



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



class   lowercase_   (  __lowercase       ,							unittest.TestCase					):
      UpperCamelCase_      :		Any										=    PerceiverTokenizer
      UpperCamelCase_      :		str										=    False



      def 						UpperCamelCase_		(      self				:    str		)   ->					List[Any]:
        super().setUp()
        _snake_case						=							PerceiverTokenizer()
        tokenizer.save_pretrained(self.tmpdirname		)



      @cached_property
      def 						UpperCamelCase_		(      self				:    Union[str, Any]		)   ->					List[Any]:
        return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , **A__				:    Union[str, Any]		)   ->					PerceiverTokenizer:
        return self.tokenizer_class.from_pretrained(self.tmpdirname      , **A__		)



      def 						UpperCamelCase_		(      self				:    Optional[int]      , A__				:    Union[str, Any]      , A__				:    Any=False      , A__				:    int=20      , A__				:    Tuple=5		)   ->					Tuple[str, list]:
        # 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.

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

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

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



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

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

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

        if FRAMEWORK != "jax":
          _snake_case						=							list(batch.input_ids.numpy()[0]		)
        else:
          _snake_case						=							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 						UpperCamelCase_		(      self				:    str		)   ->					Dict:
        _snake_case						=							self.perceiver_tokenizer
        _snake_case						=							['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        _snake_case						=							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 						UpperCamelCase_		(      self				:    Optional[int]		)   ->					Union[str, Any]:
        _snake_case						=							self.perceiver_tokenizer
        _snake_case						=							[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        _snake_case						=							tokenizer(
            text_target=A__      , max_length=32      , padding='''max_length'''      , truncation=A__      , return_tensors=A__		)
        self.assertEqual(32      , targets['''input_ids'''].shape[1]		)



      def 						UpperCamelCase_		(      self				:    int		)   ->					Optional[int]:
        # safety check on max_len default value so we are sure the test works
        _snake_case						=							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
        _snake_case						=							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
            _snake_case						=							tempfile.mkdtemp()

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

            _snake_case						=							tokenizer.__class__.from_pretrained(A__		)
            _snake_case						=							after_tokenizer.encode(A__      , add_special_tokens=A__		)
            self.assertListEqual(A__      , A__		)

            shutil.rmtree(A__		)

        _snake_case						=							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
            _snake_case						=							tempfile.mkdtemp()

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

            _snake_case						=							tokenizer.__class__.from_pretrained(A__		)
            _snake_case						=							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		)

            _snake_case						=							tokenizer.__class__.from_pretrained(A__      , model_max_length=43		)
            self.assertEqual(tokenizer.model_max_length      , 43		)

            shutil.rmtree(A__		)



      def 						UpperCamelCase_		(      self				:    Optional[Any]		)   ->					Tuple:
        _snake_case						=							[]
        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:
              _snake_case						=							json.load(A__		)

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

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

            _snake_case						=							added_tokens_extra_ids + [
                '''an_additional_special_token'''
            ]
            _snake_case						=							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
            _snake_case						=							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
            _snake_case						=							added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''      , lstrip=A__		)]
            _snake_case						=							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 						UpperCamelCase_		(      self				:    int		)   ->					Tuple:
        _snake_case						=							self.perceiver_tokenizer
        self.assertEqual(tokenizer.decode([178]		)      , '''�'''		)



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



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



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



      def 						UpperCamelCase_		(      self				:    Any		)   ->					str:
        pass







      def 						UpperCamelCase_		(      self				:    Tuple		)   ->					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
        _snake_case						=							self.get_tokenizers(fast=A__      , do_lower_case=A__		)
        for tokenizer in tokenizers:
          with self.subTest(f"""{tokenizer.__class__.__name__}"""		):
            _snake_case						=							['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
            _snake_case						=							tokenizer.convert_tokens_to_string(A__		)

            self.assertIsInstance(A__      , A__		)




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__A      		=     {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MraConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 __A      		=     [
     '''MRA_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MraForMaskedLM''',
     '''MraForMultipleChoice''',
     '''MraForQuestionAnswering''',
     '''MraForSequenceClassification''',
     '''MraForTokenClassification''',
     '''MraLayer''',
     '''MraModel''',
     '''MraPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_mra import (
      MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
      MraForMaskedLM,
      MraForMultipleChoice,
      MraForQuestionAnswering,
      MraForSequenceClassification,
      MraForTokenClassification,
      MraLayer,
      MraModel,
      MraPreTrainedModel,
  )
else:
 import sys

 __A      		=     _LazyModule(__name__, globals()['''__file__'''], _import_structure)

