





from random import shuffle

import tensorflow as tf
from numpy import array




def     __UpperCamelCase						(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)       ->     List[str]:
  lowercase__		    =						int(_SCREAMING_SNAKE_CASE	)
  assert noofclusters < len(_SCREAMING_SNAKE_CASE	)

  # Find out the dimensionality
  lowercase__		    =						len(vectors[0]	)

  # Will help select random centroids from among the available vectors
  lowercase__		    =						list(range(len(_SCREAMING_SNAKE_CASE	)	)	)
  shuffle(_SCREAMING_SNAKE_CASE	)

  # 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.

  lowercase__		    =						tf.Graph()

  with graph.as_default():
    # SESSION OF COMPUTATION

    lowercase__		    =						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
    lowercase__		    =						[
        tf.Variable(vectors[vector_indices[i]]	) for i in range(_SCREAMING_SNAKE_CASE	)
    ]
    ##These nodes will assign the centroid Variables the appropriate
    ##values
    lowercase__		    =						tf.placeholder('float64'	,		[dim]	)
    lowercase__		    =						[]
    for centroid in centroids:
      cent_assigns.append(tf.assign(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)	)

    ##Variables for cluster assignments of individual vectors(initialized
    ##to 0 at first)
    lowercase__		    =						[tf.Variable(0	) for i in range(len(_SCREAMING_SNAKE_CASE	)	)]
    ##These nodes will assign an assignment Variable the appropriate
    ##value
    lowercase__		    =						tf.placeholder('int32'	)
    lowercase__		    =						[]
    for assignment in assignments:
      cluster_assigns.append(tf.assign(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)	)

    ##Now lets construct the node that will compute the mean
    # The placeholder for the input
    lowercase__		    =						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
    lowercase__		    =						tf.reduce_mean(_SCREAMING_SNAKE_CASE	,		0	)

    ##Node for computing Euclidean distances
    # Placeholders for input
    lowercase__		    =						tf.placeholder('float'	,		[dim]	)
    lowercase__		    =						tf.placeholder('float'	,		[dim]	)
    lowercase__		    =						tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(_SCREAMING_SNAKE_CASE	,		_SCREAMING_SNAKE_CASE	)	,		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
    lowercase__		    =						tf.placeholder('float'	,		[noofclusters]	)
    lowercase__		    =						tf.argmin(_SCREAMING_SNAKE_CASE	,		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.
    lowercase__		    =						tf.initialize_all_variables()

    # Initialize all variables
    sess.run(_SCREAMING_SNAKE_CASE	)

    ##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.
    lowercase__		    =						100
    for _ in range(_SCREAMING_SNAKE_CASE	):
      ##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(_SCREAMING_SNAKE_CASE	)	):
        lowercase__		    =						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.
        lowercase__		    =						[
            sess.run(_SCREAMING_SNAKE_CASE	,		feed_dict={va: vect, va: sess.run(_SCREAMING_SNAKE_CASE	)}	)
            for centroid in centroids
        ]
        # Now use the cluster assignment node, with the distances
        # as the input
        lowercase__		    =						sess.run(
            _SCREAMING_SNAKE_CASE	,		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(_SCREAMING_SNAKE_CASE	):
        # Collect all the vectors assigned to this cluster
        lowercase__		    =						[
            vectors[i]
            for i in range(len(_SCREAMING_SNAKE_CASE	)	)
            if sess.run(assignments[i]	) == cluster_n
        ]
        # Compute new centroid location
        lowercase__		    =						sess.run(
            _SCREAMING_SNAKE_CASE	,		feed_dict={mean_input: array(_SCREAMING_SNAKE_CASE	)}	)
        # Assign value to appropriate variable
        sess.run(
            cent_assigns[cluster_n]	,		feed_dict={centroid_value: new_location}	)

        # Return centroids and assignments
    lowercase__		    =						sess.run(_SCREAMING_SNAKE_CASE	)
    lowercase__		    =						sess.run(_SCREAMING_SNAKE_CASE	)
    return centroids, assignments









import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DonutImageProcessor
class        SCREAMING_SNAKE_CASE     (unittest.TestCase   ):


   def __init__(					self    :       Any    ,  a    :       str    ,  a    :       List[Any]=7    ,  a    :       int=3    ,  a    :       int=18    ,  a    :       Optional[Any]=30    ,  a    :       Optional[int]=400    ,  a    :       int=True    ,  a    :       Tuple=None    ,  a    :       Optional[Any]=True    ,  a    :       str=False    ,  a    :       str=True    ,  a    :       int=True    ,  a    :       Tuple=[0.5, 0.5, 0.5]    ,  a    :       Any=[0.5, 0.5, 0.5]    ,  )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						parent
     lowercase__			    =						batch_size
     lowercase__			    =						num_channels
     lowercase__			    =						image_size
     lowercase__			    =						min_resolution
     lowercase__			    =						max_resolution
     lowercase__			    =						do_resize
     lowercase__			    =						size if size is not None else {'height': 18, 'width': 20}
     lowercase__			    =						do_thumbnail
     lowercase__			    =						do_align_axis
     lowercase__			    =						do_pad
     lowercase__			    =						do_normalize
     lowercase__			    =						image_mean
     lowercase__			    =						image_std


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     return {
         "do_resize": self.do_resize,
         "size": self.size,
         "do_thumbnail": self.do_thumbnail,
         "do_align_long_axis": self.do_align_axis,
         "do_pad": self.do_pad,
         "do_normalize": self.do_normalize,
         "image_mean": self.image_mean,
         "image_std": self.image_std,
     }






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE     (UpperCAmelCase    ,			unittest.TestCase   ):
   _UpperCamelCase					:	Optional[Any]				      = DonutImageProcessor if is_vision_available() else None


   def  SCREAMING_SNAKE_CASE_							(					self    :       int )->     List[Any]:





     """simple docstring"""







     lowercase__			    =						DonutImageProcessingTester(self )


   @property
   def  SCREAMING_SNAKE_CASE_							(					self    :       Union[str, Any] )->     Optional[int]:





     """simple docstring"""







     return self.image_processor_tester.prepare_image_processor_dict()


   def  SCREAMING_SNAKE_CASE_							(					self    :       Any )->     int:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(a    ,  'do_resize' ) )
     self.assertTrue(hasattr(a    ,  'size' ) )
     self.assertTrue(hasattr(a    ,  'do_thumbnail' ) )
     self.assertTrue(hasattr(a    ,  'do_align_long_axis' ) )
     self.assertTrue(hasattr(a    ,  'do_pad' ) )
     self.assertTrue(hasattr(a    ,  'do_normalize' ) )
     self.assertTrue(hasattr(a    ,  'image_mean' ) )
     self.assertTrue(hasattr(a    ,  'image_std' ) )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size    ,  {'height': 18, 'width': 20} )

     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=42 )
     self.assertEqual(image_processor.size    ,  {'height': 42, 'width': 42} )

     # Previous config had dimensions in (width, height) order
     lowercase__			    =						self.image_processing_class.from_dict(self.image_processor_dict    ,  size=(42, 84) )
     self.assertEqual(image_processor.size    ,  {'height': 84, 'width': 42} )


   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Dict:





     """simple docstring"""







     pass


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       str )->     Optional[int]:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  Image.Image )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       Optional[Any] )->     Tuple:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  numpify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  np.ndarray )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


   @is_flaky()
   def  SCREAMING_SNAKE_CASE_							(					self    :       List[str] )->     Dict:





     """simple docstring"""







     lowercase__			    =						self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			    =						prepare_image_inputs(self.image_processor_tester    ,  equal_resolution=a    ,  torchify=a )
     for image in image_inputs:
       self.assertIsInstance(a    ,  torch.Tensor )

     # Test not batched input
     lowercase__			    =						image_processing(image_inputs[0]    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )

     # Test batched
     lowercase__			    =						image_processing(a    ,  return_tensors='pt' ).pixel_values
     self.assertEqual(
         encoded_images.shape    ,  (
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size['height'],
             self.image_processor_tester.size['width'],
         )    ,  )


