Delete examples/colab directory

examples/colab/README.md

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----
-title: Image_mixer
-emoji: ⚡
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 2.9.4
-app_file: app.py
-pinned: false
----
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference

examples/colab/requirements.txt

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-numpy==1.21.2
-tensorflow==2.2.0
-tensorflow_hub==0.12.0

examples/colab/tf_hub_fast_style_transfer_for_arbitrary_styles.py

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-# -*- coding: utf-8 -*-
-"""TF-Hub: Fast Style Transfer for Arbitrary Styles.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb
-
-##### Copyright 2019 The TensorFlow Hub Authors.
-
-Licensed under the Apache License, Version 2.0 (the "License");
-"""
-
-# Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# ==============================================================================
-
-"""# Fast Style Transfer for Arbitrary Styles
-
-<table class="tfo-notebook-buttons" align="left">
-  <td>
-    <a target="_blank" href="https://www.tensorflow.org/hub/tutorials/tf2_arbitrary_image_stylization"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
-  </td>
-  <td>
-    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
-  </td>
-  <td>
-    <a target="_blank" href="https://github.com/tensorflow/hub/blob/master/examples/colab/tf2_arbitrary_image_stylization.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View on GitHub</a>
-  </td>
-  <td>
-    <a href="https://storage.googleapis.com/tensorflow_docs/hub/examples/colab/tf2_arbitrary_image_stylization.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
-  </td>
-  <td>
-    <a href="https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2"><img src="https://www.tensorflow.org/images/hub_logo_32px.png" />See TF Hub model</a>
-  </td>
-</table>
-
-Based on the model code in [magenta](https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization) and the publication:
-
-[Exploring the structure of a real-time, arbitrary neural artistic stylization
-network](https://arxiv.org/abs/1705.06830).
-*Golnaz Ghiasi, Honglak Lee,
-Manjunath Kudlur, Vincent Dumoulin, Jonathon Shlens*,
-Proceedings of the British Machine Vision Conference (BMVC), 2017.
-
-## Setup
-
-Let's start with importing TF2 and all relevant dependencies.
-"""
-
-import functools
-import os
-
-from matplotlib import gridspec
-import matplotlib.pylab as plt
-import numpy as np
-import tensorflow as tf
-import tensorflow_hub as hub
-import gradio as gr
-
-# @title Define image loading and visualization functions  { display-mode: "form" }
-
-def crop_center(image):
-  """Returns a cropped square image."""
-  shape = image.shape
-  new_shape = min(shape[1], shape[2])
-  offset_y = max(shape[1] - shape[2], 0) // 2
-  offset_x = max(shape[2] - shape[1], 0) // 2
-  image = tf.image.crop_to_bounding_box(
-      image, offset_y, offset_x, new_shape, new_shape)
-  return image
-
-@functools.lru_cache(maxsize=None)
-def load_image(image_url, image_size=(256, 256), preserve_aspect_ratio=True):
-  """Loads and preprocesses images."""
-  # Cache image file locally.
-  image_path = tf.keras.utils.get_file(os.path.basename(image_url)[-128:], image_url)
-  # Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1].
-  img = tf.io.decode_image(
-      tf.io.read_file(image_path),
-      channels=3, dtype=tf.float32)[tf.newaxis, ...]
-  img = crop_center(img)
-  img = tf.image.resize(img, image_size, preserve_aspect_ratio=True)
-  return img
-
-def show_n(images, titles=('',)):
-  n = len(images)
-  image_sizes = [image.shape[1] for image in images]
-  w = (image_sizes[0] * 6) // 320
-  plt.figure(figsize=(w * n, w))
-  gs = gridspec.GridSpec(1, n, width_ratios=image_sizes)
-  for i in range(n):
-    plt.subplot(gs[i])
-    plt.imshow(images[i][0], aspect='equal')
-    plt.axis('off')
-    plt.title(titles[i] if len(titles) > i else '')
-  plt.show()
-
-
-
-
-"""Let's get as well some images to play with."""
-
-# @title Load example images  { display-mode: "form" }
-
-#content_image_url = 'https://live.staticflickr.com/65535/52032998695_f57c61746c_c.jpg'  # @param {type:"string"}
-#style_image_url = 'https://live.staticflickr.com/65535/52032731604_a815a0b19f_c.jpg'  # @param {type:"string"}
-output_image_size = 384  # @param {type:"integer"}
-
-# The content image size can be arbitrary.
-content_img_size = (output_image_size, output_image_size)
-# The style prediction model was trained with image size 256 and it's the 
-# recommended image size for the style image (though, other sizes work as 
-# well but will lead to different results).
-style_img_size = (256, 256)  # Recommended to keep it at 256.
-
-
-
-# Load images from app
-content_image_input = gr.inputs.Image(label="Content Image")
-style_image_input = gr.inputs.Image(shape=(256, 256), label="Style Image")
-
-
-
-content_image = load_image(content_image_input, content_img_size)
-style_image = load_image(style_image_input, style_img_size)
-style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
-show_n([content_image, style_image], ['Content image', 'Style image'])
-
-"""## Import TF Hub module"""
-
-# Load TF Hub module.
-
-hub_handle = 'https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2'
-hub_module = hub.load(hub_handle)
-
-"""The signature of this hub module for image stylization is:
-```
-outputs = hub_module(content_image, style_image)
-stylized_image = outputs[0]
-```
-Where `content_image`, `style_image`, and `stylized_image` are expected to be 4-D Tensors with shapes `[batch_size, image_height, image_width, 3]`.
-
-In the current example we provide only single images and therefore the batch dimension is 1, but one can use the same module to process more images at the same time.
-
-The input and output values of the images should be in the range [0, 1].
-
-The shapes of content and style image don't have to match. Output image shape
-is the same as the content image shape.
-
-## Demonstrate image stylization
-"""
-
-# Stylize content image with given style image.
-# This is pretty fast within a few milliseconds on a GPU.
-
-def modify(imageinput,style_input):
-    content_image = load_image(imageinput, content_img_size)
-    style_image = load_image(style_input, style_img_size)
-    style_image = tf.nn.avg_pool(style_image, ksize=[3,3], strides=[1,1], padding='SAME')
-    #show_n([content_image, style_image], ['Content image', 'Style image'])
-    outputs = hub_module(tf.constant(imageinput), tf.constant(style_input))
-    return outputs[0]
-#stylized_image = outputs[0]
-
-# Visualize input images and the generated stylized image.
-
-#show_n([content_image, style_image, stylized_image], titles=['Original content image', 'Style image', 'Stylized image'])
-
-# Gradio app
-
-
-app_interface = gr.Interface(modify,
-                             inputs=[content_image_input, style_image_input],
-                             outputs="image",
-                             title="Fast Neural Style Transfer",
-                             description="Gradio demo for Fast Neural Style Transfer using a pretrained Image Stylization model from TensorFlow Hub. To use it, simply upload a content image and style image, or click one of the examples to load them. To learn more about the project, please find the references listed below.",
-                             )
-app_interface.launch()
-