


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 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						42
							_UpperCAmelCase      =						None
							# Automatically constructed
							_UpperCAmelCase      =						"dict"
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						field(default="""Translation"""	,       init=__SCREAMING_SNAKE_CASE	,       repr=__SCREAMING_SNAKE_CASE  )






							def __call__(      self       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										from .features import Value

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







@dataclass
class 	__lowercase    :





							"""simple docstring"""







							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						None
							# Automatically constructed
							_UpperCAmelCase      =						"dict"
							_UpperCAmelCase      =						None
							_UpperCAmelCase      =						field(default="""TranslationVariableLanguages"""	,       init=__SCREAMING_SNAKE_CASE	,       repr=__SCREAMING_SNAKE_CASE  )






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Tuple					=					sorted(set(self.languages       )       ) if self.languages else None
										SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					len(self.languages       ) if self.languages else None






							def __call__(      self       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					set(self.languages       )
										if self.languages and set(lowerCAmelCase__       ) - lang_set:
													raise ValueError(
													    F'''Some languages in example ({', '.join(sorted(set(lowerCAmelCase__       ) - lang_set       )       )}) are not in valid set ({', '.join(lowerCAmelCase__       )}).'''       )

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

        # Ensure translations are in ascending order by language code.
										SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     List[str]					=					zip(*sorted(lowerCAmelCase__       )       )

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






							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										from .features import Sequence, Value

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









from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 	__lowercase    (__SCREAMING_SNAKE_CASE  ):





							"""simple docstring"""







							_UpperCAmelCase      =						["""image_processor""", """tokenizer"""]
							_UpperCAmelCase      =						"""Pix2StructImageProcessor"""
							_UpperCAmelCase      =						("""T5Tokenizer""", """T5TokenizerFast""")






							def __init__(      self			,						lowerCAmelCase__			,						lowerCAmelCase__       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     Dict					=					False
										super().__init__(lowerCAmelCase__			,						lowerCAmelCase__       )






							def __call__(      self			,						lowerCAmelCase__=None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = 2_0_4_8			,						lowerCAmelCase__ = 0			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = None			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = False			,						lowerCAmelCase__ = True			,						lowerCAmelCase__ = None			,						**lowerCAmelCase__			,						):





										"""simple docstring"""




										if images is None and text is None:
													raise ValueError('You have to specify either images or text.'       )

										# Get only text
										if images is None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					self.tokenizer
													SCREAMING_SNAKE_CASE_		:     str					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)
													return text_encoding

										if not self.image_processor.is_vqa:
													# add pixel_values
													SCREAMING_SNAKE_CASE_		:     List[Any]					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						**lowerCAmelCase__       )
										else:
													# add pixel_values and bbox
													SCREAMING_SNAKE_CASE_		:     Any					=					self.image_processor(
													    lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						max_patches=lowerCAmelCase__			,						header_text=lowerCAmelCase__			,						**lowerCAmelCase__       )

										if text is not None and not self.image_processor.is_vqa:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					self.tokenizer(
													    text=lowerCAmelCase__			,						add_special_tokens=lowerCAmelCase__			,						padding=lowerCAmelCase__			,						truncation=lowerCAmelCase__			,						max_length=lowerCAmelCase__			,						stride=lowerCAmelCase__			,						pad_to_multiple_of=lowerCAmelCase__			,						return_attention_mask=lowerCAmelCase__			,						return_overflowing_tokens=lowerCAmelCase__			,						return_special_tokens_mask=lowerCAmelCase__			,						return_offsets_mapping=lowerCAmelCase__			,						return_token_type_ids=lowerCAmelCase__			,						return_length=lowerCAmelCase__			,						verbose=lowerCAmelCase__			,						return_tensors=lowerCAmelCase__			,						**lowerCAmelCase__			,						)

													if "attention_mask" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     Any					=					text_encoding.pop('attention_mask'       )
													if "input_ids" in text_encoding:
																SCREAMING_SNAKE_CASE_		:     List[str]					=					text_encoding.pop('input_ids'       )
										else:
													SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					None

										if text_encoding is not None:
													encoding_image_processor.update(lowerCAmelCase__       )

										return encoding_image_processor






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							def 					UpperCamelCase__     (      self			,						*lowerCAmelCase__			,						**lowerCAmelCase__       ):





										"""simple docstring"""




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






							@property
							def 					UpperCamelCase__     (      self       ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_		:     List[str]					=					self.tokenizer.model_input_names
										SCREAMING_SNAKE_CASE_		:     Optional[int]					=					self.image_processor.model_input_names
										return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





