




import copy
import random

from transformers import CLIPTokenizer





class     lowerCamelCase__(					__lowerCamelCase):
      def __init__(				self: List[Any]    ,       *UpperCamelCase_: Optional[int]    ,       **UpperCamelCase_: List[str]   ):
             super().__init__(*UpperCamelCase_    ,       **UpperCamelCase_   )
             __lowerCamelCase   =						{}
      def 			lowerCAmelCase__						(				self: Dict    ,       UpperCamelCase_: Optional[int]    ,       *UpperCamelCase_: Dict    ,       **UpperCamelCase_: Tuple   ):
             __lowerCamelCase   =						super().add_tokens(UpperCamelCase_    ,       *UpperCamelCase_    ,       **UpperCamelCase_   )
             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: str    ,       UpperCamelCase_: Any    ,       *UpperCamelCase_: Any    ,       UpperCamelCase_: Optional[Any]=1    ,       **UpperCamelCase_: Optional[Any]   ):
             __lowerCamelCase   =						[]
             if num_vec_per_token == 1:
                    self.try_adding_tokens(UpperCamelCase_    ,       *UpperCamelCase_    ,       **UpperCamelCase_   )
                    output.append(UpperCamelCase_   )
             else:
                    __lowerCamelCase   =						[]
                    for i in range(UpperCamelCase_   ):
                           __lowerCamelCase   =						placeholder_token + F'_{i}'
                           self.try_adding_tokens(UpperCamelCase_    ,       *UpperCamelCase_    ,       **UpperCamelCase_   )
                           output.append(UpperCamelCase_   )
        # 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'   )
             __lowerCamelCase   =						output
      def 			lowerCAmelCase__						(				self: int    ,       UpperCamelCase_: str    ,       UpperCamelCase_: List[str]=False    ,       UpperCamelCase_: Optional[Any]=1.0   ):
             if isinstance(UpperCamelCase_    ,       UpperCamelCase_   ):
                    __lowerCamelCase   =						[]
                    for i in range(len(UpperCamelCase_   )   ):
                           output.append(self.replace_placeholder_tokens_in_text(text[i]    ,       vector_shuffle=UpperCamelCase_   )   )
                    return output
             for placeholder_token in self.token_map:
                    if placeholder_token in text:
                           __lowerCamelCase   =						self.token_map[placeholder_token]
                           __lowerCamelCase   =						tokens[: 1 + int(len(UpperCamelCase_   ) * prop_tokens_to_load   )]
                           if vector_shuffle:
                                  __lowerCamelCase   =						copy.copy(UpperCamelCase_   )
                                  random.shuffle(UpperCamelCase_   )
                           __lowerCamelCase   =						text.replace(UpperCamelCase_    ,       """ """.join(UpperCamelCase_   )   )
             return text
      def __call__(				self: str    ,       UpperCamelCase_: Optional[Any]    ,       *UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: List[str]=False    ,       UpperCamelCase_: List[str]=1.0    ,       **UpperCamelCase_: Tuple   ):
             return super().__call__(
                 self.replace_placeholder_tokens_in_text(
                     UpperCamelCase_    ,       vector_shuffle=UpperCamelCase_    ,       prop_tokens_to_load=UpperCamelCase_   )    ,       *UpperCamelCase_    ,       **UpperCamelCase_    ,       )




      def 			lowerCAmelCase__						(				self: List[str]    ,       UpperCamelCase_: List[Any]    ,       *UpperCamelCase_: Optional[int]    ,       UpperCamelCase_: str=False    ,       UpperCamelCase_: int=1.0    ,       **UpperCamelCase_: List[Any]   ):
             return super().encode(
                 self.replace_placeholder_tokens_in_text(
                     UpperCamelCase_    ,       vector_shuffle=UpperCamelCase_    ,       prop_tokens_to_load=UpperCamelCase_   )    ,       *UpperCamelCase_    ,       **UpperCamelCase_    ,       )





from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable


lowerCAmelCase           =		{'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		['''GPTNeoXTokenizerFast''']

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowerCAmelCase           =		[
	    '''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''GPTNeoXForCausalLM''',
	    '''GPTNeoXForQuestionAnswering''',
	    '''GPTNeoXForSequenceClassification''',
	    '''GPTNeoXForTokenClassification''',
	    '''GPTNeoXLayer''',
	    '''GPTNeoXModel''',
	    '''GPTNeoXPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_gpt_neox import (
		    GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
		    GPTNeoXForCausalLM,
		    GPTNeoXForQuestionAnswering,
		    GPTNeoXForSequenceClassification,
		    GPTNeoXForTokenClassification,
		    GPTNeoXLayer,
		    GPTNeoXModel,
		    GPTNeoXPreTrainedModel,
		)


else:
	import sys

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






