




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():
 _lowerCamelCase      			=						'pt'
elif is_tf_available():
 _lowerCamelCase      			=						'tf'
else:
 _lowerCamelCase      			=						'jax'



class       a					(       _A     ,       unittest.TestCase      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[int]       		=  PerceiverTokenizer
     lowerCAmelCase    :	Optional[Any]       		=  False
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         super().setUp()
         UpperCAmelCase_										=					PerceiverTokenizer()
         tokenizer.save_pretrained(self.tmpdirname    )
     @cached_property
     def   lowerCamelCase_       (     self       :      Optional[int]    ):
         return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''    )
     def   lowerCamelCase_       (     self       :      Dict ,   **__snake_case       :      List[Any]    ):
         return self.tokenizer_class.from_pretrained(self.tmpdirname ,   **__snake_case    )
     def   lowerCamelCase_       (     self       :      List[Any] ,   __snake_case       :      Union[str, Any] ,   __snake_case       :      Union[str, Any]=False ,   __snake_case       :      Any=20 ,   __snake_case       :      str=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_										=					[]
         for i in range(len(__snake_case    )    ):
             try:
                 UpperCAmelCase_										=					tokenizer.decode([i] ,   clean_up_tokenization_spaces=__snake_case    )
             except UnicodeDecodeError:
                 pass
             toks.append((i, tok)    )

         UpperCAmelCase_										=					list(filter(lambda __snake_case    : re.match(R'''^[ a-zA-Z]+$''' ,   t[1]    ) ,   __snake_case    )    )
         UpperCAmelCase_										=					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:
             UpperCAmelCase_										=					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:
                 UpperCAmelCase_										=					toks + toks
        # toks_str = [t[1] for t in toks]
         UpperCAmelCase_										=					[t[0] for t in toks]

         # Ensure consistency
         UpperCAmelCase_										=					tokenizer.decode(__snake_case ,   clean_up_tokenization_spaces=__snake_case    )
         if " " not in output_txt and len(__snake_case    ) > 1:
             UpperCAmelCase_										=					(
                 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:
             UpperCAmelCase_										=					''' ''' + output_txt
         UpperCAmelCase_										=					tokenizer.encode(__snake_case ,   add_special_tokens=__snake_case    )
         return output_txt, output_ids
     def   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.perceiver_tokenizer
         UpperCAmelCase_										=					'''Unicode €.'''
         UpperCAmelCase_										=					tokenizer(__snake_case    )
         UpperCAmelCase_										=					[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
         UpperCAmelCase_										=					tokenizer.decode(__snake_case    )
         self.assertEqual(__snake_case ,   '''[CLS]Unicode €.[SEP]'''    )

         UpperCAmelCase_										=					tokenizer('''e è é ê ë'''    )
         UpperCAmelCase_										=					[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
         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      List[Any]    ):
         UpperCAmelCase_										=					self.perceiver_tokenizer
         UpperCAmelCase_										=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         # fmt: off
         UpperCAmelCase_										=					[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
         UpperCAmelCase_										=					tokenizer(__snake_case ,   padding=__snake_case ,   return_tensors=__snake_case    )
         self.assertIsInstance(__snake_case ,   __snake_case    )

         if FRAMEWORK != "jax":
             UpperCAmelCase_										=					list(batch.input_ids.numpy()[0]    )
         else:
             UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Optional[int]    ):
         UpperCAmelCase_										=					self.perceiver_tokenizer
         UpperCAmelCase_										=					['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Tuple    ):
         UpperCAmelCase_										=					self.perceiver_tokenizer
         UpperCAmelCase_										=					[
             '''Summary of the text.''',
             '''Another summary.''',
         ]
         UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Dict    ):
         # safety check on max_len default value so we are sure the test works
         UpperCAmelCase_										=					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
         UpperCAmelCase_										=					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_										=					tempfile.mkdtemp()

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

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

                 shutil.rmtree(__snake_case    )

         UpperCAmelCase_										=					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
                 UpperCAmelCase_										=					tempfile.mkdtemp()

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

                 UpperCAmelCase_										=					tokenizer.__class__.from_pretrained(__snake_case    )
                 UpperCAmelCase_										=					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    )

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

                 shutil.rmtree(__snake_case    )
     def   lowerCamelCase_       (     self       :      List[str]    ):
         UpperCAmelCase_										=					[]
         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:
                     UpperCAmelCase_										=					json.load(__snake_case    )

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

                 UpperCAmelCase_										=					[F'<extra_id_{i}>' for i in range(1_25    )]

                 UpperCAmelCase_										=					added_tokens_extra_ids + [
                     '''an_additional_special_token'''
                 ]
                 UpperCAmelCase_										=					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
                 UpperCAmelCase_										=					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
                 UpperCAmelCase_										=					added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token''' ,   lstrip=__snake_case    )]
                 UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Dict    ):
         UpperCAmelCase_										=					self.perceiver_tokenizer
         self.assertEqual(tokenizer.decode([1_78]    ) ,   '''�'''    )
     def   lowerCamelCase_       (     self       :      int    ):
         pass
     def   lowerCamelCase_       (     self       :      List[str]    ):
         pass
     def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
         pass
     def   lowerCamelCase_       (     self       :      Tuple    ):
         pass




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

                 self.assertIsInstance(__snake_case ,   __snake_case    )











from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowerCamelCase      			=						{
    'configuration_conditional_detr': [
        'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'ConditionalDetrConfig',
        'ConditionalDetrOnnxConfig',
    ]
}

try:
 if not is_vision_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						['ConditionalDetrFeatureExtractor']
 _lowerCamelCase      			=						['ConditionalDetrImageProcessor']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 _lowerCamelCase      			=						[
     'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
     'ConditionalDetrForObjectDetection',
     'ConditionalDetrForSegmentation',
     'ConditionalDetrModel',
     'ConditionalDetrPreTrainedModel',
 ]


if TYPE_CHECKING:
 from .configuration_conditional_detr import (
     CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
     ConditionalDetrConfig,
     ConditionalDetrOnnxConfig,
 )

 try:
  if not is_vision_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
  from .image_processing_conditional_detr import ConditionalDetrImageProcessor

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_conditional_detr import (
      CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
      ConditionalDetrForObjectDetection,
      ConditionalDetrForSegmentation,
      ConditionalDetrModel,
      ConditionalDetrPreTrainedModel,
  )

else:
 import sys

 _lowerCamelCase      			=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





