





from random import shuffle

import tensorflow as tf
from numpy import array




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)    ->       Tuple:
  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 argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowercase_          =		logging.get_logger(__name__)




def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		)    ->       Any:
  lowercase__           =  MobileNetVaConfig(layer_norm_eps=0.0_0_1		)

  if "_quant" in model_name:
    raise ValueError('Quantized models are not supported.'		)

  lowercase__           =  re.match(R'^mobilenet_v1_([^_]*)_([^_]*)$'		,						_SCREAMING_SNAKE_CASE		)
  if matches:
    lowercase__           =  float(matches[1]		)
    lowercase__           =  int(matches[2]		)

  # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
  # the usual 1000. The first class (index 0) is "background".
  lowercase__           =  1001
  lowercase__           =  'imagenet-1k-id2label.json'
  lowercase__           =  'huggingface/label-files'
  lowercase__           =  json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						repo_type='dataset'		)		,						'r'		)		)
  lowercase__           =  {int(_SCREAMING_SNAKE_CASE		) + 1: v for k, v in idalabel.items()}
  lowercase__           =  'background'
  lowercase__           =  idalabel
  lowercase__           =  {v: k for k, v in idalabel.items()}

  return config




def       __UpperCamelCase  ()    ->       int:
  lowercase__           =  'http://images.cocodataset.org/val2017/000000039769.jpg'
  lowercase__           =  Image.open(requests.get(_SCREAMING_SNAKE_CASE		,						stream=_SCREAMING_SNAKE_CASE		).raw		)
  return im




@torch.no_grad()
def       __UpperCamelCase  (_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE=False		)    ->       Union[str, Any]:
  lowercase__           =  get_mobilenet_va_config(_SCREAMING_SNAKE_CASE		)

  # Load 🤗 model
  lowercase__           =  MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE		).eval()

  # Load weights from TensorFlow checkpoint
  load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		,						_SCREAMING_SNAKE_CASE		)

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  lowercase__           =  MobileNetVaImageProcessor(
      crop_size={'width': config.image_size, 'height': config.image_size}		,						size={'shortest_edge': config.image_size + 32}		,						)
  lowercase__           =  image_processor(images=prepare_img()		,						return_tensors='pt'		)
  lowercase__           =  model(**_SCREAMING_SNAKE_CASE		)
  lowercase__           =  outputs.logits

  assert logits.shape == (1, 1001)

  if model_name == "mobilenet_v1_1.0_224":
    lowercase__           =  torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]		)
  elif model_name == "mobilenet_v1_0.75_192":
    lowercase__           =  torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]		)
  else:
    lowercase__           =  None

  if expected_logits is not None:
    assert torch.allclose(logits[0, :3]		,						_SCREAMING_SNAKE_CASE		,						atol=1E-4		)

  Path(_SCREAMING_SNAKE_CASE		).mkdir(exist_ok=_SCREAMING_SNAKE_CASE		)
  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
  model.save_pretrained(_SCREAMING_SNAKE_CASE		)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
  image_processor.save_pretrained(_SCREAMING_SNAKE_CASE		)

  if push_to_hub:
    print('Pushing to the hub...'		)
    lowercase__           =  'google/' + model_name
    image_processor.push_to_hub(_SCREAMING_SNAKE_CASE		)
    model.push_to_hub(_SCREAMING_SNAKE_CASE		)


if __name__ == "__main__":
      lowercase_          =		argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          """--model_name""",
          default="""mobilenet_v1_1.0_224""",
          type=str,
          help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
      )
      parser.add_argument(
          """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
      )
      parser.add_argument(
          """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
      )
      parser.add_argument(
          """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
      )

      lowercase_          =		parser.parse_args()
      convert_movilevit_checkpoint(
          args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
      )


