



from typing import TYPE_CHECKING

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


a      :  Any			     =			{
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Union[str, Any]			     =			['BlenderbotSmallTokenizerFast']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  str			     =			[
	    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'BlenderbotSmallForCausalLM',
	    'BlenderbotSmallForConditionalGeneration',
	    'BlenderbotSmallModel',
	    'BlenderbotSmallPreTrainedModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[int]			     =			[
	    'TFBlenderbotSmallForConditionalGeneration',
	    'TFBlenderbotSmallModel',
	    'TFBlenderbotSmallPreTrainedModel',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a      :  Optional[Any]			     =			[
	    'FlaxBlenderbotSmallForConditionalGeneration',
	    'FlaxBlenderbotSmallModel',
	    'FlaxBlenderbotSmallPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_blenderbot_small import (
	    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    BlenderbotSmallConfig,
	    BlenderbotSmallOnnxConfig,
	)
	from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_blenderbot_small import (
		    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    BlenderbotSmallForCausalLM,
		    BlenderbotSmallForConditionalGeneration,
		    BlenderbotSmallModel,
		    BlenderbotSmallPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_blenderbot_small import (
		    TFBlenderbotSmallForConditionalGeneration,
		    TFBlenderbotSmallModel,
		    TFBlenderbotSmallPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_blenderbot_small import (
		    FlaxBlenderbotSmallForConditionalGeneration,
		    FlaxBlenderbotSmallModel,
		    FlaxBlenderbotSmallPreTrainedModel,
		)

else:
	import sys

	a      :  Union[str, Any]			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)













import copy
import random

from transformers import CLIPTokenizer

class 							__SCREAMING_SNAKE_CASE       (  _a							):






							def __init__(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														super().__init__(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														UpperCamelCase__					  =      {}






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      super().add_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
														if num_added_tokens == 0:
																					raise ValueError(
																					    f"""The tokenizer already contains the token {placeholder_token}. Please pass a different"""
																					    """ `placeholder_token` that is not already in the tokenizer."""	)






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=1	,			**__lowerCAmelCase	):
														UpperCamelCase__					  =      []
														if num_vec_per_token == 1:
																					self.try_adding_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					output.append(__lowerCAmelCase	)
														else:
																					UpperCamelCase__					  =      []
																					for i in range(__lowerCAmelCase	):
																												UpperCamelCase__					  =      placeholder_token + f"""_{i}"""
																												self.try_adding_tokens(__lowerCAmelCase	,			*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												output.append(__lowerCAmelCase	)
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
														for token in self.token_map:
																					if token in placeholder_token:
																												raise ValueError(
																												    f"""The tokenizer already has placeholder token {token} that can get confused with"""
																												    f""" {placeholder_token}keep placeholder tokens independent"""	)
														UpperCamelCase__					  =      output






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	):
														if isinstance(__lowerCAmelCase	,			__lowerCAmelCase	):
																					UpperCamelCase__					  =      []
																					for i in range(len(__lowerCAmelCase	)	):
																												output.append(self.replace_placeholder_tokens_in_text(text[i]	,			vector_shuffle=__lowerCAmelCase	)	)
																					return output
														for placeholder_token in self.token_map:
																					if placeholder_token in text:
																												UpperCamelCase__					  =      self.token_map[placeholder_token]
																												UpperCamelCase__					  =      tokens[: 1 + int(len(__lowerCAmelCase	) * prop_tokens_to_load	)]
																												if vector_shuffle:
																																			UpperCamelCase__					  =      copy.copy(__lowerCAmelCase	)
																																			random.shuffle(__lowerCAmelCase	)
																												UpperCamelCase__					  =      text.replace(__lowerCAmelCase	,			""" """.join(__lowerCAmelCase	)	)
														return text






							def __call__(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	,			**__lowerCAmelCase	):
														return super().__call__(
														    self.replace_placeholder_tokens_in_text(
														        __lowerCAmelCase	,			vector_shuffle=__lowerCAmelCase	,			prop_tokens_to_load=__lowerCAmelCase	)	,			*__lowerCAmelCase	,			**__lowerCAmelCase	,			)







							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	,			*__lowerCAmelCase	,			__lowerCAmelCase=False	,			__lowerCAmelCase=1.0	,			**__lowerCAmelCase	):
														return super().encode(
														    self.replace_placeholder_tokens_in_text(
														        __lowerCAmelCase	,			vector_shuffle=__lowerCAmelCase	,			prop_tokens_to_load=__lowerCAmelCase	)	,			*__lowerCAmelCase	,			**__lowerCAmelCase	,			)




