
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


__lowerCamelCase							=       logging.get_logger(__name__)

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

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

__lowerCamelCase							=       {
    """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"""
        ),
    },
}
__lowerCamelCase							=       {F'funnel-transformer/{name}': 5_12 for name in _model_names}
__lowerCamelCase							=       {F'funnel-transformer/{name}': {"""do_lower_case""": True} for name in _model_names}






class   UpperCAmelCase		(						SCREAMING_SNAKE_CASE__ ):
   A__					:		List[str]						     =     VOCAB_FILES_NAMES
   A__					:		Union[str, Any]						     =     PRETRAINED_VOCAB_FILES_MAP
   A__					:		str						     =     PRETRAINED_INIT_CONFIGURATION
   A__					:		Tuple						     =     FunnelTokenizer
   A__					:		str						     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   A__					:		int						     =     2



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


          '''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__    ,						bos_token=lowerCamelCase__    ,						eos_token=lowerCamelCase__    ,						clean_text=lowerCamelCase__    ,						tokenize_chinese_chars=lowerCamelCase__    ,						strip_accents=lowerCamelCase__    ,						wordpieces_prefix=lowerCamelCase__    ,						**lowerCamelCase__    ,						)

          snake_case       :   List[str]							=      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       :   Optional[Any]							=      getattr(lowerCamelCase__    ,						normalizer_state.pop("type" ) )
                 snake_case       :   Union[str, Any]							=      do_lower_case
                 snake_case       :   List[Any]							=      strip_accents
                 snake_case       :   Optional[int]							=      tokenize_chinese_chars
                 snake_case       :   Dict							=      normalizer_class(**lowerCamelCase__ )

          snake_case       :   List[Any]							=      do_lower_case



   def 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					Union[str, Any]    ,						snake_case__		:					List[str]=None )		->	str:


          '''simple docstring'''


          snake_case       :   Union[str, 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 				_SCREAMING_SNAKE_CASE   (self		:					Tuple    ,						snake_case__		:					int    ,						snake_case__		:					List[Any] = None )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Any							=      [self.sep_token_id]
          snake_case       :   Any							=      [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 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any]    ,						snake_case__		:					Tuple    ,						snake_case__		:					List[Any] = None )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   int							=      self._tokenizer.model.save(lowerCamelCase__    ,						name=lowerCamelCase__ )
          return tuple(lowerCamelCase__ )










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 tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






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




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


