"""simple docstring"""
import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer


SCREAMING_SNAKE_CASE__   =					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =					{"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =					{
    "vocab_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"
        ),
        "squeezebert/squeezebert-mnli": "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt",
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "squeezebert/squeezebert-uncased": (
            "https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli": (
            "https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"
        ),
        "squeezebert/squeezebert-mnli-headless": (
            "https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE__   =					{
    "squeezebert/squeezebert-uncased": 512,
    "squeezebert/squeezebert-mnli": 512,
    "squeezebert/squeezebert-mnli-headless": 512,
}


SCREAMING_SNAKE_CASE__   =					{
    "squeezebert/squeezebert-uncased": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli": {"do_lower_case": True},
    "squeezebert/squeezebert-mnli-headless": {"do_lower_case": True},
}



class 					lowerCAmelCase_      (     lowerCAmelCase   ):



				"""simple docstring"""
				_lowerCAmelCase       :      Union[str, Any]     					=  VOCAB_FILES_NAMES
				_lowerCAmelCase       :      Optional[Any]     					=  PRETRAINED_VOCAB_FILES_MAP
				_lowerCAmelCase       :      List[str]     					=  PRETRAINED_INIT_CONFIGURATION
				_lowerCAmelCase       :      Dict     					=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				_lowerCAmelCase       :      Tuple     					=  SqueezeBertTokenizer

				def __init__(			self						,      lowerCAmelCase=None						,      lowerCAmelCase=None						,      lowerCAmelCase=True						,      lowerCAmelCase="[UNK]"						,      lowerCAmelCase="[SEP]"						,      lowerCAmelCase="[PAD]"						,      lowerCAmelCase="[CLS]"						,      lowerCAmelCase="[MASK]"						,      lowerCAmelCase=True						,      lowerCAmelCase=None						,      **lowerCAmelCase						,      ):






										"""simple docstring"""

										super().__init__(
										    lowerCAmelCase						,      tokenizer_file=lowerCAmelCase						,      do_lower_case=lowerCAmelCase						,      unk_token=lowerCAmelCase						,      sep_token=lowerCAmelCase						,      pad_token=lowerCAmelCase						,      cls_token=lowerCAmelCase						,      mask_token=lowerCAmelCase						,      tokenize_chinese_chars=lowerCAmelCase						,      strip_accents=lowerCAmelCase						,      **lowerCAmelCase						,      )

										snake_case        =     json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
										if (
										    normalizer_state.get('lowercase'						,      lowerCAmelCase      ) != do_lower_case
										    or normalizer_state.get('strip_accents'						,      lowerCAmelCase      ) != strip_accents
										    or normalizer_state.get('handle_chinese_chars'						,      lowerCAmelCase      ) != tokenize_chinese_chars
										):
																snake_case        =     getattr(lowerCAmelCase						,      normalizer_state.pop('type'      )      )
																snake_case        =     do_lower_case
																snake_case        =     strip_accents
																snake_case        =     tokenize_chinese_chars
																snake_case        =     normalizer_class(**lowerCAmelCase      )

										snake_case        =     do_lower_case

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase=None      ):






										"""simple docstring"""

										snake_case        =     [self.cls_token_id] + token_ids_a + [self.sep_token_id]

										if token_ids_a:
																output += token_ids_a + [self.sep_token_id]

										return output

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										snake_case        =     [self.sep_token_id]
										snake_case        =     [self.cls_token_id]
										if token_ids_a is None:
																return len(cls + token_ids_a + sep      ) * [0]
										return len(cls + token_ids_a + sep      ) * [0] + len(token_ids_a + sep      ) * [1]

				def 						snake_case	(			self						,      lowerCAmelCase						,      lowerCAmelCase = None      ):






										"""simple docstring"""

										snake_case        =     self._tokenizer.model.save(lowerCAmelCase						,      name=lowerCAmelCase      )
										return tuple(lowerCAmelCase      )



"""simple docstring"""
import os
import sys
import unittest


SCREAMING_SNAKE_CASE__   =					os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
SCREAMING_SNAKE_CASE__   =					os.path.join(git_repo_path, "src", "transformers")

SCREAMING_SNAKE_CASE__   =					"\n{0} = None\n"

SCREAMING_SNAKE_CASE__   =					"\nclass {0}(metaclass=DummyObject):\n    _backends = {1}\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, {1})\n"


SCREAMING_SNAKE_CASE__   =					"\ndef {0}(*args, **kwargs):\n    requires_backends({0}, {1})\n"



class 					lowerCAmelCase_      (     unittest.TestCase   ):



				"""simple docstring"""

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     find_backend('    _import_structure["models.albert"].append("AlbertTokenizerFast")'      )
										self.assertIsNone(lowerCAmelCase      )

										snake_case        =     find_backend('    if not is_tokenizers_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tokenizers'      )

										snake_case        =     find_backend('    if not is_tensorflow_text_available():'      )
										self.assertEqual(lowerCAmelCase						,      'tensorflow_text'      )

										snake_case        =     find_backend('    if not (is_sentencepiece_available() and is_tokenizers_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tensorflow_text_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tensorflow_text'      )

										snake_case        =     find_backend(
										    '    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):'      )
										self.assertEqual(lowerCAmelCase						,      'sentencepiece_and_tokenizers_and_vision'      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     read_init()
										# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
										self.assertIn('torch'						,      lowerCAmelCase      )
										self.assertIn('tensorflow_text'						,      lowerCAmelCase      )
										self.assertIn('sentencepiece_and_tokenizers'						,      lowerCAmelCase      )

										# Likewise, we can't assert on the exact content of a key
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertModel'						,      objects['tf']      )
										self.assertIn('FlaxBertModel'						,      objects['flax']      )
										self.assertIn('BertModel'						,      objects['torch']      )
										self.assertIn('TFBertTokenizer'						,      objects['tensorflow_text']      )
										self.assertIn('convert_slow_tokenizer'						,      objects['sentencepiece_and_tokenizers']      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     create_dummy_object('CONSTANT'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      '\nCONSTANT = None\n'      )

										snake_case        =     create_dummy_object('function'						,      '\'torch\''      )
										self.assertEqual(
										    lowerCAmelCase						,      '\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n'      )

										snake_case        =     '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
										snake_case        =     create_dummy_object('FakeClass'						,      '\'torch\''      )
										self.assertEqual(lowerCAmelCase						,      lowerCAmelCase      )

				def 						snake_case	(			self      ):






										"""simple docstring"""

										snake_case        =     '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
										snake_case        =     create_dummy_files({'torch': ['CONSTANT', 'function', 'FakeClass']}      )
										self.assertEqual(dummy_files['torch']						,      lowerCAmelCase      )


