






"""simple docstring"""




import warnings
from typing import List

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   str							=  ['''image_processor''', '''tokenizer''']
		_snake_case   :   str							=  '''OwlViTImageProcessor'''
		_snake_case   :   int							=  ('''CLIPTokenizer''', '''CLIPTokenizerFast''')



		def __init__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		**_UpperCamelCase       )     ->							Tuple:
							UpperCAmelCase_   :				str						=						None
							if "feature_extractor" in kwargs:
												warnings.warn(
												    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
												    ' instead.'    ,		_UpperCamelCase    ,		)
												UpperCAmelCase_   :				Union[str, Any]						=						kwargs.pop('feature_extractor'       )

							UpperCAmelCase_   :				Optional[int]						=						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__(_UpperCamelCase    ,		_UpperCamelCase       )



		def __call__(							self    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase=None    ,		_UpperCamelCase="max_length"    ,		_UpperCamelCase="np"    ,		**_UpperCamelCase       )     ->							Optional[Any]:

							if text is None and query_images is None and images is None:
												raise ValueError(
												    'You have to specify at least one text or query image or image. All three cannot be none.'       )

							if text is not None:
												if isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) or (isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) and not isinstance(text[0]    ,		_UpperCamelCase       )):
																	UpperCAmelCase_   :				List[Any]						=						[self.tokenizer(_UpperCamelCase    ,		padding=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		**_UpperCamelCase       )]

												elif isinstance(_UpperCamelCase    ,		_UpperCamelCase       ) and isinstance(text[0]    ,		_UpperCamelCase       ):
																	UpperCAmelCase_   :				List[Any]						=						[]

																	# Maximum number of queries across batch
																	UpperCAmelCase_   :				int						=						max([len(_UpperCamelCase       ) for t in text]       )

																	# Pad all batch samples to max number of text queries
																	for t in text:
																						if len(_UpperCamelCase       ) != max_num_queries:
																											UpperCAmelCase_   :				Union[str, Any]						=						t + [' '] * (max_num_queries - len(_UpperCamelCase       ))

																						UpperCAmelCase_   :				List[str]						=						self.tokenizer(_UpperCamelCase    ,		padding=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		**_UpperCamelCase       )
																						encodings.append(_UpperCamelCase       )
												else:
																	raise TypeError('Input text should be a string, a list of strings or a nested list of strings'       )

												if return_tensors == "np":
																	UpperCAmelCase_   :				Any						=						np.concatenate([encoding['input_ids'] for encoding in encodings]    ,		axis=0       )
																	UpperCAmelCase_   :				Any						=						np.concatenate([encoding['attention_mask'] for encoding in encodings]    ,		axis=0       )

												elif return_tensors == "jax" and is_flax_available():
																	import jax.numpy as jnp

																	UpperCAmelCase_   :				Dict						=						jnp.concatenate([encoding['input_ids'] for encoding in encodings]    ,		axis=0       )
																	UpperCAmelCase_   :				str						=						jnp.concatenate([encoding['attention_mask'] for encoding in encodings]    ,		axis=0       )

												elif return_tensors == "pt" and is_torch_available():
																	import torch

																	UpperCAmelCase_   :				Optional[int]						=						torch.cat([encoding['input_ids'] for encoding in encodings]    ,		dim=0       )
																	UpperCAmelCase_   :				int						=						torch.cat([encoding['attention_mask'] for encoding in encodings]    ,		dim=0       )

												elif return_tensors == "tf" and is_tf_available():
																	import tensorflow as tf

																	UpperCAmelCase_   :				List[str]						=						tf.stack([encoding['input_ids'] for encoding in encodings]    ,		axis=0       )
																	UpperCAmelCase_   :				str						=						tf.stack([encoding['attention_mask'] for encoding in encodings]    ,		axis=0       )

												else:
																	raise ValueError('Target return tensor type could not be returned'       )

												UpperCAmelCase_   :				List[str]						=						BatchEncoding()
												UpperCAmelCase_   :				str						=						input_ids
												UpperCAmelCase_   :				Tuple						=						attention_mask

							if query_images is not None:
												UpperCAmelCase_   :				Any						=						BatchEncoding()
												UpperCAmelCase_   :				Tuple						=						self.image_processor(
												    _UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		**_UpperCamelCase       ).pixel_values
												UpperCAmelCase_   :				Any						=						query_pixel_values

							if images is not None:
												UpperCAmelCase_   :				Tuple						=						self.image_processor(_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		**_UpperCamelCase       )

							if text is not None and images is not None:
												UpperCAmelCase_   :				Tuple						=						image_features.pixel_values
												return encoding
							elif query_images is not None and images is not None:
												UpperCAmelCase_   :				Optional[int]						=						image_features.pixel_values
												return encoding
							elif text is not None or query_images is not None:
												return encoding
							else:
												return BatchEncoding(data=dict(**_UpperCamelCase       )    ,		tensor_type=_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Optional[int]:
							return self.image_processor.post_process(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Union[str, Any]:
							return self.image_processor.post_process_object_detection(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Dict:
							return self.image_processor.post_process_image_guided_detection(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							Any:
							return self.tokenizer.batch_decode(*_UpperCamelCase    ,		**_UpperCamelCase       )



		def  __UpperCAmelCase      (							self    ,		*_UpperCamelCase    ,		**_UpperCamelCase       )     ->							str:
							return self.tokenizer.decode(*_UpperCamelCase    ,		**_UpperCamelCase       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Dict:
							warnings.warn(
							    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,		_UpperCamelCase    ,		)
							return self.image_processor_class



		@property
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							warnings.warn(
							    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,		_UpperCamelCase    ,		)
							return self.image_processor














from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
		from ...processing_utils import ProcessorMixin
		from ...utils import TensorType


__UpperCAmelCase     							=						logging.get_logger(__name__)

__UpperCAmelCase     							=						{
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}



class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Dict							=  '''layoutlmv3'''



		def __init__(							self    ,		_UpperCamelCase=5_0_2_6_5    ,		_UpperCamelCase=7_6_8    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=1_2    ,		_UpperCamelCase=3_0_7_2    ,		_UpperCamelCase="gelu"    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=0.1    ,		_UpperCamelCase=5_1_2    ,		_UpperCamelCase=2    ,		_UpperCamelCase=0.02    ,		_UpperCamelCase=1E-5    ,		_UpperCamelCase=1    ,		_UpperCamelCase=0    ,		_UpperCamelCase=2    ,		_UpperCamelCase=1_0_2_4    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=True    ,		_UpperCamelCase=3_2    ,		_UpperCamelCase=1_2_8    ,		_UpperCamelCase=6_4    ,		_UpperCamelCase=2_5_6    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=True    ,		_UpperCamelCase=2_2_4    ,		_UpperCamelCase=3    ,		_UpperCamelCase=1_6    ,		_UpperCamelCase=None    ,		**_UpperCamelCase    ,		)     ->							Optional[Any]:
							super().__init__(
							    vocab_size=_UpperCamelCase    ,		hidden_size=_UpperCamelCase    ,		num_hidden_layers=_UpperCamelCase    ,		num_attention_heads=_UpperCamelCase    ,		intermediate_size=_UpperCamelCase    ,		hidden_act=_UpperCamelCase    ,		hidden_dropout_prob=_UpperCamelCase    ,		attention_probs_dropout_prob=_UpperCamelCase    ,		max_position_embeddings=_UpperCamelCase    ,		type_vocab_size=_UpperCamelCase    ,		initializer_range=_UpperCamelCase    ,		layer_norm_eps=_UpperCamelCase    ,		pad_token_id=_UpperCamelCase    ,		bos_token_id=_UpperCamelCase    ,		eos_token_id=_UpperCamelCase    ,		**_UpperCamelCase    ,		)
							UpperCAmelCase_   :				str						=						max_ad_position_embeddings
							UpperCAmelCase_   :				Union[str, Any]						=						coordinate_size
							UpperCAmelCase_   :				Union[str, Any]						=						shape_size
							UpperCAmelCase_   :				str						=						has_relative_attention_bias
							UpperCAmelCase_   :				Tuple						=						rel_pos_bins
							UpperCAmelCase_   :				Dict						=						max_rel_pos
							UpperCAmelCase_   :				Any						=						has_spatial_attention_bias
							UpperCAmelCase_   :				Optional[Any]						=						rel_ad_pos_bins
							UpperCAmelCase_   :				List[str]						=						max_rel_ad_pos
							UpperCAmelCase_   :				List[str]						=						text_embed
							UpperCAmelCase_   :				Dict						=						visual_embed
							UpperCAmelCase_   :				Optional[int]						=						input_size
							UpperCAmelCase_   :				List[Any]						=						num_channels
							UpperCAmelCase_   :				List[Any]						=						patch_size
							UpperCAmelCase_   :				Union[str, Any]						=						classifier_dropout





class   lowerCamelCase				(_snake_case      ):







		'''simple docstring'''



		_snake_case   :   Tuple							=  version.parse('''1.12'''      )



		@property
		def  __UpperCAmelCase      (							self       )     ->							Mapping[str, Mapping[int, str]]:
							# The order of inputs is different for question answering and sequence classification
							if self.task in ["question-answering", "sequence-classification"]:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
												    ]       )
							else:
												return OrderedDict(
												    [
												        ('input_ids', {0: 'batch', 1: 'sequence'}),
												        ('bbox', {0: 'batch', 1: 'sequence'}),
												        ('attention_mask', {0: 'batch', 1: 'sequence'}),
												        ('pixel_values', {0: 'batch', 1: 'num_channels'}),
												    ]       )



		@property
		def  __UpperCAmelCase      (							self       )     ->							float:
							return 1E-5



		@property
		def  __UpperCAmelCase      (							self       )     ->							int:
							return 1_2



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = -1    ,		_UpperCamelCase = False    ,		_UpperCamelCase = None    ,		_UpperCamelCase = 3    ,		_UpperCamelCase = 4_0    ,		_UpperCamelCase = 4_0    ,		)     ->							Mapping[str, Any]:
							setattr(processor.image_processor    ,		'apply_ocr'    ,		_UpperCamelCase       )

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Optional[int]						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_batch    ,		num_token_to_add=0       )
							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							UpperCAmelCase_   :				Any						=						processor.tokenizer.num_special_tokens_to_add(_UpperCamelCase       )
							UpperCAmelCase_   :				Any						=						compute_effective_axis_dimension(
							    _UpperCamelCase    ,		fixed_dimension=OnnxConfig.default_fixed_sequence    ,		num_token_to_add=_UpperCamelCase       )
							# Generate dummy inputs according to compute batch and sequence
							UpperCAmelCase_   :				Tuple						=						[[' '.join([processor.tokenizer.unk_token]       ) * seq_length]] * batch_size

							# Generate dummy bounding boxes
							UpperCAmelCase_   :				List[str]						=						[[[4_8, 8_4, 7_3, 1_2_8]]] * batch_size

							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
							UpperCAmelCase_   :				str						=						self._generate_dummy_images(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )

							UpperCAmelCase_   :				List[str]						=						dict(
							    processor(
							        _UpperCamelCase    ,		text=_UpperCamelCase    ,		boxes=_UpperCamelCase    ,		return_tensors=_UpperCamelCase    ,		)       )

							return inputs






