





'''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__    :       List[str]              ='pt'
elif is_tf_available():
   A__    :       Any              ='tf'
else:
   A__    :       str              ='jax'




class 			UpperCAmelCase					(      lowerCamelCase__							,		unittest.TestCase       ):
      _lowercase:      int											=       PerceiverTokenizer
      _lowercase:      Optional[int]											=       False




      def     lowercase__   (      self      :			Tuple  )    ->				Any:
            super().setUp()
            _lowerCAmelCase												=							PerceiverTokenizer()
            tokenizer.save_pretrained(self.tmpdirname  )




      @cached_property
      def     lowercase__   (      self      :			Dict  )    ->				int:
            return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""  )




      def     lowercase__   (      self      :			List[Any]      ,      **__snake_case      :			Tuple  )    ->				Any:
            return self.tokenizer_class.from_pretrained(self.tmpdirname      ,      **__snake_case  )




      def     lowercase__   (      self      :			Union[str, Any]      ,      __snake_case      :			Optional[Any]      ,      __snake_case      :			Union[str, Any]=False      ,      __snake_case      :			str=20      ,      __snake_case      :			List[str]=5  )    ->				Tuple:
            # 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.

            _lowerCAmelCase												=							[]
            for i in range(len(__snake_case  )  ):
                  try:
                        _lowerCAmelCase												=							tokenizer.decode([i]      ,      clean_up_tokenization_spaces=__snake_case  )
                  except UnicodeDecodeError:
                        pass
                  toks.append((i, tok)  )

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

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




      def     lowercase__   (      self      :			Optional[Any]  )    ->				Dict:
            _lowerCAmelCase												=							self.perceiver_tokenizer
            _lowerCAmelCase												=							'Unicode €.'
            _lowerCAmelCase												=							tokenizer(__snake_case  )
            _lowerCAmelCase												=							[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
            self.assertEqual(encoded["""input_ids"""]      ,      __snake_case  )

            # decoding
            _lowerCAmelCase												=							tokenizer.decode(__snake_case  )
            self.assertEqual(__snake_case      ,      """[CLS]Unicode €.[SEP]"""  )

            _lowerCAmelCase												=							tokenizer("""e è é ê ë"""  )
            _lowerCAmelCase												=							[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
            self.assertEqual(encoded["""input_ids"""]      ,      __snake_case  )
            # decoding
            _lowerCAmelCase												=							tokenizer.decode(__snake_case  )
            self.assertEqual(__snake_case      ,      """[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     lowercase__   (      self      :			str  )    ->				Optional[int]:
            _lowerCAmelCase												=							self.perceiver_tokenizer
            _lowerCAmelCase												=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
            # fmt: off
            _lowerCAmelCase												=							[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
            # fmt: on
            _lowerCAmelCase												=							tokenizer(__snake_case      ,      padding=__snake_case      ,      return_tensors=__snake_case  )
            self.assertIsInstance(__snake_case      ,      __snake_case  )

            if FRAMEWORK != "jax":
                  _lowerCAmelCase												=							list(batch.input_ids.numpy()[0]  )
            else:
                  _lowerCAmelCase												=							list(batch.input_ids.tolist()[0]  )

            self.assertListEqual(__snake_case      ,      __snake_case  )

            self.assertEqual((2, 38)      ,      batch.input_ids.shape  )
            self.assertEqual((2, 38)      ,      batch.attention_mask.shape  )




      def     lowercase__   (      self      :			List[str]  )    ->				List[Any]:
            _lowerCAmelCase												=							self.perceiver_tokenizer
            _lowerCAmelCase												=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
            _lowerCAmelCase												=							tokenizer(__snake_case      ,      padding=__snake_case      ,      return_tensors=__snake_case  )
            # check if input_ids are returned and no decoder_input_ids
            self.assertIn("""input_ids"""      ,      __snake_case  )
            self.assertIn("""attention_mask"""      ,      __snake_case  )
            self.assertNotIn("""decoder_input_ids"""      ,      __snake_case  )
            self.assertNotIn("""decoder_attention_mask"""      ,      __snake_case  )




      def     lowercase__   (      self      :			Optional[int]  )    ->				Tuple:
            _lowerCAmelCase												=							self.perceiver_tokenizer
            _lowerCAmelCase												=							[
                'Summary of the text.',
                'Another summary.',
            ]
            _lowerCAmelCase												=							tokenizer(
                text_target=__snake_case      ,      max_length=32      ,      padding="""max_length"""      ,      truncation=__snake_case      ,      return_tensors=__snake_case  )
            self.assertEqual(32      ,      targets["""input_ids"""].shape[1]  )




      def     lowercase__   (      self      :			List[Any]  )    ->				Dict:
            # safety check on max_len default value so we are sure the test works
            _lowerCAmelCase												=							self.get_tokenizers()
            for tokenizer in tokenizers:
                  with self.subTest(f"{tokenizer.__class__.__name__}"  ):
                        self.assertNotEqual(tokenizer.model_max_length      ,      42  )

                  # Now let's start the test
            _lowerCAmelCase												=							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
                        _lowerCAmelCase												=							tempfile.mkdtemp()

                        _lowerCAmelCase												=							' He is very happy, UNwant\u00E9d,running'
                        _lowerCAmelCase												=							tokenizer.encode(__snake_case      ,      add_special_tokens=__snake_case  )
                        tokenizer.save_pretrained(__snake_case  )

                        _lowerCAmelCase												=							tokenizer.__class__.from_pretrained(__snake_case  )
                        _lowerCAmelCase												=							after_tokenizer.encode(__snake_case      ,      add_special_tokens=__snake_case  )
                        self.assertListEqual(__snake_case      ,      __snake_case  )

                        shutil.rmtree(__snake_case  )

            _lowerCAmelCase												=							self.get_tokenizers(model_max_length=42  )
            for tokenizer in tokenizers:
                  with self.subTest(f"{tokenizer.__class__.__name__}"  ):
                        # Isolate this from the other tests because we save additional tokens/etc
                        _lowerCAmelCase												=							tempfile.mkdtemp()

                        _lowerCAmelCase												=							' He is very happy, UNwant\u00E9d,running'
                        tokenizer.add_tokens(["""bim""", """bambam"""]  )
                        _lowerCAmelCase												=							tokenizer.additional_special_tokens
                        additional_special_tokens.append("""new_additional_special_token"""  )
                        tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens}  )
                        _lowerCAmelCase												=							tokenizer.encode(__snake_case      ,      add_special_tokens=__snake_case  )
                        tokenizer.save_pretrained(__snake_case  )

                        _lowerCAmelCase												=							tokenizer.__class__.from_pretrained(__snake_case  )
                        _lowerCAmelCase												=							after_tokenizer.encode(__snake_case      ,      add_special_tokens=__snake_case  )
                        self.assertListEqual(__snake_case      ,      __snake_case  )
                        self.assertIn("""new_additional_special_token"""      ,      after_tokenizer.additional_special_tokens  )
                        self.assertEqual(after_tokenizer.model_max_length      ,      42  )

                        _lowerCAmelCase												=							tokenizer.__class__.from_pretrained(__snake_case      ,      model_max_length=43  )
                        self.assertEqual(tokenizer.model_max_length      ,      43  )

                        shutil.rmtree(__snake_case  )




      def     lowercase__   (      self      :			List[str]  )    ->				Dict:
            _lowerCAmelCase												=							[]
            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(__snake_case  )

                        with open(os.path.join(__snake_case      ,      """special_tokens_map.json"""  )      ,      encoding="""utf-8"""  ) as json_file:
                              _lowerCAmelCase												=							json.load(__snake_case  )

                        with open(os.path.join(__snake_case      ,      """tokenizer_config.json"""  )      ,      encoding="""utf-8"""  ) as json_file:
                              _lowerCAmelCase												=							json.load(__snake_case  )

                        _lowerCAmelCase												=							[f"<extra_id_{i}>" for i in range(1_25  )]

                        _lowerCAmelCase												=							added_tokens_extra_ids + [
                            'an_additional_special_token'
                        ]
                        _lowerCAmelCase												=							added_tokens_extra_ids + [
                            'an_additional_special_token'
                        ]

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

                        # 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
                        _lowerCAmelCase												=							tokenizer_class.from_pretrained(
                            __snake_case      ,      )
                        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
                        _lowerCAmelCase												=							added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""      ,      lstrip=__snake_case  )]
                        _lowerCAmelCase												=							tokenizer_class.from_pretrained(
                            __snake_case      ,      additional_special_tokens=__snake_case      ,      )

                        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     lowercase__   (      self      :			Tuple  )    ->				Dict:
            _lowerCAmelCase												=							self.perceiver_tokenizer
            self.assertEqual(tokenizer.decode([1_78]  )      ,      """�"""  )




      def     lowercase__   (      self      :			Union[str, Any]  )    ->				List[str]:
            pass




      def     lowercase__   (      self      :			int  )    ->				Optional[int]:
            pass




      def     lowercase__   (      self      :			Optional[int]  )    ->				List[Any]:
            pass




      def     lowercase__   (      self      :			str  )    ->				int:
            pass




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

                        self.assertIsInstance(__snake_case      ,      __snake_case  )













'''simple docstring'''






from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							cva.getAffineTransform(lowerCAmelCase							,     lowerCAmelCase  )
      return cva.warpAffine(lowerCAmelCase							,     lowerCAmelCase							,     (rows, cols)  )


if __name__ == "__main__":
      # read original image
      A__    :       Any              =cva.imread(
          str(Path(__file__).resolve().parent.parent / '''image_data''' / '''lena.jpg''')
      )
      # turn image in gray scale value
      A__    :       Any              =cva.cvtColor(image, cva.COLOR_BGR2GRAY)
      # get image shape
      A__       ,						A__    :       Tuple              =gray_img.shape

      # set different points to rotate image
      A__    :       Optional[int]              =np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
      A__    :       Tuple              =np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
      A__    :       List[str]              =np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
      A__    :       Dict              =np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)

      # add all rotated images in a list
      A__    :       List[Any]              =[
          gray_img,
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
          get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
      ]

      # plot different image rotations
      A__    :       Tuple              =plt.figure(1)
      A__    :       int              =['''Original''', '''Rotation 1''', '''Rotation 2''', '''Rotation 3''']
      for i, image in enumerate(images):
            plt.subplot(2, 2, i + 1), plt.imshow(image, '''gray''')
            plt.title(titles[i])
            plt.axis('''off''')
            plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
      plt.show()






