

											# Test not batched input
											__magic_name__					=      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
											__magic_name__					=      image_processing(_lowerCAmelCase	,      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 logging

import datasets
from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
from tokenizers.models import Unigram
from tokenizers.trainers import UnigramTrainer

from transformers import AlbertTokenizerFast


UpperCamelCase			:					List[str]         =							logging.getLogger(__name__)
def 				UpperCamelCase_	(					)->   Dict:




						'''simple docstring'''
						__magic_name__					=      argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'  )
						parser.add_argument(
						    '--dataset_name'						,      type=__snake_case						,      default='wikitext'						,      help='Name of the training. Explore datasets at: hf.co/datasets.'						,      )
						parser.add_argument(
						    '--dataset_config'						,      type=__snake_case						,      default='wikitext-103-raw-v1'						,      help='Configuration name of the dataset.'  )
						parser.add_argument(
						    '--batch_size'						,      type=__snake_case						,      default=10_00						,      help='Batch size during training.'						,      )
						parser.add_argument(
						    '--vocab_size'						,      type=__snake_case						,      default=1_00_48						,      help='Size of the desired vocabulary.'						,      )
						parser.add_argument(
						    '--limit'						,      default=__snake_case						,      type=__snake_case						,      help='Limit the number of shards (used for debugging).'						,      )
						parser.add_argument(
						    '--export_to_hub'						,      action='store_true'						,      )

						__magic_name__					=      parser.parse_args()
						return args
def 				UpperCamelCase_	(					__snake_case      :    Any  )->   Optional[int]:




						'''simple docstring'''
						__magic_name__					=      datasets.load_dataset(args.dataset_name						,      args.dataset_config						,      split='train'  )

						if args.limit is not None:
												__magic_name__					=      min(len(__snake_case  )						,      args.limit  )
												__magic_name__					=      dataset.select(range(__snake_case  )  )
												logger.info(F'''Limiting the dataset to {args.limit} entries.'''  )

						def batch_iterator():
												for i in range(0						,      len(__snake_case  )						,      args.batch_size  ):
																		yield dataset[i : i + args.batch_size]["text"]

    # Prepare the tokenizer.
						__magic_name__					=      Tokenizer(Unigram()  )
						__magic_name__					=      normalizers.Sequence([normalizers.Replace('``'						,      '"'  ), normalizers.Replace('\'\''						,      '"'  )]  )
						__magic_name__					=      pre_tokenizers.Metaspace()

						# Prepare the trainer.
						__magic_name__					=      UnigramTrainer(
						    unk_token='<unk>'						,      special_tokens=['[CLS]', '[SEP]', '<unk>', '<pad>', '[MASK]']						,      vocab_size=args.vocab_size						,      )

						logger.info('Training the tokenizer.'  )
						tokenizer.train_from_iterator(batch_iterator()						,      trainer=__snake_case  )
						logger.info('Tokenizer training complete!'  )

						__magic_name__					=      tokenizer.token_to_id('[CLS]'  )
						__magic_name__					=      tokenizer.token_to_id('[SEP]'  )
						__magic_name__					=      processors.TemplateProcessing(
						    single='[CLS]:0 $A:0 [SEP]:0'						,      pair='[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1'						,      special_tokens=[
						        ('[CLS]', cls_token_id),
						        ('[SEP]', sep_token_id),
						    ]						,      )
						__magic_name__					=      decoders.Metaspace()

						if args.export_to_hub:
												logger.info('Exporting the trained tokenzier to Hub.'  )
												__magic_name__					=      AlbertTokenizerFast(tokenizer_object=__snake_case  )
												new_tokenizer.push_to_hub('unigram-tokenizer-dataset'  )


if __name__ == "__main__":
					UpperCamelCase			:					Dict         =							parse_args()
					main(args)

