






'''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    :      str  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			Any						):
						lowerCamelCase__         =  MobileNetVaConfig(layer_norm_eps=0.001						)

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

						lowerCamelCase__         =  re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""   ,    snake_case_						)
						if matches:
												lowerCamelCase__         =  float(matches[1]						)
												lowerCamelCase__         =  int(matches[2]						)

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

						return config
def 						A__			(				):
						lowerCamelCase__         =  """http://images.cocodataset.org/val2017/000000039769.jpg"""
						lowerCamelCase__         =  Image.open(requests.get(snake_case_   ,    stream=snake_case_						).raw						)
						return im
@torch.no_grad()
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			Dict   ,    __lowerCAmelCase		:			Optional[int]   ,    __lowerCAmelCase		:			Optional[Any]=False						):
						lowerCamelCase__         =  get_mobilenet_va_config(snake_case_						)

						# Load 🤗 model
						lowerCamelCase__         =  MobileNetVaForImageClassification(snake_case_						).eval()

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

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

						assert logits.shape == (1, 1001)

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

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

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

						if push_to_hub:
												print("""Pushing to the hub..."""						)
												lowerCamelCase__         =  """google/""" + model_name
												image_processor.push_to_hub(snake_case_						)
												model.push_to_hub(snake_case_						)


if __name__ == "__main__":
						UpperCamelCase    :      str  			=		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    :      Optional[int]  			=		parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)









'''simple docstring'''







from manim import *




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  Rectangle(height=0.5   ,width=0.5       )
								lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  VGroup(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""CPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								cpu.move_to([-2.5, -0.5, 0]       )
								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(1       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )
								lowerCamelCase__         =  Text("""GPU"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								gpu.align_to(_lowerCAmelCase   ,_lowerCAmelCase       )
								gpu.set_x(gpu.get_x() - 1       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  [mem.copy() for i in range(6       )]
								lowerCamelCase__         =  VGroup(*_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0       )

								lowerCamelCase__         =  Text("""Model"""   ,font_size=24       )
								lowerCamelCase__         =  Group(_lowerCAmelCase   ,_lowerCAmelCase       ).arrange(_lowerCAmelCase   ,buff=0.5   ,aligned_edge=_lowerCAmelCase       )
								model.move_to([3, -1.0, 0]       )

								self.play(
								    Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,Create(_lowerCAmelCase   ,run_time=1       )   ,)

								lowerCamelCase__         =  MarkupText(
								    F'''First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'''   ,font_size=24   ,)

								lowerCamelCase__         =  Square(side_length=2.2       )
								key.move_to([-5, 2, 0]       )

								lowerCamelCase__         =  MarkupText(
								    F'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'''   ,font_size=18   ,)

								key_text.move_to([-5, 2.4, 0]       )

								step_a.move_to([2, 2, 0]       )
								self.play(Write(_lowerCAmelCase   ,run_time=2.5       )   ,Write(_lowerCAmelCase       )   ,Write(_lowerCAmelCase       )       )

								self.add(_lowerCAmelCase       )

								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								lowerCamelCase__         =  []
								for i, rect in enumerate(_lowerCAmelCase       ):
														lowerCamelCase__         =  Rectangle(height=0.46   ,width=0.46       ).set_stroke(width=0.0       ).set_fill(_lowerCAmelCase   ,opacity=0.7       )
														cpu_target.move_to(_lowerCAmelCase       )
														cpu_target.generate_target()
														lowerCamelCase__         =  0.46 / 4
														lowerCamelCase__         =  0.46 / 3

														if i == 0:
																				cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT       )   ,buff=0.02   ,direction=_lowerCAmelCase       )
																				cpu_target.target.set_x(cpu_target.target.get_x() + 0.1       )
														elif i == 3:
																				cpu_target.target.next_to(cpu_targs[0].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														else:
																				cpu_target.target.next_to(cpu_targs[i - 1].target   ,direction=_lowerCAmelCase   ,buff=0.0       )
														cpu_targs.append(_lowerCAmelCase       )

														first_animations.append(rect.animate(run_time=0.5       ).set_stroke(_lowerCAmelCase       )       )
														second_animations.append(MoveToTarget(_lowerCAmelCase   ,run_time=1.5       )       )

								self.play(*_lowerCAmelCase       )
								self.play(*_lowerCAmelCase       )

								self.wait()

