




from collections.abc import Sequence
from queue import Queue







class  UpperCAmelCase:



	"""simple docstring"""






	def __init__(	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase=None			, lowerCamelCase=None							)   ->     List[str]:





								"""simple docstring"""


								lowercase__    :			Dict     =				start
								lowercase__    :			List[Any]     =				end
								lowercase__    :			Tuple     =				val
								lowercase__    :			Dict     =				(start + end) // 2
								lowercase__    :			int     =				left
								lowercase__    :			Optional[Any]     =				right







	def __repr__(	self							)   ->     int:





								"""simple docstring"""


								return f"""SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})"""







class  UpperCAmelCase:



	"""simple docstring"""






	def __init__(	self			, lowerCamelCase			, lowerCamelCase							)   ->     Dict:





								"""simple docstring"""


								lowercase__    :			Optional[Any]     =				collection
								lowercase__    :			List[str]     =				function
								if self.collection:
															lowercase__    :			Any     =				self._build_tree(0			, len(lowerCamelCase							) - 1							)

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase							)   ->     Optional[int]:





								"""simple docstring"""


								self._update_tree(self.root			, lowerCamelCase			, lowerCamelCase							)

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase							)   ->     List[Any]:





								"""simple docstring"""


								return self._query_range(self.root			, lowerCamelCase			, lowerCamelCase							)

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase							)   ->     Optional[int]:





								"""simple docstring"""


								if start == end:
															return SegmentTreeNode(lowerCamelCase			, lowerCamelCase			, self.collection[start]							)
								lowercase__    :			List[str]     =				(start + end) // 2
								lowercase__    :			List[Any]     =				self._build_tree(lowerCamelCase			, lowerCamelCase							)
								lowercase__    :			List[str]     =				self._build_tree(mid + 1			, lowerCamelCase							)
								return SegmentTreeNode(lowerCamelCase			, lowerCamelCase			, self.fn(left.val			, right.val							)			, lowerCamelCase			, lowerCamelCase							)

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase							)   ->     int:





								"""simple docstring"""


								if node.start == i and node.end == i:
															lowercase__    :			Any     =				val
															return
								if i <= node.mid:
															self._update_tree(node.left			, lowerCamelCase			, lowerCamelCase							)
								else:
															self._update_tree(node.right			, lowerCamelCase			, lowerCamelCase							)
								lowercase__    :			str     =				self.fn(node.left.val			, node.right.val							)

	def        __a      (	self			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase							)   ->     Optional[int]:





								"""simple docstring"""


								if node.start == i and node.end == j:
															return node.val

								if i <= node.mid:
															if j <= node.mid:
																						# range in left child tree
																						return self._query_range(node.left			, lowerCamelCase			, lowerCamelCase							)
															else:
																						# range in left child tree and right child tree
																						return self.fn(
																						    self._query_range(node.left			, lowerCamelCase			, node.mid							)			, self._query_range(node.right			, node.mid + 1			, lowerCamelCase							)			, )
								else:
															# range in right child tree
															return self._query_range(node.right			, lowerCamelCase			, lowerCamelCase							)







	def        __a      (	self							)   ->     Dict:





								"""simple docstring"""


								if self.root is not None:
															lowercase__    :			List[str]     =				Queue()
															queue.put(self.root							)
															while not queue.empty():
																						lowercase__    :			int     =				queue.get()
																						yield node

																						if node.left is not None:
																													queue.put(node.left							)

																						if node.right is not None:
																													queue.put(node.right							)


if __name__ == "__main__":
						import operator

						for fn in [operator.add, max, min]:
												print('''*''' * 5_0)
												__a    :						Union[str, Any]						      =       SegmentTree([2, 1, 5, 3, 4], fn)
												for node in arr.traverse():
																		print(node)
												print()

												arr.update(1, 5)
												for node in arr.traverse():
																		print(node)
												print()

												print(arr.query_range(3, 4))  # 7
												print(arr.query_range(2, 2))  # 5
												print(arr.query_range(1, 3))  # 13
												print()







'''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()
__UpperCamelCase   :							Tuple         =   logging.get_logger(__name__)



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						List[Any]     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     MobileNetVaConfig(layer_norm_eps=0.0_0_1     )

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

   __a					  =     re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$',       SCREAMING_SNAKE_CASE__     )
   if matches:
      __a					  =     float(matches[1]     )
      __a					  =     int(matches[2]     )

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

   return config



def  __UpperCAmelCase						(					)	->      Union[str, Any]:

   """simple docstring"""
   __a					  =     'http://images.cocodataset.org/val2017/000000039769.jpg'
   __a					  =     Image.open(requests.get(SCREAMING_SNAKE_CASE__,       stream=SCREAMING_SNAKE_CASE__     ).raw     )
   return im



@torch.no_grad()
def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						int,       SCREAMING_SNAKE_CASE__:						Union[str, Any],       SCREAMING_SNAKE_CASE__:						Optional[int],       SCREAMING_SNAKE_CASE__:						str=False     )	->      int:

   """simple docstring"""
   __a					  =     get_mobilenet_va_config(SCREAMING_SNAKE_CASE__     )

   # Load 🤗 model
   __a					  =     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
   __a					  =     MobileNetVaImageProcessor(
       crop_size={'width': config.image_size, 'height': config.image_size},       size={'shortest_edge': config.image_size + 32},       )
   __a					  =     image_processor(images=prepare_img(),       return_tensors='pt'     )
   __a					  =     model(**SCREAMING_SNAKE_CASE__     )
   __a					  =     outputs.logits

   assert logits.shape == (1, 1001)

   if model_name == "mobilenet_v1_1.0_224":
      __a					  =     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":
      __a					  =     torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]     )
   else:
      __a					  =     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...'     )
      __a					  =     'google/' + model_name
      image_processor.push_to_hub(SCREAMING_SNAKE_CASE__     )
      model.push_to_hub(SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 __UpperCamelCase   :							Any         =   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."""
 )

 __UpperCamelCase   :							Any         =   parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )