


'''simple docstring'''



import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin


class 						_snake_case   (       _UpperCAmelCase		):
					lowerCAmelCase :Tuple						=   ['image_processor', 'tokenizer']
					lowerCAmelCase :Union[str, Any]						=   'AutoImageProcessor'
					lowerCAmelCase :Dict						=   'AutoTokenizer'




					def __init__( self							,							_lowerCamelCase=None							,							_lowerCamelCase=None							,							**_lowerCamelCase):
								UpperCAmelCase__					:   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."""							,							SCREAMING_SNAKE_CASE_							,							)
											UpperCAmelCase__					:   Tuple       				=       kwargs.pop("""feature_extractor""")

								UpperCAmelCase__					:   Optional[Any]       				=       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__(SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_)
								UpperCAmelCase__					:   int       				=       self.image_processor
								UpperCAmelCase__					:   int       				=       False




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

								UpperCAmelCase__					:   Any       				=       kwargs.pop("""images"""							,							SCREAMING_SNAKE_CASE_)
								UpperCAmelCase__					:   str       				=       kwargs.pop("""text"""							,							SCREAMING_SNAKE_CASE_)
								if len(SCREAMING_SNAKE_CASE_) > 0:
											UpperCAmelCase__					:   List[Any]       				=       args[0]
											UpperCAmelCase__					:   Optional[int]       				=       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:
											UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor(SCREAMING_SNAKE_CASE_							,							*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_)
								if text is not None:
											UpperCAmelCase__					:   str       				=       self.tokenizer(SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_)

								if text is None:
											return inputs
								elif images is None:
											return encodings
								else:
											UpperCAmelCase__					:   str       				=       encodings["""input_ids"""]
											return inputs




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_)




					def 			snake_case__  ( self							,							*_lowerCamelCase							,							**_lowerCamelCase):
								return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_							,							**SCREAMING_SNAKE_CASE_)




					@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.""")
								UpperCAmelCase__					:   Tuple       				=       True
								UpperCAmelCase__					:   Dict       				=       self.tokenizer
								yield
								UpperCAmelCase__					:   Optional[Any]       				=       self.image_processor
								UpperCAmelCase__					:   Dict       				=       False




					def 			snake_case__  ( self							,							_lowerCamelCase							,							_lowerCamelCase=False							,							_lowerCamelCase=None):
								if added_vocab is None:
											UpperCAmelCase__					:   Tuple       				=       self.tokenizer.get_added_vocab()

								UpperCAmelCase__					:   List[str]       				=       {}

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

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

								if len(SCREAMING_SNAKE_CASE_):
											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."""							,							SCREAMING_SNAKE_CASE_							,							)
								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."""							,							SCREAMING_SNAKE_CASE_							,							)
								return self.image_processor



'''simple docstring'''



from typing import TYPE_CHECKING

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


__A		={
    'configuration_m2m_100': ['M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP', 'M2M100Config', 'M2M100OnnxConfig'],
    'tokenization_m2m_100': ['M2M100Tokenizer'],
}


try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A		=[
		    'M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'M2M100ForConditionalGeneration',
		    'M2M100Model',
		    'M2M100PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
		from .tokenization_mam_aaa import MaMaaaTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mam_aaa import (
				    M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
				    MaMaaaForConditionalGeneration,
				    MaMaaaModel,
				    MaMaaaPreTrainedModel,
				)


else:
		import sys

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