






'''simple docstring'''

from typing import Callable, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase							=logging.get_logger(__name__)

UpperCamelCase							={
    "microsoft/xprophetnet-large-wiki100-cased": (
        "https://huggingface.co/microsoft/xprophetnet-large-wiki100-cased/resolve/main/config.json"
    ),
}




class     A    (      SCREAMING_SNAKE_CASE__						):






 """simple docstring"""





 __a			:						List[Any]      =						'''xlm-prophetnet'''
 __a			:						int      =						['''past_key_values''']
 __a			:						str      =						{
     '''num_attention_heads''': '''num_encoder_attention_heads''',
 }
 def __init__(							self			,     __lowerCAmelCase = 0.1			,     __lowerCAmelCase = "gelu"			,     __lowerCAmelCase = 3_05_22			,     __lowerCAmelCase = 10_24			,     __lowerCAmelCase = 40_96			,     __lowerCAmelCase = 12			,     __lowerCAmelCase = 16			,     __lowerCAmelCase = 40_96			,     __lowerCAmelCase = 12			,     __lowerCAmelCase = 16			,     __lowerCAmelCase = 0.1			,     __lowerCAmelCase = 0.1			,     __lowerCAmelCase = 5_12			,     __lowerCAmelCase = 0.02			,     __lowerCAmelCase = True			,     __lowerCAmelCase = True			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = 2			,     __lowerCAmelCase = 32			,     __lowerCAmelCase = 1_28			,     __lowerCAmelCase = False			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = True			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = 1			,     __lowerCAmelCase = 2			,     **__lowerCAmelCase			,     ):
       UpperCamelCase_				:   List[Any]					    =							vocab_size
       UpperCamelCase_				:   Tuple					    =							hidden_size
       UpperCamelCase_				:   Dict					    =							encoder_ffn_dim
       UpperCamelCase_				:   Dict					    =							num_encoder_layers
       UpperCamelCase_				:   List[Any]					    =							num_encoder_attention_heads
       UpperCamelCase_				:   str					    =							decoder_ffn_dim
       UpperCamelCase_				:   Optional[int]					    =							num_decoder_layers
       UpperCamelCase_				:   Union[str, Any]					    =							num_decoder_attention_heads
       UpperCamelCase_				:   Union[str, Any]					    =							max_position_embeddings
       UpperCamelCase_				:   Dict					    =							init_std  # Normal(0, this parameter)
       UpperCamelCase_				:   Optional[Any]					    =							activation_function

       # parameters for xlmprophetnet
       UpperCamelCase_				:   str					    =							ngram
       UpperCamelCase_				:   Tuple					    =							num_buckets
       UpperCamelCase_				:   Dict					    =							relative_max_distance
       UpperCamelCase_				:   Any					    =							disable_ngram_loss
       UpperCamelCase_				:   Dict					    =							eps

       # 3 Types of Dropout
       UpperCamelCase_				:   Optional[int]					    =							attention_dropout
       UpperCamelCase_				:   List[Any]					    =							activation_dropout
       UpperCamelCase_				:   Optional[Any]					    =							dropout

       UpperCamelCase_				:   Any					    =							use_cache

       super().__init__(
           pad_token_id=__lowerCAmelCase			,     bos_token_id=__lowerCAmelCase			,     eos_token_id=__lowerCAmelCase			,     is_encoder_decoder=__lowerCAmelCase			,     add_cross_attention=__lowerCAmelCase			,     decoder_start_token_id=__lowerCAmelCase			,     **__lowerCAmelCase			,     )
 @property
 def   _UpperCAmelCase       (							self ):
       return self.num_encoder_layers + self.num_decoder_layers





 @num_hidden_layers.setter
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       raise NotImplementedError(
           """This model does not support the setting of `num_hidden_layers`. Please set `num_encoder_layers` and"""
           """ `num_decoder_layers`.""" )














'''simple docstring'''

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     A    (      SCREAMING_SNAKE_CASE__,   unittest.TestCase						):






 """simple docstring"""





 __a			:						int      =						PerceiverTokenizer
 __a			:						List[str]      =						False
 def   _UpperCAmelCase       (							self ):
       super().setUp()
       UpperCamelCase_				:   Optional[int]					    =							PerceiverTokenizer()
       tokenizer.save_pretrained(self.tmpdirname )
 @cached_property
 def   _UpperCAmelCase       (							self ):
       return PerceiverTokenizer.from_pretrained("""deepmind/language-perceiver""" )
 def   _UpperCAmelCase       (							self			,     **__lowerCAmelCase ):
       return self.tokenizer_class.from_pretrained(self.tmpdirname			,     **__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase			,     __lowerCAmelCase=False			,     __lowerCAmelCase=20			,     __lowerCAmelCase=5 ):
       # 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.

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

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

       # Ensure consistency
       UpperCamelCase_				:   str					    =							tokenizer.decode(__lowerCAmelCase			,     clean_up_tokenization_spaces=__lowerCAmelCase )
       if " " not in output_txt and len(__lowerCAmelCase ) > 1:
             UpperCamelCase_				:   Tuple					    =							(
                 tokenizer.decode([toks_ids[0]]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
                 + """ """
                 + tokenizer.decode(toks_ids[1:]			,     clean_up_tokenization_spaces=__lowerCAmelCase )
             )
       if with_prefix_space:
             UpperCamelCase_				:   Tuple					    =							""" """ + output_txt
       UpperCamelCase_				:   List[str]					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
       return output_txt, output_ids
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Dict					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[int]					    =							"""Unicode €."""
       UpperCamelCase_				:   Dict					    =							tokenizer(__lowerCAmelCase )
       UpperCamelCase_				:   Tuple					    =							[4, 91, 1_16, 1_11, 1_05, 1_17, 1_06, 1_07, 38, 2_32, 1_36, 1_78, 52, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )

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

       UpperCamelCase_				:   str					    =							tokenizer("""e è é ê ë""" )
       UpperCamelCase_				:   List[str]					    =							[4, 1_07, 38, 2_01, 1_74, 38, 2_01, 1_75, 38, 2_01, 1_76, 38, 2_01, 1_77, 5]
       self.assertEqual(encoded["""input_ids"""]			,     __lowerCAmelCase )
       # decoding
       UpperCamelCase_				:   List[str]					    =							tokenizer.decode(__lowerCAmelCase )
       self.assertEqual(__lowerCAmelCase			,     """[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 ):
       UpperCamelCase_				:   Union[str, Any]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Optional[Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       # fmt: off
       UpperCamelCase_				:   int					    =							[4, 71, 38, 1_14, 1_17, 1_16, 1_09, 38, 1_18, 1_03, 1_20, 1_03, 1_09, 1_20, 1_03, 1_18, 1_10, 38, 1_08, 1_17, 1_20, 38, 1_21, 1_23, 1_15, 1_15, 1_03, 1_20, 1_11, 1_28, 1_03, 1_22, 1_11, 1_17, 1_16, 52, 5, 0]
       # fmt: on
       UpperCamelCase_				:   Any					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )

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

       self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

       self.assertEqual((2, 38)			,     batch.input_ids.shape )
       self.assertEqual((2, 38)			,     batch.attention_mask.shape )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   Optional[int]					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   Union[str, Any]					    =							["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
       UpperCamelCase_				:   int					    =							tokenizer(__lowerCAmelCase			,     padding=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       # check if input_ids are returned and no decoder_input_ids
       self.assertIn("""input_ids"""			,     __lowerCAmelCase )
       self.assertIn("""attention_mask"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_input_ids"""			,     __lowerCAmelCase )
       self.assertNotIn("""decoder_attention_mask"""			,     __lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   int					    =							self.perceiver_tokenizer
       UpperCamelCase_				:   List[str]					    =							[
           """Summary of the text.""",
           """Another summary.""",
       ]
       UpperCamelCase_				:   Dict					    =							tokenizer(
           text_target=__lowerCAmelCase			,     max_length=32			,     padding="""max_length"""			,     truncation=__lowerCAmelCase			,     return_tensors=__lowerCAmelCase )
       self.assertEqual(32			,     targets["""input_ids"""].shape[1] )
 def   _UpperCAmelCase       (							self ):
       # safety check on max_len default value so we are sure the test works
       UpperCamelCase_				:   Tuple					    =							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_				:   Tuple					    =							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_				:   Tuple					    =							tempfile.mkdtemp()

                   UpperCamelCase_				:   Optional[int]					    =							""" He is very happy, UNwant\u00E9d,running"""
                   UpperCamelCase_				:   Dict					    =							tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   tokenizer.save_pretrained(__lowerCAmelCase )

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   Union[str, Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )

                   shutil.rmtree(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							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_				:   Tuple					    =							tempfile.mkdtemp()

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

                   UpperCamelCase_				:   Tuple					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase )
                   UpperCamelCase_				:   List[Any]					    =							after_tokenizer.encode(__lowerCAmelCase			,     add_special_tokens=__lowerCAmelCase )
                   self.assertListEqual(__lowerCAmelCase			,     __lowerCAmelCase )
                   self.assertIn("""new_additional_special_token"""			,     after_tokenizer.additional_special_tokens )
                   self.assertEqual(after_tokenizer.model_max_length			,     42 )

                   UpperCamelCase_				:   int					    =							tokenizer.__class__.from_pretrained(__lowerCAmelCase			,     model_max_length=43 )
                   self.assertEqual(tokenizer.model_max_length			,     43 )

                   shutil.rmtree(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self ):
       UpperCamelCase_				:   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(__lowerCAmelCase )

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

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

                   UpperCamelCase_				:   Tuple					    =							[F"<extra_id_{i}>" for i in range(1_25 )]

                   UpperCamelCase_				:   Union[str, Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]
                   UpperCamelCase_				:   List[Any]					    =							added_tokens_extra_ids + [
                       """an_additional_special_token"""
                   ]

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

                   # 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_				:   Optional[int]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     )
                   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_				:   Optional[Any]					    =							added_tokens_extra_ids + [AddedToken("""a_new_additional_special_token"""			,     lstrip=__lowerCAmelCase )]
                   UpperCamelCase_				:   Union[str, Any]					    =							tokenizer_class.from_pretrained(
                       __lowerCAmelCase			,     additional_special_tokens=__lowerCAmelCase			,     )

                   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 ):
       UpperCamelCase_				:   List[Any]					    =							self.perceiver_tokenizer
       self.assertEqual(tokenizer.decode([1_78] )			,     """�""" )
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass
 def   _UpperCAmelCase       (							self ):
       pass





 def   _UpperCAmelCase       (							self ):
       # The default common tokenizer tests uses invalid tokens for Perceiver that can only accept one-character
       # strings and special added tokens as tokens
       UpperCamelCase_				:   Dict					    =							self.get_tokenizers(fast=__lowerCAmelCase			,     do_lower_case=__lowerCAmelCase )
       for tokenizer in tokenizers:
             with self.subTest(F"{tokenizer.__class__.__name__}" ):
                   UpperCamelCase_				:   str					    =							["""[CLS]""", """t""", """h""", """i""", """s""", """ """, """i""", """s""", """ """, """a""", """ """, """t""", """e""", """s""", """t""", """[SEP]"""]
                   UpperCamelCase_				:   Optional[Any]					    =							tokenizer.convert_tokens_to_string(__lowerCAmelCase )

                   self.assertIsInstance(__lowerCAmelCase			,     __lowerCAmelCase )






