
'''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


lowercase__						       =      logging.get_logger(__name__)

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

lowercase__						       =      {
    "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"
        ),
    },
}

lowercase__						       =      {
    "squeezebert/squeezebert-uncased": 5_1_2,
    "squeezebert/squeezebert-mnli": 5_1_2,
    "squeezebert/squeezebert-mnli-headless": 5_1_2,
}


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






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						VOCAB_FILES_NAMES
		lowerCamelCase     							=						PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase     							=						PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase     							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase     							=						SqueezeBertTokenizer



		def __init__(      self				:			Optional[Any]  ,	UpperCamelCase__				:			int=None  ,	UpperCamelCase__				:			Union[str, Any]=None  ,	UpperCamelCase__				:			int=True  ,	UpperCamelCase__				:			Optional[Any]="[UNK]"  ,	UpperCamelCase__				:			Optional[int]="[SEP]"  ,	UpperCamelCase__				:			str="[PAD]"  ,	UpperCamelCase__				:			int="[CLS]"  ,	UpperCamelCase__				:			List[Any]="[MASK]"  ,	UpperCamelCase__				:			str=True  ,	UpperCamelCase__				:			Optional[int]=None  ,	**UpperCamelCase__				:			Optional[int]  ,	)						->			str:






				"""simple docstring"""







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

				snake_case  :       Tuple     			=						json.loads(self.backend_tokenizer.normalizer.__getstate__()      )
				if (
				    normalizer_state.get('''lowercase'''  ,	UpperCamelCase__      ) != do_lower_case
				    or normalizer_state.get('''strip_accents'''  ,	UpperCamelCase__      ) != strip_accents
				    or normalizer_state.get('''handle_chinese_chars'''  ,	UpperCamelCase__      ) != tokenize_chinese_chars
				):
						snake_case  :       Union[str, Any]     			=						getattr(UpperCamelCase__  ,	normalizer_state.pop('''type'''      )      )
						snake_case  :       Tuple     			=						do_lower_case
						snake_case  :       Optional[int]     			=						strip_accents
						snake_case  :       Any     			=						tokenize_chinese_chars
						snake_case  :       List[str]     			=						normalizer_class(**UpperCamelCase__      )

				snake_case  :       Dict     			=						do_lower_case



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			List[str]  ,	UpperCamelCase__				:			int=None      )						->			Any:






				"""simple docstring"""







				snake_case  :       str     			=						[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       lowerCAmelCase				(      self				:			List[str]  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None      )						->			List[int]:






				"""simple docstring"""







				snake_case  :       Dict     			=						[self.sep_token_id]
				snake_case  :       str     			=						[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       lowerCAmelCase				(      self				:			Dict  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[str] = None      )						->			Tuple[str]:






				"""simple docstring"""







				snake_case  :       Tuple     			=						self._tokenizer.model.save(UpperCamelCase__  ,	name=UpperCamelCase__      )
				return tuple(UpperCamelCase__      )




'''simple docstring'''
from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
						import numpy as np
						import tensorflow as tf

						from transformers import TFCamembertModel






@require_tf
@require_sentencepiece
@require_tokenizers
class 	snake_case__       (      unittest.TestCase					):







		"""simple docstring"""

		@slow
		def       lowerCAmelCase				(      self				:			Optional[Any]      )						->			Optional[Any]:






				"""simple docstring"""







				snake_case  :       Any     			=						TFCamembertModel.from_pretrained('''jplu/tf-camembert-base'''      )

				snake_case  :       List[Any]     			=						tf.convert_to_tensor(
				    [[5, 121, 11, 660, 16, 730, 2_5543, 110, 83, 6]]  ,	dtype=tf.intaa  ,	)  # J'aime le camembert !"

				snake_case  :       Any     			=						model(UpperCamelCase__      )['''last_hidden_state''']
				snake_case  :       int     			=						tf.TensorShape((1, 10, 768)      )
				self.assertEqual(output.shape  ,	UpperCamelCase__      )
				# compare the actual values for a slice.
				snake_case  :       Dict     			=						tf.convert_to_tensor(
				    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]]  ,	dtype=tf.floataa  ,	)
				# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
				# camembert.eval()
				# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

				self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,	expected_slice.numpy()  ,	atol=1e-4      )      )


