



'''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():
  lowercase_		       =  "pt"
elif is_tf_available():
  lowercase_		       =  "tf"
else:
  lowercase_		       =  "jax"






class __A			(  A   ,   unittest.TestCase						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[int]          =       PerceiverTokenizer
 __lowerCamelCase    :       Optional[Any]          =       False



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      super().setUp()
      _a      =  PerceiverTokenizer()
      tokenizer.save_pretrained(self.tmpdirname       )



 @cached_property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''       )



 def 					a__  (self ,   **A       )    ->     PerceiverTokenizer:


      """simple docstring"""



      return self.tokenizer_class.from_pretrained(self.tmpdirname ,   **A       )



 def 					a__  (self ,   A ,   A=False ,   A=20 ,   A=5       )    ->     Tuple[str, list]:


      """simple docstring"""



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

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

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



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  self.perceiver_tokenizer
      _a      =  '''Unicode €.'''
      _a      =  tokenizer(A       )
      _a      =  [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
      self.assertEqual(encoded['''input_ids'''] ,   A       )

      # decoding
      _a      =  tokenizer.decode(A       )
      self.assertEqual(A ,   '''[CLS]Unicode €.[SEP]'''       )

      _a      =  tokenizer('''e è é ê ë'''       )
      _a      =  [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
      self.assertEqual(encoded['''input_ids'''] ,   A       )
      # decoding
      _a      =  tokenizer.decode(A       )
      self.assertEqual(A ,   '''[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 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  self.perceiver_tokenizer
      _a      =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      # fmt: off
      _a      =  [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
      # fmt: on
      _a      =  tokenizer(A ,   padding=A ,   return_tensors=A       )
      self.assertIsInstance(A ,   A       )

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

      self.assertListEqual(A ,   A       )

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



 def 					a__  (self       )    ->     str:


      """simple docstring"""



      _a      =  self.perceiver_tokenizer
      _a      =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
      _a      =  tokenizer(A ,   padding=A ,   return_tensors=A       )
      # check if input_ids are returned and no decoder_input_ids
      self.assertIn('''input_ids''' ,   A       )
      self.assertIn('''attention_mask''' ,   A       )
      self.assertNotIn('''decoder_input_ids''' ,   A       )
      self.assertNotIn('''decoder_attention_mask''' ,   A       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      _a      =  self.perceiver_tokenizer
      _a      =  [
          '''Summary of the text.''',
          '''Another summary.''',
      ]
      _a      =  tokenizer(
          text_target=A ,   max_length=32 ,   padding='''max_length''' ,   truncation=A ,   return_tensors=A       )
      self.assertEqual(32 ,   targets['''input_ids'''].shape[1]       )



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  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
      _a      =  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
                _a      =  tempfile.mkdtemp()

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

                _a      =  tokenizer.__class__.from_pretrained(A       )
                _a      =  after_tokenizer.encode(A ,   add_special_tokens=A       )
                self.assertListEqual(A ,   A       )

                shutil.rmtree(A       )

      _a      =  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
                _a      =  tempfile.mkdtemp()

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

                _a      =  tokenizer.__class__.from_pretrained(A       )
                _a      =  after_tokenizer.encode(A ,   add_special_tokens=A       )
                self.assertListEqual(A ,   A       )
                self.assertIn('''new_additional_special_token''' ,   after_tokenizer.additional_special_tokens       )
                self.assertEqual(after_tokenizer.model_max_length ,   42       )

                _a      =  tokenizer.__class__.from_pretrained(A ,   model_max_length=43       )
                self.assertEqual(tokenizer.model_max_length ,   43       )

                shutil.rmtree(A       )



 def 					a__  (self       )    ->     int:


      """simple docstring"""



      _a      =  []
      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(A       )

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

                with open(os.path.join(A ,   '''tokenizer_config.json'''       ) ,   encoding='''utf-8'''       ) as json_file:
                     _a      =  json.load(A       )

                _a      =  [f'''<extra_id_{i}>''' for i in range(125       )]

                _a      =  added_tokens_extra_ids + [
                    '''an_additional_special_token'''
                ]
                _a      =  added_tokens_extra_ids + [
                    '''an_additional_special_token'''
                ]

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

                # 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
                _a      =  tokenizer_class.from_pretrained(
                    A ,   )
                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
                _a      =  added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' ,   lstrip=A       )]
                _a      =  tokenizer_class.from_pretrained(
                    A ,   additional_special_tokens=A ,   )

                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 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      _a      =  self.perceiver_tokenizer
      self.assertEqual(tokenizer.decode([178]       ) ,   '''�'''       )



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     Optional[int]:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      pass



 def 					a__  (self       )    ->     Union[str, Any]:


      """simple docstring"""



      pass



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


      """simple docstring"""



      _a      =  self.get_tokenizers(fast=A ,   do_lower_case=A       )
      for tokenizer in tokenizers:
           with self.subTest(f'''{tokenizer.__class__.__name__}'''       ):
                _a      =  ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
                _a      =  tokenizer.convert_tokens_to_string(A       )

                self.assertIsInstance(A ,   A       )








'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  {
    "microsoft/resnet-50": "https://huggingface.co/microsoft/resnet-50/blob/main/config.json",
}






class __A			(  A   ,   A						):



 '''simple docstring'''







 __lowerCamelCase    :       Tuple          =       'resnet'
 __lowerCamelCase    :       Any          =       ['basic', 'bottleneck']



 def __init__(self ,   A=3 ,   A=64 ,   A=[256, 512, 1_024, 2_048] ,   A=[3, 4, 6, 3] ,   A="bottleneck" ,   A="relu" ,   A=False ,   A=None ,   A=None ,   **A ,   )    ->     Dict:


      """simple docstring"""



      super().__init__(**A       )
      if layer_type not in self.layer_types:
           raise ValueError(f'''layer_type={layer_type} is not one of {','.join(self.layer_types       )}'''       )
      _a      =  num_channels
      _a      =  embedding_size
      _a      =  hidden_sizes
      _a      =  depths
      _a      =  layer_type
      _a      =  hidden_act
      _a      =  downsample_in_first_stage
      _a      =  ['''stem'''] + [f'''stage{idx}''' for idx in range(1 ,   len(A       ) + 1       )]
      _a  ,    _a      =  get_aligned_output_features_output_indices(
          out_features=A ,   out_indices=A ,   stage_names=self.stage_names       )






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Any          =       version.parse('1.11'						)



 @property
 def 					a__  (self       )    ->     Mapping[str, Mapping[int, str]]:


      """simple docstring"""



      return OrderedDict(
          [
              ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
          ]       )



 @property
 def 					a__  (self       )    ->     float:


      """simple docstring"""



      return 1E-3



