


import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__a				(    UpperCAmelCase      ):
		_a     :  Any           =     ['image_processor', 'tokenizer']
		_a     :  List[str]           =     'AutoImageProcessor'
		_a     :  Tuple           =     'AutoTokenizer'
		def __init__(      self      ,   _SCREAMING_SNAKE_CASE=None      ,   _SCREAMING_SNAKE_CASE=None      ,   **_SCREAMING_SNAKE_CASE						)			->	Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase					=      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					=      kwargs.pop('feature_extractor'						)

									_UpperCAmelCase					=      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					=      self.image_processor
									_UpperCAmelCase					=      False
		def __call__(      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	List[Any]:




									"""simple docstring"""





									if self._in_target_context_manager:
																return self.current_processor(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)

									_UpperCAmelCase					=      kwargs.pop('images'      ,   _SCREAMING_SNAKE_CASE						)
									_UpperCAmelCase					=      kwargs.pop('text'      ,   _SCREAMING_SNAKE_CASE						)
									if len(_SCREAMING_SNAKE_CASE						) > 0:
																_UpperCAmelCase					=      args[0]
																_UpperCAmelCase					=      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					=      self.image_processor(_SCREAMING_SNAKE_CASE      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
									if text is not None:
																_UpperCAmelCase					=      self.tokenizer(_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)

									if text is None:
																return inputs
									elif images is None:
																return encodings
									else:
																_UpperCAmelCase					=      encodings['input_ids']
																return inputs
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	int:




									"""simple docstring"""





									return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		def      UpperCAmelCase__    (      self      ,   *_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)			->	Dict:




									"""simple docstring"""





									return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE      ,   **_SCREAMING_SNAKE_CASE						)
		@contextmanager
		def      UpperCAmelCase__    (      self						)			->	Optional[Any]:




									"""simple docstring"""





									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					=      True
									_UpperCAmelCase					=      self.tokenizer
									yield
									_UpperCAmelCase					=      self.image_processor
									_UpperCAmelCase					=      False
		def      UpperCAmelCase__    (      self      ,   _SCREAMING_SNAKE_CASE      ,   _SCREAMING_SNAKE_CASE=False      ,   _SCREAMING_SNAKE_CASE=None						)			->	Optional[Any]:




									"""simple docstring"""





									if added_vocab is None:
																_UpperCAmelCase					=      self.tokenizer.get_added_vocab()

									_UpperCAmelCase					=      {}

									while tokens:
																_UpperCAmelCase					=      re.search(R'<s_(.*?)>'      ,   _SCREAMING_SNAKE_CASE      ,   re.IGNORECASE						)
																if start_token is None:
																							break
																_UpperCAmelCase					=      start_token.group(1						)
																_UpperCAmelCase					=      re.search(Rf'''</s_{key}>'''      ,   _SCREAMING_SNAKE_CASE      ,   re.IGNORECASE						)
																_UpperCAmelCase					=      start_token.group()
																if end_token is None:
																							_UpperCAmelCase					=      tokens.replace(_SCREAMING_SNAKE_CASE      ,   ''						)
																else:
																							_UpperCAmelCase					=      end_token.group()
																							_UpperCAmelCase					=      re.escape(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      re.escape(_SCREAMING_SNAKE_CASE						)
																							_UpperCAmelCase					=      re.search(f'''{start_token_escaped}(.*?){end_token_escaped}'''      ,   _SCREAMING_SNAKE_CASE      ,   re.IGNORECASE						)
																							if content is not None:
																														_UpperCAmelCase					=      content.group(1						).strip()
																														if r"<s_" in content and r"</s_" in content:  # non-leaf node
																																					_UpperCAmelCase					=      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					=      value[0]
																																												_UpperCAmelCase					=      value
																														else:  # leaf nodes
																																					_UpperCAmelCase					=      []
																																					for leaf in content.split(R'<sep/>'						):
																																												_UpperCAmelCase					=      leaf.strip()
																																												if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
																																																			_UpperCAmelCase					=      leaf[1:-2]  # for categorical special tokens
																																												output[key].append(_SCREAMING_SNAKE_CASE						)
																																					if len(output[key]						) == 1:
																																												_UpperCAmelCase					=      output[key][0]

																							_UpperCAmelCase					=      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      UpperCAmelCase__    (      self						)			->	str:




									"""simple docstring"""





									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      UpperCAmelCase__    (      self						)			->	Dict:




									"""simple docstring"""





									warnings.warn(
									    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'      ,   _SCREAMING_SNAKE_CASE      ,   )
									return self.image_processor




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCAmelCase__  :Any             =							{'''configuration_xglm''': ['''XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XGLMConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :Optional[Any]             =							['''XGLMTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :str             =							['''XGLMTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[str]             =							[
		    '''XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''XGLMForCausalLM''',
		    '''XGLMModel''',
		    '''XGLMPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :str             =							[
		    '''FlaxXGLMForCausalLM''',
		    '''FlaxXGLMModel''',
		    '''FlaxXGLMPreTrainedModel''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowerCAmelCase__  :List[Any]             =							[
		    '''TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFXGLMForCausalLM''',
		    '''TFXGLMModel''',
		    '''TFXGLMPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm import XGLMTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_xglm_fast import XGLMTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_xglm import (
				    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFXGLMForCausalLM,
				    TFXGLMModel,
				    TFXGLMPreTrainedModel,
				)


else:
		import sys

		lowerCAmelCase__  :Any             =							_LazyModule(__name__, globals()['''__file__'''], _import_structure)
