





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	:	List[str]		     =       """pt"""
elif is_tf_available():
     UpperCAmelCase	:	Optional[Any]		     =       """tf"""
else:
     UpperCAmelCase	:	List[str]		     =       """jax"""







class 				__lowerCAmelCase       (    _UpperCAmelCase						,				unittest.TestCase):
      _lowercase				:					Union[str, Any]						=			PerceiverTokenizer
      _lowercase				:					Dict						=			False







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        super().setUp()
        a__	:					Any										=PerceiverTokenizer()
        tokenizer.save_pretrained(self.tmpdirname							)







      @cached_property
      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      **lowerCAmelCase__							)   ->						List[str]:



        '''simple docstring'''




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







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__=False		,      lowerCAmelCase__=2_0		,      lowerCAmelCase__=5							)   ->						Optional[Any]:



        '''simple docstring'''




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

        a__	:					int										=list(filter(lambda lowerCAmelCase__							: re.match(r"^[ a-zA-Z]+$"		,      t[1]							)		,      _UpperCAmelCase							)							)
        a__	:					Tuple										=list(filter(lambda lowerCAmelCase__							: [t[0]] == tokenizer.encode(t[1]		,      add_special_tokens=_UpperCAmelCase							)		,      _UpperCAmelCase							)							)
        if max_length is not None and len(_UpperCAmelCase							) > max_length:
          a__	:					str										=toks[:max_length]
        if min_length is not None and len(_UpperCAmelCase							) < min_length and len(_UpperCAmelCase							) > 0:
          while len(_UpperCAmelCase							) < min_length:
            a__	:					List[Any]										=toks + toks
        # toks_str = [t[1] for t in toks]
        a__	:					Dict										=[t[0] for t in toks]

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







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					int										=self.perceiver_tokenizer
        a__	:					Tuple										='''Unicode €.'''
        a__	:					Optional[int]										=tokenizer(_UpperCAmelCase							)
        a__	:					List[str]										=[4, 9_1, 1_1_6, 1_1_1, 1_0_5, 1_1_7, 1_0_6, 1_0_7, 3_8, 2_3_2, 1_3_6, 1_7_8, 5_2, 5]
        self.assertEqual(encoded["input_ids"]		,      _UpperCAmelCase							)

        # decoding
        a__	:					Optional[int]										=tokenizer.decode(_UpperCAmelCase							)
        self.assertEqual(_UpperCAmelCase		,      "[CLS]Unicode €.[SEP]"							)

        a__	:					List[Any]										=tokenizer("e è é ê ë"							)
        a__	:					Optional[int]										=[4, 1_0_7, 3_8, 2_0_1, 1_7_4, 3_8, 2_0_1, 1_7_5, 3_8, 2_0_1, 1_7_6, 3_8, 2_0_1, 1_7_7, 5]
        self.assertEqual(encoded["input_ids"]		,      _UpperCAmelCase							)
        # decoding
        a__	:					List[Any]										=tokenizer.decode(_UpperCAmelCase							)
        self.assertEqual(_UpperCAmelCase		,      "[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							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.perceiver_tokenizer
        a__	:					List[str]										=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        # fmt: off
        a__	:					List[str]										=[4, 7_1, 3_8, 1_1_4, 1_1_7, 1_1_6, 1_0_9, 3_8, 1_1_8, 1_0_3, 1_2_0, 1_0_3, 1_0_9, 1_2_0, 1_0_3, 1_1_8, 1_1_0, 3_8, 1_0_8, 1_1_7, 1_2_0, 3_8, 1_2_1, 1_2_3, 1_1_5, 1_1_5, 1_0_3, 1_2_0, 1_1_1, 1_2_8, 1_0_3, 1_2_2, 1_1_1, 1_1_7, 1_1_6, 5_2, 5, 0]
        # fmt: on
        a__	:					Union[str, Any]										=tokenizer(_UpperCAmelCase		,      padding=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase							)
        self.assertIsInstance(_UpperCAmelCase		,      _UpperCAmelCase							)

        if FRAMEWORK != "jax":
          a__	:					Tuple										=list(batch.input_ids.numpy()[0]							)
        else:
          a__	:					int										=list(batch.input_ids.tolist()[0]							)

        self.assertListEqual(_UpperCAmelCase		,      _UpperCAmelCase							)

        self.assertEqual((2, 3_8)		,      batch.input_ids.shape							)
        self.assertEqual((2, 3_8)		,      batch.attention_mask.shape							)







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Optional[int]										=self.perceiver_tokenizer
        a__	:					int										=['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
        a__	:					List[str]										=tokenizer(_UpperCAmelCase		,      padding=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase							)
        # check if input_ids are returned and no decoder_input_ids
        self.assertIn("input_ids"		,      _UpperCAmelCase							)
        self.assertIn("attention_mask"		,      _UpperCAmelCase							)
        self.assertNotIn("decoder_input_ids"		,      _UpperCAmelCase							)
        self.assertNotIn("decoder_attention_mask"		,      _UpperCAmelCase							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        a__	:					Optional[Any]										=self.perceiver_tokenizer
        a__	:					Tuple										=[
            '''Summary of the text.''',
            '''Another summary.''',
        ]
        a__	:					List[Any]										=tokenizer(
            text_target=_UpperCAmelCase		,      max_length=3_2		,      padding="max_length"		,      truncation=_UpperCAmelCase		,      return_tensors=_UpperCAmelCase							)
        self.assertEqual(3_2		,      targets["input_ids"].shape[1]							)







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					Union[str, Any]										=self.get_tokenizers()
        for tokenizer in tokenizers:
          with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
            self.assertNotEqual(tokenizer.model_max_length		,      4_2							)

        # 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__	:					Dict										=tempfile.mkdtemp()

            a__	:					Dict										=''' He is very happy, UNwant\u00E9d,running'''
            a__	:					List[str]										=tokenizer.encode(_UpperCAmelCase		,      add_special_tokens=_UpperCAmelCase							)
            tokenizer.save_pretrained(_UpperCAmelCase							)

            a__	:					Dict										=tokenizer.__class__.from_pretrained(_UpperCAmelCase							)
            a__	:					Union[str, Any]										=after_tokenizer.encode(_UpperCAmelCase		,      add_special_tokens=_UpperCAmelCase							)
            self.assertListEqual(_UpperCAmelCase		,      _UpperCAmelCase							)

            shutil.rmtree(_UpperCAmelCase							)

        a__	:					Optional[Any]										=self.get_tokenizers(model_max_length=4_2							)
        for tokenizer in tokenizers:
          with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
            # Isolate this from the other tests because we save additional tokens/etc
            a__	:					str										=tempfile.mkdtemp()

            a__	:					Tuple										=''' He is very happy, UNwant\u00E9d,running'''
            tokenizer.add_tokens(["bim", "bambam"]							)
            a__	:					Dict										=tokenizer.additional_special_tokens
            additional_special_tokens.append("new_additional_special_token"							)
            tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens}							)
            a__	:					Optional[int]										=tokenizer.encode(_UpperCAmelCase		,      add_special_tokens=_UpperCAmelCase							)
            tokenizer.save_pretrained(_UpperCAmelCase							)

            a__	:					Tuple										=tokenizer.__class__.from_pretrained(_UpperCAmelCase							)
            a__	:					Union[str, Any]										=after_tokenizer.encode(_UpperCAmelCase		,      add_special_tokens=_UpperCAmelCase							)
            self.assertListEqual(_UpperCAmelCase		,      _UpperCAmelCase							)
            self.assertIn("new_additional_special_token"		,      after_tokenizer.additional_special_tokens							)
            self.assertEqual(after_tokenizer.model_max_length		,      4_2							)

            a__	:					List[str]										=tokenizer.__class__.from_pretrained(_UpperCAmelCase		,      model_max_length=4_3							)
            self.assertEqual(tokenizer.model_max_length		,      4_3							)

            shutil.rmtree(_UpperCAmelCase							)







      def    _lowercase  (    self							)   ->						List[str]:



        '''simple docstring'''




        a__	:					List[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(_UpperCAmelCase							)

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

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

            a__	:					Union[str, Any]										=[F'''<extra_id_{i}>''' for i in range(1_2_5							)]

            a__	:					Optional[Any]										=added_tokens_extra_ids + [
                '''an_additional_special_token'''
            ]
            a__	:					Optional[int]										=added_tokens_extra_ids + [
                '''an_additional_special_token'''
            ]

            with open(os.path.join(_UpperCAmelCase		,      "special_tokens_map.json"							)		,      "w"		,      encoding="utf-8"							) as outfile:
              json.dump(_UpperCAmelCase		,      _UpperCAmelCase							)
            with open(os.path.join(_UpperCAmelCase		,      "tokenizer_config.json"							)		,      "w"		,      encoding="utf-8"							) as outfile:
              json.dump(_UpperCAmelCase		,      _UpperCAmelCase							)

            # 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__	:					Dict										=tokenizer_class.from_pretrained(
                _UpperCAmelCase		,      )
            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__	:					Optional[Any]										=added_tokens_extra_ids + [AddedToken("a_new_additional_special_token"		,      lstrip=_UpperCAmelCase							)]
            a__	:					str										=tokenizer_class.from_pretrained(
                _UpperCAmelCase		,      additional_special_tokens=_UpperCAmelCase		,      )

            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							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					List[Any]										=self.perceiver_tokenizer
        self.assertEqual(tokenizer.decode([1_7_8]							)		,      "�"							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Any										=self.get_tokenizers(fast=_UpperCAmelCase		,      do_lower_case=_UpperCAmelCase							)
        for tokenizer in tokenizers:
          with self.subTest(F'''{tokenizer.__class__.__name__}'''							):
            a__	:					Union[str, Any]										=['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
            a__	:					List[Any]										=tokenizer.convert_tokens_to_string(_UpperCAmelCase							)

            self.assertIsInstance(_UpperCAmelCase		,      _UpperCAmelCase							)







"""simple docstring"""






import unittest

import numpy as np

from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
       import jax.numpy as jnp

       from transformers.models.roformer.modeling_flax_roformer import (
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerModel,
       )


class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    def __init__(   self    ,			_UpperCAmelCase    ,			_UpperCAmelCase=13    ,			_UpperCAmelCase=7    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=True    ,			_UpperCAmelCase=99    ,			_UpperCAmelCase=32    ,			_UpperCAmelCase=5    ,			_UpperCAmelCase=4    ,			_UpperCAmelCase=37    ,			_UpperCAmelCase="gelu"    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=0.1    ,			_UpperCAmelCase=512    ,			_UpperCAmelCase=16    ,			_UpperCAmelCase=2    ,			_UpperCAmelCase=0.02    ,			_UpperCAmelCase=4    ,			):
       lowercase__:   Dict					=      parent
       lowercase__:   List[str]					=      batch_size
       lowercase__:   Optional[Any]					=      seq_length
       lowercase__:   List[Any]					=      is_training
       lowercase__:   int					=      use_attention_mask
       lowercase__:   Tuple					=      use_token_type_ids
       lowercase__:   Union[str, Any]					=      use_labels
       lowercase__:   str					=      vocab_size
       lowercase__:   str					=      hidden_size
       lowercase__:   str					=      num_hidden_layers
       lowercase__:   Optional[int]					=      num_attention_heads
       lowercase__:   List[str]					=      intermediate_size
       lowercase__:   List[str]					=      hidden_act
       lowercase__:   Tuple					=      hidden_dropout_prob
       lowercase__:   int					=      attention_probs_dropout_prob
       lowercase__:   int					=      max_position_embeddings
       lowercase__:   Union[str, Any]					=      type_vocab_size
       lowercase__:   List[Any]					=      type_sequence_label_size
       lowercase__:   Any					=      initializer_range
       lowercase__:   str					=      num_choices






    def _snake_case			(   self							):
       lowercase__:   int					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.vocab_size							)

       lowercase__:   List[Any]					=      None
       if self.use_attention_mask:
          lowercase__:   Tuple					=      random_attention_mask([self.batch_size, self.seq_length]							)

       lowercase__:   List[Any]					=      None
       if self.use_token_type_ids:
          lowercase__:   str					=      ids_tensor([self.batch_size, self.seq_length]    ,			self.type_vocab_size							)

       lowercase__:   Optional[int]					=      RoFormerConfig(
           vocab_size=self.vocab_size    ,			hidden_size=self.hidden_size    ,			num_hidden_layers=self.num_hidden_layers    ,			num_attention_heads=self.num_attention_heads    ,			intermediate_size=self.intermediate_size    ,			hidden_act=self.hidden_act    ,			hidden_dropout_prob=self.hidden_dropout_prob    ,			attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,			max_position_embeddings=self.max_position_embeddings    ,			type_vocab_size=self.type_vocab_size    ,			is_decoder=_UpperCAmelCase    ,			initializer_range=self.initializer_range    ,			)

       return config, input_ids, token_type_ids, attention_mask






    def _snake_case			(   self							):
       lowercase__:   str					=      self.prepare_config_and_inputs()
       lowercase__, lowercase__, lowercase__, lowercase__:   Optional[Any]					=      config_and_inputs
       lowercase__:   Dict					=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
       return config, inputs_dict



@require_flax
class     UpperCAmelCase						(_UpperCAmelCase    ,unittest.TestCase  ):



    """simple docstring"""

    _UpperCAmelCase	:List[str]						    = True

    _UpperCAmelCase	:Union[str, Any]						    = (
        (
            FlaxRoFormerModel,
            FlaxRoFormerForMaskedLM,
            FlaxRoFormerForSequenceClassification,
            FlaxRoFormerForTokenClassification,
            FlaxRoFormerForMultipleChoice,
            FlaxRoFormerForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )






    def _snake_case			(   self							):
       lowercase__:   str					=      FlaxRoFormerModelTester(self							)






    @slow
    def _snake_case			(   self							):
       for model_class_name in self.all_model_classes:
          lowercase__:   Dict					=      model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''    ,			from_pt=_UpperCAmelCase							)
          lowercase__:   int					=      model(np.ones((1, 1)							)							)
          self.assertIsNotNone(_UpperCAmelCase							)



@require_flax
class     UpperCAmelCase						(unittest.TestCase  ):



    """simple docstring"""

    @slow
    def _snake_case			(   self							):
       lowercase__:   Any					=      FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''							)
       lowercase__:   Optional[int]					=      jnp.array([[0, 1, 2, 3, 4, 5]]							)
       lowercase__:   List[Any]					=      model(_UpperCAmelCase							)[0]

       lowercase__:   str					=      50000

       lowercase__:   Tuple					=      (1, 6, vocab_size)
       self.assertEqual(output.shape    ,			_UpperCAmelCase							)

       lowercase__:   List[Any]					=      jnp.array(
           [[[-0.1_205, -1.0_265, 0.2_922], [-1.5_134, 0.1_974, 0.1_519], [-5.0_135, -3.9_003, -0.8_404]]]							)

       self.assertTrue(jnp.allclose(output[:, :3, :3]    ,			_UpperCAmelCase    ,			atol=1e-4							)							)
