
'''simple docstring'''




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

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
      from transformers.pipelines.conversational import Conversation


lowercase			:				int    		=			logging.get_logger(__name__)

lowercase			:				Any    		=			{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

lowercase			:				str    		=			{
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

lowercase			:				Optional[Any]    		=			{
    'gpt-neox-20b': 20_48,
}





class  A				(							__snake_case						):
     __magic_name__						=		VOCAB_FILES_NAMES
     __magic_name__						=		PRETRAINED_VOCAB_FILES_MAP
     __magic_name__						=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __magic_name__						=		['''input_ids''', '''attention_mask''']





     def __init__(							self						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE=None						,			SCREAMING_SNAKE_CASE="<|endoftext|>"						,			SCREAMING_SNAKE_CASE="<|endoftext|>"						,			SCREAMING_SNAKE_CASE="<|endoftext|>"						,			SCREAMING_SNAKE_CASE=False						,			**SCREAMING_SNAKE_CASE						,			) ->				Union[str, Any]:






         """simple docstring"""






         super().__init__(
             SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			tokenizer_file=SCREAMING_SNAKE_CASE						,			unk_token=SCREAMING_SNAKE_CASE						,			bos_token=SCREAMING_SNAKE_CASE						,			eos_token=SCREAMING_SNAKE_CASE						,			add_prefix_space=SCREAMING_SNAKE_CASE						,			**SCREAMING_SNAKE_CASE						,			)

         A       :   Tuple					=						json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
         if pre_tok_state.get('''add_prefix_space'''						,			SCREAMING_SNAKE_CASE	) != add_prefix_space:
             A       :   Optional[int]					=						getattr(SCREAMING_SNAKE_CASE						,			pre_tok_state.pop('''type'''	)	)
             A       :   Dict					=						add_prefix_space
             A       :   Dict					=						pre_tok_class(**SCREAMING_SNAKE_CASE	)

         A       :   List[str]					=						add_prefix_space





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE = None	) ->				Tuple[str]:






         """simple docstring"""






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





     def 				__lowerCAmelCase							(							self						,			SCREAMING_SNAKE_CASE	) ->				List[int]:






         """simple docstring"""






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

         if len(SCREAMING_SNAKE_CASE	) > self.model_max_length:
             A       :   List[str]					=						input_ids[-self.model_max_length :]
         return input_ids








"""simple docstring"""




from typing import TYPE_CHECKING

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


__UpperCamelCase						=      {
    '''configuration_blip''': [
        '''BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''BlipConfig''',
        '''BlipTextConfig''',
        '''BlipVisionConfig''',
    ],
    '''processing_blip''': ['''BlipProcessor'''],
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      ['''BlipImageProcessor''']


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''BlipModel''',
	    '''BlipPreTrainedModel''',
	    '''BlipForConditionalGeneration''',
	    '''BlipForQuestionAnswering''',
	    '''BlipVisionModel''',
	    '''BlipTextModel''',
	    '''BlipForImageTextRetrieval''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__UpperCamelCase						=      [
	    '''TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFBlipModel''',
	    '''TFBlipPreTrainedModel''',
	    '''TFBlipForConditionalGeneration''',
	    '''TFBlipForQuestionAnswering''',
	    '''TFBlipVisionModel''',
	    '''TFBlipTextModel''',
	    '''TFBlipForImageTextRetrieval''',
	]

if TYPE_CHECKING:
	from .configuration_blip import BLIP_PRETRAINED_CONFIG_ARCHIVE_MAP, BlipConfig, BlipTextConfig, BlipVisionConfig
	from .processing_blip import BlipProcessor

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .image_processing_blip import BlipImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blip import (
		    BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlipForConditionalGeneration,
		    BlipForImageTextRetrieval,
		    BlipForQuestionAnswering,
		    BlipModel,
		    BlipPreTrainedModel,
		    BlipTextModel,
		    BlipVisionModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blip import (
		    TF_BLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFBlipForConditionalGeneration,
		    TFBlipForImageTextRetrieval,
		    TFBlipForQuestionAnswering,
		    TFBlipModel,
		    TFBlipPreTrainedModel,
		    TFBlipTextModel,
		    TFBlipVisionModel,
		)

else:
	import sys

	__UpperCamelCase						=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


