






"""simple docstring"""







import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a       :	int             = 16
__a       :	Optional[int]             = 32





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ = 16):
					a__          =  AutoTokenizer.from_pretrained('''bert-base-cased''')
					a__          =  load_dataset('''glue''' ,     '''mrpc''')

					def tokenize_function(lowerCamelCase_):
										# max_length=None => use the model max length (it's actually the default)
										a__          =  tokenizer(examples['''sentence1'''] ,     examples['''sentence2'''] ,     truncation=UpperCamelCase__ ,     max_length=UpperCamelCase__)
										return outputs

					# Apply the method we just defined to all the examples in all the splits of the dataset
					# starting with the main process first:
					with accelerator.main_process_first():
										a__          =  datasets.map(
										    UpperCamelCase__ ,     batched=UpperCamelCase__ ,     remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,     )

					# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
					# transformers library
					a__          =  tokenized_datasets.rename_column('''label''' ,     '''labels''')

					def collate_fn(lowerCamelCase_):
										# On TPU it's best to pad everything to the same length or training will be very slow.
										a__          =  128 if accelerator.distributed_type == DistributedType.TPU else None
										# When using mixed precision we want round multiples of 8/16
										if accelerator.mixed_precision == "fp8":
															a__          =  16
										elif accelerator.mixed_precision != "no":
															a__          =  8
										else:
															a__          =  None

										return tokenizer.pad(
										    UpperCamelCase__ ,     padding='''longest''' ,     max_length=UpperCamelCase__ ,     pad_to_multiple_of=UpperCamelCase__ ,     return_tensors='''pt''' ,     )

					# Instantiate dataloaders.
					a__          =  DataLoader(
					    tokenized_datasets['''train'''] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__)
					a__          =  DataLoader(
					    tokenized_datasets['''validation'''] ,     shuffle=UpperCamelCase__ ,     collate_fn=UpperCamelCase__ ,     batch_size=UpperCamelCase__)

					return train_dataloader, eval_dataloader


# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
	from accelerate.test_utils.training import mocked_dataloaders

	__a       :	Union[str, Any]             = mocked_dataloaders  # noqa: F811





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):





					if os.environ.get('''TESTING_MOCKED_DATALOADERS''' ,     UpperCamelCase__) == "1":
										a__          =  2
					# Initialize accelerator
					a__          =  Accelerator(cpu=args.cpu ,     mixed_precision=args.mixed_precision)
					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					a__          =  config['''lr''']
					a__          =  int(config['''num_epochs'''])
					a__          =  int(config['''seed'''])
					a__          =  int(config['''batch_size'''])

					a__          =  evaluate.load('''glue''' ,     '''mrpc''')

					# If the batch size is too big we use gradient accumulation
					a__          =  1
					if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
										a__          =  batch_size // MAX_GPU_BATCH_SIZE
										a__          =  MAX_GPU_BATCH_SIZE

					set_seed(UpperCamelCase__)
					a__       ,a__          =  get_dataloaders(UpperCamelCase__ ,     UpperCamelCase__)
					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					a__          =  AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' ,     return_dict=UpperCamelCase__)

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					a__          =  model.to(accelerator.device)

					# Instantiate optimizer
					a__          =  AdamW(params=model.parameters() ,     lr=UpperCamelCase__)

					# Instantiate scheduler
					a__          =  get_linear_schedule_with_warmup(
					    optimizer=UpperCamelCase__ ,     num_warmup_steps=100 ,     num_training_steps=(len(UpperCamelCase__) * num_epochs) // gradient_accumulation_steps ,     )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					a__       ,a__       ,a__       ,a__       ,a__          =  accelerator.prepare(
					    UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__ ,     UpperCamelCase__)

					# Now we train the model
					for epoch in range(UpperCamelCase__):
										model.train()
										for step, batch in enumerate(UpperCamelCase__):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device)
															a__          =  model(**UpperCamelCase__)
															a__          =  outputs.loss
															a__          =  loss / gradient_accumulation_steps
															accelerator.backward(UpperCamelCase__)
															if step % gradient_accumulation_steps == 0:
																				optimizer.step()
																				lr_scheduler.step()
																				optimizer.zero_grad()

										model.eval()
										a__          =  0
										for step, batch in enumerate(UpperCamelCase__):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															batch.to(accelerator.device)
															with torch.no_grad():
																				a__          =  model(**UpperCamelCase__)
															a__          =  outputs.logits.argmax(dim=-1)
															a__       ,a__          =  accelerator.gather((predictions, batch['''labels''']))
															# New Code #
															# First we check if it's a distributed system
															if accelerator.use_distributed:
																				# Then see if we're on the last batch of our eval dataloader
																				if step == len(UpperCamelCase__) - 1:
																									# Last batch needs to be truncated on distributed systems as it contains additional samples
																									a__          =  predictions[: len(eval_dataloader.dataset) - samples_seen]
																									a__          =  references[: len(eval_dataloader.dataset) - samples_seen]
																				else:
																									# Otherwise we add the number of samples seen
																									samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
															metric.add_batch(
															    predictions=UpperCamelCase__ ,     references=UpperCamelCase__ ,     )

										a__          =  metric.compute()
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'epoch {epoch}:' ,     UpperCamelCase__)





def        SCREAMING_SNAKE_CASE       (      ):
					a__          =  argparse.ArgumentParser(description='''Simple example of training script.''')
					parser.add_argument(
					    '''--mixed_precision''' ,     type=UpperCamelCase__ ,     default=UpperCamelCase__ ,     choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] ,     help='''Whether to use mixed precision. Choose'''
					    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
					    '''and an Nvidia Ampere GPU.''' ,     )
					parser.add_argument('''--cpu''' ,     action='''store_true''' ,     help='''If passed, will train on the CPU.''')
					a__          =  parser.parse_args()
					a__          =  {'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
					training_function(UpperCamelCase__ ,     UpperCamelCase__)


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
	)


