






"""simple docstring"""







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()
_lowerCAmelCase            =	logging.get_logger(__name__)


def      UpperCamelCase       ( _A )     ->       Optional[Any]:
				lowercase    :      Any       = MobileNetVaConfig(layer_norm_eps=0.001 )

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

				lowercase    :      Union[str, Any]       = re.match(r"""^mobilenet_v1_([^_]*)_([^_]*)$"""  ,				_A )
				if matches:
								lowercase    :      Any       = float(matches[1] )
								lowercase    :      int       = int(matches[2] )

				# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
				# the usual 1000. The first class (index 0) is "background".
				lowercase    :      List[str]       = 1_001
				lowercase    :      str       = """imagenet-1k-id2label.json"""
				lowercase    :      List[str]       = """huggingface/label-files"""
				lowercase    :      Tuple       = json.load(open(hf_hub_download(_A  ,				_A  ,				repo_type="""dataset""" )  ,				"""r""" ) )
				lowercase    :      int       = {int(_A ) + 1: v for k, v in idalabel.items()}
				lowercase    :      List[Any]       = """background"""
				lowercase    :      int       = idalabel
				lowercase    :      List[str]       = {v: k for k, v in idalabel.items()}

				return config


def      UpperCamelCase       ( )     ->       str:
				lowercase    :      Optional[int]       = """http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase    :      Union[str, Any]       = Image.open(requests.get(_A  ,				stream=_A ).raw )
				return im


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A=False )     ->       List[str]:
				lowercase    :      Any       = get_mobilenet_va_config(_A )

				# Load 🤗 model
				lowercase    :      Dict       = MobileNetVaForImageClassification(_A ).eval()

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

				# Check outputs on an image, prepared by MobileNetV1ImageProcessor
				lowercase    :      str       = MobileNetVaImageProcessor(
				    crop_size={"""width""": config.image_size, """height""": config.image_size}  ,				size={"""shortest_edge""": config.image_size + 32}  ,				)
				lowercase    :      List[Any]       = image_processor(images=prepare_img()  ,				return_tensors="""pt""" )
				lowercase    :      Any       = model(**_A )
				lowercase    :      Optional[Any]       = outputs.logits

				assert logits.shape == (1, 1_001)

				if model_name == "mobilenet_v1_1.0_224":
								lowercase    :      Any       = torch.tensor([-4.1739, -1.1233, 3.1205] )
				elif model_name == "mobilenet_v1_0.75_192":
								lowercase    :      Union[str, Any]       = torch.tensor([-3.9440, -2.3141, -0.3333] )
				else:
								lowercase    :      List[str]       = None

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

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

				if push_to_hub:
								print("""Pushing to the hub...""" )
								lowercase    :      Optional[int]       = """google/""" + model_name
								image_processor.push_to_hub(_A )
								model.push_to_hub(_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	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.'
				)

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








"""simple docstring"""







def      UpperCamelCase       ( _A  ,				_A )     ->       int:
				lowercase    :      int       = 1  # To kept the Calculated Value
				# Since C(n, k) = C(n, n-k)
				if k > (n - k):
								lowercase    :      List[Any]       = n - k
				# Calculate C(n,k)
				for i in range(_A ):
								result *= n - i
								result //= i + 1
				return result


def      UpperCamelCase       ( _A )     ->       int:
				return binomial_coefficient(2 * node_count  ,				_A ) // (node_count + 1)


def      UpperCamelCase       ( _A )     ->       int:
				if n < 0:
								raise ValueError("""factorial() not defined for negative values""" )
				lowercase    :      Union[str, Any]       = 1
				for i in range(1  ,				n + 1 ):
								result *= i
				return result


def      UpperCamelCase       ( _A )     ->       int:
				return catalan_number(_A ) * factorial(_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	int(input('Enter the number of nodes: ').strip() or 0)
				if node_count <= 0:
								raise ValueError('We need some nodes to work with.')
				print(
				    F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
				    F'binary trees and {catalan_number(node_count)} binary search trees.'
				)
