




'''simple docstring'''





import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_ta import TaTokenizer
else:
			__SCREAMING_SNAKE_CASE 						=None


__SCREAMING_SNAKE_CASE 						=logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE 						={"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}

__SCREAMING_SNAKE_CASE 						={
    """vocab_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
    },
    """tokenizer_file""": {
        """t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
        """t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
        """t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
        """t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
        """t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__SCREAMING_SNAKE_CASE 						={
    """t5-small""": 512,
    """t5-base""": 512,
    """t5-large""": 512,
    """t5-3b""": 512,
    """t5-11b""": 512,
}






class  __magic_name__						(  __UpperCAmelCase):
				'''simple docstring'''







				SCREAMING_SNAKE_CASE__					:							Dict	      =					VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE__					:							List[Any]	      =					PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE__					:							Union[str, Any]	      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE__					:							Optional[Any]	      =					["input_ids", "attention_mask"]
				SCREAMING_SNAKE_CASE__					:							Any	      =					TaTokenizer

				SCREAMING_SNAKE_CASE__					:							List[int]	      =					[]


				def __init__(							self:   Dict		,					_lowerCamelCase:   Dict=None		,					_lowerCamelCase:   Dict=None		,					_lowerCamelCase:   Optional[int]="</s>"		,					_lowerCamelCase:   Any="<unk>"		,					_lowerCamelCase:   Optional[Any]="<pad>"		,					_lowerCamelCase:   Tuple=1_00		,					_lowerCamelCase:   Tuple=None		,					**_lowerCamelCase:   str		,					):
											# Add extra_ids to the special token list
											if extra_ids > 0 and additional_special_tokens is None:
																		SCREAMING_SNAKE_CASE_   	=    [f"<extra_id_{i}>" for i in range(_lowerCamelCase		)]
											elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra special tokens
																		SCREAMING_SNAKE_CASE_   	=    len(set(filter(lambda _lowerCamelCase		: bool('''extra_id_''' in str(_lowerCamelCase		)		)		,					_lowerCamelCase		)		)		)
																		if extra_tokens != extra_ids:
																									raise ValueError(
																									    f"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
																									    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																									    ''' tokens'''		)

											super().__init__(
											    _lowerCamelCase		,					tokenizer_file=_lowerCamelCase		,					eos_token=_lowerCamelCase		,					unk_token=_lowerCamelCase		,					pad_token=_lowerCamelCase		,					extra_ids=_lowerCamelCase		,					additional_special_tokens=_lowerCamelCase		,					**_lowerCamelCase		,					)

											SCREAMING_SNAKE_CASE_   	=    vocab_file
											SCREAMING_SNAKE_CASE_   	=    False if not self.vocab_file else True
											SCREAMING_SNAKE_CASE_   	=    extra_ids


				@staticmethod
				def     _A							(							_lowerCamelCase:   Dict		,					_lowerCamelCase:   Optional[int]		,					_lowerCamelCase:   int		):
											if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																		SCREAMING_SNAKE_CASE_   	=    TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																		if init_max_model_length is not None and init_max_model_length != max_model_length:
																									return init_max_model_length
																		elif init_max_model_length is None:
																									warnings.warn(
																									    '''This tokenizer was incorrectly instantiated with a model max length of'''
																									    f" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
																									    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																									    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																									    f" {pretrained_model_name_or_path} automatically truncating your input to"
																									    f" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
																									    f" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
																									    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																									    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''		,					_lowerCamelCase		,					)

											return max_model_length


				def     _A							(							self:   Dict		,					_lowerCamelCase:   str		,					_lowerCamelCase:   Optional[str] = None		):
											if not self.can_save_slow_tokenizer:
																		raise ValueError(
																		    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
																		    '''tokenizer.'''		)

											if not os.path.isdir(_lowerCamelCase		):
																		logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
																		return
											SCREAMING_SNAKE_CASE_   	=    os.path.join(
											    _lowerCamelCase		,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

											if os.path.abspath(self.vocab_file		) != os.path.abspath(_lowerCamelCase		):
																		copyfile(self.vocab_file		,					_lowerCamelCase		)
																		logger.info(f"Copy vocab file to {out_vocab_file}"		)

											return (out_vocab_file,)


				def     _A							(							self:   Optional[Any]		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		):
											SCREAMING_SNAKE_CASE_   	=    token_ids_a + [self.eos_token_id]
											if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a
											else:
																		SCREAMING_SNAKE_CASE_   	=    token_ids_a + [self.eos_token_id]
																		return self.prefix_tokens + token_ids_a + token_ids_a


				def     _A							(							self:   str		,					_lowerCamelCase:   List[int]		,					_lowerCamelCase:   Optional[List[int]] = None		):
											SCREAMING_SNAKE_CASE_   	=    [self.eos_token_id]

											if token_ids_a is None:
																		return len(token_ids_a + eos		) * [0]
											return len(token_ids_a + eos + token_ids_a + eos		) * [0]


				def     _A							(							self:   Union[str, Any]		):
											return list(
											    set(filter(lambda _lowerCamelCase		: bool(re.search(R'''<extra_id_\d+>'''		,					_lowerCamelCase		)		) is not None		,					self.additional_special_tokens		)		)		)





				def     _A							(							self:   Any		):
											return [self.convert_tokens_to_ids(_lowerCamelCase		) for token in self.get_sentinel_tokens()]








import requests

def 			a   (_lowerCAmelCase  ,    _lowerCAmelCase     ):
							SCREAMING_SNAKE_CASE_   	=    {'''Content-Type''': '''application/json'''}
							SCREAMING_SNAKE_CASE_   	=    requests.post(_lowerCAmelCase  ,    json={'''text''': message_body}  ,    headers=_lowerCAmelCase     )
							if response.status_code != 2_0_0:
														SCREAMING_SNAKE_CASE_   	=    (
														    '''Request to slack returned an error '''
														    F"{response.status_code}, the response is:\n{response.text}"
														)
														raise ValueError(_lowerCAmelCase     )


if __name__ == "__main__":
			# Set the slack url to the one provided by Slack when you create the webhook at
			# https://my.slack.com/services/new/incoming-webhook/
			send_slack_message("""<YOUR MESSAGE BODY>""", """<SLACK CHANNEL URL>""")


