



'''simple docstring'''

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
	from transformers.pipelines.conversational import Conversation


snake_case_			=     logging.get_logger(__name__)

snake_case_			=     {"""tokenizer_file""": """tokenizer.json"""}

snake_case_			=     {
    """tokenizer_file""": {
        """bigscience/tokenizer""": """https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json""",
        """bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json""",
        """bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json""",
        """bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json""",
        """bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json""",
        """bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json""",
        """bigscience/bloom""": """https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json""",
    },
}


class 	a__     (				_lowercase	):
	__magic_name__		:    Dict	    =     VOCAB_FILES_NAMES
	__magic_name__		:    Union[str, Any]	    =     PRETRAINED_VOCAB_FILES_MAP
	__magic_name__		:    str	    =     ["input_ids", "attention_mask"]
	__magic_name__		:    str	    =     None


	def __init__(self : Optional[Any],					__UpperCAmelCase : Tuple=None,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : Tuple="<unk>",					__UpperCAmelCase : List[str]="<s>",					__UpperCAmelCase : Optional[Any]="</s>",					__UpperCAmelCase : int="<pad>",					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : int=False,					**__UpperCAmelCase : Tuple,					)     ->			List[str]:

					"""simple docstring"""

					super().__init__(
					    __UpperCAmelCase,					__UpperCAmelCase,					tokenizer_file=__UpperCAmelCase,					unk_token=__UpperCAmelCase,					bos_token=__UpperCAmelCase,					eos_token=__UpperCAmelCase,					pad_token=__UpperCAmelCase,					add_prefix_space=__UpperCAmelCase,					clean_up_tokenization_spaces=__UpperCAmelCase,					**__UpperCAmelCase,					)
					SCREAMING_SNAKE_CASE  :						str      					= json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()      )
					if pre_tok_state.get('''add_prefix_space''',					__UpperCAmelCase      ) != add_prefix_space:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= getattr(__UpperCAmelCase,					pre_tok_state.pop('''type'''      )      )
									SCREAMING_SNAKE_CASE  :						int      					= add_prefix_space
									SCREAMING_SNAKE_CASE  :						int      					= pre_tok_class(**__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= add_prefix_space


	def 				lowercase__  (self : Any,					*__UpperCAmelCase : List[Any],					**__UpperCAmelCase : Any      )     ->			BatchEncoding:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= kwargs.get('''is_split_into_words''',					__UpperCAmelCase      )
					if not (self.add_prefix_space or not is_split_into_words):
									raise Exception(
									    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
									    ''' pretokenized inputs.'''      )

					return super()._batch_encode_plus(*__UpperCAmelCase,					**__UpperCAmelCase      )


	def 				lowercase__  (self : List[Any],					*__UpperCAmelCase : str,					**__UpperCAmelCase : Optional[Any]      )     ->			BatchEncoding:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= kwargs.get('''is_split_into_words''',					__UpperCAmelCase      )

					if not (self.add_prefix_space or not is_split_into_words):
									raise Exception(
									    F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with'''
									    ''' pretokenized inputs.'''      )

					return super()._encode_plus(*__UpperCAmelCase,					**__UpperCAmelCase      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Tuple      					= self._tokenizer.model.save(__UpperCAmelCase,					name=__UpperCAmelCase      )
					return tuple(__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : "Conversation"      )     ->			List[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Any      					= []
					for is_user, text in conversation.iter_texts():
									input_ids.extend(self.encode(__UpperCAmelCase,					add_special_tokens=__UpperCAmelCase      ) + [self.eos_token_id]      )

					if len(__UpperCAmelCase      ) > self.model_max_length:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= input_ids[-self.model_max_length :]
					return input_ids









'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case_			=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	snake_case_			=     [
	    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimesformerModel""",
	    """TimesformerForVideoClassification""",
	    """TimesformerPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_timesformer import (
		    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimesformerForVideoClassification,
		    TimesformerModel,
		    TimesformerPreTrainedModel,
		)

else:
	import sys

	snake_case_			=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




