





import os
import shutil
import sys
import tempfile
import unittest
from pathlib import Path

import pytest

import transformers
from transformers import (
    BERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
    GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
    AutoTokenizer,
    BertConfig,
    BertTokenizer,
    BertTokenizerFast,
    CTRLTokenizer,
    GPTaTokenizer,
    GPTaTokenizerFast,
    PreTrainedTokenizerFast,
    RobertaTokenizer,
    RobertaTokenizerFast,
    is_tokenizers_available,
)
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.auto.tokenization_auto import (
    TOKENIZER_MAPPING,
    get_tokenizer_config,
    tokenizer_class_from_name,
)
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import (
    DUMMY_DIFF_TOKENIZER_IDENTIFIER,
    DUMMY_UNKNOWN_IDENTIFIER,
    SMALL_MODEL_IDENTIFIER,
    RequestCounter,
    require_tokenizers,
    slow,
)


sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


if is_tokenizers_available():
    from test_module.custom_tokenization_fast import CustomTokenizerFast



class 					A__   (       unittest.TestCase			):





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   0





      @slow
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for model_name in (x for x in BERT_PRETRAINED_CONFIG_ARCHIVE_MAP.keys() if "japanese" not in x):
        UpperCamelCase	:					int       	=   AutoTokenizer.from_pretrained(A_   )
        self.assertIsNotNone(A_   )
        self.assertIsInstance(A_       ,							(BertTokenizer, BertTokenizerFast)   )
        self.assertGreater(len(A_   )       ,							0   )

       for model_name in GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP.keys():
        UpperCamelCase	:					List[str]       	=   AutoTokenizer.from_pretrained(A_   )
        self.assertIsNotNone(A_   )
        self.assertIsInstance(A_       ,							(GPTaTokenizer, GPTaTokenizerFast)   )
        self.assertGreater(len(A_   )       ,							0   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Union[str, Any]       	=   AutoTokenizer.from_pretrained(A_   )
       self.assertIsInstance(A_       ,							(BertTokenizer, BertTokenizerFast)   )
       self.assertEqual(tokenizer.vocab_size       ,							12   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   AutoTokenizer.from_pretrained(A_   )
       self.assertIsInstance(A_       ,							(RobertaTokenizer, RobertaTokenizerFast)   )
       self.assertEqual(tokenizer.vocab_size       ,							20   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Any       	=   AutoConfig.from_pretrained(A_   )
       self.assertIsInstance(A_       ,							A_   )
       # Check that tokenizer_type ≠ model_type
       UpperCamelCase	:					Optional[Any]       	=   AutoTokenizer.from_pretrained(A_       ,							config=A_   )
       self.assertIsInstance(A_       ,							(BertTokenizer, BertTokenizerFast)   )
       self.assertEqual(tokenizer.vocab_size       ,							12   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmp_dir:
        shutil.copy("./tests/fixtures/vocab.txt"       ,							os.path.join(A_       ,							"vocab.txt"   )   )

        UpperCamelCase	:					List[Any]       	=   AutoTokenizer.from_pretrained(A_       ,							tokenizer_type="bert"       ,							use_fast=A_   )
        self.assertIsInstance(A_       ,							A_   )

       with tempfile.TemporaryDirectory() as tmp_dir:
        shutil.copy("./tests/fixtures/vocab.json"       ,							os.path.join(A_       ,							"vocab.json"   )   )
        shutil.copy("./tests/fixtures/merges.txt"       ,							os.path.join(A_       ,							"merges.txt"   )   )

        UpperCamelCase	:					int       	=   AutoTokenizer.from_pretrained(A_       ,							tokenizer_type="gpt2"       ,							use_fast=A_   )
        self.assertIsInstance(A_       ,							A_   )





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       with tempfile.TemporaryDirectory() as tmp_dir:
        shutil.copy("./tests/fixtures/vocab.txt"       ,							os.path.join(A_       ,							"vocab.txt"   )   )

        UpperCamelCase	:					int       	=   AutoTokenizer.from_pretrained(A_       ,							tokenizer_type="bert"   )
        self.assertIsInstance(A_       ,							A_   )

       with tempfile.TemporaryDirectory() as tmp_dir:
        shutil.copy("./tests/fixtures/vocab.json"       ,							os.path.join(A_       ,							"vocab.json"   )   )
        shutil.copy("./tests/fixtures/merges.txt"       ,							os.path.join(A_       ,							"merges.txt"   )   )

        UpperCamelCase	:					Optional[int]       	=   AutoTokenizer.from_pretrained(A_       ,							tokenizer_type="gpt2"   )
        self.assertIsInstance(A_       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       with pytest.raises(A_   ):
        AutoTokenizer.from_pretrained("./"       ,							tokenizer_type="xxx"   )





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
        UpperCamelCase	:					Optional[int]       	=   tokenizer_class.from_pretrained("wietsedv/bert-base-dutch-cased"   )
        self.assertIsInstance(A_       ,							(BertTokenizer, BertTokenizerFast)   )

        if isinstance(A_       ,							A_   ):
         self.assertEqual(tokenizer.basic_tokenizer.do_lower_case       ,							A_   )
        else:
         self.assertEqual(tokenizer.do_lower_case       ,							A_   )

        self.assertEqual(tokenizer.model_max_length       ,							512   )





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       for tokenizer_class in [BertTokenizer, BertTokenizerFast, AutoTokenizer]:
        with self.assertRaisesRegex(
            A_       ,							"julien-c/herlolip-not-exists is not a local folder and is not a valid model identifier"       ,							):
         UpperCamelCase	:					Any       	=   tokenizer_class.from_pretrained("julien-c/herlolip-not-exists"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Dict       	=   TOKENIZER_MAPPING.values()
       UpperCamelCase	:					Tuple       	=   []

       for slow_tok, fast_tok in tokenizers:
        if slow_tok is not None:
         tokenizer_names.append(slow_tok.__name__   )

        if fast_tok is not None:
         tokenizer_names.append(fast_tok.__name__   )

       for tokenizer_name in tokenizer_names:
        # must find the right class
        tokenizer_class_from_name(A_   )





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"       ,							use_fast=A_   )       ,							A_   )
       self.assertIsInstance(AutoTokenizer.from_pretrained("bert-base-cased"   )       ,							A_   )





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   AutoTokenizer.from_pretrained("distilbert-base-uncased"       ,							do_lower_case=A_   )
       UpperCamelCase	:					List[Any]       	=   """Hello, world. How are you?"""
       UpperCamelCase	:					Optional[int]       	=   tokenizer.tokenize(A_   )
       self.assertEqual("[UNK]"       ,							tokens[0]   )

       UpperCamelCase	:					Optional[int]       	=   AutoTokenizer.from_pretrained("microsoft/mpnet-base"       ,							do_lower_case=A_   )
       UpperCamelCase	:					int       	=   tokenizer.tokenize(A_   )
       self.assertEqual("[UNK]"       ,							tokens[0]   )





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   AutoTokenizer.from_pretrained("robot-test/dummy-tokenizer-fast-with-model-config"   )
       self.assertEqual(type(A_   )       ,							A_   )
       self.assertEqual(tokenizer.model_max_length       ,							512   )
       self.assertEqual(tokenizer.vocab_size       ,							3_0000   )
       self.assertEqual(tokenizer.unk_token       ,							"[UNK]"   )
       self.assertEqual(tokenizer.padding_side       ,							"right"   )
       self.assertEqual(tokenizer.truncation_side       ,							"right"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   AutoTokenizer.from_pretrained(A_   )
       self.assertIsInstance(A_       ,							(BertTokenizer, BertTokenizerFast)   )
       with tempfile.TemporaryDirectory() as tmp_dir:
        tokenizer.save_pretrained(A_   )
        UpperCamelCase	:					Optional[Any]       	=   AutoTokenizer.from_pretrained(A_   )

       self.assertIsInstance(A_       ,							tokenizer.__class__   )
       self.assertEqual(tokenizera.vocab_size       ,							12   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					str       	=   AutoTokenizer.from_pretrained("ctrl"   )
       # There is no fast CTRL so this always gives us a slow tokenizer.
       self.assertIsInstance(A_       ,							A_   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					Optional[int]       	=   get_tokenizer_config("bert-base-cased"   )
       UpperCamelCase	:					Any       	=   config.pop("_commit_hash"       ,							A_   )
       # If we ever update bert-base-cased tokenizer config, this dict here will need to be updated.
       self.assertEqual(A_       ,							{"do_lower_case": False}   )

       # This model does not have a tokenizer_config so we get back an empty dict.
       UpperCamelCase	:					List[str]       	=   get_tokenizer_config(A_   )
       self.assertDictEqual(A_       ,							{}   )

       # A tokenizer saved with `save_pretrained` always creates a tokenizer config.
       UpperCamelCase	:					str       	=   AutoTokenizer.from_pretrained(A_   )
       with tempfile.TemporaryDirectory() as tmp_dir:
        tokenizer.save_pretrained(A_   )
        UpperCamelCase	:					Any       	=   get_tokenizer_config(A_   )

       # Check the class of the tokenizer was properly saved (note that it always saves the slow class).
       self.assertEqual(config["tokenizer_class"]       ,							"BertTokenizer"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       try:
        AutoConfig.register("custom"       ,							A_   )

        AutoTokenizer.register(A_       ,							slow_tokenizer_class=A_   )
        # Trying to register something existing in the Transformers library will raise an error
        with self.assertRaises(A_   ):
         AutoTokenizer.register(A_       ,							slow_tokenizer_class=A_   )

        UpperCamelCase	:					List[str]       	=   CustomTokenizer.from_pretrained(A_   )
        with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(A_   )

         UpperCamelCase	:					Any       	=   AutoTokenizer.from_pretrained(A_   )
         self.assertIsInstance(A_       ,							A_   )

       finally:
        if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
        if CustomConfig in TOKENIZER_MAPPING._extra_content:
         del TOKENIZER_MAPPING._extra_content[CustomConfig]





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       try:
        AutoConfig.register("custom"       ,							A_   )

        # Can register in two steps
        AutoTokenizer.register(A_       ,							slow_tokenizer_class=A_   )
        self.assertEqual(TOKENIZER_MAPPING[CustomConfig]       ,							(CustomTokenizer, None)   )
        AutoTokenizer.register(A_       ,							fast_tokenizer_class=A_   )
        self.assertEqual(TOKENIZER_MAPPING[CustomConfig]       ,							(CustomTokenizer, CustomTokenizerFast)   )

        del TOKENIZER_MAPPING._extra_content[CustomConfig]
        # Can register in one step
        AutoTokenizer.register(
            A_       ,							slow_tokenizer_class=A_       ,							fast_tokenizer_class=A_   )
        self.assertEqual(TOKENIZER_MAPPING[CustomConfig]       ,							(CustomTokenizer, CustomTokenizerFast)   )

        # Trying to register something existing in the Transformers library will raise an error
        with self.assertRaises(A_   ):
         AutoTokenizer.register(A_       ,							fast_tokenizer_class=A_   )

        # We pass through a bert tokenizer fast cause there is no converter slow to fast for our new toknizer
        # and that model does not have a tokenizer.json
        with tempfile.TemporaryDirectory() as tmp_dir:
         UpperCamelCase	:					int       	=   BertTokenizerFast.from_pretrained(A_   )
         bert_tokenizer.save_pretrained(A_   )
         UpperCamelCase	:					int       	=   CustomTokenizerFast.from_pretrained(A_   )

        with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(A_   )

         UpperCamelCase	:					Tuple       	=   AutoTokenizer.from_pretrained(A_   )
         self.assertIsInstance(A_       ,							A_   )

         UpperCamelCase	:					Any       	=   AutoTokenizer.from_pretrained(A_       ,							use_fast=A_   )
         self.assertIsInstance(A_       ,							A_   )

       finally:
        if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
        if CustomConfig in TOKENIZER_MAPPING._extra_content:
         del TOKENIZER_MAPPING._extra_content[CustomConfig]





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       with self.assertRaises(A_   ):
        UpperCamelCase	:					Dict       	=   AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"   )
       # If remote code is disabled, we can't load this config.
       with self.assertRaises(A_   ):
        UpperCamelCase	:					Optional[Any]       	=   AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer"       ,							trust_remote_code=A_   )

       UpperCamelCase	:					Any       	=   AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"       ,							trust_remote_code=A_   )
       self.assertTrue(tokenizer.special_attribute_present   )
       # Test tokenizer can be reloaded.
       with tempfile.TemporaryDirectory() as tmp_dir:
        tokenizer.save_pretrained(A_   )
        UpperCamelCase	:					Union[str, Any]       	=   AutoTokenizer.from_pretrained(A_       ,							trust_remote_code=A_   )
       self.assertTrue(reloaded_tokenizer.special_attribute_present   )

       if is_tokenizers_available():
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizerFast"   )
        self.assertEqual(reloaded_tokenizer.__class__.__name__       ,							"NewTokenizerFast"   )

        # Test we can also load the slow version
        UpperCamelCase	:					List[Any]       	=   AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer"       ,							trust_remote_code=A_       ,							use_fast=A_   )
        self.assertTrue(tokenizer.special_attribute_present   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizer"   )
        # Test tokenizer can be reloaded.
        with tempfile.TemporaryDirectory() as tmp_dir:
         tokenizer.save_pretrained(A_   )
         UpperCamelCase	:					Tuple       	=   AutoTokenizer.from_pretrained(A_       ,							trust_remote_code=A_       ,							use_fast=A_   )
        self.assertEqual(reloaded_tokenizer.__class__.__name__       ,							"NewTokenizer"   )
        self.assertTrue(reloaded_tokenizer.special_attribute_present   )
       else:
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizer"   )
        self.assertEqual(reloaded_tokenizer.__class__.__name__       ,							"NewTokenizer"   )





      @require_tokenizers
      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       class 					A__   (       UpperCAmelCase__			):
             _UpperCAmelCase	:str						    =   False



       class 					A__   (       UpperCAmelCase__			):
             _UpperCAmelCase	:List[str]						    =   NewTokenizer
             _UpperCAmelCase	:Tuple						    =   False

       try:
        AutoConfig.register("custom"       ,							A_   )
        AutoTokenizer.register(A_       ,							slow_tokenizer_class=A_   )
        AutoTokenizer.register(A_       ,							fast_tokenizer_class=A_   )
        # If remote code is not set, the default is to use local
        UpperCamelCase	:					str       	=   AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizerFast"   )
        self.assertFalse(tokenizer.special_attribute_present   )
        UpperCamelCase	:					Optional[int]       	=   AutoTokenizer.from_pretrained("hf-internal-testing/test_dynamic_tokenizer"       ,							use_fast=A_   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizer"   )
        self.assertFalse(tokenizer.special_attribute_present   )

        # If remote code is disabled, we load the local one.
        UpperCamelCase	:					Tuple       	=   AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer"       ,							trust_remote_code=A_   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizerFast"   )
        self.assertFalse(tokenizer.special_attribute_present   )
        UpperCamelCase	:					List[str]       	=   AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer"       ,							trust_remote_code=A_       ,							use_fast=A_   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizer"   )
        self.assertFalse(tokenizer.special_attribute_present   )

        # If remote is enabled, we load from the Hub
        UpperCamelCase	:					Optional[int]       	=   AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer"       ,							trust_remote_code=A_   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizerFast"   )
        self.assertTrue(tokenizer.special_attribute_present   )
        UpperCamelCase	:					Dict       	=   AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer"       ,							trust_remote_code=A_       ,							use_fast=A_   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizer"   )
        self.assertTrue(tokenizer.special_attribute_present   )

       finally:
        if "custom" in CONFIG_MAPPING._extra_content:
         del CONFIG_MAPPING._extra_content["custom"]
        if CustomConfig in TOKENIZER_MAPPING._extra_content:
         del TOKENIZER_MAPPING._extra_content[CustomConfig]





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					List[str]       	=   AutoTokenizer.from_pretrained(
           "hf-internal-testing/test_dynamic_tokenizer_legacy"       ,							trust_remote_code=A_   )
       self.assertTrue(tokenizer.special_attribute_present   )
       if is_tokenizers_available():
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizerFast"   )

        # Test we can also load the slow version
        UpperCamelCase	:					str       	=   AutoTokenizer.from_pretrained(
            "hf-internal-testing/test_dynamic_tokenizer_legacy"       ,							trust_remote_code=A_       ,							use_fast=A_   )
        self.assertTrue(tokenizer.special_attribute_present   )
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizer"   )
       else:
        self.assertEqual(tokenizer.__class__.__name__       ,							"NewTokenizer"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           A_       ,							"bert-base is not a local folder and is not a valid model identifier"   ):
        UpperCamelCase	:					Dict       	=   AutoTokenizer.from_pretrained("bert-base"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       with self.assertRaisesRegex(
           A_       ,							R"aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"   ):
        UpperCamelCase	:					int       	=   AutoTokenizer.from_pretrained(A_       ,							revision="aaaaaa"   )





      def        __UpperCamelCase( self   ):
       '''simple docstring'''




       UpperCamelCase	:					int       	=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"   )
       with RequestCounter() as counter:
        UpperCamelCase	:					Optional[Any]       	=   AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert"   )
        self.assertEqual(counter.get_request_count       ,							0   )
        self.assertEqual(counter.head_request_count       ,							1   )
        self.assertEqual(counter.other_request_count       ,							0   )




'''simple docstring'''







import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


A__:    int					    =       logging.getLogger(__name__)







@dataclass
class A__       :
      __UpperCamelCase       :  str
      __UpperCamelCase       :  List[str]
      __UpperCamelCase       :  Optional[List[str]]







@dataclass
class A__       :
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  List[int]
      __UpperCamelCase       :  Optional[List[int]]	       =							None
      __UpperCamelCase       :  Optional[List[int]]	       =							None







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  str	       =							"train"
      __UpperCamelCase       :  Tuple	       =							"dev"
      __UpperCamelCase       :  str	       =							"test"







class A__       :






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:Dict     ,						SCREAMING_SNAKE_CASE			:Union[Split, str]	)       ->					List[InputExample]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:str	)       ->					List[str]:




             '''simple docstring'''
             raise NotImplementedError






      @staticmethod
      def __UpperCAmelCase       ( SCREAMING_SNAKE_CASE			:List[InputExample]     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:int     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:Optional[Any]="[CLS]"     ,						SCREAMING_SNAKE_CASE			:Optional[int]=1     ,						SCREAMING_SNAKE_CASE			:Any="[SEP]"     ,						SCREAMING_SNAKE_CASE			:List[Any]=False     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=False     ,						SCREAMING_SNAKE_CASE			:List[str]=0     ,						SCREAMING_SNAKE_CASE			:str=0     ,						SCREAMING_SNAKE_CASE			:Dict=-1_0_0     ,						SCREAMING_SNAKE_CASE			:Optional[int]=0     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						)       ->					List[InputFeatures]:




             '''simple docstring'''
             _a    : str     					={label: i for i, label in enumerate(SCREAMING_SNAKE_CASE	)}

             _a    : Tuple     					=[]
             for ex_index, example in enumerate(SCREAMING_SNAKE_CASE	):
                    if ex_index % 1_0_0_0_0 == 0:
                           logger.info("""Writing example %d of %d"""     ,						SCREAMING_SNAKE_CASE     ,						len(SCREAMING_SNAKE_CASE	)	)

                    _a    : Optional[Any]     					=[]
                    _a    : List[Any]     					=[]
                    for word, label in zip(example.words     ,						example.labels	):
                           _a    : Optional[int]     					=tokenizer.tokenize(SCREAMING_SNAKE_CASE	)

                           # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                           if len(SCREAMING_SNAKE_CASE	) > 0:
                                  tokens.extend(SCREAMING_SNAKE_CASE	)
                                  # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                                  label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(SCREAMING_SNAKE_CASE	) - 1)	)

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                    _a    : Optional[int]     					=tokenizer.num_special_tokens_to_add()
                    if len(SCREAMING_SNAKE_CASE	) > max_seq_length - special_tokens_count:
                           _a    : List[Any]     					=tokens[: (max_seq_length - special_tokens_count)]
                           _a    : Tuple     					=label_ids[: (max_seq_length - special_tokens_count)]

                    # The convention in BERT is:
                    # (a) For sequence pairs:
                    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                    #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                    # (b) For single sequences:
                    #  tokens:   [CLS] the dog is hairy . [SEP]
                    #  type_ids:   0   0   0   0  0     0   0
                    #
                    # Where "type_ids" are used to indicate whether this is the first
                    # sequence or the second sequence. The embedding vectors for `type=0` and
                    # `type=1` were learned during pre-training and are added to the wordpiece
                    # embedding vector (and position vector). This is not *strictly* necessary
                    # since the [SEP] token unambiguously separates the sequences, but it makes
                    # it easier for the model to learn the concept of sequences.
                    #
                    # For classification tasks, the first vector (corresponding to [CLS]) is
                    # used as the "sentence vector". Note that this only makes sense because
                    # the entire model is fine-tuned.
                    tokens += [sep_token]
                    label_ids += [pad_token_label_id]
                    if sep_token_extra:
                           # roberta uses an extra separator b/w pairs of sentences
                           tokens += [sep_token]
                           label_ids += [pad_token_label_id]
                    _a    : Dict     					=[sequence_a_segment_id] * len(SCREAMING_SNAKE_CASE	)

                    if cls_token_at_end:
                           tokens += [cls_token]
                           label_ids += [pad_token_label_id]
                           segment_ids += [cls_token_segment_id]
                    else:
                           _a    : Any     					=[cls_token] + tokens
                           _a    : Dict     					=[pad_token_label_id] + label_ids
                           _a    : Union[str, Any]     					=[cls_token_segment_id] + segment_ids

                    _a    : List[str]     					=tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE	)

                    # The mask has 1 for real tokens and 0 for padding tokens. Only real
                    # tokens are attended to.
                    _a    : Optional[int]     					=[1 if mask_padding_with_zero else 0] * len(SCREAMING_SNAKE_CASE	)

                    # Zero-pad up to the sequence length.
                    _a    : Union[str, Any]     					=max_seq_length - len(SCREAMING_SNAKE_CASE	)
                    if pad_on_left:
                           _a    : Optional[Any]     					=([pad_token] * padding_length) + input_ids
                           _a    : Optional[int]     					=([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                           _a    : Union[str, Any]     					=([pad_token_segment_id] * padding_length) + segment_ids
                           _a    : Dict     					=([pad_token_label_id] * padding_length) + label_ids
                    else:
                           input_ids += [pad_token] * padding_length
                           input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                           segment_ids += [pad_token_segment_id] * padding_length
                           label_ids += [pad_token_label_id] * padding_length

                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length
                    assert len(SCREAMING_SNAKE_CASE	) == max_seq_length

                    if ex_index < 5:
                           logger.info("""*** Example ***"""	)
                           logger.info("""guid: %s"""     ,						example.guid	)
                           logger.info("""tokens: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in tokens]	)	)
                           logger.info("""input_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in input_ids]	)	)
                           logger.info("""input_mask: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in input_mask]	)	)
                           logger.info("""segment_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in segment_ids]	)	)
                           logger.info("""label_ids: %s"""     ,						""" """.join([str(SCREAMING_SNAKE_CASE	) for x in label_ids]	)	)

                    if "token_type_ids" not in tokenizer.model_input_names:
                           _a    : Tuple     					=None

                    features.append(
                        InputFeatures(
                            input_ids=SCREAMING_SNAKE_CASE     ,						attention_mask=SCREAMING_SNAKE_CASE     ,						token_type_ids=SCREAMING_SNAKE_CASE     ,						label_ids=SCREAMING_SNAKE_CASE	)	)
             return features


if is_torch_available():
   import torch
   from torch import nn
   from torch.utils.data import Dataset







   class A__       (						UpperCAmelCase__      ):
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							nn.CrossEntropyLoss().ignore_index






         def __init__( self			:Dict     ,						SCREAMING_SNAKE_CASE			:TokenClassificationTask     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:int=False     ,						SCREAMING_SNAKE_CASE			:Split = Split.train     ,						)       ->					List[str]:




                '''simple docstring'''
                # Load data features from cache or dataset file
                _a    : Optional[Any]     					=os.path.join(
                    SCREAMING_SNAKE_CASE     ,						"""cached_{}_{}_{}""".format(mode.value     ,						tokenizer.__class__.__name__     ,						str(SCREAMING_SNAKE_CASE	)	)     ,						)

                # Make sure only the first process in distributed training processes the dataset,
                # and the others will use the cache.
                _a    : List[str]     					=cached_features_file + """.lock"""
                with FileLock(SCREAMING_SNAKE_CASE	):
                       if os.path.exists(SCREAMING_SNAKE_CASE	) and not overwrite_cache:
                              logger.info(f"Loading features from cached file {cached_features_file}"	)
                              _a    : Any     					=torch.load(SCREAMING_SNAKE_CASE	)
                       else:
                              logger.info(f"Creating features from dataset file at {data_dir}"	)
                              _a    : Any     					=token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                              # TODO clean up all this to leverage built-in features of tokenizers
                              _a    : List[str]     					=token_classification_task.convert_examples_to_features(
                                  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						cls_token_at_end=bool(model_type in ["""xlnet"""]	)     ,						cls_token=tokenizer.cls_token     ,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,						sep_token=tokenizer.sep_token     ,						sep_token_extra=SCREAMING_SNAKE_CASE     ,						pad_on_left=bool(tokenizer.padding_side == """left"""	)     ,						pad_token=tokenizer.pad_token_id     ,						pad_token_segment_id=tokenizer.pad_token_type_id     ,						pad_token_label_id=self.pad_token_label_id     ,						)
                              logger.info(f"Saving features into cached file {cached_features_file}"	)
                              torch.save(self.features     ,						SCREAMING_SNAKE_CASE	)






         def __len__( self			:Optional[int]	)       ->					Union[str, Any]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:Dict     ,						SCREAMING_SNAKE_CASE			:int	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


if is_tf_available():
   import tensorflow as tf







   class A__       :
         __UpperCamelCase       :  List[InputFeatures]
         __UpperCamelCase       :  int	       =							-100






         def __init__( self			:str     ,						SCREAMING_SNAKE_CASE			:TokenClassificationTask     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:PreTrainedTokenizer     ,						SCREAMING_SNAKE_CASE			:List[str]     ,						SCREAMING_SNAKE_CASE			:str     ,						SCREAMING_SNAKE_CASE			:Optional[int] = None     ,						SCREAMING_SNAKE_CASE			:str=False     ,						SCREAMING_SNAKE_CASE			:Split = Split.train     ,						)       ->					Any:




                '''simple docstring'''
                _a    : Tuple     					=token_classification_task.read_examples_from_file(SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE	)
                # TODO clean up all this to leverage built-in features of tokenizers
                _a    : List[Any]     					=token_classification_task.convert_examples_to_features(
                    SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE     ,						cls_token_at_end=bool(model_type in ["""xlnet"""]	)     ,						cls_token=tokenizer.cls_token     ,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0     ,						sep_token=tokenizer.sep_token     ,						sep_token_extra=SCREAMING_SNAKE_CASE     ,						pad_on_left=bool(tokenizer.padding_side == """left"""	)     ,						pad_token=tokenizer.pad_token_id     ,						pad_token_segment_id=tokenizer.pad_token_type_id     ,						pad_token_label_id=self.pad_token_label_id     ,						)

                def gen():
                       for ex in self.features:
                              if ex.token_type_ids is None:
                                     yield (
                                         {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                                         ex.label_ids,
                                     )
                              else:
                                     yield (
                                         {
                                             "input_ids": ex.input_ids,
                                             "attention_mask": ex.attention_mask,
                                             "token_type_ids": ex.token_type_ids,
                                         },
                                         ex.label_ids,
                                     )

                if "token_type_ids" not in tokenizer.model_input_names:
                       _a    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)     ,						(
                               {"""input_ids""": tf.TensorShape([None]	), """attention_mask""": tf.TensorShape([None]	)},
                               tf.TensorShape([None]	),
                           )     ,						)
                else:
                       _a    : Union[str, Any]     					=tf.data.Dataset.from_generator(
                           SCREAMING_SNAKE_CASE     ,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)     ,						(
                               {
                                   """input_ids""": tf.TensorShape([None]	),
                                   """attention_mask""": tf.TensorShape([None]	),
                                   """token_type_ids""": tf.TensorShape([None]	),
                               },
                               tf.TensorShape([None]	),
                           )     ,						)






         def __UpperCAmelCase       ( self			:Tuple	)       ->					Any:




                '''simple docstring'''
                _a    : List[Any]     					=self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features	)	)	)

                return self.dataset






         def __len__( self			:str	)       ->					Optional[int]:




                '''simple docstring'''
                return len(self.features	)






         def __getitem__( self			:int     ,						SCREAMING_SNAKE_CASE			:str	)       ->					InputFeatures:




                '''simple docstring'''
                return self.features[i]


