


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 TvltImageProcessor
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case=10	,		__snake_case=10	,		__snake_case=False	,		__snake_case=False		)	->     Union[str, Any]:







 """simple docstring"""


 _lowercase				 =[]
 for i in range(image_processor_tester.num_frames		):
  video.append(np.random.randint(255	,		size=(image_processor_tester.num_channels, width, height)	,		dtype=np.uinta		)		)

 if not numpify and not torchify:
  # PIL expects the channel dimension as last dimension
  _lowercase				 =[Image.fromarray(np.moveaxis(UpperCamelCase_	,		0	,		-1		)		) for frame in video]

 if torchify:
  _lowercase				 =[torch.from_numpy(UpperCamelCase_		) for frame in video]

 return video
def 							UpperCAmelCase_   (				__snake_case	,		__snake_case=False	,		__snake_case=False	,		__snake_case=False		)	->     int:







 """simple docstring"""


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

 _lowercase				 =[]
 for i in range(image_processor_tester.batch_size		):
  if equal_resolution:
   _lowercase				 =image_processor_tester.max_resolution
  else:
   _lowercase				 =np.random.choice(
       np.arange(image_processor_tester.min_resolution	,		image_processor_tester.max_resolution		)	,		2		)
   _lowercase				 =prepare_video(
       image_processor_tester=UpperCamelCase_	,		width=UpperCamelCase_	,		height=UpperCamelCase_	,		numpify=UpperCamelCase_	,		torchify=UpperCamelCase_	,		)
  video_inputs.append(UpperCamelCase_		)

 return video_inputs







class  lowerCamelCase__  (			unittest.TestCase):





     def __init__(self						,	UpperCAmelCase						,	UpperCAmelCase=7						,	UpperCAmelCase=3						,	UpperCAmelCase=4						,	UpperCAmelCase=1_8						,	UpperCAmelCase=3_0						,	UpperCAmelCase=4_0_0						,	UpperCAmelCase=True						,	UpperCAmelCase=None						,	UpperCAmelCase=True						,	UpperCAmelCase=[0.5, 0.5, 0.5]						,	UpperCAmelCase=[0.5, 0.5, 0.5]						,	UpperCAmelCase=True						,	UpperCAmelCase=None						,	)	->  Any:
      _lowercase				 =size if size is not None else {'shortest_edge': 1_8}
      _lowercase				 =crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

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





     def 	__A   (self			)	->  Dict:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "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  lowerCamelCase__  (			snake_case_  ,				unittest.TestCase):
     SCREAMING_SNAKE_CASE__      =     TvltImageProcessor if is_vision_available() else None





     def 	__A   (self			)	->  Union[str, Any]:
      _lowercase				 =TvltImageProcessorTester(self			)





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





     def 	__A   (self			)	->  Any:
      _lowercase				 =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_						,	'''do_center_crop'''			)			)
      self.assertTrue(hasattr(lowercase_						,	'''size'''			)			)





     def 	__A   (self			)	->  List[str]:
      # Initialize image_processor
      _lowercase				 =self.image_processing_class(**self.image_processor_dict			)
      # create random PIL videos
      _lowercase				 =prepare_video_inputs(self.image_processor_tester						,	equal_resolution=lowercase_			)
      for video in video_inputs:
       self.assertIsInstance(lowercase_						,	lowercase_			)
       self.assertIsInstance(video[0]						,	Image.Image			)

      # Test not batched input
      _lowercase				 =image_processor(video_inputs[0]						,	return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_videos.shape						,	(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )						,	)

      # Test batched
      _lowercase				 =image_processor(lowercase_						,	return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_videos.shape						,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )						,	)





     def 	__A   (self			)	->  str:
      # Initialize image_processor
      _lowercase				 =self.image_processing_class(**self.image_processor_dict			)
      # create random numpy tensors
      _lowercase				 =prepare_video_inputs(self.image_processor_tester						,	equal_resolution=lowercase_						,	numpify=lowercase_			)
      for video in video_inputs:
       self.assertIsInstance(lowercase_						,	lowercase_			)
       self.assertIsInstance(video[0]						,	np.ndarray			)

      # Test not batched input
      _lowercase				 =image_processor(video_inputs[0]						,	return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_videos.shape						,	(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )						,	)

      # Test batched
      _lowercase				 =image_processor(lowercase_						,	return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_videos.shape						,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )						,	)





     def 	__A   (self			)	->  List[str]:
      # Initialize image_processor
      _lowercase				 =self.image_processing_class(**self.image_processor_dict			)
      # create random PyTorch tensors
      _lowercase				 =prepare_video_inputs(self.image_processor_tester						,	equal_resolution=lowercase_						,	torchify=lowercase_			)
      for video in video_inputs:
       self.assertIsInstance(lowercase_						,	lowercase_			)
       self.assertIsInstance(video[0]						,	torch.Tensor			)

      # Test not batched input
      _lowercase				 =image_processor(video_inputs[0]						,	return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_videos.shape						,	(
              1,
              self.image_processor_tester.num_frames,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size['''height'''],
              self.image_processor_tester.crop_size['''width'''],
          )						,	)

      # Test batched
      _lowercase				 =image_processor(lowercase_						,	return_tensors='''pt'''			).pixel_values
      self.assertEqual(
          encoded_videos.shape						,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_frames,
              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 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 					a			(  )	->		Union[str, Any]:
 __A						:							int      =						argparse.ArgumentParser(description='Train a unigram tokenizer on the wikitext dataset.'		)
 parser.add_argument(
     '--dataset_name'  ,						type=UpperCamelCase_  ,						default='wikitext'  ,						help='Name of the training. Explore datasets at: hf.co/datasets.'  ,						)
 parser.add_argument(
     '--dataset_config'  ,						type=UpperCamelCase_  ,						default='wikitext-103-raw-v1'  ,						help='Configuration name of the dataset.'		)
 parser.add_argument(
     '--batch_size'  ,						type=UpperCamelCase_  ,						default=1_0_0_0  ,						help='Batch size during training.'  ,						)
 parser.add_argument(
     '--vocab_size'  ,						type=UpperCamelCase_  ,						default=1_0_0_4_8  ,						help='Size of the desired vocabulary.'  ,						)
 parser.add_argument(
     '--limit'  ,						default=UpperCamelCase_  ,						type=UpperCamelCase_  ,						help='Limit the number of shards (used for debugging).'  ,						)
 parser.add_argument(
     '--export_to_hub'  ,						action='store_true'  ,						)

 __A						:							List[Any]      =						parser.parse_args()
 return args




def 					a			(  UpperCamelCase_				:					Optional[Any]		)	->		Optional[Any]:
 __A						:							Union[str, Any]      =						datasets.load_dataset(args.dataset_name  ,						args.dataset_config  ,						split='train'		)

 if args.limit is not None:
  __A						:							Tuple      =						min(len(UpperCamelCase_		)  ,						args.limit		)
  __A						:							Optional[int]      =						dataset.select(range(UpperCamelCase_		)		)
  logger.info(F"Limiting the dataset to {args.limit} entries."		)

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

    # Prepare the tokenizer.
 __A						:							Any      =						Tokenizer(Unigram()		)
 __A						:							List[Any]      =						normalizers.Sequence([normalizers.Replace('``'  ,						'"'		), normalizers.Replace('\'\''  ,						'"'		)]		)
 __A						:							Optional[int]      =						pre_tokenizers.Metaspace()

 # Prepare the trainer.
 __A						:							int      =						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=UpperCamelCase_		)
 logger.info('Tokenizer training complete!'		)

 __A						:							Union[str, Any]      =						tokenizer.token_to_id('[CLS]'		)
 __A						:							str      =						tokenizer.token_to_id('[SEP]'		)
 __A						:							Tuple      =						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),
     ]  ,						)
 __A						:							List[str]      =						decoders.Metaspace()

 if args.export_to_hub:
  logger.info('Exporting the trained tokenzier to Hub.'		)
  __A						:							Union[str, Any]      =						AlbertTokenizerFast(tokenizer_object=UpperCamelCase_		)
  new_tokenizer.push_to_hub('unigram-tokenizer-dataset'		)


if __name__ == "__main__":
   _UpperCamelCase :    Optional[Any]      =			parse_args()
   main(args)





