






"""simple docstring"""







import argparse
import csv
import logging
import os
import random

import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange

from transformers import (
    CONFIG_NAME,
    WEIGHTS_NAME,
    AdamW,
    OpenAIGPTDoubleHeadsModel,
    OpenAIGPTTokenizer,
    get_linear_schedule_with_warmup,
)


logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
__a       :	List[Any]             = logging.getLogger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  np.argmax(SCREAMING_SNAKE_CASE_ ,     axis=1)
					return np.sum(outputs == labels)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					with open(SCREAMING_SNAKE_CASE_ ,     encoding='''utf_8''') as f:
										a__          =  csv.reader(SCREAMING_SNAKE_CASE_)
										a__          =  []
										next(SCREAMING_SNAKE_CASE_)  # skip the first line
										for line in tqdm(SCREAMING_SNAKE_CASE_):
															output.append((''' '''.join(line[1:5]), line[5], line[6], int(line[-1]) - 1))
					return output





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  []
					for dataset in encoded_datasets:
										a__          =  len(SCREAMING_SNAKE_CASE_)
										a__          =  np.zeros((n_batch, 2, input_len) ,     dtype=np.intaa)
										a__          =  np.zeros((n_batch, 2) ,     dtype=np.intaa)
										a__          =  np.full((n_batch, 2, input_len) ,     fill_value=-100 ,     dtype=np.intaa)
										a__          =  np.zeros((n_batch,) ,     dtype=np.intaa)
										for (
										    i,
										    (story, conta, conta, mc_label),
										) in enumerate(SCREAMING_SNAKE_CASE_):
															a__          =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															a__          =  [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
															a__          =  with_conta
															a__          =  with_conta
															a__          =  len(SCREAMING_SNAKE_CASE_) - 1
															a__          =  len(SCREAMING_SNAKE_CASE_) - 1
															a__          =  with_conta
															a__          =  with_conta
															a__          =  mc_label
										a__          =  (input_ids, mc_token_ids, lm_labels, mc_labels)
										tensor_datasets.append(tuple(torch.tensor(SCREAMING_SNAKE_CASE_) for t in all_inputs))
					return tensor_datasets





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  argparse.ArgumentParser()
					parser.add_argument('''--model_name''' ,     type=SCREAMING_SNAKE_CASE_ ,     default='''openai-gpt''' ,     help='''pretrained model name''')
					parser.add_argument('''--do_train''' ,     action='''store_true''' ,     help='''Whether to run training.''')
					parser.add_argument('''--do_eval''' ,     action='''store_true''' ,     help='''Whether to run eval on the dev set.''')
					parser.add_argument(
					    '''--output_dir''' ,     default=SCREAMING_SNAKE_CASE_ ,     type=SCREAMING_SNAKE_CASE_ ,     required=SCREAMING_SNAKE_CASE_ ,     help='''The output directory where the model predictions and checkpoints will be written.''' ,     )
					parser.add_argument('''--train_dataset''' ,     type=SCREAMING_SNAKE_CASE_ ,     default='''''')
					parser.add_argument('''--eval_dataset''' ,     type=SCREAMING_SNAKE_CASE_ ,     default='''''')
					parser.add_argument('''--seed''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=42)
					parser.add_argument('''--num_train_epochs''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=3)
					parser.add_argument('''--train_batch_size''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=8)
					parser.add_argument('''--eval_batch_size''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=16)
					parser.add_argument('''--adam_epsilon''' ,     default=1E-8 ,     type=SCREAMING_SNAKE_CASE_ ,     help='''Epsilon for Adam optimizer.''')
					parser.add_argument('''--max_grad_norm''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=1)
					parser.add_argument(
					    '''--max_steps''' ,     default=-1 ,     type=SCREAMING_SNAKE_CASE_ ,     help=(
					        '''If > 0: set total number of training                         steps to perform. Override num_train_epochs.'''
					    ) ,     )
					parser.add_argument(
					    '''--gradient_accumulation_steps''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=1 ,     help='''Number of updates steps to accumulate before                        performing a backward/update pass.''' ,     )
					parser.add_argument('''--learning_rate''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=6.2_5E-5)
					parser.add_argument('''--warmup_steps''' ,     default=0 ,     type=SCREAMING_SNAKE_CASE_ ,     help='''Linear warmup over warmup_steps.''')
					parser.add_argument('''--lr_schedule''' ,     type=SCREAMING_SNAKE_CASE_ ,     default='''warmup_linear''')
					parser.add_argument('''--weight_decay''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=0.01)
					parser.add_argument('''--lm_coef''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=0.9)
					parser.add_argument('''--n_valid''' ,     type=SCREAMING_SNAKE_CASE_ ,     default=374)

					parser.add_argument('''--server_ip''' ,     type=SCREAMING_SNAKE_CASE_ ,     default='''''' ,     help='''Can be used for distant debugging.''')
					parser.add_argument('''--server_port''' ,     type=SCREAMING_SNAKE_CASE_ ,     default='''''' ,     help='''Can be used for distant debugging.''')
					a__          =  parser.parse_args()
					print(SCREAMING_SNAKE_CASE_)

					if args.server_ip and args.server_port:
										# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
										import ptvsd

										print('''Waiting for debugger attach''')
										ptvsd.enable_attach(address=(args.server_ip, args.server_port) ,     redirect_output=SCREAMING_SNAKE_CASE_)
										ptvsd.wait_for_attach()

					random.seed(args.seed)
					np.random.seed(args.seed)
					torch.manual_seed(args.seed)
					torch.cuda.manual_seed_all(args.seed)

					a__          =  torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''')
					a__          =  torch.cuda.device_count()
					logger.info('''device: {}, n_gpu {}'''.format(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_))

					if not args.do_train and not args.do_eval:
										raise ValueError('''At least one of `do_train` or `do_eval` must be True.''')

					if not os.path.exists(args.output_dir):
										os.makedirs(args.output_dir)

					# Load tokenizer and model
					# This loading functions also add new tokens and embeddings called `special tokens`
					# These new embeddings will be fine-tuned on the RocStories dataset
					a__          =  ['_start_', '_delimiter_', '_classify_']
					a__          =  OpenAIGPTTokenizer.from_pretrained(args.model_name)
					tokenizer.add_tokens(SCREAMING_SNAKE_CASE_)
					a__          =  tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_)
					a__          =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name)
					model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE_))
					model.to(SCREAMING_SNAKE_CASE_)

					# Load and encode the datasets
					def tokenize_and_encode(lowerCamelCase_):
										if isinstance(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_):
															return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(SCREAMING_SNAKE_CASE_))
										elif isinstance(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_):
															return obj
										return [tokenize_and_encode(SCREAMING_SNAKE_CASE_) for o in obj]

					logger.info('''Encoding dataset...''')
					a__          =  load_rocstories_dataset(args.train_dataset)
					a__          =  load_rocstories_dataset(args.eval_dataset)
					a__          =  (train_dataset, eval_dataset)
					a__          =  tokenize_and_encode(SCREAMING_SNAKE_CASE_)

					# Compute the max input length for the Transformer
					a__          =  model.config.n_positions // 2 - 2
					a__          =  max(
					    len(story[:max_length]) + max(len(conta[:max_length]) ,     len(conta[:max_length])) + 3
					    for dataset in encoded_datasets
					    for story, conta, conta, _ in dataset)
					a__          =  min(SCREAMING_SNAKE_CASE_ ,     model.config.n_positions)  # Max size of input for the pre-trained model

					# Prepare inputs tensors and dataloaders
					a__          =  pre_process_datasets(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_ ,     *SCREAMING_SNAKE_CASE_)
					a__          =  tensor_datasets[0], tensor_datasets[1]

					a__          =  TensorDataset(*SCREAMING_SNAKE_CASE_)
					a__          =  RandomSampler(SCREAMING_SNAKE_CASE_)
					a__          =  DataLoader(SCREAMING_SNAKE_CASE_ ,     sampler=SCREAMING_SNAKE_CASE_ ,     batch_size=args.train_batch_size)

					a__          =  TensorDataset(*SCREAMING_SNAKE_CASE_)
					a__          =  SequentialSampler(SCREAMING_SNAKE_CASE_)
					a__          =  DataLoader(SCREAMING_SNAKE_CASE_ ,     sampler=SCREAMING_SNAKE_CASE_ ,     batch_size=args.eval_batch_size)

					# Prepare optimizer
					if args.do_train:
										if args.max_steps > 0:
															a__          =  args.max_steps
															a__          =  args.max_steps // (len(SCREAMING_SNAKE_CASE_) // args.gradient_accumulation_steps) + 1
										else:
															a__          =  len(SCREAMING_SNAKE_CASE_) // args.gradient_accumulation_steps * args.num_train_epochs

										a__          =  list(model.named_parameters())
										a__          =  ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
										a__          =  [
										    {
										        'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)],
										        'weight_decay': args.weight_decay,
										    },
										    {'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], 'weight_decay': 0.0},
										]
										a__          =  AdamW(SCREAMING_SNAKE_CASE_ ,     lr=args.learning_rate ,     eps=args.adam_epsilon)
										a__          =  get_linear_schedule_with_warmup(
										    SCREAMING_SNAKE_CASE_ ,     num_warmup_steps=args.warmup_steps ,     num_training_steps=SCREAMING_SNAKE_CASE_)

					if args.do_train:
										a__          =  0, 0, None
										model.train()
										for _ in trange(int(args.num_train_epochs) ,     desc='''Epoch'''):
															a__          =  0
															a__          =  0
															a__          =  tqdm(SCREAMING_SNAKE_CASE_ ,     desc='''Training''')
															for step, batch in enumerate(SCREAMING_SNAKE_CASE_):
																				a__          =  tuple(t.to(SCREAMING_SNAKE_CASE_) for t in batch)
																				a__          =  batch
																				a__          =  model(SCREAMING_SNAKE_CASE_ ,     mc_token_ids=SCREAMING_SNAKE_CASE_ ,     lm_labels=SCREAMING_SNAKE_CASE_ ,     mc_labels=SCREAMING_SNAKE_CASE_)
																				a__          =  args.lm_coef * losses[0] + losses[1]
																				loss.backward()
																				optimizer.step()
																				scheduler.step()
																				optimizer.zero_grad()
																				tr_loss += loss.item()
																				a__          =  (
																				    loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
																				)
																				nb_tr_steps += 1
																				a__          =  'Training loss: {:.2e} lr: {:.2e}'.format(SCREAMING_SNAKE_CASE_ ,     scheduler.get_lr()[0])

    # Save a trained model
					if args.do_train:
										# Save a trained model, configuration and tokenizer
										a__          =  model.module if hasattr(SCREAMING_SNAKE_CASE_ ,     '''module''') else model  # Only save the model itself

										# If we save using the predefined names, we can load using `from_pretrained`
										a__          =  os.path.join(args.output_dir ,     SCREAMING_SNAKE_CASE_)
										a__          =  os.path.join(args.output_dir ,     SCREAMING_SNAKE_CASE_)

										torch.save(model_to_save.state_dict() ,     SCREAMING_SNAKE_CASE_)
										model_to_save.config.to_json_file(SCREAMING_SNAKE_CASE_)
										tokenizer.save_vocabulary(args.output_dir)

										# Load a trained model and vocabulary that you have fine-tuned
										a__          =  OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir)
										a__          =  OpenAIGPTTokenizer.from_pretrained(args.output_dir)
										model.to(SCREAMING_SNAKE_CASE_)

					if args.do_eval:
										model.eval()
										a__          =  0, 0
										a__          =  0, 0
										for batch in tqdm(SCREAMING_SNAKE_CASE_ ,     desc='''Evaluating'''):
															a__          =  tuple(t.to(SCREAMING_SNAKE_CASE_) for t in batch)
															a__          =  batch
															with torch.no_grad():
																				a__          =  model(
																				    SCREAMING_SNAKE_CASE_ ,     mc_token_ids=SCREAMING_SNAKE_CASE_ ,     lm_labels=SCREAMING_SNAKE_CASE_ ,     mc_labels=SCREAMING_SNAKE_CASE_)

															a__          =  mc_logits.detach().cpu().numpy()
															a__          =  mc_labels.to('''cpu''').numpy()
															a__          =  accuracy(SCREAMING_SNAKE_CASE_ ,     SCREAMING_SNAKE_CASE_)

															eval_loss += mc_loss.mean().item()
															eval_accuracy += tmp_eval_accuracy

															nb_eval_examples += input_ids.size(0)
															nb_eval_steps += 1

										a__          =  eval_loss / nb_eval_steps
										a__          =  eval_accuracy / nb_eval_examples
										a__          =  tr_loss / nb_tr_steps if args.do_train else None
										a__          =  {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}

										a__          =  os.path.join(args.output_dir ,     '''eval_results.txt''')
										with open(SCREAMING_SNAKE_CASE_ ,     '''w''') as writer:
															logger.info('''***** Eval results *****''')
															for key in sorted(result.keys()):
																				logger.info('''  %s = %s''' ,     SCREAMING_SNAKE_CASE_ ,     str(result[key]))
																				writer.write('''%s = %s\n''' % (key, str(result[key])))


if __name__ == "__main__":
	main()









"""simple docstring"""







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       :	Optional[Any]             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_):
					a__          =  MobileViTConfig()

					# size of the architecture
					if "mobilevit_s" in mobilevit_name:
										a__          =  [144, 192, 240]
										a__          =  [16, 32, 64, 96, 128, 160, 640]
					elif "mobilevit_xs" in mobilevit_name:
										a__          =  [96, 120, 144]
										a__          =  [16, 32, 48, 64, 80, 96, 384]
					elif "mobilevit_xxs" in mobilevit_name:
										a__          =  [64, 80, 96]
										a__          =  [16, 16, 24, 48, 64, 80, 320]
										a__          =  0.05
										a__          =  2.0

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

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

					return config





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False):
					for i in range(1 ,     6):
										if f'layer_{i}.' in name:
															a__          =  name.replace(f'layer_{i}.' ,     f'encoder.layer.{i - 1}.')

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

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

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

					for i in range(2 ,     5):
										if f'.global_rep.{i}.weight' in name:
															a__          =  name.replace(f'.global_rep.{i}.weight' ,     '''.layernorm.weight''')
										if f'.global_rep.{i}.bias' in name:
															a__          =  name.replace(f'.global_rep.{i}.bias' ,     '''.layernorm.bias''')

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

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

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

					return name





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					if base_model:
										a__          =  ''''''
					else:
										a__          =  '''mobilevit.'''

					for key in orig_state_dict.copy().keys():
										a__          =  orig_state_dict.pop(lowerCamelCase_)

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

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

					return orig_state_dict





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					a__          =  Image.open(requests.get(lowerCamelCase_ ,     stream=lowerCamelCase_).raw)
					return im





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  get_mobilevit_config(lowerCamelCase_)

					# load original state_dict
					a__          =  torch.load(lowerCamelCase_ ,     map_location='''cpu''')

					# load 🤗 model
					if mobilevit_name.startswith('''deeplabv3_'''):
										a__          =  MobileViTForSemanticSegmentation(lowerCamelCase_).eval()
					else:
										a__          =  MobileViTForImageClassification(lowerCamelCase_).eval()

					a__          =  convert_state_dict(lowerCamelCase_ ,     lowerCamelCase_)
					model.load_state_dict(lowerCamelCase_)

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

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

										if mobilevit_name == "deeplabv3_mobilevit_s":
															a__          =  torch.tensor(
															    [
															        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
															        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
															        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xs":
															a__          =  torch.tensor(
															    [
															        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
															        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
															        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
															    ])
										elif mobilevit_name == "deeplabv3_mobilevit_xxs":
															a__          =  torch.tensor(
															    [
															        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
															        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
															        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
															    ])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

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

										if mobilevit_name == "mobilevit_s":
															a__          =  torch.tensor([-0.9866, 0.2392, -1.1241])
										elif mobilevit_name == "mobilevit_xs":
															a__          =  torch.tensor([-2.4761, -0.9399, -1.9587])
										elif mobilevit_name == "mobilevit_xxs":
															a__          =  torch.tensor([-1.9364, -1.2327, -0.4653])
										else:
															raise ValueError(f'Unknown mobilevit_name: {mobilevit_name}')

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

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model {mobilevit_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:
										a__          =  {
										    '''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...''')
										a__          =  model_mapping[mobilevit_name]
										image_processor.push_to_hub(lowerCamelCase_ ,     organization='''apple''')
										model.push_to_hub(lowerCamelCase_ ,     organization='''apple''')


if __name__ == "__main__":
	__a       :	Optional[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       :	List[str]             = parser.parse_args()
	convert_movilevit_checkpoint(
	    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


