
'''simple docstring'''
from typing import TYPE_CHECKING

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


A__   :		Optional[int] 		={
    'configuration_maskformer': ['MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MaskFormerConfig'],
    'configuration_maskformer_swin': ['MaskFormerSwinConfig'],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__   :		List[Any] 		=['MaskFormerFeatureExtractor']
     A__   :		Union[str, Any] 		=['MaskFormerImageProcessor']


try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__   :		Dict 		=[
         'MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MaskFormerForInstanceSegmentation',
         'MaskFormerModel',
         'MaskFormerPreTrainedModel',
     ]
     A__   :		Optional[Any] 		=[
         'MaskFormerSwinBackbone',
         'MaskFormerSwinModel',
         'MaskFormerSwinPreTrainedModel',
     ]

if TYPE_CHECKING:
     from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
     from .configuration_maskformer_swin import MaskFormerSwinConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_maskformer import MaskFormerFeatureExtractor
          from .image_processing_maskformer import MaskFormerImageProcessor
     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_maskformer import (
              MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              MaskFormerForInstanceSegmentation,
              MaskFormerModel,
              MaskFormerPreTrainedModel,
          )
          from .modeling_maskformer_swin import (
              MaskFormerSwinBackbone,
              MaskFormerSwinModel,
              MaskFormerSwinPreTrainedModel,
          )


else:
     import sys

     A__   :		int 		=_LazyModule(__name__, globals()['__file__'], _import_structure)





'''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__   :		List[str] 		='tf'
else:
     A__   :		List[str] 		='jax'







class  __A				( _SCREAMING_SNAKE_CASE    ,						unittest.TestCase			):
 lowerCamelCase				       =PerceiverTokenizer
 lowerCamelCase				       =False
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   super().setUp()
   __A		:     Optional[int]												=		PerceiverTokenizer()
   tokenizer.save_pretrained(self.tmpdirname       )
 @cached_property
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""       )
 def 		lowercase_(   self			:    Optional[Any]				,   **lowerCamelCase			:    Union[str, Any]       ):



   """simple docstring"""





   return self.tokenizer_class.from_pretrained(self.tmpdirname				,   **lowerCamelCase       )
 def 		lowercase_(   self			:    Any				,   lowerCamelCase			:    List[Any]				,   lowerCamelCase			:    Any=False				,   lowerCamelCase			:    List[Any]=20				,   lowerCamelCase			:    List[str]=5       ):



   """simple docstring"""





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

   __A		:     List[str]												=		list(filter(lambda lowerCamelCase       : re.match(r"""^[ a-zA-Z]+$"""				,   t[1]       )				,   lowerCamelCase       )       )
   __A		:     List[str]												=		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:
     __A		:     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:
       __A		:     Dict												=		toks + toks
        # toks_str = [t[1] for t in toks]
   __A		:     Optional[int]												=		[t[0] for t in toks]

   # Ensure consistency
   __A		:     Optional[Any]												=		tokenizer.decode(lowerCamelCase				,   clean_up_tokenization_spaces=lowerCamelCase       )
   if " " not in output_txt and len(lowerCamelCase       ) > 1:
     __A		:     int												=		(
         tokenizer.decode([toks_ids[0]]				,   clean_up_tokenization_spaces=lowerCamelCase       )
         + """ """
         + tokenizer.decode(toks_ids[1:]				,   clean_up_tokenization_spaces=lowerCamelCase       )
     )
   if with_prefix_space:
     __A		:     Union[str, Any]												=		""" """ + output_txt
   __A		:     Any												=		tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
   return output_txt, output_ids
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     int												=		self.perceiver_tokenizer
   __A		:     List[str]												=		"""Unicode €."""
   __A		:     Dict												=		tokenizer(lowerCamelCase       )
   __A		:     Any												=		[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"""]				,   lowerCamelCase       )

   # decoding
   __A		:     Dict												=		tokenizer.decode(lowerCamelCase       )
   self.assertEqual(lowerCamelCase				,   """[CLS]Unicode €.[SEP]"""       )

   __A		:     int												=		tokenizer("""e è é ê ë"""       )
   __A		:     str												=		[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"""]				,   lowerCamelCase       )
   # decoding
   __A		:     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 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Optional[Any]												=		self.perceiver_tokenizer
   __A		:     int												=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   # fmt: off
   __A		:     Any												=		[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
   __A		:     Optional[int]												=		tokenizer(lowerCamelCase				,   padding=lowerCamelCase				,   return_tensors=lowerCamelCase       )
   self.assertIsInstance(lowerCamelCase				,   lowerCamelCase       )

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

   self.assertListEqual(lowerCamelCase				,   lowerCamelCase       )

   self.assertEqual((2, 38)				,   batch.input_ids.shape       )
   self.assertEqual((2, 38)				,   batch.attention_mask.shape       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     str												=		self.perceiver_tokenizer
   __A		:     Any												=		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   __A		:     Optional[int]												=		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 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.perceiver_tokenizer
   __A		:     Optional[Any]												=		[
       """Summary of the text.""",
       """Another summary.""",
   ]
   __A		:     Union[str, Any]												=		tokenizer(
       text_target=lowerCamelCase				,   max_length=32				,   padding="""max_length"""				,   truncation=lowerCamelCase				,   return_tensors=lowerCamelCase       )
   self.assertEqual(32				,   targets["""input_ids"""].shape[1]       )
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   __A		:     int												=		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		:     Optional[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
       __A		:     Optional[int]												=		tempfile.mkdtemp()

       __A		:     List[str]												=		""" He is very happy, UNwant\u00E9d,running"""
       __A		:     str												=		tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
       tokenizer.save_pretrained(lowerCamelCase       )

       __A		:     Optional[int]												=		tokenizer.__class__.from_pretrained(lowerCamelCase       )
       __A		:     Optional[int]												=		after_tokenizer.encode(lowerCamelCase				,   add_special_tokens=lowerCamelCase       )
       self.assertListEqual(lowerCamelCase				,   lowerCamelCase       )

       shutil.rmtree(lowerCamelCase       )

   __A		:     Optional[Any]												=		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		:     List[Any]												=		tempfile.mkdtemp()

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

       __A		:     Tuple												=		tokenizer.__class__.from_pretrained(lowerCamelCase       )
       __A		:     Union[str, Any]												=		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				,   42       )

       __A		:     List[str]												=		tokenizer.__class__.from_pretrained(lowerCamelCase				,   model_max_length=43       )
       self.assertEqual(tokenizer.model_max_length				,   43       )

       shutil.rmtree(lowerCamelCase       )
 def 		lowercase_(   self			:    int       ):



   """simple docstring"""





   __A		:     str												=		[]
   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:
         __A		:     int												=		json.load(lowerCamelCase       )

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

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

       __A		:     int												=		added_tokens_extra_ids + [
           """an_additional_special_token"""
       ]
       __A		:     Any												=		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
       __A		:     int												=		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
       __A		:     int												=		added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""				,   lstrip=lowerCamelCase       )]
       __A		:     str												=		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 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   __A		:     Dict												=		self.perceiver_tokenizer
   self.assertEqual(tokenizer.decode([1_78]       )				,   """�"""       )
 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Optional[Any]       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Any       ):



   """simple docstring"""





   pass
 def 		lowercase_(   self			:    Union[str, Any]       ):



   """simple docstring"""





   pass






 def 		lowercase_(   self			:    Optional[int]       ):



   """simple docstring"""





   __A		:     Optional[int]												=		self.get_tokenizers(fast=lowerCamelCase				,   do_lower_case=lowerCamelCase       )
   for tokenizer in tokenizers:
     with self.subTest(f"{tokenizer.__class__.__name__}"       ):
       __A		:     Optional[int]												=		["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
       __A		:     List[Any]												=		tokenizer.convert_tokens_to_string(lowerCamelCase       )

       self.assertIsInstance(lowerCamelCase				,   lowerCamelCase       )



