





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       A_			(a_     ):
	UpperCAmelCase__   =     ['''image_processor''', '''tokenizer''']
	UpperCAmelCase__   =     '''OwlViTImageProcessor'''
	UpperCAmelCase__   =     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
	def __init__(       self				,   _A=None				,   _A=None				,   **_A						):






				'''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.'''				,   lowerCamelCase__				,   )
							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__(lowerCamelCase__				,   lowerCamelCase__						)
	def __call__(       self				,   _A=None				,   _A=None				,   _A=None				,   _A="max_length"				,   _A="np"				,   **_A						):






				'''simple docstring'''







				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(lowerCamelCase__				,   lowerCamelCase__						) or (isinstance(lowerCamelCase__				,   lowerCamelCase__						) and not isinstance(text[0]				,   lowerCamelCase__						)):
										UpperCAmelCase  						=							[self.tokenizer(lowerCamelCase__				,   padding=lowerCamelCase__				,   return_tensors=lowerCamelCase__				,   **lowerCamelCase__						)]

							elif isinstance(lowerCamelCase__				,   lowerCamelCase__						) and isinstance(text[0]				,   lowerCamelCase__						):
										UpperCAmelCase  						=							[]

										# Maximum number of queries across batch
										UpperCAmelCase  						=							max([len(lowerCamelCase__						) for t in text]						)

										# Pad all batch samples to max number of text queries
										for t in text:
													if len(lowerCamelCase__						) != max_num_queries:
																UpperCAmelCase  						=							t + [''' '''] * (max_num_queries - len(lowerCamelCase__						))

													UpperCAmelCase  						=							self.tokenizer(lowerCamelCase__				,   padding=lowerCamelCase__				,   return_tensors=lowerCamelCase__				,   **lowerCamelCase__						)
													encodings.append(lowerCamelCase__						)
							else:
										raise TypeError('''Input text should be a string, a list of strings or a nested list of strings'''						)

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

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

										UpperCAmelCase  						=							torch.cat([encoding['''input_ids'''] for encoding in encodings]				,   dim=0						)
										UpperCAmelCase  						=							torch.cat([encoding['''attention_mask'''] for encoding in encodings]				,   dim=0						)

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

										UpperCAmelCase  						=							tf.stack([encoding['''input_ids'''] for encoding in encodings]				,   axis=0						)
										UpperCAmelCase  						=							tf.stack([encoding['''attention_mask'''] for encoding in encodings]				,   axis=0						)

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

							UpperCAmelCase  						=							BatchEncoding()
							UpperCAmelCase  						=							input_ids
							UpperCAmelCase  						=							attention_mask

				if query_images is not None:
							UpperCAmelCase  						=							BatchEncoding()
							UpperCAmelCase  						=							self.image_processor(
							    lowerCamelCase__				,   return_tensors=lowerCamelCase__				,   **lowerCamelCase__						).pixel_values
							UpperCAmelCase  						=							query_pixel_values

				if images is not None:
							UpperCAmelCase  						=							self.image_processor(lowerCamelCase__				,   return_tensors=lowerCamelCase__				,   **lowerCamelCase__						)

				if text is not None and images is not None:
							UpperCAmelCase  						=							image_features.pixel_values
							return encoding
				elif query_images is not None and images is not None:
							UpperCAmelCase  						=							image_features.pixel_values
							return encoding
				elif text is not None or query_images is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowerCamelCase__						)				,   tensor_type=lowerCamelCase__						)
	def 	_lowercase							(       self				,   *_A				,   **_A						):






				'''simple docstring'''







				return self.image_processor.post_process(*lowerCamelCase__				,   **lowerCamelCase__						)
	def 	_lowercase							(       self				,   *_A				,   **_A						):






				'''simple docstring'''







				return self.image_processor.post_process_object_detection(*lowerCamelCase__				,   **lowerCamelCase__						)
	def 	_lowercase							(       self				,   *_A				,   **_A						):






				'''simple docstring'''







				return self.image_processor.post_process_image_guided_detection(*lowerCamelCase__				,   **lowerCamelCase__						)
	def 	_lowercase							(       self				,   *_A				,   **_A						):






				'''simple docstring'''







				return self.tokenizer.batch_decode(*lowerCamelCase__				,   **lowerCamelCase__						)
	def 	_lowercase							(       self				,   *_A				,   **_A						):






				'''simple docstring'''







				return self.tokenizer.decode(*lowerCamelCase__				,   **lowerCamelCase__						)
	@property
	def 	_lowercase							(       self						):






				'''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						):






				'''simple docstring'''







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









from collections import OrderedDict
from typing import Mapping

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


__A								=       logging.get_logger(__name__)

__A								=       {
    "facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
    "facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			'''xlm-roberta-xl'''


			def __init__( self     ,		lowerCamelCase__=250_880     ,		lowerCamelCase__=2_560     ,		lowerCamelCase__=36     ,		lowerCamelCase__=32     ,		lowerCamelCase__=10_240     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=514     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=1e-05     ,		lowerCamelCase__=1     ,		lowerCamelCase__=0     ,		lowerCamelCase__=2     ,		lowerCamelCase__="absolute"     ,		lowerCamelCase__=True     ,		lowerCamelCase__=None     ,		**lowerCamelCase__     ,		)       -> str:



							'''simple docstring'''
							super().__init__(pad_token_id=lowerCamelCase__     ,		bos_token_id=lowerCamelCase__     ,		eos_token_id=lowerCamelCase__     ,		**lowerCamelCase__				)
							__lowerCamelCase									=       vocab_size
							__lowerCamelCase									=       hidden_size
							__lowerCamelCase									=       num_hidden_layers
							__lowerCamelCase									=       num_attention_heads
							__lowerCamelCase									=       hidden_act
							__lowerCamelCase									=       intermediate_size
							__lowerCamelCase									=       hidden_dropout_prob
							__lowerCamelCase									=       attention_probs_dropout_prob
							__lowerCamelCase									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       layer_norm_eps
							__lowerCamelCase									=       position_embedding_type
							__lowerCamelCase									=       use_cache
							__lowerCamelCase									=       classifier_dropout







class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			@property
			def  lowercase_       ( self				)       -> Mapping[str, Mapping[int, str]]:



							'''simple docstring'''
							if self.task == "multiple-choice":
											__lowerCamelCase									=       {0: 'batch', 1: 'choice', 2: 'sequence'}
							else:
											__lowerCamelCase									=       {0: 'batch', 1: 'sequence'}
							return OrderedDict(
							    [
							        ('input_ids', dynamic_axis),
							        ('attention_mask', dynamic_axis),
							    ]				)


