





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






class        snake_case_    (		__lowercase					,unittest.TestCase						):
  A_      							=   PerceiverTokenizer
  A_      							=   False






  def 				UpperCAmelCase__						(							self			:					Union[str, Any]      )->Optional[int]:






   '''simple docstring'''

   super().setUp()
   __lowerCAmelCase					:	Tuple      =       PerceiverTokenizer()
   tokenizer.save_pretrained(self.tmpdirname      )






  @cached_property
  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Optional[int]:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					Optional[Any]       ,  **_snake_case			:					Dict      )->PerceiverTokenizer:






   '''simple docstring'''

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






  def 				UpperCAmelCase__						(							self			:					List[Any]       ,  _snake_case			:					Dict       ,  _snake_case			:					Union[str, Any]=False       ,  _snake_case			:					Dict=20       ,  _snake_case			:					List[str]=5      )->Tuple[str, list]:






   '''simple docstring'''

   __lowerCAmelCase					:	List[str]      =       []
   for i in range(len(_snake_case      )      ):
    try:
     __lowerCAmelCase					:	Dict      =       tokenizer.decode([i]       ,  clean_up_tokenization_spaces=_snake_case      )
    except UnicodeDecodeError:
     pass
    toks.append((i, tok)      )

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

   # Ensure consistency
   __lowerCAmelCase					:	List[Any]      =       tokenizer.decode(_snake_case       ,  clean_up_tokenization_spaces=_snake_case      )
   if " " not in output_txt and len(_snake_case      ) > 1:
    __lowerCAmelCase					:	Optional[int]      =       (
        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					:	Tuple      =       """ """ + output_txt
   __lowerCAmelCase					:	Optional[int]      =       tokenizer.encode(_snake_case       ,  add_special_tokens=_snake_case      )
   return output_txt, output_ids






  def 				UpperCAmelCase__						(							self			:					int      )->Optional[Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       self.perceiver_tokenizer
   __lowerCAmelCase					:	Optional[int]      =       """Unicode €."""
   __lowerCAmelCase					:	Optional[Any]      =       tokenizer(_snake_case      )
   __lowerCAmelCase					:	int      =       [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
   self.assertEqual(encoded["""input_ids"""]       ,  _snake_case      )

   # decoding
   __lowerCAmelCase					:	List[Any]      =       tokenizer.decode(_snake_case      )
   self.assertEqual(_snake_case       ,  """[CLS]Unicode €.[SEP]"""      )

   __lowerCAmelCase					:	Any      =       tokenizer("""e è é ê ë"""      )
   __lowerCAmelCase					:	str      =       [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
   self.assertEqual(encoded["""input_ids"""]       ,  _snake_case      )
   # decoding
   __lowerCAmelCase					:	List[str]      =       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 				UpperCAmelCase__						(							self			:					str      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	str      =       self.perceiver_tokenizer
   __lowerCAmelCase					:	Tuple      =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   # fmt: off
   __lowerCAmelCase					:	Dict      =       [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
   __lowerCAmelCase					:	Optional[int]      =       tokenizer(_snake_case       ,  padding=_snake_case       ,  return_tensors=_snake_case      )
   self.assertIsInstance(_snake_case       ,  _snake_case      )

   if FRAMEWORK != "jax":
    __lowerCAmelCase					:	int      =       list(batch.input_ids.numpy()[0]      )
   else:
    __lowerCAmelCase					:	Dict      =       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 				UpperCAmelCase__						(							self			:					Any      )->int:






   '''simple docstring'''

   __lowerCAmelCase					:	Optional[int]      =       self.perceiver_tokenizer
   __lowerCAmelCase					:	Any      =       ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
   __lowerCAmelCase					:	List[Any]      =       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 				UpperCAmelCase__						(							self			:					str      )->Tuple:






   '''simple docstring'''

   __lowerCAmelCase					:	Union[str, Any]      =       self.perceiver_tokenizer
   __lowerCAmelCase					:	Dict      =       [
       """Summary of the text.""",
       """Another summary.""",
   ]
   __lowerCAmelCase					:	Any      =       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 				UpperCAmelCase__						(							self			:					Dict      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	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
   __lowerCAmelCase					:	Dict      =       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					:	Any      =       tempfile.mkdtemp()

     __lowerCAmelCase					:	int      =       """ He is very happy, UNwant\u00E9d,running"""
     __lowerCAmelCase					:	Any      =       tokenizer.encode(_snake_case       ,  add_special_tokens=_snake_case      )
     tokenizer.save_pretrained(_snake_case      )

     __lowerCAmelCase					:	int      =       tokenizer.__class__.from_pretrained(_snake_case      )
     __lowerCAmelCase					:	str      =       after_tokenizer.encode(_snake_case       ,  add_special_tokens=_snake_case      )
     self.assertListEqual(_snake_case       ,  _snake_case      )

     shutil.rmtree(_snake_case      )

   __lowerCAmelCase					:	int      =       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					:	Any      =       tempfile.mkdtemp()

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

     __lowerCAmelCase					:	List[str]      =       tokenizer.__class__.from_pretrained(_snake_case      )
     __lowerCAmelCase					:	Dict      =       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					:	Tuple      =       tokenizer.__class__.from_pretrained(_snake_case       ,  model_max_length=43      )
     self.assertEqual(tokenizer.model_max_length       ,  43      )

     shutil.rmtree(_snake_case      )






  def 				UpperCAmelCase__						(							self			:					Optional[int]      )->Union[str, Any]:






   '''simple docstring'''

   __lowerCAmelCase					:	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(_snake_case      )

     with open(os.path.join(_snake_case       ,  """special_tokens_map.json"""      )       ,  encoding="""utf-8"""      ) as json_file:
      __lowerCAmelCase					:	List[Any]      =       json.load(_snake_case      )

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

     __lowerCAmelCase					:	List[str]      =       [F'''<extra_id_{i}>''' for i in range(125      )]

     __lowerCAmelCase					:	Tuple      =       added_tokens_extra_ids + [
         """an_additional_special_token"""
     ]
     __lowerCAmelCase					:	str      =       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					:	List[str]      =       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					:	int      =       added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""       ,  lstrip=_snake_case      )]
     __lowerCAmelCase					:	List[str]      =       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 				UpperCAmelCase__						(							self			:					str      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Any      =       self.perceiver_tokenizer
   self.assertEqual(tokenizer.decode([178]      )       ,  """�"""      )






  def 				UpperCAmelCase__						(							self			:					int      )->Tuple:






   '''simple docstring'''

   pass






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






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Tuple      )->List[str]:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->List[Any]:






   '''simple docstring'''

   pass






  def 				UpperCAmelCase__						(							self			:					Optional[Any]      )->Any:






   '''simple docstring'''

   __lowerCAmelCase					:	Tuple      =       self.get_tokenizers(fast=_snake_case       ,  do_lower_case=_snake_case      )
   for tokenizer in tokenizers:
    with self.subTest(F'''{tokenizer.__class__.__name__}'''      ):
     __lowerCAmelCase					:	Dict      =       ["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
     __lowerCAmelCase					:	Optional[int]      =       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,
)


_UpperCAmelCase             =							{
    'configuration_gpt_bigcode': ['GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTBigCodeConfig'],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _UpperCAmelCase             =							[
          'GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST',
          'GPTBigCodeForSequenceClassification',
          'GPTBigCodeForTokenClassification',
          'GPTBigCodeForCausalLM',
          'GPTBigCodeModel',
          'GPTBigCodePreTrainedModel',
      ]

if TYPE_CHECKING:
      from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_gpt_bigcode import (
                GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
                GPTBigCodeForCausalLM,
                GPTBigCodeForSequenceClassification,
                GPTBigCodeForTokenClassification,
                GPTBigCodeModel,
                GPTBigCodePreTrainedModel,
            )


else:
      import sys

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