

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


SCREAMING_SNAKE_CASE_            = logging.get_logger(__name__)


if is_vision_available():
     import PIL

class 			a					(		UpperCAmelCase			):
      _lowercase        =     ["pixel_values"]


      def __init__(    self  ,   A_ = True  ,   A_ = None  ,   A_ = PILImageResampling.BICUBIC  ,   A_ = True  ,   A_ = None  ,   A_ = True  ,   A_ = 1 / 255  ,   A_ = True  ,   A_ = None  ,   A_ = None  ,   A_ = True  ,   **A_  ,   ):







          '''simple docstring'''


          super().__init__(**A_		)
          _UpperCAmelCase    :						List[str]							=      size if size is not None else {"shortest_edge": 224}
          _UpperCAmelCase    :						Dict							=      get_size_dict(A_  ,   default_to_square=A_		)
          _UpperCAmelCase    :						Optional[int]							=      crop_size if crop_size is not None else {"height": 224, "width": 224}
          _UpperCAmelCase    :						Dict							=      get_size_dict(A_  ,   default_to_square=A_  ,   param_name="crop_size"		)

          _UpperCAmelCase    :						str							=      do_resize
          _UpperCAmelCase    :						Dict							=      size
          _UpperCAmelCase    :						Union[str, Any]							=      resample
          _UpperCAmelCase    :						Optional[int]							=      do_center_crop
          _UpperCAmelCase    :						Optional[int]							=      crop_size
          _UpperCAmelCase    :						List[str]							=      do_rescale
          _UpperCAmelCase    :						Union[str, Any]							=      rescale_factor
          _UpperCAmelCase    :						Optional[Any]							=      do_normalize
          _UpperCAmelCase    :						Dict							=      image_mean if image_mean is not None else OPENAI_CLIP_MEAN
          _UpperCAmelCase    :						Optional[Any]							=      image_std if image_std is not None else OPENAI_CLIP_STD
          _UpperCAmelCase    :						Tuple							=      do_convert_rgb


      def     _UpperCAmelCase			(    self  ,   A_  ,   A_  ,   A_ = PILImageResampling.BICUBIC  ,   A_ = None  ,   **A_  ,   ):







          '''simple docstring'''


          _UpperCAmelCase    :						int							=      get_size_dict(A_  ,   default_to_square=A_		)
          if "shortest_edge" not in size:
              raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'		)
          _UpperCAmelCase    :						Optional[int]							=      get_resize_output_image_size(A_  ,   size=size["shortest_edge"]  ,   default_to_square=A_		)
          return resize(A_  ,   size=A_  ,   resample=A_  ,   data_format=A_  ,   **A_		)


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







          '''simple docstring'''


          _UpperCAmelCase    :						str							=      get_size_dict(A_		)
          if "height" not in size or "width" not in size:
              raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'		)
          return center_crop(A_  ,   size=(size["height"], size["width"])  ,   data_format=A_  ,   **A_		)


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







          '''simple docstring'''


          return rescale(A_  ,   scale=A_  ,   data_format=A_  ,   **A_		)


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







          '''simple docstring'''


          return normalize(A_  ,   mean=A_  ,   std=A_  ,   data_format=A_  ,   **A_		)





      def     _UpperCAmelCase			(    self  ,   A_  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = None  ,   A_ = ChannelDimension.FIRST  ,   **A_  ,   ):







          '''simple docstring'''


          _UpperCAmelCase    :						List[Any]							=      do_resize if do_resize is not None else self.do_resize
          _UpperCAmelCase    :						int							=      size if size is not None else self.size
          _UpperCAmelCase    :						Optional[Any]							=      get_size_dict(A_  ,   param_name="size"  ,   default_to_square=A_		)
          _UpperCAmelCase    :						Tuple							=      resample if resample is not None else self.resample
          _UpperCAmelCase    :						Any							=      do_center_crop if do_center_crop is not None else self.do_center_crop
          _UpperCAmelCase    :						str							=      crop_size if crop_size is not None else self.crop_size
          _UpperCAmelCase    :						Optional[int]							=      get_size_dict(A_  ,   param_name="crop_size"  ,   default_to_square=A_		)
          _UpperCAmelCase    :						Dict							=      do_rescale if do_rescale is not None else self.do_rescale
          _UpperCAmelCase    :						List[str]							=      rescale_factor if rescale_factor is not None else self.rescale_factor
          _UpperCAmelCase    :						Dict							=      do_normalize if do_normalize is not None else self.do_normalize
          _UpperCAmelCase    :						Tuple							=      image_mean if image_mean is not None else self.image_mean
          _UpperCAmelCase    :						int							=      image_std if image_std is not None else self.image_std
          _UpperCAmelCase    :						int							=      do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

          _UpperCAmelCase    :						List[str]							=      make_list_of_images(A_		)

          if not valid_images(A_		):
              raise ValueError(
                  "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
                  "torch.Tensor, tf.Tensor or jax.ndarray."		)

          if do_resize and size is None:
              raise ValueError("Size must be specified if do_resize is True."		)

          if do_center_crop and crop_size is None:
              raise ValueError("Crop size must be specified if do_center_crop is True."		)

          if do_rescale and rescale_factor is None:
              raise ValueError("Rescale factor must be specified if do_rescale is True."		)

          if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("Image mean and std must be specified if do_normalize is True."		)

          # PIL RGBA images are converted to RGB
          if do_convert_rgb:
              _UpperCAmelCase    :						Optional[Any]							=      [convert_to_rgb(A_		) for image in images]

          # All transformations expect numpy arrays.
          _UpperCAmelCase    :						Optional[Any]							=      [to_numpy_array(A_		) for image in images]

          if do_resize:
              _UpperCAmelCase    :						Optional[int]							=      [self.resize(image=A_  ,   size=A_  ,   resample=A_		) for image in images]

          if do_center_crop:
              _UpperCAmelCase    :						Any							=      [self.center_crop(image=A_  ,   size=A_		) for image in images]

          if do_rescale:
              _UpperCAmelCase    :						Optional[Any]							=      [self.rescale(image=A_  ,   scale=A_		) for image in images]

          if do_normalize:
              _UpperCAmelCase    :						str							=      [self.normalize(image=A_  ,   mean=A_  ,   std=A_		) for image in images]

          _UpperCAmelCase    :						Optional[int]							=      [to_channel_dimension_format(A_  ,   A_		) for image in images]

          _UpperCAmelCase    :						List[Any]							=      {"pixel_values": images}
          return BatchFeature(data=A_  ,   tensor_type=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


