




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







class 		lowercase							(     UpperCamelCase__,unittest.TestCase	):
     _a											=		PerceiverTokenizer
     _a											=		False

     def       a__			(						self       )		->							int:
           super().setUp()
           _A							:  int    =		PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname       )

     @cached_property
     def       a__			(						self       )		->							Optional[Any]:
           return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver"""       )

     def       a__			(						self	,  **_a       )		->							List[str]:
           return self.tokenizer_class.from_pretrained(self.tmpdirname	,  **A__       )

     def       a__			(						self	,  _a	,  _a=False	,  _a=20	,  _a=5       )		->							Optional[int]:
           # 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.

           _A							:  int    =		[]
           for i in range(len(A__       )       ):
                 try:
                       _A							:  List[Any]    =		tokenizer.decode([i]	,  clean_up_tokenization_spaces=A__       )
                 except UnicodeDecodeError:
                       pass
                 toks.append((i, tok)       )

           _A							:  Dict    =		list(filter(lambda _a       : re.match(R"""^[ a-zA-Z]+$"""	,  t[1]       )	,  A__       )       )
           _A							:  str    =		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							:  List[Any]    =		toks[:max_length]
           if min_length is not None and len(A__       ) < min_length and len(A__       ) > 0:
                 while len(A__       ) < min_length:
                       _A							:  List[Any]    =		toks + toks
        # toks_str = [t[1] for t in toks]
           _A							:  Tuple    =		[t[0] for t in toks]

           # Ensure consistency
           _A							:  Union[str, Any]    =		tokenizer.decode(A__	,  clean_up_tokenization_spaces=A__       )
           if " " not in output_txt and len(A__       ) > 1:
                 _A							:  List[Any]    =		(
                     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							:  List[Any]    =		""" """ + output_txt
           _A							:  Optional[Any]    =		tokenizer.encode(A__	,  add_special_tokens=A__       )
           return output_txt, output_ids

     def       a__			(						self       )		->							int:
           _A							:  Union[str, Any]    =		self.perceiver_tokenizer
           _A							:  str    =		"""Unicode €."""
           _A							:  Union[str, Any]    =		tokenizer(A__       )
           _A							:  Any    =		[4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded["""input_ids"""]	,  A__       )

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

           _A							:  Any    =		tokenizer("""e è é ê ë"""       )
           _A							:  str    =		[4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded["""input_ids"""]	,  A__       )
           # decoding
           _A							:  Tuple    =		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       )		->							str:
           _A							:  int    =		self.perceiver_tokenizer
           _A							:  Optional[int]    =		["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
           # fmt: off
           _A							:  str    =		[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							:  Union[str, Any]    =		tokenizer(A__	,  padding=A__	,  return_tensors=A__       )
           self.assertIsInstance(A__	,  A__       )

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

                       _A							:  Tuple    =		""" He is very happy, UNwant\u00E9d,running"""
                       _A							:  Optional[Any]    =		tokenizer.encode(A__	,  add_special_tokens=A__       )
                       tokenizer.save_pretrained(A__       )

                       _A							:  List[str]    =		tokenizer.__class__.from_pretrained(A__       )
                       _A							:  int    =		after_tokenizer.encode(A__	,  add_special_tokens=A__       )
                       self.assertListEqual(A__	,  A__       )

                       shutil.rmtree(A__       )

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

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

                       _A							:  List[Any]    =		tokenizer.__class__.from_pretrained(A__       )
                       _A							:  Optional[int]    =		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							:  Dict    =		tokenizer.__class__.from_pretrained(A__	,  model_max_length=43       )
                       self.assertEqual(tokenizer.model_max_length	,  43       )

                       shutil.rmtree(A__       )

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

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

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

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

                       _A							:  Optional[Any]    =		added_tokens_extra_ids + [
                           """an_additional_special_token"""
                       ]
                       _A							:  Dict    =		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							:  List[str]    =		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							:  Tuple    =		added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""	,  lstrip=A__       )]
                       _A							:  Tuple    =		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       )		->							Optional[int]:
           _A							:  Any    =		self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178]       )	,  """�"""       )

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

     def       a__			(						self       )		->							List[str]:
           pass

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

     def       a__			(						self       )		->							Dict:
           pass



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

                       self.assertIsInstance(A__	,  A__       )










from unittest import TestCase

from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset







class 		lowercase							(     UpperCamelCase__	):

     def       a__			(						self       )		->							Optional[int]:
           return [
               {"col_1": 3, "col_2": "a"},
               {"col_1": 2, "col_2": "b"},
               {"col_1": 1, "col_2": "c"},
               {"col_1": 0, "col_2": "d"},
           ]

     def       a__			(						self       )		->							Optional[int]:
           _A							:  Union[str, Any]    =		{"""col_1""": [3, 2, 1, 0], """col_2""": ["""a""", """b""", """c""", """d"""]}
           return Dataset.from_dict(_a       )

     def       a__			(						self       )		->							Any:
           _A							:  str    =		self._create_example_records()
           _A							:  List[Any]    =		Dataset.from_list(_a       )
           self.assertListEqual(dset.column_names	,  ["""col_1""", """col_2"""]       )
           for i, r in enumerate(_a       ):
                 self.assertDictEqual(_a	,  example_records[i]       )

     def       a__			(						self       )		->							List[str]:
           _A							:  Dict    =		self._create_example_records()
           _A							:  List[str]    =		Dataset.from_list(_a       )
           _A							:  str    =		Dataset.from_dict({k: [r[k] for r in example_records] for k in example_records[0]}       )
           self.assertEqual(dset.info	,  dset_from_dict.info       )

     def       a__			(						self       )		->							str:  # checks what happens with missing columns
           _A							:  List[str]    =		[{"""col_1""": 1}, {"""col_2""": """x"""}]
           _A							:  List[str]    =		Dataset.from_list(_a       )
           self.assertDictEqual(dset[0]	,  {"""col_1""": 1}       )
           self.assertDictEqual(dset[1]	,  {"""col_1""": None}       )  # NB: first record is used for columns

     def       a__			(						self       )		->							Dict:  # checks if the type can be inferred from the second record
           _A							:  List[Any]    =		[{"""col_1""": []}, {"""col_1""": [1, 2]}]
           _A							:  str    =		Dataset.from_list(_a       )
           self.assertEqual(dset.info.features["""col_1"""]	,  Sequence(Value("""int64"""       )       )       )



     def       a__			(						self       )		->							Dict:
           _A							:  List[str]    =		Dataset.from_list([]       )
           self.assertEqual(len(_a       )	,  0       )
           self.assertListEqual(dset.column_names	,  []       )

