


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_lowercase      :      List[Any]            =					{
    """configuration_funnel""": ["""FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FunnelConfig"""],
    """convert_funnel_original_tf_checkpoint_to_pytorch""": [],
    """tokenization_funnel""": ["""FunnelTokenizer"""],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					["""FunnelTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[Any]            =					[
	    """FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FunnelBaseModel""",
	    """FunnelForMaskedLM""",
	    """FunnelForMultipleChoice""",
	    """FunnelForPreTraining""",
	    """FunnelForQuestionAnswering""",
	    """FunnelForSequenceClassification""",
	    """FunnelForTokenClassification""",
	    """FunnelModel""",
	    """FunnelPreTrainedModel""",
	    """load_tf_weights_in_funnel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[Any]            =					[
	    """TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFFunnelBaseModel""",
	    """TFFunnelForMaskedLM""",
	    """TFFunnelForMultipleChoice""",
	    """TFFunnelForPreTraining""",
	    """TFFunnelForQuestionAnswering""",
	    """TFFunnelForSequenceClassification""",
	    """TFFunnelForTokenClassification""",
	    """TFFunnelModel""",
	    """TFFunnelPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
	from .tokenization_funnel import FunnelTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_funnel_fast import FunnelTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_funnel import (
		    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FunnelBaseModel,
		    FunnelForMaskedLM,
		    FunnelForMultipleChoice,
		    FunnelForPreTraining,
		    FunnelForQuestionAnswering,
		    FunnelForSequenceClassification,
		    FunnelForTokenClassification,
		    FunnelModel,
		    FunnelPreTrainedModel,
		    load_tf_weights_in_funnel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_funnel import (
		    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFFunnelBaseModel,
		    TFFunnelForMaskedLM,
		    TFFunnelForMultipleChoice,
		    TFFunnelForPreTraining,
		    TFFunnelForQuestionAnswering,
		    TFFunnelForSequenceClassification,
		    TFFunnelForTokenClassification,
		    TFFunnelModel,
		    TFFunnelPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Dict            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)









'''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


_lowercase      :      Optional[Any]            =					logging.get_logger(__name__)

_lowercase      :      List[str]            =					{"""tokenizer_file""": """tokenizer.json"""}

_lowercase      :      Optional[Any]            =					{
    """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 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				List[str]  		=	VOCAB_FILES_NAMES
				__magic_name__    :				Optional[int]  		=	PRETRAINED_VOCAB_FILES_MAP
				__magic_name__    :				Any  		=	["input_ids", "attention_mask"]
				__magic_name__    :				Optional[int]  		=	None




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

										"""simple docstring"""




										super().__init__(
										    lowerCAmelCase				,   lowerCAmelCase				,   tokenizer_file=lowerCAmelCase				,   unk_token=lowerCAmelCase				,   bos_token=lowerCAmelCase				,   eos_token=lowerCAmelCase				,   pad_token=lowerCAmelCase				,   add_prefix_space=lowerCAmelCase				,   clean_up_tokenization_spaces=lowerCAmelCase				,   **lowerCAmelCase				,   )
										UpperCAmelCase					    =     json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()				)
										if pre_tok_state.get('''add_prefix_space'''				,   lowerCAmelCase				) != add_prefix_space:
																UpperCAmelCase					    =     getattr(lowerCAmelCase				,   pre_tok_state.pop('''type'''				)				)
																UpperCAmelCase					    =     add_prefix_space
																UpperCAmelCase					    =     pre_tok_class(**lowerCAmelCase				)

										UpperCAmelCase					    =     add_prefix_space




				def 					a__(		self      :			str				,   *lowerCAmelCase      :			Dict				,   **lowerCAmelCase      :			List[str]				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)
										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(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Union[str, Any]				,   *lowerCAmelCase      :			List[str]				,   **lowerCAmelCase      :			Union[str, Any]				)->					BatchEncoding:

										"""simple docstring"""




										UpperCAmelCase					    =     kwargs.get('''is_split_into_words'''				,   lowerCAmelCase				)

										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(*lowerCAmelCase				,   **lowerCAmelCase				)




				def 					a__(		self      :			Union[str, Any]				,   lowerCAmelCase      :			str				,   lowerCAmelCase      :			Optional[str] = None				)->					Tuple[str]:

										"""simple docstring"""




										UpperCAmelCase					    =     self._tokenizer.model.save(lowerCAmelCase				,   name=lowerCAmelCase				)
										return tuple(lowerCAmelCase				)





				def 					a__(		self      :			List[Any]				,   lowerCAmelCase      :			"Conversation"				)->					List[int]:

										"""simple docstring"""




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

										if len(lowerCAmelCase				) > self.model_max_length:
																UpperCAmelCase					    =     input_ids[-self.model_max_length :]
										return input_ids





