


"""simple docstring"""






import json
import os
import random
import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
					from PIL import Image

					from transformers import FlavaImageProcessor, FlavaProcessor
					from transformers.models.flava.image_processing_flava import (
					    FLAVA_CODEBOOK_MEAN,
					    FLAVA_CODEBOOK_STD,
					    FLAVA_IMAGE_MEAN,
					    FLAVA_IMAGE_STD,
					)






@require_vision
class     A(     unittest.TestCase		):

		"""simple docstring"""





		def      _UpperCamelCase(   self      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:List[str]				    =  tempfile.mkdtemp()

					# fmt: off
					_UpperCamelCase				:Dict				    =  ['''[UNK]''', '''[CLS]''', '''[SEP]''', '''[PAD]''', '''[MASK]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''', ''',''', '''low''', '''lowest''']
					# fmt: on
					_UpperCamelCase				:Optional[Any]				    =  os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['''vocab_file''']      )

					with open(self.vocab_file				,							'''w'''				,							encoding='''utf-8'''      ) as fp:
								fp.write(''''''.join([x + '''\n''' for x in vocab_tokens]      )      )

					_UpperCamelCase				:Union[str, Any]				    =  {
					    '''image_mean''': FLAVA_IMAGE_MEAN,
					    '''image_std''': FLAVA_IMAGE_STD,
					    '''do_normalize''': True,
					    '''do_resize''': True,
					    '''size''': 2_24,
					    '''do_center_crop''': True,
					    '''crop_size''': 2_24,
					    '''input_size_patches''': 14,
					    '''total_mask_patches''': 75,
					    '''mask_group_max_patches''': None,
					    '''mask_group_min_patches''': 16,
					    '''mask_group_min_aspect_ratio''': 0.3,
					    '''mask_group_max_aspect_ratio''': None,
					    '''codebook_do_resize''': True,
					    '''codebook_size''': 1_12,
					    '''codebook_do_center_crop''': True,
					    '''codebook_crop_size''': 1_12,
					    '''codebook_do_map_pixels''': True,
					    '''codebook_do_normalize''': True,
					    '''codebook_image_mean''': FLAVA_CODEBOOK_MEAN,
					    '''codebook_image_std''': FLAVA_CODEBOOK_STD,
					}

					_UpperCamelCase				:Optional[int]				    =  os.path.join(self.tmpdirname				,							SCREAMING_SNAKE_CASE__      )
					with open(self.image_processor_file				,							'''w'''				,							encoding='''utf-8'''      ) as fp:
								json.dump(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  Optional[Any]:

					"""simple docstring"""



					return BertTokenizer.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  str:

					"""simple docstring"""



					return BertTokenizerFast.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self				,							**SCREAMING_SNAKE_CASE__      )			->  int:

					"""simple docstring"""



					return FlavaImageProcessor.from_pretrained(self.tmpdirname				,							**SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					shutil.rmtree(self.tmpdirname      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  [np.random.randint(2_55				,							size=(3, 30, 4_00)				,							dtype=np.uinta      )]

					_UpperCamelCase				:int				    =  [Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__				,							0				,							-1      )      ) for x in image_inputs]

					return image_inputs
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:List[Any]				    =  self.get_tokenizer()
					_UpperCamelCase				:Any				    =  self.get_rust_tokenizer()
					_UpperCamelCase				:Dict				    =  self.get_image_processor()

					_UpperCamelCase				:Union[str, Any]				    =  FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__      )
					processor_slow.save_pretrained(self.tmpdirname      )
					_UpperCamelCase				:Optional[Any]				    =  FlavaProcessor.from_pretrained(self.tmpdirname				,							use_fast=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Tuple				    =  FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__      )
					processor_fast.save_pretrained(self.tmpdirname      )
					_UpperCamelCase				:Tuple				    =  FlavaProcessor.from_pretrained(self.tmpdirname      )

					self.assertEqual(processor_slow.tokenizer.get_vocab()				,							tokenizer_slow.get_vocab()      )
					self.assertEqual(processor_fast.tokenizer.get_vocab()				,							tokenizer_fast.get_vocab()      )
					self.assertEqual(tokenizer_slow.get_vocab()				,							tokenizer_fast.get_vocab()      )
					self.assertIsInstance(processor_slow.tokenizer				,							SCREAMING_SNAKE_CASE__      )
					self.assertIsInstance(processor_fast.tokenizer				,							SCREAMING_SNAKE_CASE__      )

					self.assertEqual(processor_slow.image_processor.to_json_string()				,							image_processor.to_json_string()      )
					self.assertEqual(processor_fast.image_processor.to_json_string()				,							image_processor.to_json_string()      )
					self.assertIsInstance(processor_slow.image_processor				,							SCREAMING_SNAKE_CASE__      )
					self.assertIsInstance(processor_fast.image_processor				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  str:

					"""simple docstring"""



					_UpperCamelCase				:Optional[Any]				    =  FlavaProcessor(tokenizer=self.get_tokenizer()				,							image_processor=self.get_image_processor()      )
					processor.save_pretrained(self.tmpdirname      )

					_UpperCamelCase				:Any				    =  self.get_tokenizer(bos_token='''(BOS)'''				,							eos_token='''(EOS)'''      )
					_UpperCamelCase				:str				    =  self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__				,							padding_value=1.0      )

					_UpperCamelCase				:Any				    =  FlavaProcessor.from_pretrained(
					    self.tmpdirname				,							bos_token='''(BOS)'''				,							eos_token='''(EOS)'''				,							do_normalize=SCREAMING_SNAKE_CASE__				,							padding_value=1.0      )

					self.assertEqual(processor.tokenizer.get_vocab()				,							tokenizer_add_kwargs.get_vocab()      )
					self.assertIsInstance(processor.tokenizer				,							SCREAMING_SNAKE_CASE__      )

					self.assertEqual(processor.image_processor.to_json_string()				,							image_processor_add_kwargs.to_json_string()      )
					self.assertIsInstance(processor.image_processor				,							SCREAMING_SNAKE_CASE__      )
		def      _UpperCamelCase(   self      )			->  Dict:

					"""simple docstring"""



					_UpperCamelCase				:Tuple				    =  self.get_image_processor()
					_UpperCamelCase				:Optional[Any]				    =  self.get_tokenizer()

					_UpperCamelCase				:Tuple				    =  FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Tuple				    =  self.prepare_image_inputs()

					_UpperCamelCase				:int				    =  image_processor(SCREAMING_SNAKE_CASE__				,							return_tensors='''np'''      )
					_UpperCamelCase				:Any				    =  processor(images=SCREAMING_SNAKE_CASE__				,							return_tensors='''np'''      )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()				,							input_processor[key].sum()				,							delta=1E-2      )

					# With rest of the args
					random.seed(12_34      )
					_UpperCamelCase				:str				    =  image_processor(
					    SCREAMING_SNAKE_CASE__				,							return_image_mask=SCREAMING_SNAKE_CASE__				,							return_codebook_pixels=SCREAMING_SNAKE_CASE__				,							return_tensors='''np'''      )
					random.seed(12_34      )
					_UpperCamelCase				:Union[str, Any]				    =  processor(
					    images=SCREAMING_SNAKE_CASE__				,							return_image_mask=SCREAMING_SNAKE_CASE__				,							return_codebook_pixels=SCREAMING_SNAKE_CASE__				,							return_tensors='''np'''      )

					for key in input_feat_extract.keys():
								self.assertAlmostEqual(input_feat_extract[key].sum()				,							input_processor[key].sum()				,							delta=1E-2      )
		def      _UpperCamelCase(   self      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  self.get_image_processor()
					_UpperCamelCase				:Tuple				    =  self.get_tokenizer()

					_UpperCamelCase				:Tuple				    =  FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Union[str, Any]				    =  '''lower newer'''

					_UpperCamelCase				:Union[str, Any]				    =  processor(text=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Optional[int]				    =  tokenizer(SCREAMING_SNAKE_CASE__      )

					for key in encoded_tok.keys():
								self.assertListEqual(encoded_tok[key]				,							encoded_processor[key]      )
		def      _UpperCamelCase(   self      )			->  int:

					"""simple docstring"""



					_UpperCamelCase				:Optional[int]				    =  self.get_image_processor()
					_UpperCamelCase				:Any				    =  self.get_tokenizer()

					_UpperCamelCase				:List[Any]				    =  FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:Optional[Any]				    =  '''lower newer'''
					_UpperCamelCase				:List[str]				    =  self.prepare_image_inputs()

					_UpperCamelCase				:Dict				    =  processor(text=SCREAMING_SNAKE_CASE__				,							images=SCREAMING_SNAKE_CASE__      )

					self.assertListEqual(list(inputs.keys()      )				,							['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values''']      )

					# add extra args
					_UpperCamelCase				:Union[str, Any]				    =  processor(text=SCREAMING_SNAKE_CASE__				,							images=SCREAMING_SNAKE_CASE__				,							return_codebook_pixels=SCREAMING_SNAKE_CASE__				,							return_image_mask=SCREAMING_SNAKE_CASE__      )

					self.assertListEqual(
					    list(inputs.keys()      )				,							[
					        '''input_ids''',
					        '''token_type_ids''',
					        '''attention_mask''',
					        '''pixel_values''',
					        '''codebook_pixel_values''',
					        '''bool_masked_pos''',
					    ]				,							)

					# test if it raises when no input is passed
					with pytest.raises(SCREAMING_SNAKE_CASE__      ):
								processor()
		def      _UpperCamelCase(   self      )			->  Union[str, Any]:

					"""simple docstring"""



					_UpperCamelCase				:Union[str, Any]				    =  self.get_image_processor()
					_UpperCamelCase				:Union[str, Any]				    =  self.get_tokenizer()

					_UpperCamelCase				:List[Any]				    =  FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:List[Any]				    =  [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

					_UpperCamelCase				:List[Any]				    =  processor.batch_decode(SCREAMING_SNAKE_CASE__      )
					_UpperCamelCase				:str				    =  tokenizer.batch_decode(SCREAMING_SNAKE_CASE__      )

					self.assertListEqual(SCREAMING_SNAKE_CASE__				,							SCREAMING_SNAKE_CASE__      )





		def      _UpperCamelCase(   self      )			->  List[Any]:

					"""simple docstring"""



					_UpperCamelCase				:str				    =  self.get_image_processor()
					_UpperCamelCase				:Dict				    =  self.get_tokenizer()

					_UpperCamelCase				:str				    =  FlavaProcessor(tokenizer=SCREAMING_SNAKE_CASE__				,							image_processor=SCREAMING_SNAKE_CASE__      )

					_UpperCamelCase				:List[str]				    =  '''lower newer'''
					_UpperCamelCase				:Any				    =  self.prepare_image_inputs()

					_UpperCamelCase				:Union[str, Any]				    =  processor(text=SCREAMING_SNAKE_CASE__				,							images=SCREAMING_SNAKE_CASE__      )

					self.assertListEqual(list(inputs.keys()      )				,							processor.model_input_names      )





"""simple docstring"""






def 							A_					(   snake_case__		)				->     str:
			_UpperCamelCase				:Tuple				    =  word.upper()
			_UpperCamelCase				:Dict				    =  0
			_UpperCamelCase				:Union[str, Any]				    =  {}
			_UpperCamelCase				:Optional[int]				    =  []

			for letter in word:
						if letter not in letter_nums:
									_UpperCamelCase				:Dict				    =  str(snake_case__		)
									next_num += 1
						word_pattern.append(letter_nums[letter]		)
			return ".".join(snake_case__		)


if __name__ == "__main__":
					import pprint
					import time

					UpperCamelCase__							:Dict       =  time.time()
					with open("""dictionary.txt""") as in_file:
										UpperCamelCase__							:str       =  in_file.read().splitlines()

					UpperCamelCase__							:dict       =  {}
					for word in word_list:
										UpperCamelCase__							:List[Any]       =  get_word_pattern(word)
										if pattern in all_patterns:
															all_patterns[pattern].append(word)
										else:
															UpperCamelCase__							:Tuple       =  [word]

					with open("""word_patterns.txt""", """w""") as out_file:
										out_file.write(pprint.pformat(all_patterns))

					UpperCamelCase__							:Dict       =  round(time.time() - start_time, 2)
					print(F"""Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.""")
					# Done!  9,581 word patterns found in 0.58 seconds.

