

import copy
import random

from transformers import CLIPTokenizer





class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				int							,   *_UpperCamelCase :				int							,   **_UpperCamelCase :				int							)      ->Dict:
									super().__init__(*_UpperCamelCase							,   **_UpperCamelCase							)
									snake_case_   =	{}







			def 				snake_case__(     self :				Optional[Any]							,   _UpperCamelCase :				Optional[Any]							,   *_UpperCamelCase :				Optional[int]							,   **_UpperCamelCase :				List[str]							)      ->Any:
									snake_case_   =	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 				snake_case__(     self :				Union[str, Any]							,   _UpperCamelCase :				Any							,   *_UpperCamelCase :				Dict							,   _UpperCamelCase :				Optional[Any]=1							,   **_UpperCamelCase :				Optional[Any]							)      ->Any:
									snake_case_   =	[]
									if num_vec_per_token == 1:
															self.try_adding_tokens(_UpperCamelCase							,   *_UpperCamelCase							,   **_UpperCamelCase							)
															output.append(_UpperCamelCase							)
									else:
															snake_case_   =	[]
															for i in range(_UpperCamelCase							):
																					snake_case_   =	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'''							)
									snake_case_   =	output







			def 				snake_case__(     self :				List[str]							,   _UpperCamelCase :				Tuple							,   _UpperCamelCase :				Dict=False							,   _UpperCamelCase :				Union[str, Any]=1.0							)      ->Optional[Any]:
									if isinstance(_UpperCamelCase							,   _UpperCamelCase							):
															snake_case_   =	[]
															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:
																					snake_case_   =	self.token_map[placeholder_token]
																					snake_case_   =	tokens[: 1 + int(len(_UpperCamelCase							) * prop_tokens_to_load							)]
																					if vector_shuffle:
																											snake_case_   =	copy.copy(_UpperCamelCase							)
																											random.shuffle(_UpperCamelCase							)
																					snake_case_   =	text.replace(_UpperCamelCase							,   ''' '''.join(_UpperCamelCase							)							)
									return text







			def __call__(     self :				List[str]							,   _UpperCamelCase :				Any							,   *_UpperCamelCase :				int							,   _UpperCamelCase :				Optional[Any]=False							,   _UpperCamelCase :				Tuple=1.0							,   **_UpperCamelCase :				Optional[int]							)      ->Optional[int]:
									return super().__call__(
									    self.replace_placeholder_tokens_in_text(
									        _UpperCamelCase							,   vector_shuffle=_UpperCamelCase							,   prop_tokens_to_load=_UpperCamelCase							)							,   *_UpperCamelCase							,   **_UpperCamelCase							,   )







			def 				snake_case__(     self :				str							,   _UpperCamelCase :				Any							,   *_UpperCamelCase :				Any							,   _UpperCamelCase :				Union[str, Any]=False							,   _UpperCamelCase :				List[Any]=1.0							,   **_UpperCamelCase :				Any							)      ->int:
									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 ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase_       	=	{'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''OPTForCausalLM''',
					    '''OPTModel''',
					    '''OPTPreTrainedModel''',
					    '''OPTForSequenceClassification''',
					    '''OPTForQuestionAnswering''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_       	=	[
					    '''FlaxOPTForCausalLM''',
					    '''FlaxOPTModel''',
					    '''FlaxOPTPreTrainedModel''',
					]


if TYPE_CHECKING:
					from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_opt import (
										    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    OPTForCausalLM,
										    OPTForQuestionAnswering,
										    OPTForSequenceClassification,
										    OPTModel,
										    OPTPreTrainedModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
					import sys

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