




import warnings
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     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       ['''image_processor''', '''tokenizer''']
			snake_case_   		=       '''LayoutLMv2ImageProcessor'''
			snake_case_   		=       ('''LayoutXLMTokenizer''', '''LayoutXLMTokenizerFast''')


			def __init__(      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						**lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							if "feature_extractor" in kwargs:
											warnings.warn(
											    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
											    ' instead.'			,						lowerCamelCase__			,						)
											__lowerCamelCase		       =    kwargs.pop('feature_extractor'							)

							__lowerCamelCase		       =    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__(lowerCamelCase__			,						lowerCamelCase__							)


			def __call__(      self			,						lowerCamelCase__			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = True			,						lowerCamelCase__ = False			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = 0			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = None			,						lowerCamelCase__ = False			,						lowerCamelCase__ = False			,						lowerCamelCase__ = False			,						lowerCamelCase__ = False			,						lowerCamelCase__ = True			,						lowerCamelCase__ = None			,						**lowerCamelCase__			,						)    ->      BatchEncoding:



							'''simple docstring'''
							# verify input
							if self.image_processor.apply_ocr and (boxes is not None):
											raise ValueError(
											    'You cannot provide bounding boxes '
											    'if you initialized the image processor with apply_ocr set to True.'							)

							if self.image_processor.apply_ocr and (word_labels is not None):
											raise ValueError(
											    'You cannot provide word labels if you initialized the image processor with apply_ocr set to True.'							)

							if return_overflowing_tokens is True and return_offsets_mapping is False:
											raise ValueError('You cannot return overflowing tokens without returning the offsets mapping.'							)

							# first, apply the image processor
							__lowerCamelCase		       =    self.image_processor(images=lowerCamelCase__			,						return_tensors=lowerCamelCase__							)

							# second, apply the tokenizer
							if text is not None and self.image_processor.apply_ocr and text_pair is None:
											if isinstance(lowerCamelCase__			,						lowerCamelCase__							):
															__lowerCamelCase		       =    [text]  # add batch dimension (as the image processor always adds a batch dimension)
											__lowerCamelCase		       =    features['words']

							__lowerCamelCase		       =    self.tokenizer(
							    text=text if text is not None else features['words']			,						text_pair=text_pair if text_pair is not None else None			,						boxes=boxes if boxes is not None else features['boxes']			,						word_labels=lowerCamelCase__			,						add_special_tokens=lowerCamelCase__			,						padding=lowerCamelCase__			,						truncation=lowerCamelCase__			,						max_length=lowerCamelCase__			,						stride=lowerCamelCase__			,						pad_to_multiple_of=lowerCamelCase__			,						return_token_type_ids=lowerCamelCase__			,						return_attention_mask=lowerCamelCase__			,						return_overflowing_tokens=lowerCamelCase__			,						return_special_tokens_mask=lowerCamelCase__			,						return_offsets_mapping=lowerCamelCase__			,						return_length=lowerCamelCase__			,						verbose=lowerCamelCase__			,						return_tensors=lowerCamelCase__			,						**lowerCamelCase__			,						)

							# add pixel values
							__lowerCamelCase		       =    features.pop('pixel_values'							)
							if return_overflowing_tokens is True:
											__lowerCamelCase		       =    self.get_overflowing_images(lowerCamelCase__			,						encoded_inputs['overflow_to_sample_mapping']							)
							__lowerCamelCase		       =    images

							return encoded_inputs


			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      Optional[int]:



							'''simple docstring'''
							# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
							__lowerCamelCase		       =    []
							for sample_idx in overflow_to_sample_mapping:
											images_with_overflow.append(images[sample_idx]							)

							if len(lowerCamelCase__							) != len(lowerCamelCase__							):
											raise ValueError(
											    'Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got'
											    f""" {len(lowerCamelCase__							)} and {len(lowerCamelCase__							)}"""							)

							return images_with_overflow


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.batch_decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			@property
			def       lowercase_    (      self							)    ->      int:



							'''simple docstring'''
							return ["input_ids", "bbox", "attention_mask", "image"]


			@property
			def       lowercase_    (      self							)    ->      List[str]:



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






			@property
			def       lowercase_    (      self							)    ->      List[Any]:



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








from typing import List, Optional

import numpy as np

from ...processing_utils import ProcessorMixin
from ...utils import to_numpy







class     __lowerCAmelCase (       __magic_name__ ):

			"""simple docstring"""







			snake_case_   		=       '''EncodecFeatureExtractor'''
			snake_case_   		=       ('''T5Tokenizer''', '''T5TokenizerFast''')


			def __init__(      self			,						lowerCamelCase__			,						lowerCamelCase__							)    ->      int:



							'''simple docstring'''
							super().__init__(lowerCamelCase__			,						lowerCamelCase__							)
							__lowerCamelCase		       =    self.feature_extractor
							__lowerCamelCase		       =    False


			def       lowercase_    (      self			,						lowerCamelCase__=None			,						lowerCamelCase__=None			,						lowerCamelCase__=True							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.get_decoder_prompt_ids(task=lowerCamelCase__			,						language=lowerCamelCase__			,						no_timestamps=lowerCamelCase__							)


			def __call__(      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Dict:



							'''simple docstring'''
							# For backward compatibility
							if self._in_target_context_manager:
											return self.current_processor(*lowerCamelCase__			,						**lowerCamelCase__							)

							__lowerCamelCase		       =    kwargs.pop('audio'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    kwargs.pop('sampling_rate'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    kwargs.pop('text'			,						lowerCamelCase__							)
							if len(lowerCamelCase__							) > 0:
											__lowerCamelCase		       =    args[0]
											__lowerCamelCase		       =    args[1:]

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

							if text is not None:
											__lowerCamelCase		       =    self.tokenizer(lowerCamelCase__			,						**lowerCamelCase__							)

							if audio is not None:
											__lowerCamelCase		       =    self.feature_extractor(lowerCamelCase__			,						*lowerCamelCase__			,						sampling_rate=lowerCamelCase__			,						**lowerCamelCase__							)

							if audio is None:
											return inputs

							elif text is None:
											return audio_inputs

							else:
											__lowerCamelCase		       =    audio_inputs['input_values']
											if "padding_mask" in audio_inputs:
															__lowerCamelCase		       =    audio_inputs['padding_mask']
											return inputs


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase		       =    kwargs.pop('audio'			,						lowerCamelCase__							)
							__lowerCamelCase		       =    kwargs.pop('padding_mask'			,						lowerCamelCase__							)

							if len(lowerCamelCase__							) > 0:
											__lowerCamelCase		       =    args[0]
											__lowerCamelCase		       =    args[1:]

							if audio_values is not None:
											return self._decode_audio(lowerCamelCase__			,						padding_mask=lowerCamelCase__							)
							else:
											return self.tokenizer.batch_decode(*lowerCamelCase__			,						**lowerCamelCase__							)


			def       lowercase_    (      self			,						*lowerCamelCase__			,						**lowerCamelCase__							)    ->      List[Any]:



							'''simple docstring'''
							return self.tokenizer.decode(*lowerCamelCase__			,						**lowerCamelCase__							)






			def       lowercase_    (      self			,						lowerCamelCase__			,						lowerCamelCase__ = None							)    ->      List[np.ndarray]:



							'''simple docstring'''
							__lowerCamelCase		       =    to_numpy(lowerCamelCase__							)
							__lowerCamelCase       ,						__lowerCamelCase       ,						__lowerCamelCase		       =    audio_values.shape

							if padding_mask is None:
											return list(lowerCamelCase__							)

							__lowerCamelCase		       =    to_numpy(lowerCamelCase__							)

							# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
							# token (so that the generated audio values are **not** treated as padded tokens)
							__lowerCamelCase		       =    seq_len - padding_mask.shape[-1]
							__lowerCamelCase		       =    1 - self.feature_extractor.padding_value
							__lowerCamelCase		       =    np.pad(lowerCamelCase__			,						((0, 0), (0, difference))			,						'constant'			,						constant_values=lowerCamelCase__							)

							__lowerCamelCase		       =    audio_values.tolist()
							for i in range(lowerCamelCase__							):
											__lowerCamelCase		       =    np.asarray(audio_values[i]							)[
											    padding_mask[i][None, :] != self.feature_extractor.padding_value
											]
											__lowerCamelCase		       =    sliced_audio.reshape(lowerCamelCase__			,						-1							)

							return audio_values


