


'''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    lowercase	(							A__					):

		"""simple docstring"""

		_a     =				["image_processor", "tokenizer"]
		_a     =				"OwlViTImageProcessor"
		_a     =				("CLIPTokenizer", "CLIPTokenizerFast")




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

							'''simple docstring'''


							UpperCamelCase__   :Tuple												= 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__   :List[Any]												= kwargs.pop('''feature_extractor'''							)

							UpperCamelCase__   :List[Any]												= 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_							):

							'''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(UpperCAmelCase__    ,   UpperCAmelCase__							) or (isinstance(UpperCAmelCase__    ,   UpperCAmelCase__							) and not isinstance(text[0]    ,   UpperCAmelCase__							)):
																	UpperCamelCase__   :Optional[Any]												= [self.tokenizer(UpperCAmelCase__    ,   padding=UpperCAmelCase__    ,   return_tensors=UpperCAmelCase__    ,   **UpperCAmelCase__							)]

												elif isinstance(UpperCAmelCase__    ,   UpperCAmelCase__							) and isinstance(text[0]    ,   UpperCAmelCase__							):
																	UpperCamelCase__   :Any												= []

																	# Maximum number of queries across batch
																	UpperCamelCase__   :List[str]												= 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__   :Dict												= t + [''' '''] * (max_num_queries - len(UpperCAmelCase__							))

																						UpperCamelCase__   :Optional[Any]												= 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__   :Union[str, Any]												= np.concatenate([encoding['''input_ids'''] for encoding in encodings]    ,   axis=0							)
																	UpperCamelCase__   :List[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__   :Any												= jnp.concatenate([encoding['''input_ids'''] for encoding in encodings]    ,   axis=0							)
																	UpperCamelCase__   :int												= jnp.concatenate([encoding['''attention_mask'''] for encoding in encodings]    ,   axis=0							)

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

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

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

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

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

												UpperCamelCase__   :Optional[int]												= BatchEncoding()
												UpperCamelCase__   :Optional[Any]												= input_ids
												UpperCamelCase__   :Dict												= attention_mask

							if query_images is not None:
												UpperCamelCase__   :int												= BatchEncoding()
												UpperCamelCase__   :Optional[Any]												= self.image_processor(
												    UpperCAmelCase__    ,   return_tensors=UpperCAmelCase__    ,   **UpperCAmelCase__							).pixel_values
												UpperCamelCase__   :Tuple												= query_pixel_values

							if images is not None:
												UpperCamelCase__   :List[Any]												= self.image_processor(UpperCAmelCase__    ,   return_tensors=UpperCAmelCase__    ,   **UpperCAmelCase__							)

							if text is not None and images is not None:
												UpperCamelCase__   :List[str]												= image_features.pixel_values
												return encoding
							elif query_images is not None and images is not None:
												UpperCamelCase__   :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(**UpperCAmelCase__							)    ,   tensor_type=UpperCAmelCase__							)




		def     lowerCAmelCase__				(		self    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		def     lowerCAmelCase__				(		self    ,   *UpperCamelCase_    ,   **UpperCamelCase_							):

							'''simple docstring'''


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




		@property
		def     lowerCAmelCase__				(		self							):

							'''simple docstring'''


							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     lowerCAmelCase__				(		self							):

							'''simple docstring'''


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



"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a__				:							str						=     {
    '''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoFormerConfig''', '''RoFormerOnnxConfig'''],
    '''tokenization_roformer''': ['''RoFormerTokenizer'''],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     ['''RoFormerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							int						=     [
		    '''ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''RoFormerForCausalLM''',
		    '''RoFormerForMaskedLM''',
		    '''RoFormerForMultipleChoice''',
		    '''RoFormerForQuestionAnswering''',
		    '''RoFormerForSequenceClassification''',
		    '''RoFormerForTokenClassification''',
		    '''RoFormerLayer''',
		    '''RoFormerModel''',
		    '''RoFormerPreTrainedModel''',
		    '''load_tf_weights_in_roformer''',
		]


try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							List[Any]						=     [
		    '''TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''TFRoFormerForCausalLM''',
		    '''TFRoFormerForMaskedLM''',
		    '''TFRoFormerForMultipleChoice''',
		    '''TFRoFormerForQuestionAnswering''',
		    '''TFRoFormerForSequenceClassification''',
		    '''TFRoFormerForTokenClassification''',
		    '''TFRoFormerLayer''',
		    '''TFRoFormerModel''',
		    '''TFRoFormerPreTrainedModel''',
		]


try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__				:							Tuple						=     [
		    '''FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''FlaxRoFormerForMaskedLM''',
		    '''FlaxRoFormerForMultipleChoice''',
		    '''FlaxRoFormerForQuestionAnswering''',
		    '''FlaxRoFormerForSequenceClassification''',
		    '''FlaxRoFormerForTokenClassification''',
		    '''FlaxRoFormerModel''',
		    '''FlaxRoFormerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
		from .tokenization_roformer import RoFormerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_roformer_fast import RoFormerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roformer import (
				    ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoFormerForCausalLM,
				    RoFormerForMaskedLM,
				    RoFormerForMultipleChoice,
				    RoFormerForQuestionAnswering,
				    RoFormerForSequenceClassification,
				    RoFormerForTokenClassification,
				    RoFormerLayer,
				    RoFormerModel,
				    RoFormerPreTrainedModel,
				    load_tf_weights_in_roformer,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_roformer import (
				    TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFRoFormerForCausalLM,
				    TFRoFormerForMaskedLM,
				    TFRoFormerForMultipleChoice,
				    TFRoFormerForQuestionAnswering,
				    TFRoFormerForSequenceClassification,
				    TFRoFormerForTokenClassification,
				    TFRoFormerLayer,
				    TFRoFormerModel,
				    TFRoFormerPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_roformer import (
				    FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    FlaxRoFormerForMaskedLM,
				    FlaxRoFormerForMultipleChoice,
				    FlaxRoFormerForQuestionAnswering,
				    FlaxRoFormerForSequenceClassification,
				    FlaxRoFormerForTokenClassification,
				    FlaxRoFormerModel,
				    FlaxRoFormerPreTrainedModel,
				)


else:
		import sys

		a__				:							int						=     _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
