

from typing import Any, Dict, List, Union

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


if is_vision_available():
      from ..image_utils import load_image


if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

__a    :str      =			logging.get_logger(__name__)


__a    :Any      =			Dict[str, Any]
__a    :int      =			List[Prediction]

@add_end_docstrings(snake_case_				)
class   _a					(			snake_case_				):




      """simple docstring"""

      def __init__(   self							:				Tuple						,			*UpperCAmelCase							:				Optional[int]						,			**UpperCAmelCase							:				Optional[Any]	):
            super().__init__(*UpperCAmelCase						,			**UpperCAmelCase	)

            if self.framework == "tf":
                  raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''	)

            requires_backends(self						,			"vision"	)
            self.check_model_type(
                dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()	)	)





      def 		__A	(   self							:				str						,			**UpperCAmelCase							:				str	):
            A_						  =				{}
            if "threshold" in kwargs:
                  A_						  =				kwargs["threshold"]
            return {}, {}, postprocess_kwargs





      def __call__(   self							:				Union[str, Any]						,			*UpperCAmelCase							:				Union[str, Any]						,			**UpperCAmelCase							:				Optional[Any]	):
            return super().__call__(*UpperCAmelCase						,			**UpperCAmelCase	)





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				Any	):
            A_						  =				load_image(UpperCAmelCase	)
            A_						  =				torch.IntTensor([[image.height, image.width]]	)
            A_						  =				self.image_processor(images=[image]						,			return_tensors="pt"	)
            if self.tokenizer is not None:
                  A_						  =				self.tokenizer(text=inputs["words"]						,			boxes=inputs["boxes"]						,			return_tensors="pt"	)
            A_						  =				target_size
            return inputs





      def 		__A	(   self							:				Optional[Any]						,			UpperCAmelCase							:				Optional[int]	):
            A_						  =				model_inputs.pop("target_size"	)
            A_						  =				self.model(**UpperCAmelCase	)
            A_						  =				outputs.__class__({"target_size": target_size, **outputs}	)
            if self.tokenizer is not None:
                  A_						  =				model_inputs["bbox"]
            return model_outputs





      def 		__A	(   self							:				str						,			UpperCAmelCase							:				Optional[int]						,			UpperCAmelCase							:				Union[str, Any]=0.9	):
            A_						  =				model_outputs["target_size"]
            if self.tokenizer is not None:
                  # This is a LayoutLMForTokenClassification variant.
                  # The OCR got the boxes and the model classified the words.
                  A_  ,     A_						  =				target_size[0].tolist()

                  def unnormalize(UpperCAmelCase							:				Any	):
                        return self._get_bounding_box(
                            torch.Tensor(
                                [
                                    (width * bbox[0] / 1000),
                                    (height * bbox[1] / 1000),
                                    (width * bbox[2] / 1000),
                                    (height * bbox[3] / 1000),
                                ]	)	)

                  A_  ,     A_						  =				model_outputs["logits"].squeeze(0	).softmax(dim=-1	).max(dim=-1	)
                  A_						  =				[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
                  A_						  =				[unnormalize(UpperCAmelCase	) for bbox in model_outputs["bbox"].squeeze(0	)]
                  A_						  =				["score", "label", "box"]
                  A_						  =				[dict(zip(UpperCAmelCase						,			UpperCAmelCase	)	) for vals in zip(scores.tolist()						,			UpperCAmelCase						,			UpperCAmelCase	) if vals[0] > threshold]
            else:
                  # This is a regular ForObjectDetectionModel
                  A_						  =				self.image_processor.post_process_object_detection(UpperCAmelCase						,			UpperCAmelCase						,			UpperCAmelCase	)
                  A_						  =				raw_annotations[0]
                  A_						  =				raw_annotation["scores"]
                  A_						  =				raw_annotation["labels"]
                  A_						  =				raw_annotation["boxes"]

                  A_						  =				scores.tolist()
                  A_						  =				[self.model.config.idalabel[label.item()] for label in labels]
                  A_						  =				[self._get_bounding_box(UpperCAmelCase	) for box in boxes]

                  # {"scores": [...], ...} --> [{"score":x, ...}, ...]
                  A_						  =				["score", "label", "box"]
                  A_						  =				[
                      dict(zip(UpperCAmelCase						,			UpperCAmelCase	)	)
                      for vals in zip(raw_annotation["scores"]						,			raw_annotation["labels"]						,			raw_annotation["boxes"]	)
                  ]

            return annotation







      def 		__A	(   self							:				Tuple						,			UpperCAmelCase							:				"torch.Tensor"	):
            if self.framework != "pt":
                  raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."	)
            A_  ,     A_  ,     A_  ,     A_						  =				box.int().tolist()
            A_						  =				{
                "xmin": xmin,
                "ymin": ymin,
                "xmax": xmax,
                "ymax": ymax,
            }
            return bbox






from random import shuffle

import tensorflow as tf
from numpy import array



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase	)		->					Optional[Any]:
 UpperCamelCase      :   List[Any]			      =  int(_lowerCAmelCase	)
 assert noofclusters < len(_lowerCAmelCase	)

 # Find out the dimensionality
 UpperCamelCase      :   str			      =  len(vectors[0]	)

 # Will help select random centroids from among the available vectors
 UpperCamelCase      :   List[str]			      =  list(range(len(_lowerCAmelCase	)	)	)
 shuffle(_lowerCAmelCase	)

 # GRAPH OF COMPUTATION
 # We initialize a new graph and set it as the default during each run
 # of this algorithm. This ensures that as this function is called
 # multiple times, the default graph doesn't keep getting crowded with
 # unused ops and Variables from previous function calls.

 UpperCamelCase      :   Any			      =  tf.Graph()

 with graph.as_default():
  # SESSION OF COMPUTATION

  UpperCamelCase      :   Union[str, Any]			      =  tf.Session()

  ##CONSTRUCTING THE ELEMENTS OF COMPUTATION

  ##First lets ensure we have a Variable vector for each centroid,
  ##initialized to one of the vectors from the available data points
  UpperCamelCase      :   Optional[int]			      =  [
      tf.Variable(vectors[vector_indices[i]]	) for i in range(_lowerCAmelCase	)
  ]
  ##These nodes will assign the centroid Variables the appropriate
  ##values
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float64"				,  [dim]	)
  UpperCamelCase      :   str			      =  []
  for centroid in centroids:
   cent_assigns.append(tf.assign(_lowerCAmelCase				,  _lowerCAmelCase	)	)

  ##Variables for cluster assignments of individual vectors(initialized
  ##to 0 at first)
  UpperCamelCase      :   List[str]			      =  [tf.Variable(0	) for i in range(len(_lowerCAmelCase	)	)]
  ##These nodes will assign an assignment Variable the appropriate
  ##value
  UpperCamelCase      :   Any			      =  tf.placeholder("int32"	)
  UpperCamelCase      :   Optional[int]			      =  []
  for assignment in assignments:
   cluster_assigns.append(tf.assign(_lowerCAmelCase				,  _lowerCAmelCase	)	)

  ##Now lets construct the node that will compute the mean
  # The placeholder for the input
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float"				,  [None, dim]	)
  # The Node/op takes the input and computes a mean along the 0th
  # dimension, i.e. the list of input vectors
  UpperCamelCase      :   List[str]			      =  tf.reduce_mean(_lowerCAmelCase				,  0	)

  ##Node for computing Euclidean distances
  # Placeholders for input
  UpperCamelCase      :   Optional[int]			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   Any			      =  tf.placeholder("float"				,  [dim]	)
  UpperCamelCase      :   List[str]			      =  tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_lowerCAmelCase				,  _lowerCAmelCase	)				,  2	)	)	)

  ##This node will figure out which cluster to assign a vector to,
  ##based on Euclidean distances of the vector from the centroids.
  # Placeholder for input
  UpperCamelCase      :   Tuple			      =  tf.placeholder("float"				,  [noofclusters]	)
  UpperCamelCase      :   str			      =  tf.argmin(_lowerCAmelCase				,  0	)

  ##INITIALIZING STATE VARIABLES

  ##This will help initialization of all Variables defined with respect
  ##to the graph. The Variable-initializer should be defined after
  ##all the Variables have been constructed, so that each of them
  ##will be included in the initialization.
  UpperCamelCase      :   Optional[Any]			      =  tf.initialize_all_variables()

  # Initialize all variables
  sess.run(_lowerCAmelCase	)

  ##CLUSTERING ITERATIONS

  # Now perform the Expectation-Maximization steps of K-Means clustering
  # iterations. To keep things simple, we will only do a set number of
  # iterations, instead of using a Stopping Criterion.
  UpperCamelCase      :   Any			      =  100
  for _ in range(_lowerCAmelCase	):
   ##EXPECTATION STEP
   ##Based on the centroid locations till last iteration, compute
   ##the _expected_ centroid assignments.
   # Iterate over each vector
   for vector_n in range(len(_lowerCAmelCase	)	):
    UpperCamelCase      :   Dict			      =  vectors[vector_n]
    # Compute Euclidean distance between this vector and each
    # centroid. Remember that this list cannot be named
    #'centroid_distances', since that is the input to the
    # cluster assignment node.
    UpperCamelCase      :   Optional[Any]			      =  [
        sess.run(_lowerCAmelCase				,  feed_dict={va: vect, va: sess.run(_lowerCAmelCase	)}	)
        for centroid in centroids
    ]
    # Now use the cluster assignment node, with the distances
    # as the input
    UpperCamelCase      :   Tuple			      =  sess.run(
        _lowerCAmelCase				,  feed_dict={centroid_distances: distances}	)
    # Now assign the value to the appropriate state variable
    sess.run(
        cluster_assigns[vector_n]				,  feed_dict={assignment_value: assignment}	)

   ##MAXIMIZATION STEP
   # Based on the expected state computed from the Expectation Step,
   # compute the locations of the centroids so as to maximize the
   # overall objective of minimizing within-cluster Sum-of-Squares
   for cluster_n in range(_lowerCAmelCase	):
    # Collect all the vectors assigned to this cluster
    UpperCamelCase      :   str			      =  [
        vectors[i]
        for i in range(len(_lowerCAmelCase	)	)
        if sess.run(assignments[i]	) == cluster_n
    ]
    # Compute new centroid location
    UpperCamelCase      :   Optional[int]			      =  sess.run(
        _lowerCAmelCase				,  feed_dict={mean_input: array(_lowerCAmelCase	)}	)
    # Assign value to appropriate variable
    sess.run(
        cent_assigns[cluster_n]				,  feed_dict={centroid_value: new_location}	)

        # Return centroids and assignments
  UpperCamelCase      :   Union[str, Any]			      =  sess.run(_lowerCAmelCase	)
  UpperCamelCase      :   Tuple			      =  sess.run(_lowerCAmelCase	)
  return centroids, assignments
