

"""simple docstring"""




import copy
import random

from transformers import CLIPTokenizer


class 		_A       (			__a					):





      def __init__(  self    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					int:
         super().__init__(*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			{}





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			super().add_tokens(SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
         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    ,						SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=1    ,						**SCREAMING_SNAKE_CASE__		)   ->					Optional[Any]:
         lowerCamelCase__						=			[]
         if num_vec_per_token == 1:
            self.try_adding_tokens(SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
            output.append(SCREAMING_SNAKE_CASE__		)
         else:
            lowerCamelCase__						=			[]
            for i in range(SCREAMING_SNAKE_CASE__		):
               lowerCamelCase__						=			placeholder_token + f'_{i}'
               self.try_adding_tokens(SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)
               output.append(SCREAMING_SNAKE_CASE__		)
        # 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    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=1.0		)   ->					List[Any]:
         if isinstance(SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		):
            lowerCamelCase__						=			[]
            for i in range(len(SCREAMING_SNAKE_CASE__		)		):
               output.append(self.replace_placeholder_tokens_in_text(text[i]    ,						vector_shuffle=SCREAMING_SNAKE_CASE__		)		)
            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(SCREAMING_SNAKE_CASE__		) * prop_tokens_to_load		)]
               if vector_shuffle:
                  lowerCamelCase__						=			copy.copy(SCREAMING_SNAKE_CASE__		)
                  random.shuffle(SCREAMING_SNAKE_CASE__		)
               lowerCamelCase__						=			text.replace(SCREAMING_SNAKE_CASE__    ,						" ".join(SCREAMING_SNAKE_CASE__		)		)
         return text





      def __call__(  self    ,						SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=1.0    ,						**SCREAMING_SNAKE_CASE__		)   ->					int:
         return super().__call__(
             self.replace_placeholder_tokens_in_text(
                 SCREAMING_SNAKE_CASE__    ,						vector_shuffle=SCREAMING_SNAKE_CASE__    ,						prop_tokens_to_load=SCREAMING_SNAKE_CASE__		)    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__    ,						)





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						*SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__=False    ,						SCREAMING_SNAKE_CASE__=1.0    ,						**SCREAMING_SNAKE_CASE__		)   ->					str:
         return super().encode(
             self.replace_placeholder_tokens_in_text(
                 SCREAMING_SNAKE_CASE__    ,						vector_shuffle=SCREAMING_SNAKE_CASE__    ,						prop_tokens_to_load=SCREAMING_SNAKE_CASE__		)    ,						*SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__    ,						)







"""simple docstring"""




from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_     :			List[Any]	  =			{
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			[
         '''RagModel''',
         '''RagPreTrainedModel''',
         '''RagSequenceForGeneration''',
         '''RagTokenForGeneration''',
     ]

try:
     if not is_tf_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     SCREAMING_SNAKE_CASE_     :			str	  =			[
         '''TFRagModel''',
         '''TFRagPreTrainedModel''',
         '''TFRagSequenceForGeneration''',
         '''TFRagTokenForGeneration''',
     ]


if TYPE_CHECKING:
     from .configuration_rag import RagConfig
     from .retrieval_rag import RagRetriever
     from .tokenization_rag import RagTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

     try:
          if not is_tf_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_tf_rag import (
              TFRagModel,
              TFRagPreTrainedModel,
              TFRagSequenceForGeneration,
              TFRagTokenForGeneration,
          )

else:
     import sys

     SCREAMING_SNAKE_CASE_     :			List[str]	  =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




