



"""simple docstring"""
import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin




class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					["image_processor", "tokenizer"]
							__lowerCAmelCase    					=					"AutoImageProcessor"
							__lowerCAmelCase    					=					"AutoTokenizer"






							def __init__(   self			,     a_=None			,     a_=None			,     **a_      ):
									a_     :      int 						=     None
									if "feature_extractor" in kwargs:
											warnings.warn(
											    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
											    " instead."			,     a_			,     )
											a_     :      List[Any] 						=     kwargs.pop("feature_extractor"      )

									a_     :      List[str] 						=     image_processor if image_processor is not None else feature_extractor
									if image_processor is None:
											raise ValueError("You need to specify an `image_processor`."      )
									if tokenizer is None:
											raise ValueError("You need to specify a `tokenizer`."      )

									super().__init__(a_			,     a_      )
									a_     :      str 						=     self.image_processor
									a_     :      Optional[Any] 						=     False






							def __call__(   self			,     *a_			,     **a_      ):
									# For backward compatibility
									if self._in_target_context_manager:
											return self.current_processor(*a_			,     **a_      )

									a_     :      List[str] 						=     kwargs.pop("images"			,     a_      )
									a_     :      List[Any] 						=     kwargs.pop("text"			,     a_      )
									if len(a_      ) > 0:
											a_     :      int 						=     args[0]
											a_     :      Dict 						=     args[1:]

									if images is None and text is None:
											raise ValueError("You need to specify either an `images` or `text` input to process."      )

									if images is not None:
											a_     :      int 						=     self.image_processor(a_			,     *a_			,     **a_      )
									if text is not None:
											a_     :      List[str] 						=     self.tokenizer(a_			,     **a_      )

									if text is None:
											return inputs
									elif images is None:
											return encodings
									else:
											a_     :      Union[str, Any] 						=     encodings["input_ids"]
											return inputs






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.batch_decode(*a_			,     **a_      )






							def snake_case_  (   self			,     *a_			,     **a_      ):
									return self.tokenizer.decode(*a_			,     **a_      )






							@contextmanager
							def snake_case_  (   self      ):
									warnings.warn(
									    "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
									    "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
									    "your images inputs, or in a separate call."      )
									a_     :      Dict 						=     True
									a_     :      Any 						=     self.tokenizer
									yield
									a_     :      List[str] 						=     self.image_processor
									a_     :      Any 						=     False






							def snake_case_  (   self			,     a_			,     a_=False			,     a_=None      ):
									if added_vocab is None:
											a_     :      Optional[Any] 						=     self.tokenizer.get_added_vocab()

									a_     :      int 						=     {}

									while tokens:
											a_     :      Union[str, Any] 						=     re.search(R"<s_(.*?)>"			,     a_			,     re.IGNORECASE      )
											if start_token is None:
													break
											a_     :      int 						=     start_token.group(1      )
											a_     :      str 						=     re.search(RF"""</s_{key}>"""			,     a_			,     re.IGNORECASE      )
											a_     :      Any 						=     start_token.group()
											if end_token is None:
													a_     :      Optional[int] 						=     tokens.replace(a_			,     ""      )
											else:
													a_     :      Dict 						=     end_token.group()
													a_     :      List[str] 						=     re.escape(a_      )
													a_     :      Tuple 						=     re.escape(a_      )
													a_     :      Any 						=     re.search(F"""{start_token_escaped}(.*?){end_token_escaped}"""			,     a_			,     re.IGNORECASE      )
													if content is not None:
															a_     :      str 						=     content.group(1      ).strip()
															if r"<s_" in content and r"</s_" in content:  # non-leaf node
																	a_     :      Union[str, Any] 						=     self.tokenajson(a_			,     is_inner_value=a_			,     added_vocab=a_      )
																	if value:
																			if len(a_      ) == 1:
																					a_     :      List[Any] 						=     value[0]
																			a_     :      Any 						=     value
															else:  # leaf nodes
																	a_     :      Union[str, Any] 						=     []
																	for leaf in content.split(R"<sep/>"      ):
																			a_     :      List[str] 						=     leaf.strip()
																			if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																					a_     :      Tuple 						=     leaf[1:-2]  # for categorical special tokens
																			output[key].append(a_      )
																	if len(output[key]      ) == 1:
																			a_     :      Optional[Any] 						=     output[key][0]

													a_     :      Dict 						=     tokens[tokens.find(a_      ) + len(a_      ) :].strip()
													if tokens[:6] == r"<sep/>":  # non-leaf nodes
															return [output] + self.tokenajson(tokens[6:]			,     is_inner_value=a_			,     added_vocab=a_      )

									if len(a_      ):
											return [output] if is_inner_value else output
									else:
											return [] if is_inner_value else {"text_sequence": tokens}






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."			,     a_			,     )
									return self.image_processor_class






							@property
							def snake_case_  (   self      ):
									warnings.warn(
									    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."			,     a_			,     )
									return self.image_processor




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_				= {
    """configuration_bigbird_pegasus""": [
        """BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """BigBirdPegasusConfig""",
        """BigBirdPegasusOnnxConfig""",
    ],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_				= [
					    """BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """BigBirdPegasusForCausalLM""",
					    """BigBirdPegasusForConditionalGeneration""",
					    """BigBirdPegasusForQuestionAnswering""",
					    """BigBirdPegasusForSequenceClassification""",
					    """BigBirdPegasusModel""",
					    """BigBirdPegasusPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_bigbird_pegasus import (
					    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    BigBirdPegasusConfig,
					    BigBirdPegasusOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_bigbird_pegasus import (
										    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
										    BigBirdPegasusForCausalLM,
										    BigBirdPegasusForConditionalGeneration,
										    BigBirdPegasusForQuestionAnswering,
										    BigBirdPegasusForSequenceClassification,
										    BigBirdPegasusModel,
										    BigBirdPegasusPreTrainedModel,
										)


else:
					import sys

					SCREAMING_SNAKE_CASE_				= _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)