

from typing import Tuple, Union

from ...modeling_outputs import BackboneOutput
from ...modeling_utils import PreTrainedModel
from ...utils import is_timm_available, is_torch_available, requires_backends
from ...utils.backbone_utils import BackboneMixin
from .configuration_timm_backbone import TimmBackboneConfig


if is_timm_available():
					import timm


if is_torch_available():
					from torch import Tensor

class 			a					(		UpperCAmelCase							,	UpperCAmelCase			):
						_lowercase        =     "pixel_values"
						_lowercase        =     False
						_lowercase        =     TimmBackboneConfig


						def __init__(    self  ,   A_  ,   **A_		):







										'''simple docstring'''


										requires_backends(self  ,   "timm"		)
										super().__init__(A_		)
										_UpperCAmelCase    :						Union[str, Any]							=      config

										if config.backbone is None:
														raise ValueError("backbone is not set in the config. Please set it to a timm model name."		)

										if config.backbone not in timm.list_models():
														raise ValueError(f'backbone {config.backbone} is not supported by timm.'		)

										if hasattr(A_  ,   "out_features"		) and config.out_features is not None:
														raise ValueError("out_features is not supported by TimmBackbone. Please use out_indices instead."		)

										_UpperCAmelCase    :						List[Any]							=      getattr(A_  ,   "use_pretrained_backbone"  ,   A_		)
										if pretrained is None:
														raise ValueError("use_pretrained_backbone is not set in the config. Please set it to True or False."		)

										# We just take the final layer by default. This matches the default for the transformers models.
										_UpperCAmelCase    :						Any							=      config.out_indices if getattr(A_  ,   "out_indices"  ,   A_		) is not None else (-1,)

										_UpperCAmelCase    :						Union[str, Any]							=      timm.create_model(
										    config.backbone  ,   pretrained=A_  ,   features_only=config.features_only  ,   in_chans=config.num_channels  ,   out_indices=A_  ,   **A_  ,   )
										# These are used to control the output of the model when called. If output_hidden_states is True, then
										# return_layers is modified to include all layers.
										_UpperCAmelCase    :						List[str]							=      self._backbone.return_layers
										_UpperCAmelCase    :						Tuple							=      {layer["module"]: str(A_		) for i, layer in enumerate(self._backbone.feature_info.info		)}
										super()._init_backbone(A_		)


						@classmethod
						def     _UpperCAmelCase			(    cls  ,   A_  ,   *A_  ,   **A_		):







										'''simple docstring'''


										requires_backends(cls  ,   ["vision", "timm"]		)
										from ...models.timm_backbone import TimmBackboneConfig

										_UpperCAmelCase    :						Any							=      kwargs.pop("config"  ,   TimmBackboneConfig()		)

										_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("use_timm_backbone"  ,   A_		)
										if not use_timm:
														raise ValueError("use_timm_backbone must be True for timm backbones"		)

										_UpperCAmelCase    :						Optional[Any]							=      kwargs.pop("num_channels"  ,   config.num_channels		)
										_UpperCAmelCase    :						int							=      kwargs.pop("features_only"  ,   config.features_only		)
										_UpperCAmelCase    :						Optional[Any]							=      kwargs.pop("use_pretrained_backbone"  ,   config.use_pretrained_backbone		)
										_UpperCAmelCase    :						List[str]							=      kwargs.pop("out_indices"  ,   config.out_indices		)
										_UpperCAmelCase    :						str							=      TimmBackboneConfig(
										    backbone=A_  ,   num_channels=A_  ,   features_only=A_  ,   use_pretrained_backbone=A_  ,   out_indices=A_  ,   )
										return super()._from_config(A_  ,   **A_		)


						def     _UpperCAmelCase			(    self  ,   A_		):







										'''simple docstring'''


										pass





						def     _UpperCAmelCase			(    self  ,   A_  ,   A_=None  ,   A_=None  ,   A_=None  ,   **A_		):







										'''simple docstring'''


										_UpperCAmelCase    :						Tuple							=      return_dict if return_dict is not None else self.config.use_return_dict
										_UpperCAmelCase    :						Optional[Any]							=      (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										_UpperCAmelCase    :						Optional[Any]							=      output_attentions if output_attentions is not None else self.config.output_attentions

										if output_attentions:
														raise ValueError("Cannot output attentions for timm backbones at the moment"		)

										if output_hidden_states:
														# We modify the return layers to include all the stages of the backbone
														_UpperCAmelCase    :						Optional[int]							=      self._all_layers
														_UpperCAmelCase    :						Any							=      self._backbone(A_  ,   **A_		)
														_UpperCAmelCase    :						Union[str, Any]							=      self._return_layers
														_UpperCAmelCase    :						List[str]							=      tuple(hidden_states[i] for i in self.out_indices		)
										else:
														_UpperCAmelCase    :						int							=      self._backbone(A_  ,   **A_		)
														_UpperCAmelCase    :						Optional[Any]							=      None

										_UpperCAmelCase    :						Optional[Any]							=      tuple(A_		)
										_UpperCAmelCase    :						List[str]							=      tuple(A_		) if hidden_states is not None else None

										if not return_dict:
														_UpperCAmelCase    :						Tuple							=      (feature_maps,)
														if output_hidden_states:
																		_UpperCAmelCase    :						Optional[int]							=      output + (hidden_states,)
														return output

										return BackboneOutput(feature_maps=A_  ,   hidden_states=A_  ,   attentions=A_		)





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					(		UpperCAmelCase			):
						_lowercase        =     ["image_processor", "tokenizer"]
						_lowercase        =     "OwlViTImageProcessor"
						_lowercase        =     ("CLIPTokenizer", "CLIPTokenizerFast")


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







										'''simple docstring'''


										_UpperCAmelCase    :						List[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."  ,   A_  ,   )
														_UpperCAmelCase    :						Union[str, Any]							=      kwargs.pop("feature_extractor"		)

										_UpperCAmelCase    :						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__(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_		)):
																		_UpperCAmelCase    :						Optional[int]							=      [self.tokenizer(A_  ,   padding=A_  ,   return_tensors=A_  ,   **A_		)]

														elif isinstance(A_  ,   A_		) and isinstance(text[0]  ,   A_		):
																		_UpperCAmelCase    :						Optional[int]							=      []

																		# Maximum number of queries across batch
																		_UpperCAmelCase    :						Optional[Any]							=      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:
																										_UpperCAmelCase    :						Optional[int]							=      t + [" "] * (max_num_queries - len(A_		))

																						_UpperCAmelCase    :						str							=      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":
																		_UpperCAmelCase    :						List[str]							=      np.concatenate([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						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

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

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

																		_UpperCAmelCase    :						Union[str, Any]							=      tf.stack([encoding["input_ids"] for encoding in encodings]  ,   axis=0		)
																		_UpperCAmelCase    :						Optional[int]							=      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    :						str							=      input_ids
														_UpperCAmelCase    :						Optional[Any]							=      attention_mask

										if query_images is not None:
														_UpperCAmelCase    :						int							=      BatchEncoding()
														_UpperCAmelCase    :						str							=      self.image_processor(
														    A_  ,   return_tensors=A_  ,   **A_		).pixel_values
														_UpperCAmelCase    :						Optional[Any]							=      query_pixel_values

										if images is not None:
														_UpperCAmelCase    :						int							=      self.image_processor(A_  ,   return_tensors=A_  ,   **A_		)

										if text is not None and images is not None:
														_UpperCAmelCase    :						Optional[int]							=      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(**A_		)  ,   tensor_type=A_		)


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						def     _UpperCAmelCase			(    self  ,   *A_  ,   **A_		):







										'''simple docstring'''


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


						@property
						def     _UpperCAmelCase			(    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     _UpperCAmelCase			(    self		):







										'''simple docstring'''


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


