




"""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,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


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

def 		lowerCAmelCase							( lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Optional[int]										=			MobileNetVaConfig(layer_norm_eps=0.001		)

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

						snake_case__       :					List[str]										=			re.match(R'''^.*mobilenet_v2_([^_]*)_([^_]*)$'''      ,			lowerCamelCase		)
						if matches:
												snake_case__       :					Optional[Any]										=			float(matches[1]		)
												snake_case__       :					Dict										=			int(matches[2]		)

						if model_name.startswith('''deeplabv3_'''		):
												snake_case__       :					Optional[Any]										=			8
												snake_case__       :					Tuple										=			21
												snake_case__       :					Any										=			'''pascal-voc-id2label.json'''
						else:
												# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
												# of the usual 1000. The first class (index 0) is "background".
												snake_case__       :					str										=			1001
												snake_case__       :					int										=			'''imagenet-1k-id2label.json'''

						snake_case__       :					str										=			'''huggingface/label-files'''
						snake_case__       :					Dict										=			json.load(open(hf_hub_download(lowerCamelCase      ,			lowerCamelCase      ,			repo_type='''dataset'''		)      ,			'''r'''		)		)

						if config.num_labels == 1001:
												snake_case__       :					str										=			{int(lowerCamelCase		) + 1: v for k, v in idalabel.items()}
												snake_case__       :					Tuple										=			'''background'''
						else:
												snake_case__       :					List[Any]										=			{int(lowerCamelCase		): v for k, v in idalabel.items()}

						snake_case__       :					Union[str, Any]										=			idalabel
						snake_case__       :					Optional[int]										=			{v: k for k, v in idalabel.items()}

						return config

def 		lowerCAmelCase							( ):
						'''simple docstring'''


						snake_case__       :					Tuple										=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
						snake_case__       :					str										=			Image.open(requests.get(lowerCamelCase      ,			stream=lowerCamelCase		).raw		)
						return im

@torch.no_grad()
def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase      ,			lowerCamelCase=False		):
						'''simple docstring'''


						snake_case__       :					List[Any]										=			get_mobilenet_va_config(lowerCamelCase		)

						# Load 🤗 model
						if model_name.startswith('''deeplabv3_'''		):
												snake_case__       :					List[Any]										=			MobileNetVaForSemanticSegmentation(lowerCamelCase		).eval()
						else:
												snake_case__       :					List[Any]										=			MobileNetVaForImageClassification(lowerCamelCase		).eval()

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

						# Check outputs on an image, prepared by MobileNetV2ImageProcessor
						snake_case__       :					int										=			MobileNetVaImageProcessor(
						    crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,			size={'''shortest_edge''': config.image_size + 32}      ,			)
						snake_case__       :					Tuple										=			image_processor(images=prepare_img()      ,			return_tensors='''pt'''		)
						snake_case__       :					List[Any]										=			model(**lowerCamelCase		)
						snake_case__       :					Any										=			outputs.logits

						if model_name.startswith('''deeplabv3_'''		):
												assert logits.shape == (1, 21, 65, 65)

												if model_name == "deeplabv3_mobilenet_v2_1.0_513":
																		snake_case__       :					int										=			torch.tensor(
																		    [
																		        [[17.5_790, 17.7_581, 18.3_355], [18.3_257, 18.4_230, 18.8_973], [18.6_169, 18.8_650, 19.2_187]],
																		        [[-2.1_595, -2.0_977, -2.3_741], [-2.4_226, -2.3_028, -2.6_835], [-2.7_819, -2.5_991, -2.7_706]],
																		        [[4.2_058, 4.8_317, 4.7_638], [4.4_136, 5.0_361, 4.9_383], [4.5_028, 4.9_644, 4.8_734]],
																		    ]		)

												else:
																		raise ValueError(f'''Unknown model name: {model_name}'''		)

												assert torch.allclose(logits[0, :3, :3, :3]      ,			lowerCamelCase      ,			atol=1e-4		)
						else:
												assert logits.shape == (1, 1001)

												if model_name == "mobilenet_v2_1.4_224":
																		snake_case__       :					List[str]										=			torch.tensor([0.0_181, -1.0_015, 0.4_688]		)
												elif model_name == "mobilenet_v2_1.0_224":
																		snake_case__       :					Dict										=			torch.tensor([0.2_445, -1.1_993, 0.1_905]		)
												elif model_name == "mobilenet_v2_0.75_160":
																		snake_case__       :					List[str]										=			torch.tensor([0.2_482, 0.4_136, 0.6_669]		)
												elif model_name == "mobilenet_v2_0.35_96":
																		snake_case__       :					Optional[int]										=			torch.tensor([0.1_451, -0.4_624, 0.7_192]		)
												else:
																		snake_case__       :					Union[str, Any]										=			None

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

						Path(lowerCamelCase		).mkdir(exist_ok=lowerCamelCase		)
						print(f'''Saving model {model_name} to {pytorch_dump_folder_path}'''		)
						model.save_pretrained(lowerCamelCase		)
						print(f'''Saving image processor to {pytorch_dump_folder_path}'''		)
						image_processor.save_pretrained(lowerCamelCase		)

						if push_to_hub:
												print('''Pushing to the hub...'''		)
												snake_case__       :					Optional[Any]										=			'''google/''' + model_name
												image_processor.push_to_hub(lowerCamelCase		)
												model.push_to_hub(lowerCamelCase		)


if __name__ == "__main__":
							_lowercase	      =    argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--model_name""",
							    default="""mobilenet_v2_1.0_224""",
							    type=str,
							    help="""Name of the MobileNetV2 model you'd like to convert. Should in the form 'mobilenet_v2_<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
							)








"""simple docstring"""





def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						snake_case__       :					Any										=					len(lowerCamelCase		)
						# Create a stack and push index of fist element to it
						snake_case__       :					str										=					[]
						st.append(0		)

						# Span value of first element is always 1
						snake_case__       :					Union[str, Any]										=					1

						# Calculate span values for rest of the elements
						for i in range(1      ,			lowerCamelCase		):
												# Pop elements from stack while stack is not
												# empty and top of stack is smaller than price[i]
												while len(lowerCamelCase		) > 0 and price[st[0]] <= price[i]:
																		st.pop()

												# If stack becomes empty, then price[i] is greater
												# than all elements on left of it, i.e. price[0],
												# price[1], ..price[i-1]. Else the price[i]  is
												# greater than elements after top of stack
												snake_case__       :					Dict										=					i + 1 if len(lowerCamelCase		) <= 0 else (i - st[0])

												# Push this element to stack
												st.append(lowerCamelCase		)

def 		lowerCAmelCase							( lowerCamelCase      ,			lowerCamelCase		):
						'''simple docstring'''


						for i in range(0      ,			lowerCamelCase		):
												print(arr[i]      ,			end=''' '''		)


# Driver program to test above function
_lowercase	      =    [10, 4, 5, 90, 1_20, 80]
_lowercase	      =    [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)

print_array(S, len(price))


