






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():
       __magic_name__   :  str        =							'''pt'''
elif is_tf_available():
       __magic_name__   :  Dict        =							'''tf'''
else:
       __magic_name__   :  Tuple        =							'''jax'''





class 					A__     (     __snake_case   ,    unittest.TestCase ):

     '''simple docstring'''






     snake_case__					=					PerceiverTokenizer
     snake_case__					=					False







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           super().setUp()
           UpperCamelCase					       =  PerceiverTokenizer()
           tokenizer.save_pretrained(self.tmpdirname )







     @cached_property
     def   _SCREAMING_SNAKE_CASE     ( self      :    int ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int]   ,						**_SCREAMING_SNAKE_CASE      :    List[str] ):






           """simple docstring"""




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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any]   ,						_SCREAMING_SNAKE_CASE      :    Dict   ,						_SCREAMING_SNAKE_CASE      :    List[str]=False   ,						_SCREAMING_SNAKE_CASE      :    Dict=20   ,						_SCREAMING_SNAKE_CASE      :    List[str]=5 ):






           """simple docstring"""




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

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

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







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[Any] ):






           """simple docstring"""




           UpperCamelCase					       =  self.perceiver_tokenizer
           UpperCamelCase					       =  'Unicode €.'
           UpperCamelCase					       =  tokenizer(_SCREAMING_SNAKE_CASE )
           UpperCamelCase					       =  [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
           self.assertEqual(encoded['input_ids']   ,						_SCREAMING_SNAKE_CASE )

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

           UpperCamelCase					       =  tokenizer('e è é ê ë' )
           UpperCamelCase					       =  [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
           self.assertEqual(encoded['input_ids']   ,						_SCREAMING_SNAKE_CASE )
           # decoding
           UpperCamelCase					       =  tokenizer.decode(_SCREAMING_SNAKE_CASE )
           self.assertEqual(_SCREAMING_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   _SCREAMING_SNAKE_CASE     ( self      :    Any ):






           """simple docstring"""




           UpperCamelCase					       =  self.perceiver_tokenizer
           UpperCamelCase					       =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
           # fmt: off
           UpperCamelCase					       =  [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
           UpperCamelCase					       =  tokenizer(_SCREAMING_SNAKE_CASE   ,						padding=_SCREAMING_SNAKE_CASE   ,						return_tensors=_SCREAMING_SNAKE_CASE )
           self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

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

           self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

           self.assertEqual((2, 38)   ,						batch.input_ids.shape )
           self.assertEqual((2, 38)   ,						batch.attention_mask.shape )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  self.perceiver_tokenizer
           UpperCamelCase					       =  ['A long paragraph for summarization.', 'Another paragraph for summarization.']
           UpperCamelCase					       =  tokenizer(_SCREAMING_SNAKE_CASE   ,						padding=_SCREAMING_SNAKE_CASE   ,						return_tensors=_SCREAMING_SNAKE_CASE )
           # check if input_ids are returned and no decoder_input_ids
           self.assertIn('input_ids'   ,						_SCREAMING_SNAKE_CASE )
           self.assertIn('attention_mask'   ,						_SCREAMING_SNAKE_CASE )
           self.assertNotIn('decoder_input_ids'   ,						_SCREAMING_SNAKE_CASE )
           self.assertNotIn('decoder_attention_mask'   ,						_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  self.perceiver_tokenizer
           UpperCamelCase					       =  [
               'Summary of the text.',
               'Another summary.',
           ]
           UpperCamelCase					       =  tokenizer(
               text_target=_SCREAMING_SNAKE_CASE   ,						max_length=32   ,						padding='max_length'   ,						truncation=_SCREAMING_SNAKE_CASE   ,						return_tensors=_SCREAMING_SNAKE_CASE )
           self.assertEqual(32   ,						targets['input_ids'].shape[1] )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           UpperCamelCase					       =  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
           UpperCamelCase					       =  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
                       UpperCamelCase					       =  tempfile.mkdtemp()

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

                       UpperCamelCase					       =  tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  after_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
                       self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )

                       shutil.rmtree(_SCREAMING_SNAKE_CASE )

           UpperCamelCase					       =  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
                       UpperCamelCase					       =  tempfile.mkdtemp()

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

                       UpperCamelCase					       =  tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE )
                       UpperCamelCase					       =  after_tokenizer.encode(_SCREAMING_SNAKE_CASE   ,						add_special_tokens=_SCREAMING_SNAKE_CASE )
                       self.assertListEqual(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                       self.assertIn('new_additional_special_token'   ,						after_tokenizer.additional_special_tokens )
                       self.assertEqual(after_tokenizer.model_max_length   ,						42 )

                       UpperCamelCase					       =  tokenizer.__class__.from_pretrained(_SCREAMING_SNAKE_CASE   ,						model_max_length=43 )
                       self.assertEqual(tokenizer.model_max_length   ,						43 )

                       shutil.rmtree(_SCREAMING_SNAKE_CASE )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  []
           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(_SCREAMING_SNAKE_CASE )

                       with open(os.path.join(_SCREAMING_SNAKE_CASE   ,						'special_tokens_map.json' )   ,						encoding='utf-8' ) as json_file:
                             UpperCamelCase					       =  json.load(_SCREAMING_SNAKE_CASE )

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

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

                       UpperCamelCase					       =  added_tokens_extra_ids + [
                           'an_additional_special_token'
                       ]
                       UpperCamelCase					       =  added_tokens_extra_ids + [
                           'an_additional_special_token'
                       ]

                       with open(os.path.join(_SCREAMING_SNAKE_CASE   ,						'special_tokens_map.json' )   ,						'w'   ,						encoding='utf-8' ) as outfile:
                             json.dump(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )
                       with open(os.path.join(_SCREAMING_SNAKE_CASE   ,						'tokenizer_config.json' )   ,						'w'   ,						encoding='utf-8' ) as outfile:
                             json.dump(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_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
                       UpperCamelCase					       =  tokenizer_class.from_pretrained(
                           _SCREAMING_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
                       UpperCamelCase					       =  added_tokens_extra_ids + [AddedToken('a_new_additional_special_token'   ,						lstrip=_SCREAMING_SNAKE_CASE )]
                       UpperCamelCase					       =  tokenizer_class.from_pretrained(
                           _SCREAMING_SNAKE_CASE   ,						additional_special_tokens=_SCREAMING_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   _SCREAMING_SNAKE_CASE     ( self      :    Dict ):






           """simple docstring"""




           UpperCamelCase					       =  self.perceiver_tokenizer
           self.assertEqual(tokenizer.decode([178] )   ,						'�' )







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    str ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    List[Any] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Optional[int] ):






           """simple docstring"""




           pass







     def   _SCREAMING_SNAKE_CASE     ( self      :    Tuple ):






           """simple docstring"""




           UpperCamelCase					       =  self.get_tokenizers(fast=_SCREAMING_SNAKE_CASE   ,						do_lower_case=_SCREAMING_SNAKE_CASE )
           for tokenizer in tokenizers:
                 with self.subTest(f'{tokenizer.__class__.__name__}' ):
                       UpperCamelCase					       =  ['[CLS]', 't', 'h', 'i', 's', ' ', 'i', 's', ' ', 'a', ' ', 't', 'e', 's', 't', '[SEP]']
                       UpperCamelCase					       =  tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE )

                       self.assertIsInstance(_SCREAMING_SNAKE_CASE   ,						_SCREAMING_SNAKE_CASE )











import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__magic_name__   :  Optional[int]        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  r'\w+[.]\d+'
      UpperCamelCase					       =  re.findall(_UpperCamelCase       ,							_UpperCamelCase)
      for pat in pats:
            UpperCamelCase					       =  key.replace(_UpperCamelCase       ,							'_'.join(pat.split('.')))
      return key
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
      if (
          any('norm' in str_ for str_ in pt_tuple_key)
          and (pt_tuple_key[-1] == "bias")
          and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
          and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
      ):
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor
      elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('embedding',)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
            UpperCamelCase					       =  pt_tensor.transpose(2       ,							3       ,							1       ,							0)
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight":
            UpperCamelCase					       =  pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('weight',)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('bias',)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=42) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  {k: v.numpy() for k, v in pt_state_dict.items()}

      # Step 2: Since the model is stateless, get random Flax params
      UpperCamelCase					       =  flax_model.init_weights(PRNGKey(_UpperCamelCase))

      UpperCamelCase					       =  flatten_dict(_UpperCamelCase)
      UpperCamelCase					       =  {}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            UpperCamelCase					       =  rename_key(_UpperCamelCase)
            UpperCamelCase					       =  tuple(renamed_pt_key.split('.'))

            # Correctly rename weight parameters
            UpperCamelCase       ,  UpperCamelCase					       =  rename_key_and_reshape_tensor(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                            F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.')

        # also add unexpected weight so that warning is thrown
            UpperCamelCase					       =  jnp.asarray(_UpperCamelCase)

      return unflatten_dict(_UpperCamelCase)



