import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
    import torch

if is_vision_available():
    import PIL


lowerCAmelCase			:							Union[str, Any]     =logging.get_logger(__name__)







def 						A__  (					__A   , __A   , __A   , __A	):







    '''simple docstring'''
    def constraint_to_multiple_of(__A   , __A   , __A=0   , __A=None	):
        _lowerCamelCase     :		Optional[int] 						=						round(val / multiple	) * multiple

        if max_val is not None and x > max_val:
            _lowerCamelCase     :		Optional[int] 						=						math.floor(val / multiple	) * multiple

        if x < min_val:
            _lowerCamelCase     :		Union[str, Any] 						=						math.ceil(val / multiple	) * multiple

        return x

    _lowerCamelCase     :		str 						=						(output_size, output_size) if isinstance(__A   , __A	) else output_size

    _lowerCamelCase						,							_lowerCamelCase     :		Optional[int] 						=						get_image_size(__A	)
    _lowerCamelCase						,							_lowerCamelCase     :		int 						=						output_size

    # determine new height and width
    _lowerCamelCase     :		Dict 						=						output_height / input_height
    _lowerCamelCase     :		Union[str, Any] 						=						output_width / input_width

    if keep_aspect_ratio:
        # scale as little as possible
        if abs(1 - scale_width	) < abs(1 - scale_height	):
            # fit width
            _lowerCamelCase     :		Optional[int] 						=						scale_width
        else:
            # fit height
            _lowerCamelCase     :		List[Any] 						=						scale_height

    _lowerCamelCase     :		str 						=						constraint_to_multiple_of(scale_height * input_height   , multiple=__A	)
    _lowerCamelCase     :		Tuple 						=						constraint_to_multiple_of(scale_width * input_width   , multiple=__A	)

    return (new_height, new_width)




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      _snake_case    		=       ['pixel_values']



      def __init__(					self     :				int    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Dict[str, int] = None    ,  _UpperCamelCase     :				PILImageResampling = PILImageResampling.BILINEAR    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				int = 1    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Union[int, float] = 1 / 255    ,  _UpperCamelCase     :				bool = True    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  **_UpperCamelCase     :				List[str]    ,  )				->None:


          """simple docstring"""


          super().__init__(**_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						size if size is not None else {"""height""": 384, """width""": 384}
          _lowerCamelCase     :		Any 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		Optional[int] 						=						do_resize
          _lowerCamelCase     :		Any 						=						size
          _lowerCamelCase     :		List[Any] 						=						keep_aspect_ratio
          _lowerCamelCase     :		Optional[Any] 						=						ensure_multiple_of
          _lowerCamelCase     :		Dict 						=						resample
          _lowerCamelCase     :		Any 						=						do_rescale
          _lowerCamelCase     :		Any 						=						rescale_factor
          _lowerCamelCase     :		List[Any] 						=						do_normalize
          _lowerCamelCase     :		str 						=						image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
          _lowerCamelCase     :		List[Any] 						=						image_std if image_std is not None else IMAGENET_STANDARD_STD



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Dict[str, int]    ,  _UpperCamelCase     :				bool = False    ,  _UpperCamelCase     :				int = 1    ,  _UpperCamelCase     :				PILImageResampling = PILImageResampling.BICUBIC    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				List[str]    ,  )				->np.ndarray:


          """simple docstring"""


          _lowerCamelCase     :		List[Any] 						=						get_size_dict(_UpperCamelCase)
          if "height" not in size or "width" not in size:
              raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""")
          _lowerCamelCase     :		List[Any] 						=						get_resize_output_image_size(
              _UpperCamelCase    ,  output_size=(size["""height"""], size["""width"""])    ,  keep_aspect_ratio=_UpperCamelCase    ,  multiple=_UpperCamelCase    ,  )
          return resize(_UpperCamelCase    ,  size=_UpperCamelCase    ,  resample=_UpperCamelCase    ,  data_format=_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[int, float]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				Optional[int]    ,  )				->int:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				np.ndarray    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Union[float, List[float]]    ,  _UpperCamelCase     :				Optional[Union[str, ChannelDimension]] = None    ,  **_UpperCamelCase     :				int    ,  )				->np.ndarray:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple    ,  _UpperCamelCase     :				ImageInput    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				int = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				int = None    ,  _UpperCamelCase     :				PILImageResampling = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				float = None    ,  _UpperCamelCase     :				bool = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[float, List[float]]] = None    ,  _UpperCamelCase     :				Optional[Union[str, TensorType]] = None    ,  _UpperCamelCase     :				ChannelDimension = ChannelDimension.FIRST    ,  **_UpperCamelCase     :				Dict    ,  )				->PIL.Image.Image:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						do_resize if do_resize is not None else self.do_resize
          _lowerCamelCase     :		Tuple 						=						size if size is not None else self.size
          _lowerCamelCase     :		Any 						=						get_size_dict(_UpperCamelCase)
          _lowerCamelCase     :		List[str] 						=						keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
          _lowerCamelCase     :		List[str] 						=						ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
          _lowerCamelCase     :		int 						=						resample if resample is not None else self.resample
          _lowerCamelCase     :		Tuple 						=						do_rescale if do_rescale is not None else self.do_rescale
          _lowerCamelCase     :		str 						=						rescale_factor if rescale_factor is not None else self.rescale_factor
          _lowerCamelCase     :		Optional[int] 						=						do_normalize if do_normalize is not None else self.do_normalize
          _lowerCamelCase     :		Union[str, Any] 						=						image_mean if image_mean is not None else self.image_mean
          _lowerCamelCase     :		Union[str, Any] 						=						image_std if image_std is not None else self.image_std

          _lowerCamelCase     :		str 						=						make_list_of_images(_UpperCamelCase)

          if not valid_images(_UpperCamelCase):
              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 or resample is None:
              raise ValueError("""Size and resample must be specified if do_resize 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.""")

          # All transformations expect numpy arrays.
          _lowerCamelCase     :		Union[str, Any] 						=						[to_numpy_array(_UpperCamelCase) for image in images]

          if do_resize:
              _lowerCamelCase     :		Union[str, Any] 						=						[self.resize(image=_UpperCamelCase    ,  size=_UpperCamelCase    ,  resample=_UpperCamelCase) for image in images]

          if do_rescale:
              _lowerCamelCase     :		Any 						=						[self.rescale(image=_UpperCamelCase    ,  scale=_UpperCamelCase) for image in images]

          if do_normalize:
              _lowerCamelCase     :		List[str] 						=						[self.normalize(image=_UpperCamelCase    ,  mean=_UpperCamelCase    ,  std=_UpperCamelCase) for image in images]

          _lowerCamelCase     :		int 						=						[to_channel_dimension_format(_UpperCamelCase    ,  _UpperCamelCase) for image in images]

          _lowerCamelCase     :		str 						=						{"""pixel_values""": images}
          return BatchFeature(data=_UpperCamelCase    ,  tensor_type=_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				Tuple    ,  _UpperCamelCase     :				List[Tuple] = None)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						outputs.logits

          # Resize logits and compute semantic segmentation maps
          if target_sizes is not None:
              if len(_UpperCamelCase) != len(_UpperCamelCase):
                  raise ValueError(
                      """Make sure that you pass in as many target sizes as the batch dimension of the logits""")

              if is_torch_tensor(_UpperCamelCase):
                  _lowerCamelCase     :		Any 						=						target_sizes.numpy()

              _lowerCamelCase     :		Optional[Any] 						=						[]

              for idx in range(len(_UpperCamelCase)):
                  _lowerCamelCase     :		Union[str, Any] 						=						torch.nn.functional.interpolate(
                      logits[idx].unsqueeze(dim=0)    ,  size=target_sizes[idx]    ,  mode="""bilinear"""    ,  align_corners=_UpperCamelCase)
                  _lowerCamelCase     :		Union[str, Any] 						=						resized_logits[0].argmax(dim=0)
                  semantic_segmentation.append(_UpperCamelCase)
          else:
              _lowerCamelCase     :		Optional[int] 						=						logits.argmax(dim=1)
              _lowerCamelCase     :		List[str] 						=						[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]

          return semantic_segmentation





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
    from PIL import Image

    from ..image_utils import load_image

if is_tf_available():
    import tensorflow as tf

    from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
    from ..tf_utils import stable_softmax

if is_torch_available():
    from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

lowerCAmelCase			:							Optional[Any]     =logging.get_logger(__name__)




@add_end_docstrings(__lowerCAmelCase		)
class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def __init__(					self     :				str    ,  *_UpperCamelCase     :				int    ,  **_UpperCamelCase     :				List[str])				->Tuple:


          """simple docstring"""


          super().__init__(*_UpperCamelCase    ,  **_UpperCamelCase)
          requires_backends(self    ,  """vision""")
          self.check_model_type(
              TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
              if self.framework == """tf"""
              else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict    ,  _UpperCamelCase     :				List[str]=None)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						{}
          if top_k is not None:
              _lowerCamelCase     :		str 						=						top_k
          return {}, {}, postprocess_params



      def __call__(					self     :				Optional[int]    ,  _UpperCamelCase     :				Union[str, List[str], "Image.Image", List["Image.Image"]]    ,  **_UpperCamelCase     :				Optional[int])				->Dict:


          """simple docstring"""


          return super().__call__(_UpperCamelCase    ,  **_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int])				->str:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						load_image(_UpperCamelCase)
          _lowerCamelCase     :		Any 						=						self.image_processor(images=_UpperCamelCase    ,  return_tensors=self.framework)
          return model_inputs



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				Union[str, Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						self.model(**_UpperCamelCase)
          return model_outputs




      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				List[str]=5)				->str:


          """simple docstring"""


          if top_k > self.model.config.num_labels:
              _lowerCamelCase     :		Union[str, Any] 						=						self.model.config.num_labels

          if self.framework == "pt":
              _lowerCamelCase     :		Optional[Any] 						=						model_outputs.logits.softmax(-1)[0]
              _lowerCamelCase						,							_lowerCamelCase     :		Dict 						=						probs.topk(_UpperCamelCase)
          elif self.framework == "tf":
              _lowerCamelCase     :		List[Any] 						=						stable_softmax(model_outputs.logits    ,  axis=-1)[0]
              _lowerCamelCase     :		List[Any] 						=						tf.math.top_k(_UpperCamelCase    ,  k=_UpperCamelCase)
              _lowerCamelCase						,							_lowerCamelCase     :		str 						=						topk.values.numpy(), topk.indices.numpy()
          else:
              raise ValueError(F"""Unsupported framework: {self.framework}""")

          _lowerCamelCase     :		str 						=						scores.tolist()
          _lowerCamelCase     :		str 						=						ids.tolist()
          return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_UpperCamelCase    ,  _UpperCamelCase)]




