



"""simple docstring"""







import copy
import random

from transformers import CLIPTokenizer






class      UpperCAmelCase__		(      _UpperCAmelCase     ):



		def __init__( self  :    Union[str, Any]      ,							*snake_case  :    Optional[Any]      ,							**snake_case  :    str				)			->						int:






									'''simple docstring'''

									super().__init__(*A_      ,							**A_				)
									A         =					{}



		def 					A_		( self  :    Dict      ,							snake_case  :    List[str]      ,							*snake_case  :    int      ,							**snake_case  :    Optional[Any]				)			->						str:






									'''simple docstring'''

									A         =					super().add_tokens(A_      ,							*A_      ,							**A_				)
									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 					A_		( self  :    Dict      ,							snake_case  :    int      ,							*snake_case  :    List[Any]      ,							snake_case  :    List[str]=1      ,							**snake_case  :    Union[str, Any]				)			->						Dict:






									'''simple docstring'''

									A         =					[]
									if num_vec_per_token == 1:
																self.try_adding_tokens(A_      ,							*A_      ,							**A_				)
																output.append(A_				)
									else:
																A         =					[]
																for i in range(A_				):
																							A         =					placeholder_token + f"""_{i}"""
																							self.try_adding_tokens(A_      ,							*A_      ,							**A_				)
																							output.append(A_				)
        # 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"""				)
									A         =					output



		def 					A_		( self  :    Optional[Any]      ,							snake_case  :    Dict      ,							snake_case  :    Tuple=False      ,							snake_case  :    Optional[Any]=1.0				)			->						Optional[Any]:






									'''simple docstring'''

									if isinstance(A_      ,							A_				):
																A         =					[]
																for i in range(len(A_				)				):
																							output.append(self.replace_placeholder_tokens_in_text(text[i]      ,							vector_shuffle=A_				)				)
																return output
									for placeholder_token in self.token_map:
																if placeholder_token in text:
																							A         =					self.token_map[placeholder_token]
																							A         =					tokens[: 1 + int(len(A_				) * prop_tokens_to_load				)]
																							if vector_shuffle:
																														A         =					copy.copy(A_				)
																														random.shuffle(A_				)
																							A         =					text.replace(A_      ,							' '.join(A_				)				)
									return text



		def __call__( self  :    Optional[Any]      ,							snake_case  :    List[str]      ,							*snake_case  :    int      ,							snake_case  :    Tuple=False      ,							snake_case  :    List[str]=1.0      ,							**snake_case  :    str				)			->						List[str]:






									'''simple docstring'''

									return super().__call__(
									    self.replace_placeholder_tokens_in_text(
									        A_      ,							vector_shuffle=A_      ,							prop_tokens_to_load=A_				)      ,							*A_      ,							**A_      ,							)



		def 					A_		( self  :    List[Any]      ,							snake_case  :    List[Any]      ,							*snake_case  :    Optional[Any]      ,							snake_case  :    Optional[Any]=False      ,							snake_case  :    Dict=1.0      ,							**snake_case  :    Tuple				)			->						int:






									'''simple docstring'''

									return super().encode(
									    self.replace_placeholder_tokens_in_text(
									        A_      ,							vector_shuffle=A_      ,							prop_tokens_to_load=A_				)      ,							*A_      ,							**A_      ,							)











"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available


A    =			{
    'configuration_gpt_neo': ['GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GPTNeoConfig', 'GPTNeoOnnxConfig'],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'GPTNeoForCausalLM',
							    'GPTNeoForQuestionAnswering',
							    'GPTNeoForSequenceClassification',
							    'GPTNeoForTokenClassification',
							    'GPTNeoModel',
							    'GPTNeoPreTrainedModel',
							    'load_tf_weights_in_gpt_neo',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							A    =			[
							    'FlaxGPTNeoForCausalLM',
							    'FlaxGPTNeoModel',
							    'FlaxGPTNeoPreTrainedModel',
							]


if TYPE_CHECKING:
							from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_neo import (
														    GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTNeoForCausalLM,
														    GPTNeoForQuestionAnswering,
														    GPTNeoForSequenceClassification,
														    GPTNeoForTokenClassification,
														    GPTNeoModel,
														    GPTNeoPreTrainedModel,
														    load_tf_weights_in_gpt_neo,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
							import sys

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



