



"""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        __A       (	A_    ):







		'''simple docstring'''





		lowerCAmelCase			:					Dict   	=				["image_processor", "tokenizer"]
		lowerCAmelCase			:					Dict   	=				"OwlViTImageProcessor"
		lowerCAmelCase			:					Optional[int]   	=				("CLIPTokenizer", "CLIPTokenizerFast")







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






				"""simple docstring"""



				lowercase__   :    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.'''			,_snake_case			,)
						lowercase__   :    int				=			kwargs.pop('''feature_extractor'''						)

				lowercase__   :    Tuple				=			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__(_snake_case			,_snake_case						)







		def __call__(	self						:  Tuple			,_snake_case						:  int=None			,_snake_case						:  Optional[int]=None			,_snake_case						:  Optional[Any]=None			,_snake_case						:  Any="max_length"			,_snake_case						:  Optional[Any]="np"			,**_snake_case						:  List[Any]						)					->		Any:






				"""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(_snake_case			,_snake_case						) or (isinstance(_snake_case			,_snake_case						) and not isinstance(text[0]			,_snake_case						)):
								lowercase__   :    List[Any]				=			[self.tokenizer(_snake_case			,padding=_snake_case			,return_tensors=_snake_case			,**_snake_case						)]

						elif isinstance(_snake_case			,_snake_case						) and isinstance(text[0]			,_snake_case						):
								lowercase__   :    int				=			[]

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

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

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

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

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

								lowercase__   :    Union[str, Any]				=			jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]			,axis=0						)
								lowercase__   :    Optional[Any]				=			jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]			,axis=0						)

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

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

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

								lowercase__   :    Dict				=			tf.stack([encoding['''input_ids'''] for encoding in encodings]			,axis=0						)
								lowercase__   :    Optional[Any]				=			tf.stack([encoding['''attention_mask'''] for encoding in encodings]			,axis=0						)

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

						lowercase__   :    Dict				=			BatchEncoding()
						lowercase__   :    Tuple				=			input_ids
						lowercase__   :    Any				=			attention_mask

				if query_images is not None:
						lowercase__   :    Tuple				=			BatchEncoding()
						lowercase__   :    Dict				=			self.image_processor(
						    _snake_case			,return_tensors=_snake_case			,**_snake_case						).pixel_values
						lowercase__   :    Dict				=			query_pixel_values

				if images is not None:
						lowercase__   :    Union[str, Any]				=			self.image_processor(_snake_case			,return_tensors=_snake_case			,**_snake_case						)

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







		def 					UpperCAmelCase    (	self						:  Tuple			,*_snake_case						:  Union[str, Any]			,**_snake_case						:  Tuple						)					->		Tuple:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  List[Any]			,*_snake_case						:  Optional[Any]			,**_snake_case						:  Dict						)					->		Union[str, Any]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Dict			,*_snake_case						:  Optional[int]			,**_snake_case						:  Optional[int]						)					->		Union[str, Any]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  List[Any]			,*_snake_case						:  Dict			,**_snake_case						:  Dict						)					->		Union[str, Any]:






				"""simple docstring"""



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







		def 					UpperCAmelCase    (	self						:  Optional[int]			,*_snake_case						:  Dict			,**_snake_case						:  Optional[Any]						)					->		str:






				"""simple docstring"""



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







		@property
		def 					UpperCAmelCase    (	self						:  str						)					->		Optional[Any]:






				"""simple docstring"""



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







		@property
		def 					UpperCAmelCase    (	self						:  Any						)					->		str:






				"""simple docstring"""



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





"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


lowerCAmelCase_   						=   {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   ['SpeechT5Tokenizer']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase_   						=   [
							    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'SpeechT5ForSpeechToText',
							    'SpeechT5ForSpeechToSpeech',
							    'SpeechT5ForTextToSpeech',
							    'SpeechT5Model',
							    'SpeechT5PreTrainedModel',
							    'SpeechT5HifiGan',
							]

if TYPE_CHECKING:
							from .configuration_speechta import (
							    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
							    SpeechTaConfig,
							    SpeechTaHifiGanConfig,
							)
							from .feature_extraction_speechta import SpeechTaFeatureExtractor
							from .processing_speechta import SpeechTaProcessor

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_speechta import SpeechTaTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_speechta import (
														    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
														    SpeechTaForSpeechToSpeech,
														    SpeechTaForSpeechToText,
														    SpeechTaForTextToSpeech,
														    SpeechTaHifiGan,
														    SpeechTaModel,
														    SpeechTaPreTrainedModel,
														)

else:
							import sys

							lowerCAmelCase_   						=   _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
