
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
					from .features import FeatureType







@dataclass
class 			__A:
				snake_case_						   =    42
				snake_case_						   =    None
				# Automatically constructed
				snake_case_						   =    "dict"
				snake_case_						   =    None
				snake_case_						   =    field(default='''Translation'''  ,   init=a  ,   repr=a			)




				def __call__(     self				)    ->      Tuple:


								'''simple docstring'''




								return pa.struct({lang: pa.string() for lang in sorted(self.languages				)}				)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union["FeatureType", Dict[str, "FeatureType"]]:


								'''simple docstring'''




								from .features import Value

								return {k: Value('''string'''				) for k in sorted(self.languages				)}







@dataclass
class 			__A:
				snake_case_						   =    None
				snake_case_						   =    None
				snake_case_						   =    None
				# Automatically constructed
				snake_case_						   =    "dict"
				snake_case_						   =    None
				snake_case_						   =    field(default='''TranslationVariableLanguages'''  ,   init=a  ,   repr=a			)




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Dict:


								'''simple docstring'''




								__a										=    sorted(set(self.languages				)				) if self.languages else None
								__a										=    len(self.languages				) if self.languages else None




				def __call__(     self				)    ->      Any:


								'''simple docstring'''




								return pa.struct({'''language''': pa.list_(pa.string()				), '''translation''': pa.list_(pa.string()				)}				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     _snake_case				)    ->      str:


								'''simple docstring'''




								__a										=    set(self.languages				)
								if self.languages and set(_snake_case				) - lang_set:
												raise ValueError(
												    F"""Some languages in example ({', '.join(sorted(set(_snake_case				) - lang_set				)				)}) are not in valid set ({', '.join(_snake_case				)})."""				)

								# Convert dictionary into tuples, splitting out cases where there are
								# multiple translations for a single language.
								__a										=    []
								for lang, text in translation_dict.items():
												if isinstance(_snake_case		,     _snake_case				):
																translation_tuples.append((lang, text)				)
												else:
																translation_tuples.extend([(lang, el) for el in text]				)

        # Ensure translations are in ascending order by language code.
								__a							,     __a										=    zip(*sorted(_snake_case				)				)

								return {"language": languages, "translation": translations}




				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Union["FeatureType", Dict[str, "FeatureType"]]:


								'''simple docstring'''




								from .features import Sequence, Value

								return {
								    "language": Sequence(Value('''string'''				)				),
								    "translation": Sequence(Value('''string'''				)				),
								}

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding







class 			__A( a			):
				snake_case_						   =    ['''image_processor''', '''tokenizer''']
				snake_case_						   =    '''ChineseCLIPImageProcessor'''
				snake_case_						   =    ('''BertTokenizer''', '''BertTokenizerFast''')




				def __init__(     self		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Tuple:


								'''simple docstring'''




								__a										=    None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
												    ''' instead.'''		,     _snake_case		,     )
												__a										=    kwargs.pop('''feature_extractor'''				)

								__a										=    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__(_snake_case		,     _snake_case				)
								__a										=    self.image_processor




				def __call__(     self		,     _snake_case=None		,     _snake_case=None		,     _snake_case=None		,     **_snake_case				)    ->      Optional[Any]:


								'''simple docstring'''




								if text is None and images is None:
												raise ValueError('''You have to specify either text or images. Both cannot be none.'''				)

								if text is not None:
												__a										=    self.tokenizer(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if images is not None:
												__a										=    self.image_processor(_snake_case		,     return_tensors=_snake_case		,     **_snake_case				)

								if text is not None and images is not None:
												__a										=    image_features.pixel_values
												return encoding
								elif text is not None:
												return encoding
								else:
												return BatchEncoding(data=dict(**_snake_case				)		,     tensor_type=_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      str:


								'''simple docstring'''




								return self.tokenizer.batch_decode(*_snake_case		,     **_snake_case				)




				def 				SCREAMING_SNAKE_CASE_		(     self		,     *_snake_case		,     **_snake_case				)    ->      Dict:


								'''simple docstring'''




								return self.tokenizer.decode(*_snake_case		,     **_snake_case				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Any:


								'''simple docstring'''




								__a										=    self.tokenizer.model_input_names
								__a										=    self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)




				@property
				def 				SCREAMING_SNAKE_CASE_		(     self				)    ->      Optional[Any]:


								'''simple docstring'''




								warnings.warn(
								    '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''		,     _snake_case		,     )
								return self.image_processor_class