



import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
a_    :  List[str]						  =		logging.get_logger(__name__)

def      __lowercase(						UpperCAmelCase__			):


      """simple docstring"""



      lowerCamelCase             =		MobileViTConfig()

      # size of the architecture
      if "mobilevit_s" in mobilevit_name:
            lowerCamelCase             =		[144, 192, 240]
            lowerCamelCase             =		[16, 32, 64, 96, 128, 160, 640]
      elif "mobilevit_xs" in mobilevit_name:
            lowerCamelCase             =		[96, 120, 144]
            lowerCamelCase             =		[16, 32, 48, 64, 80, 96, 384]
      elif "mobilevit_xxs" in mobilevit_name:
            lowerCamelCase             =		[64, 80, 96]
            lowerCamelCase             =		[16, 16, 24, 48, 64, 80, 320]
            lowerCamelCase             =		0.0_5
            lowerCamelCase             =		2.0

      if mobilevit_name.startswith("deeplabv3_"			):
            lowerCamelCase             =		512
            lowerCamelCase             =		16
            lowerCamelCase             =		21
            lowerCamelCase             =		"pascal-voc-id2label.json"
      else:
            lowerCamelCase             =		1000
            lowerCamelCase             =		"imagenet-1k-id2label.json"

      lowerCamelCase             =		"huggingface/label-files"
      lowerCamelCase             =		json.load(open(hf_hub_download(UpperCAmelCase__ , UpperCAmelCase__ , repo_type="dataset"			) , "r"			)			)
      lowerCamelCase             =		{int(UpperCAmelCase__			): v for k, v in idalabel.items()}
      lowerCamelCase             =		idalabel
      lowerCamelCase             =		{v: k for k, v in idalabel.items()}

      return config

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__=False			):


      """simple docstring"""



      for i in range(1 , 6			):
            if F"""layer_{i}.""" in name:
                  lowerCamelCase             =		name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}."""			)

      if "conv_1." in name:
            lowerCamelCase             =		name.replace("conv_1." , "conv_stem."			)
      if ".block." in name:
            lowerCamelCase             =		name.replace(".block." , "."			)

      if "exp_1x1" in name:
            lowerCamelCase             =		name.replace("exp_1x1" , "expand_1x1"			)
      if "red_1x1" in name:
            lowerCamelCase             =		name.replace("red_1x1" , "reduce_1x1"			)
      if ".local_rep.conv_3x3." in name:
            lowerCamelCase             =		name.replace(".local_rep.conv_3x3." , ".conv_kxk."			)
      if ".local_rep.conv_1x1." in name:
            lowerCamelCase             =		name.replace(".local_rep.conv_1x1." , ".conv_1x1."			)
      if ".norm." in name:
            lowerCamelCase             =		name.replace(".norm." , ".normalization."			)
      if ".conv." in name:
            lowerCamelCase             =		name.replace(".conv." , ".convolution."			)
      if ".conv_proj." in name:
            lowerCamelCase             =		name.replace(".conv_proj." , ".conv_projection."			)

      for i in range(0 , 2			):
            for j in range(0 , 4			):
                  if F""".{i}.{j}.""" in name:
                        lowerCamelCase             =		name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}."""			)

      for i in range(2 , 6			):
            for j in range(0 , 4			):
                  if F""".{i}.{j}.""" in name:
                        lowerCamelCase             =		name.replace(F""".{i}.{j}.""" , F""".{i}."""			)
                        if "expand_1x1" in name:
                              lowerCamelCase             =		name.replace("expand_1x1" , "downsampling_layer.expand_1x1"			)
                        if "conv_3x3" in name:
                              lowerCamelCase             =		name.replace("conv_3x3" , "downsampling_layer.conv_3x3"			)
                        if "reduce_1x1" in name:
                              lowerCamelCase             =		name.replace("reduce_1x1" , "downsampling_layer.reduce_1x1"			)

      for i in range(2 , 5			):
            if F""".global_rep.{i}.weight""" in name:
                  lowerCamelCase             =		name.replace(F""".global_rep.{i}.weight""" , ".layernorm.weight"			)
            if F""".global_rep.{i}.bias""" in name:
                  lowerCamelCase             =		name.replace(F""".global_rep.{i}.bias""" , ".layernorm.bias"			)

      if ".global_rep." in name:
            lowerCamelCase             =		name.replace(".global_rep." , ".transformer."			)
      if ".pre_norm_mha.0." in name:
            lowerCamelCase             =		name.replace(".pre_norm_mha.0." , ".layernorm_before."			)
      if ".pre_norm_mha.1.out_proj." in name:
            lowerCamelCase             =		name.replace(".pre_norm_mha.1.out_proj." , ".attention.output.dense."			)
      if ".pre_norm_ffn.0." in name:
            lowerCamelCase             =		name.replace(".pre_norm_ffn.0." , ".layernorm_after."			)
      if ".pre_norm_ffn.1." in name:
            lowerCamelCase             =		name.replace(".pre_norm_ffn.1." , ".intermediate.dense."			)
      if ".pre_norm_ffn.4." in name:
            lowerCamelCase             =		name.replace(".pre_norm_ffn.4." , ".output.dense."			)
      if ".transformer." in name:
            lowerCamelCase             =		name.replace(".transformer." , ".transformer.layer."			)

      if ".aspp_layer." in name:
            lowerCamelCase             =		name.replace(".aspp_layer." , "."			)
      if ".aspp_pool." in name:
            lowerCamelCase             =		name.replace(".aspp_pool." , "."			)
      if "seg_head." in name:
            lowerCamelCase             =		name.replace("seg_head." , "segmentation_head."			)
      if "segmentation_head.classifier.classifier." in name:
            lowerCamelCase             =		name.replace("segmentation_head.classifier.classifier." , "segmentation_head.classifier."			)

      if "classifier.fc." in name:
            lowerCamelCase             =		name.replace("classifier.fc." , "classifier."			)
      elif (not base_model) and ("segmentation_head." not in name):
            lowerCamelCase             =		"mobilevit." + name

      return name

def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=False			):


      """simple docstring"""



      if base_model:
            lowerCamelCase             =		""
      else:
            lowerCamelCase             =		"mobilevit."

      for key in orig_state_dict.copy().keys():
            lowerCamelCase             =		orig_state_dict.pop(UpperCAmelCase__			)

            if key[:8] == "encoder.":
                  lowerCamelCase             =		key[8:]

            if "qkv" in key:
                  lowerCamelCase             =		key.split("."			)
                  lowerCamelCase             =		int(key_split[0][6:]			) - 1
                  lowerCamelCase             =		int(key_split[3]			)
                  lowerCamelCase             =		model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}"""			)
                  lowerCamelCase             =		layer.transformer.layer[transformer_num].attention.attention.all_head_size
                  lowerCamelCase             =		(
                      F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
                  )
                  if "weight" in key:
                        lowerCamelCase             =		val[:dim, :]
                        lowerCamelCase             =		val[dim : dim * 2, :]
                        lowerCamelCase             =		val[-dim:, :]
                  else:
                        lowerCamelCase             =		val[:dim]
                        lowerCamelCase             =		val[dim : dim * 2]
                        lowerCamelCase             =		val[-dim:]
            else:
                  lowerCamelCase             =		val

      return orig_state_dict

def      __lowercase(						):


      """simple docstring"""



      lowerCamelCase             =		"http://images.cocodataset.org/val2017/000000039769.jpg"
      lowerCamelCase             =		Image.open(requests.get(UpperCAmelCase__ , stream=UpperCAmelCase__			).raw			)
      return im

@torch.no_grad()
def      __lowercase(						UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__=False			):


      """simple docstring"""



      lowerCamelCase             =		get_mobilevit_config(UpperCAmelCase__			)

      # load original state_dict
      lowerCamelCase             =		torch.load(UpperCAmelCase__ , map_location="cpu"			)

      # load 🤗 model
      if mobilevit_name.startswith("deeplabv3_"			):
            lowerCamelCase             =		MobileViTForSemanticSegmentation(UpperCAmelCase__			).eval()
      else:
            lowerCamelCase             =		MobileViTForImageClassification(UpperCAmelCase__			).eval()

      lowerCamelCase             =		convert_state_dict(UpperCAmelCase__ , UpperCAmelCase__			)
      model.load_state_dict(UpperCAmelCase__			)

      # Check outputs on an image, prepared by MobileViTImageProcessor
      lowerCamelCase             =		MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32			)
      lowerCamelCase             =		image_processor(images=prepare_img() , return_tensors="pt"			)
      lowerCamelCase             =		model(**UpperCAmelCase__			)
      lowerCamelCase             =		outputs.logits

      if mobilevit_name.startswith("deeplabv3_"			):
            assert logits.shape == (1, 21, 32, 32)

            if mobilevit_name == "deeplabv3_mobilevit_s":
                  lowerCamelCase             =		torch.tensor(
                      [
                          [[6.2_0_6_5, 6.1_2_9_2, 6.2_0_7_0], [6.1_0_7_9, 6.1_2_5_4, 6.1_7_4_7], [6.0_0_4_2, 6.1_0_7_1, 6.1_0_3_4]],
                          [[-6.9_2_5_3, -6.8_6_5_3, -7.0_3_9_8], [-7.3_2_1_8, -7.3_9_8_3, -7.3_6_7_0], [-7.1_9_6_1, -7.2_4_8_2, -7.1_5_6_9]],
                          [[-4.4_7_2_3, -4.4_3_4_8, -4.3_7_6_9], [-5.3_6_2_9, -5.4_6_3_2, -5.4_5_9_8], [-5.1_5_8_7, -5.3_4_0_2, -5.5_0_5_9]],
                      ]			)
            elif mobilevit_name == "deeplabv3_mobilevit_xs":
                  lowerCamelCase             =		torch.tensor(
                      [
                          [[5.4_4_4_9, 5.5_7_3_3, 5.6_3_1_4], [5.1_8_1_5, 5.3_9_3_0, 5.5_9_6_3], [5.1_6_5_6, 5.4_3_3_3, 5.4_8_5_3]],
                          [[-9.4_4_2_3, -9.7_7_6_6, -9.6_7_1_4], [-9.1_5_8_1, -9.5_7_2_0, -9.5_5_1_9], [-9.1_0_0_6, -9.6_4_5_8, -9.5_7_0_3]],
                          [[-7.7_7_2_1, -7.3_7_1_6, -7.1_5_8_3], [-8.4_5_9_9, -8.0_6_2_4, -7.7_9_4_4], [-8.4_1_7_2, -7.8_3_6_6, -7.5_0_2_5]],
                      ]			)
            elif mobilevit_name == "deeplabv3_mobilevit_xxs":
                  lowerCamelCase             =		torch.tensor(
                      [
                          [[6.9_8_1_1, 6.9_7_4_3, 7.3_1_2_3], [7.1_7_7_7, 7.1_9_3_1, 7.3_9_3_8], [7.5_6_3_3, 7.8_0_5_0, 7.8_9_0_1]],
                          [[-1_0.5_5_3_6, -1_0.2_3_3_2, -1_0.2_9_2_4], [-1_0.2_3_3_6, -9.8_6_2_4, -9.5_9_6_4], [-1_0.8_8_4_0, -1_0.8_1_5_8, -1_0.6_6_5_9]],
                          [[-3.4_9_3_8, -3.0_6_3_1, -2.8_6_2_0], [-3.4_2_0_5, -2.8_1_3_5, -2.6_8_7_5], [-3.4_1_7_9, -2.7_9_4_5, -2.8_7_5_0]],
                      ]			)
            else:
                  raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""			)

            assert torch.allclose(logits[0, :3, :3, :3] , UpperCAmelCase__ , atol=1E-4			)
      else:
            assert logits.shape == (1, 1000)

            if mobilevit_name == "mobilevit_s":
                  lowerCamelCase             =		torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]			)
            elif mobilevit_name == "mobilevit_xs":
                  lowerCamelCase             =		torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]			)
            elif mobilevit_name == "mobilevit_xxs":
                  lowerCamelCase             =		torch.tensor([-1.9_3_6_4, -1.2_3_2_7, -0.4_6_5_3]			)
            else:
                  raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}"""			)

            assert torch.allclose(logits[0, :3] , UpperCAmelCase__ , atol=1E-4			)

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

      if push_to_hub:
            lowerCamelCase             =		{
                "mobilevit_s": "mobilevit-small",
                "mobilevit_xs": "mobilevit-x-small",
                "mobilevit_xxs": "mobilevit-xx-small",
                "deeplabv3_mobilevit_s": "deeplabv3-mobilevit-small",
                "deeplabv3_mobilevit_xs": "deeplabv3-mobilevit-x-small",
                "deeplabv3_mobilevit_xxs": "deeplabv3-mobilevit-xx-small",
            }

            print("Pushing to the hub..."			)
            lowerCamelCase             =		model_mapping[mobilevit_name]
            image_processor.push_to_hub(UpperCAmelCase__ , organization="apple"			)
            model.push_to_hub(UpperCAmelCase__ , organization="apple"			)


if __name__ == "__main__":
     a_    :  int						  =		argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--mobilevit_name',
         default='mobilevit_s',
         type=str,
         help=(
             'Name of the MobileViT model you\'d like to convert. Should be one of \'mobilevit_s\', \'mobilevit_xs\','
             ' \'mobilevit_xxs\', \'deeplabv3_mobilevit_s\', \'deeplabv3_mobilevit_xs\', \'deeplabv3_mobilevit_xxs\'.'
         ),
     )
     parser.add_argument(
         '--checkpoint_path', required=True, type=str, help='Path to the original state dict (.pt 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.'
     )

     a_    :  int						  =		parser.parse_args()
     convert_movilevit_checkpoint(
         args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
     )




from itertools import zip_longest

import requests
from bsa import BeautifulSoup
from pandas import DataFrame

def      __lowercase(						UpperCAmelCase__ = "laptop"			):


      """simple docstring"""



      lowerCamelCase             =		F"""https://www.amazon.in/laptop/s?k={product}"""
      lowerCamelCase             =		{
          "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36\n        (KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36",
          "Accept-Language": "en-US, en;q=0.5",
      }
      lowerCamelCase             =		BeautifulSoup(requests.get(UpperCAmelCase__ , headers=UpperCAmelCase__			).text			)
      # Initialize a Pandas dataframe with the column titles
      lowerCamelCase             =		DataFrame(
          columns=[
              "Product Title",
              "Product Link",
              "Current Price of the product",
              "Product Rating",
              "MRP of the product",
              "Discount",
          ]			)
      # Loop through each entry and store them in the dataframe
      for item, _ in zip_longest(
          soup.find_all(
              "div" , attrs={"class": "s-result-item", "data-component-type": "s-search-result"} , ) , soup.find_all("div" , attrs={"class": "a-row a-size-base a-color-base"}			) , ):
            try:
                  lowerCamelCase             =		item.ha.text
                  lowerCamelCase             =		"https://www.amazon.in/" + item.ha.a["href"]
                  lowerCamelCase             =		item.find("span" , attrs={"class": "a-offscreen"}			).text
                  try:
                        lowerCamelCase             =		item.find("span" , attrs={"class": "a-icon-alt"}			).text
                  except AttributeError:
                        lowerCamelCase             =		"Not available"
                  try:
                        lowerCamelCase             =		(
                            "₹"
                            + item.find(
                                "span" , attrs={"class": "a-price a-text-price"}			).text.split("₹"			)[1]
                        )
                  except AttributeError:
                        lowerCamelCase             =		""
                  try:
                        lowerCamelCase             =		float(
                            (
                                (
                                    float(product_mrp.strip("₹"			).replace("," , ""			)			)
                                    - float(product_price.strip("₹"			).replace("," , ""			)			)
                                )
                                / float(product_mrp.strip("₹"			).replace("," , ""			)			)
                            )
                            * 100			)
                  except ValueError:
                        lowerCamelCase             =		float("nan"			)
            except AttributeError:
                  pass
            lowerCamelCase             =		[
                product_title,
                product_link,
                product_price,
                product_rating,
                product_mrp,
                discount,
            ]
      lowerCamelCase             =		" "
      lowerCamelCase             =		" "
      data_frame.index += 1
      return data_frame


if __name__ == "__main__":
     a_    :  Dict						  =		'headphones'
     get_amazon_product_data(product).to_csv(f"""Amazon Product Data for {product}.csv""")