'''simple docstring'''
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
  a=  '''pt'''
elif is_tf_available():
  a=  '''tf'''
else:
  a=  '''jax'''






class 			__lowercase		(						_lowerCamelCase			,							unittest.TestCase						):







     """simple docstring"""






     SCREAMING_SNAKE_CASE__		   =						PerceiverTokenizer
     SCREAMING_SNAKE_CASE__		   =						False







     def lowerCAmelCase     (		self    ):
         super().setUp()
         __UpperCamelCase	:	Any		    =  PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname    )







     @cached_property
     def lowerCAmelCase     (		self    ):
         return PerceiverTokenizer.from_pretrained('deepmind/language-perceiver'    )







     def lowerCAmelCase     (		self     ,			**_lowerCamelCase    ):
         return self.tokenizer_class.from_pretrained(self.tmpdirname     ,			**_lowerCamelCase    )







     def lowerCAmelCase     (		self     ,			_lowerCamelCase     ,			_lowerCamelCase=False     ,			_lowerCamelCase=2_0     ,			_lowerCamelCase=5    ):
         # XXX The default common tokenizer tests assume that every ID is decodable on its own.
         # This assumption is invalid for Perceiver because single bytes might not be
         # valid utf-8 (byte 128 for instance).
         # Here we're overriding the smallest possible method to provide
         # a clean sequence without making the same assumption.

         __UpperCamelCase	:	Optional[Any]		    =  []
         for i in range(len(_lowerCamelCase    )    ):
             try:
                 __UpperCamelCase	:	Dict		    =  tokenizer.decode([i]     ,			clean_up_tokenization_spaces=_lowerCamelCase    )
             except UnicodeDecodeError:
                 pass
             toks.append((i, tok)    )

         __UpperCamelCase	:	str		    =  list(filter(lambda _lowerCamelCase    : re.match(R'^[ a-zA-Z]+$'     ,			t[1]    )     ,			_lowerCamelCase    )    )
         __UpperCamelCase	:	Optional[int]		    =  list(filter(lambda _lowerCamelCase    : [t[0]] == tokenizer.encode(t[1]     ,			add_special_tokens=_lowerCamelCase    )     ,			_lowerCamelCase    )    )
         if max_length is not None and len(_lowerCamelCase    ) > max_length:
             __UpperCamelCase	:	Optional[Any]		    =  toks[:max_length]
         if min_length is not None and len(_lowerCamelCase    ) < min_length and len(_lowerCamelCase    ) > 0:
             while len(_lowerCamelCase    ) < min_length:
                 __UpperCamelCase	:	Any		    =  toks + toks
        # toks_str = [t[1] for t in toks]
         __UpperCamelCase	:	int		    =  [t[0] for t in toks]

         # Ensure consistency
         __UpperCamelCase	:	Optional[int]		    =  tokenizer.decode(_lowerCamelCase     ,			clean_up_tokenization_spaces=_lowerCamelCase    )
         if " " not in output_txt and len(_lowerCamelCase    ) > 1:
             __UpperCamelCase	:	str		    =  (
                 tokenizer.decode([toks_ids[0]]     ,			clean_up_tokenization_spaces=_lowerCamelCase    )
                 + ' '
                 + tokenizer.decode(toks_ids[1:]     ,			clean_up_tokenization_spaces=_lowerCamelCase    )
             )
         if with_prefix_space:
             __UpperCamelCase	:	str		    =  ' ' + output_txt
         __UpperCamelCase	:	List[str]		    =  tokenizer.encode(_lowerCamelCase     ,			add_special_tokens=_lowerCamelCase    )
         return output_txt, output_ids







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[int]		    =  self.perceiver_tokenizer
         __UpperCamelCase	:	Any		    =  'Unicode €.'
         __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(_lowerCamelCase    )
         __UpperCamelCase	:	List[str]		    =  [4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
         self.assertEqual(encoded['input_ids']     ,			_lowerCamelCase    )

         # decoding
         __UpperCamelCase	:	Union[str, Any]		    =  tokenizer.decode(_lowerCamelCase    )
         self.assertEqual(_lowerCamelCase     ,			'[CLS]Unicode €.[SEP]'    )

         __UpperCamelCase	:	Optional[Any]		    =  tokenizer('e è é ê ë'    )
         __UpperCamelCase	:	Dict		    =  [4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
         self.assertEqual(encoded['input_ids']     ,			_lowerCamelCase    )
         # decoding
         __UpperCamelCase	:	Optional[Any]		    =  tokenizer.decode(_lowerCamelCase    )
         self.assertEqual(_lowerCamelCase     ,			'[CLS]e è é ê ë[SEP]'    )

         # encode/decode, but with `encode` instead of `__call__`
         self.assertEqual(tokenizer.decode(tokenizer.encode('e è é ê ë'    )    )     ,			'[CLS]e è é ê ë[SEP]'    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.perceiver_tokenizer
         __UpperCamelCase	:	Union[str, Any]		    =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         # fmt: off
         __UpperCamelCase	:	Dict		    =  [4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
         # fmt: on
         __UpperCamelCase	:	Union[str, Any]		    =  tokenizer(_lowerCamelCase     ,			padding=_lowerCamelCase     ,			return_tensors=_lowerCamelCase    )
         self.assertIsInstance(_lowerCamelCase     ,			_lowerCamelCase    )

         if FRAMEWORK != "jax":
             __UpperCamelCase	:	List[str]		    =  list(batch.input_ids.numpy()[0]    )
         else:
             __UpperCamelCase	:	Optional[Any]		    =  list(batch.input_ids.tolist()[0]    )

         self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

         self.assertEqual((2, 3_8)     ,			batch.input_ids.shape    )
         self.assertEqual((2, 3_8)     ,			batch.attention_mask.shape    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Tuple		    =  self.perceiver_tokenizer
         __UpperCamelCase	:	List[Any]		    =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
         __UpperCamelCase	:	Dict		    =  tokenizer(_lowerCamelCase     ,			padding=_lowerCamelCase     ,			return_tensors=_lowerCamelCase    )
         # check if input_ids are returned and no decoder_input_ids
         self.assertIn('input_ids'     ,			_lowerCamelCase    )
         self.assertIn('attention_mask'     ,			_lowerCamelCase    )
         self.assertNotIn('decoder_input_ids'     ,			_lowerCamelCase    )
         self.assertNotIn('decoder_attention_mask'     ,			_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.perceiver_tokenizer
         __UpperCamelCase	:	List[str]		    =  [
             'Summary of the text.',
             'Another summary.',
         ]
         __UpperCamelCase	:	List[str]		    =  tokenizer(
             text_target=_lowerCamelCase     ,			max_length=3_2     ,			padding='max_length'     ,			truncation=_lowerCamelCase     ,			return_tensors=_lowerCamelCase    )
         self.assertEqual(3_2     ,			targets['input_ids'].shape[1]    )







     def lowerCAmelCase     (		self    ):
         # safety check on max_len default value so we are sure the test works
         __UpperCamelCase	:	List[str]		    =  self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
                 self.assertNotEqual(tokenizer.model_max_length     ,			4_2    )

        # Now let's start the test
         __UpperCamelCase	:	int		    =  self.get_tokenizers()
         for tokenizer in tokenizers:
             with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 __UpperCamelCase	:	Optional[Any]		    =  tempfile.mkdtemp()

                 __UpperCamelCase	:	str		    =  ' He is very happy, UNwant\u00E9d,running'
                 __UpperCamelCase	:	Any		    =  tokenizer.encode(_lowerCamelCase     ,			add_special_tokens=_lowerCamelCase    )
                 tokenizer.save_pretrained(_lowerCamelCase    )

                 __UpperCamelCase	:	int		    =  tokenizer.__class__.from_pretrained(_lowerCamelCase    )
                 __UpperCamelCase	:	List[str]		    =  after_tokenizer.encode(_lowerCamelCase     ,			add_special_tokens=_lowerCamelCase    )
                 self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )

                 shutil.rmtree(_lowerCamelCase    )

         __UpperCamelCase	:	Optional[int]		    =  self.get_tokenizers(model_max_length=4_2    )
         for tokenizer in tokenizers:
             with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
                 # Isolate this from the other tests because we save additional tokens/etc
                 __UpperCamelCase	:	Tuple		    =  tempfile.mkdtemp()

                 __UpperCamelCase	:	List[str]		    =  ' He is very happy, UNwant\u00E9d,running'
                 tokenizer.add_tokens(['bim', 'bambam']    )
                 __UpperCamelCase	:	Optional[Any]		    =  tokenizer.additional_special_tokens
                 additional_special_tokens.append('new_additional_special_token'    )
                 tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens}    )
                 __UpperCamelCase	:	Tuple		    =  tokenizer.encode(_lowerCamelCase     ,			add_special_tokens=_lowerCamelCase    )
                 tokenizer.save_pretrained(_lowerCamelCase    )

                 __UpperCamelCase	:	Optional[Any]		    =  tokenizer.__class__.from_pretrained(_lowerCamelCase    )
                 __UpperCamelCase	:	Optional[int]		    =  after_tokenizer.encode(_lowerCamelCase     ,			add_special_tokens=_lowerCamelCase    )
                 self.assertListEqual(_lowerCamelCase     ,			_lowerCamelCase    )
                 self.assertIn('new_additional_special_token'     ,			after_tokenizer.additional_special_tokens    )
                 self.assertEqual(after_tokenizer.model_max_length     ,			4_2    )

                 __UpperCamelCase	:	Optional[int]		    =  tokenizer.__class__.from_pretrained(_lowerCamelCase     ,			model_max_length=4_3    )
                 self.assertEqual(tokenizer.model_max_length     ,			4_3    )

                 shutil.rmtree(_lowerCamelCase    )







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Dict		    =  []
         if self.test_slow_tokenizer:
             tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())    )

         if self.test_rust_tokenizer:
             tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())    )

         for tokenizer_class, tokenizer_utils in tokenizer_list:
             with tempfile.TemporaryDirectory() as tmp_dir:
                 tokenizer_utils.save_pretrained(_lowerCamelCase    )

                 with open(os.path.join(_lowerCamelCase     ,			'special_tokens_map.json'    )     ,			encoding='utf-8'    ) as json_file:
                     __UpperCamelCase	:	str		    =  json.load(_lowerCamelCase    )

                 with open(os.path.join(_lowerCamelCase     ,			'tokenizer_config.json'    )     ,			encoding='utf-8'    ) as json_file:
                     __UpperCamelCase	:	List[Any]		    =  json.load(_lowerCamelCase    )

                 __UpperCamelCase	:	Tuple		    =  [f"""<extra_id_{i}>""" for i in range(1_2_5    )]

                 __UpperCamelCase	:	int		    =  added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]
                 __UpperCamelCase	:	Optional[int]		    =  added_tokens_extra_ids + [
                     'an_additional_special_token'
                 ]

                 with open(os.path.join(_lowerCamelCase     ,			'special_tokens_map.json'    )     ,			'w'     ,			encoding='utf-8'    ) as outfile:
                     json.dump(_lowerCamelCase     ,			_lowerCamelCase    )
                 with open(os.path.join(_lowerCamelCase     ,			'tokenizer_config.json'    )     ,			'w'     ,			encoding='utf-8'    ) as outfile:
                     json.dump(_lowerCamelCase     ,			_lowerCamelCase    )

                 # the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
                 # into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
                 # "special_tokens_map.json" files
                 __UpperCamelCase	:	Any		    =  tokenizer_class.from_pretrained(
                     _lowerCamelCase     ,			)
                 self.assertIn(
                     'an_additional_special_token'     ,			tokenizer_without_change_in_init.additional_special_tokens    )
                 self.assertEqual(
                     ['an_additional_special_token']     ,			tokenizer_without_change_in_init.convert_ids_to_tokens(
                         tokenizer_without_change_in_init.convert_tokens_to_ids(['an_additional_special_token']    )    )     ,			)

                 # Now we test that we can change the value of additional_special_tokens in the from_pretrained
                 __UpperCamelCase	:	int		    =  added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'     ,			lstrip=_lowerCamelCase    )]
                 __UpperCamelCase	:	Dict		    =  tokenizer_class.from_pretrained(
                     _lowerCamelCase     ,			additional_special_tokens=_lowerCamelCase     ,			)

                 self.assertIn('a_new_additional_special_token'     ,			tokenizer.additional_special_tokens    )
                 self.assertEqual(
                     ['a_new_additional_special_token']     ,			tokenizer.convert_ids_to_tokens(
                         tokenizer.convert_tokens_to_ids(['a_new_additional_special_token']    )    )     ,			)







     def lowerCAmelCase     (		self    ):
         __UpperCamelCase	:	Optional[Any]		    =  self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_7_8]    )     ,			'�'    )







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):
         pass







     def lowerCAmelCase     (		self    ):
         # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
         # strings and special added tokens as tokens
         __UpperCamelCase	:	List[str]		    =  self.get_tokenizers(fast=_lowerCamelCase     ,			do_lower_case=_lowerCamelCase    )
         for tokenizer in tokenizers:
             with self.subTest(f"""{tokenizer.__class__.__name__}"""    ):
                 __UpperCamelCase	:	int		    =  ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                 __UpperCamelCase	:	str		    =  tokenizer.convert_tokens_to_string(_lowerCamelCase    )

                 self.assertIsInstance(_lowerCamelCase     ,			_lowerCamelCase    )




'''simple docstring'''
import glob
import os
import random
from string import ascii_lowercase, digits

import cva
a=  ''''''
a=  ''''''
a=  ''''''
a=  1  # (0 is vertical, 1 is horizontal)







def  _UpperCamelCase (     ):




    """simple docstring"""



    __UpperCamelCase    ,					__UpperCamelCase	:	str		    =  get_dataset(_a				,     _a			)
    print('Processing...'			)
    __UpperCamelCase    ,					__UpperCamelCase    ,					__UpperCamelCase	:	List[Any]		    =  update_image_and_anno(_a				,     _a				,     _a			)

    for index, image in enumerate(_a			):
        # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
        __UpperCamelCase	:	Tuple		    =  random_chars(3_2			)
        __UpperCamelCase	:	Tuple		    =  paths[index].split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        __UpperCamelCase	:	List[str]		    =  f"""{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"""
        cva.imwrite(f"""/{file_root}.jpg"""				,     _a				,     [cva.IMWRITE_JPEG_QUALITY, 8_5]			)
        print(f"""Success {index+1}/{len(_a			)} with {file_name}"""			)
        __UpperCamelCase	:	Tuple		    =  []
        for anno in new_annos[index]:
            __UpperCamelCase	:	Any		    =  f"""{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"""
            annos_list.append(_a			)
        with open(f"""/{file_root}.txt"""				,     'w'			) as outfile:
            outfile.write('\n'.join(line for line in annos_list			)			)







def  _UpperCamelCase (     _a	:	str				,     _a	:	str			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	Union[str, Any]		    =  []
    for label_file in glob.glob(os.path.join(_a				,     '*.txt'			)			):
        __UpperCamelCase	:	Any		    =  label_file.split(os.sep			)[-1].rsplit('.'				,     1			)[0]
        with open(_a			) as in_file:
            __UpperCamelCase	:	Tuple		    =  in_file.readlines()
        __UpperCamelCase	:	Dict		    =  os.path.join(_a				,     f"""{label_name}.jpg"""			)

        __UpperCamelCase	:	Optional[Any]		    =  []
        for obj_list in obj_lists:
            __UpperCamelCase	:	Optional[Any]		    =  obj_list.rstrip('\n'			).split(' '			)
            boxes.append(
                [
                    int(obj[0]			),
                    float(obj[1]			),
                    float(obj[2]			),
                    float(obj[3]			),
                    float(obj[4]			),
                ]			)
        if not boxes:
            continue
        img_paths.append(_a			)
        labels.append(_a			)
    return img_paths, labels







def  _UpperCamelCase (     _a	:	list				,     _a	:	list				,     _a	:	int = 1			):




    """simple docstring"""



    __UpperCamelCase	:	List[str]		    =  []
    __UpperCamelCase	:	str		    =  []
    __UpperCamelCase	:	str		    =  []
    for idx in range(len(_a			)			):
        __UpperCamelCase	:	Union[str, Any]		    =  []
        __UpperCamelCase	:	List[str]		    =  img_list[idx]
        path_list.append(_a			)
        __UpperCamelCase	:	Dict		    =  anno_list[idx]
        __UpperCamelCase	:	List[str]		    =  cva.imread(_a			)
        if flip_type == 1:
            __UpperCamelCase	:	List[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[1]
                new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]			)
        elif flip_type == 0:
            __UpperCamelCase	:	Optional[Any]		    =  cva.flip(_a				,     _a			)
            for bbox in img_annos:
                __UpperCamelCase	:	List[str]		    =  1 - bbox[2]
                new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]			)
        new_annos_lists.append(_a			)
        new_imgs_list.append(_a			)
    return new_imgs_list, new_annos_lists, path_list







def  _UpperCamelCase (     _a	:	int = 3_2			):




    """simple docstring"""



    assert number_char > 1, "The number of character should greater than 1"
    __UpperCamelCase	:	Optional[Any]		    =  ascii_lowercase + digits
    return "".join(random.choice(_a			) for _ in range(_a			)			)


if __name__ == "__main__":
  main()
  print('''DONE ✅''')



