




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__    (					snake_case__      ):
						_a							:    List[Any]					=    ["""image_processor""", """tokenizer"""]
						_a							:    int					=    """OwlViTImageProcessor"""
						_a							:    Optional[Any]					=    ("""CLIPTokenizer""", """CLIPTokenizerFast""")






						def __init__(   self		,			_A=None		,			_A=None		,			**_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       None
											if "feature_extractor" in kwargs:
																warnings.warn(
																    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
																    " instead."		,			_A		,			)
																__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__(_A		,			_A			)






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

																elif isinstance(_A		,			_A			) and isinstance(text[0]		,			_A			):
																					__lowerCAmelCase				   =       []

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

																					# Pad all batch samples to max number of text queries
																					for t in text:
																										if len(_A			) != max_num_queries:
																															__lowerCAmelCase				   =       t + [" "] * (max_num_queries - len(_A			))

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

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

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

																					__lowerCAmelCase				   =       jnp.concatenate([encoding["input_ids"] for encoding in encodings]		,			axis=0			)
																					__lowerCAmelCase				   =       jnp.concatenate([encoding["attention_mask"] for encoding in encodings]		,			axis=0			)

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

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

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

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

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

																__lowerCAmelCase				   =       BatchEncoding()
																__lowerCAmelCase				   =       input_ids
																__lowerCAmelCase				   =       attention_mask

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

											if images is not None:
																__lowerCAmelCase				   =       self.image_processor(_A		,			return_tensors=_A		,			**_A			)

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






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.image_processor.post_process(*_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.image_processor.post_process_object_detection(*_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



											return self.image_processor.post_process_image_guided_detection(*_A		,			**_A			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



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






						def 				__SCREAMING_SNAKE_CASE(   self		,			*_A		,			**_A			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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










from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available


UpperCamelCase__    			=			{
    """configuration_audio_spectrogram_transformer""": [
        """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ASTConfig""",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__    			=			[
					    """AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """ASTForAudioClassification""",
					    """ASTModel""",
					    """ASTPreTrainedModel""",
					]

try:
					if not is_speech_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__    			=			["""ASTFeatureExtractor"""]

if TYPE_CHECKING:
					from .configuration_audio_spectrogram_transformer import (
					    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    ASTConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_audio_spectrogram_transformer import (
										    AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    ASTForAudioClassification,
										    ASTModel,
										    ASTPreTrainedModel,
										)

					try:
										if not is_speech_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .feature_extraction_audio_spectrogram_transformer import ASTFeatureExtractor


else:
					import sys

					UpperCamelCase__    			=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




