
'''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_funnel import FunnelTokenizer


lowercase__						       =      logging.get_logger(__name__)

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

lowercase__						       =      [
    "small",
    "small-base",
    "medium",
    "medium-base",
    "intermediate",
    "intermediate-base",
    "large",
    "large-base",
    "xlarge",
    "xlarge-base",
]

lowercase__						       =      {
    "vocab_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/vocab.txt",
        "funnel-transformer/small-base": "https://huggingface.co/funnel-transformer/small-base/resolve/main/vocab.txt",
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/vocab.txt",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/vocab.txt"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/vocab.txt"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/vocab.txt",
        "funnel-transformer/large-base": "https://huggingface.co/funnel-transformer/large-base/resolve/main/vocab.txt",
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/vocab.txt",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "funnel-transformer/small": "https://huggingface.co/funnel-transformer/small/resolve/main/tokenizer.json",
        "funnel-transformer/small-base": (
            "https://huggingface.co/funnel-transformer/small-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/medium": "https://huggingface.co/funnel-transformer/medium/resolve/main/tokenizer.json",
        "funnel-transformer/medium-base": (
            "https://huggingface.co/funnel-transformer/medium-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate": (
            "https://huggingface.co/funnel-transformer/intermediate/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/intermediate-base": (
            "https://huggingface.co/funnel-transformer/intermediate-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/large": "https://huggingface.co/funnel-transformer/large/resolve/main/tokenizer.json",
        "funnel-transformer/large-base": (
            "https://huggingface.co/funnel-transformer/large-base/resolve/main/tokenizer.json"
        ),
        "funnel-transformer/xlarge": "https://huggingface.co/funnel-transformer/xlarge/resolve/main/tokenizer.json",
        "funnel-transformer/xlarge-base": (
            "https://huggingface.co/funnel-transformer/xlarge-base/resolve/main/tokenizer.json"
        ),
    },
}
lowercase__						       =      {f"funnel-transformer/{name}": 5_1_2 for name in _model_names}
lowercase__						       =      {f"funnel-transformer/{name}": {"do_lower_case": True} for name in _model_names}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						VOCAB_FILES_NAMES
		lowerCamelCase     							=						PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase     							=						PRETRAINED_INIT_CONFIGURATION
		lowerCamelCase     							=						FunnelTokenizer
		lowerCamelCase     							=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase     							=						2



		def __init__(      self				:			Optional[int]  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			Optional[Any]=None  ,	UpperCamelCase__				:			List[str]=True  ,	UpperCamelCase__				:			Optional[int]="<unk>"  ,	UpperCamelCase__				:			Union[str, Any]="<sep>"  ,	UpperCamelCase__				:			int="<pad>"  ,	UpperCamelCase__				:			Any="<cls>"  ,	UpperCamelCase__				:			List[str]="<mask>"  ,	UpperCamelCase__				:			str="<s>"  ,	UpperCamelCase__				:			str="</s>"  ,	UpperCamelCase__				:			Optional[int]=True  ,	UpperCamelCase__				:			str=True  ,	UpperCamelCase__				:			Tuple=None  ,	UpperCamelCase__				:			str="##"  ,	**UpperCamelCase__				:			int  ,	)						->			Optional[int]:






				"""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__  ,	bos_token=UpperCamelCase__  ,	eos_token=UpperCamelCase__  ,	clean_text=UpperCamelCase__  ,	tokenize_chinese_chars=UpperCamelCase__  ,	strip_accents=UpperCamelCase__  ,	wordpieces_prefix=UpperCamelCase__  ,	**UpperCamelCase__  ,	)

				snake_case  :       Optional[Any]     			=						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  :       Any     			=						getattr(UpperCamelCase__  ,	normalizer_state.pop('''type'''      )      )
						snake_case  :       int     			=						do_lower_case
						snake_case  :       Any     			=						strip_accents
						snake_case  :       Optional[int]     			=						tokenize_chinese_chars
						snake_case  :       Optional[Any]     			=						normalizer_class(**UpperCamelCase__      )

				snake_case  :       Optional[Any]     			=						do_lower_case



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






				"""simple docstring"""







				snake_case  :       Optional[Any]     			=						[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				:			Any  ,	UpperCamelCase__				:			List[int]  ,	UpperCamelCase__				:			Optional[List[int]] = None      )						->			List[int]:






				"""simple docstring"""







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



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






				"""simple docstring"""







				snake_case  :       Union[str, Any]     			=						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      )      )


