

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()
_UpperCAmelCase		:   int										=       logging.get_logger(__name__)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    MobileViTConfig()

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

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

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

							return config





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






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

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

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

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

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

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

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

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

							return name





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






							if base_model:
														snake_case_							=    ''
							else:
														snake_case_							=    'mobilevit.'

							for key in orig_state_dict.copy().keys():
														snake_case_							=    orig_state_dict.pop(UpperCamelCase__      )

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

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

							return orig_state_dict





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    'http://images.cocodataset.org/val2017/000000039769.jpg'
							snake_case_							=    Image.open(requests.get(UpperCamelCase__   ,							stream=UpperCamelCase__      ).raw      )
							return im





@torch.no_grad()
def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






							snake_case_							=    get_mobilevit_config(UpperCamelCase__      )

							# load original state_dict
							snake_case_							=    torch.load(UpperCamelCase__   ,							map_location='cpu'      )

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

							snake_case_							=    convert_state_dict(UpperCamelCase__   ,							UpperCamelCase__      )
							model.load_state_dict(UpperCamelCase__      )

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

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

														if mobilevit_name == "deeplabv3_mobilevit_s":
																					snake_case_							=    torch.tensor(
																					    [
																					        [[6.20_65, 6.12_92, 6.20_70], [6.10_79, 6.12_54, 6.17_47], [6.00_42, 6.10_71, 6.10_34]],
																					        [[-6.92_53, -6.86_53, -7.03_98], [-7.32_18, -7.39_83, -7.36_70], [-7.19_61, -7.24_82, -7.15_69]],
																					        [[-4.47_23, -4.43_48, -4.37_69], [-5.36_29, -5.46_32, -5.45_98], [-5.15_87, -5.34_02, -5.50_59]],
																					    ]      )
														elif mobilevit_name == "deeplabv3_mobilevit_xs":
																					snake_case_							=    torch.tensor(
																					    [
																					        [[5.44_49, 5.57_33, 5.63_14], [5.18_15, 5.39_30, 5.59_63], [5.16_56, 5.43_33, 5.48_53]],
																					        [[-9.44_23, -9.77_66, -9.67_14], [-9.15_81, -9.57_20, -9.55_19], [-9.10_06, -9.64_58, -9.57_03]],
																					        [[-7.77_21, -7.37_16, -7.15_83], [-8.45_99, -8.06_24, -7.79_44], [-8.41_72, -7.83_66, -7.50_25]],
																					    ]      )
														elif mobilevit_name == "deeplabv3_mobilevit_xxs":
																					snake_case_							=    torch.tensor(
																					    [
																					        [[6.98_11, 6.97_43, 7.31_23], [7.17_77, 7.19_31, 7.39_38], [7.56_33, 7.80_50, 7.89_01]],
																					        [[-10.55_36, -10.23_32, -10.29_24], [-10.23_36, -9.86_24, -9.59_64], [-10.88_40, -10.81_58, -10.66_59]],
																					        [[-3.49_38, -3.06_31, -2.86_20], [-3.42_05, -2.81_35, -2.68_75], [-3.41_79, -2.79_45, -2.87_50]],
																					    ]      )
														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":
																					snake_case_							=    torch.tensor([-0.98_66, 0.23_92, -1.12_41]      )
														elif mobilevit_name == "mobilevit_xs":
																					snake_case_							=    torch.tensor([-2.47_61, -0.93_99, -1.95_87]      )
														elif mobilevit_name == "mobilevit_xxs":
																					snake_case_							=    torch.tensor([-1.93_64, -1.23_27, -0.46_53]      )
														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:
														snake_case_							=    {
														    '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...'      )
														snake_case_							=    model_mapping[mobilevit_name]
														image_processor.push_to_hub(UpperCamelCase__   ,							organization='apple'      )
														model.push_to_hub(UpperCamelCase__   ,							organization='apple'      )


if __name__ == "__main__":
					_UpperCAmelCase		:   List[Any]										=       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."""
					)

					_UpperCAmelCase		:   Tuple										=       parser.parse_args()
					convert_movilevit_checkpoint(
					    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
					)







import multiprocessing
import time

from arguments import PretokenizationArguments
from datasets import load_dataset

from transformers import AutoTokenizer, HfArgumentParser





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    {}
							snake_case_							=    tokenizer(example['content']   ,							truncation=UpperCamelCase__      )['input_ids']
							snake_case_							=    len(example['content']      ) / len(output['input_ids']      )
							return output


_UpperCAmelCase		:   Dict										=       HfArgumentParser(PretokenizationArguments)
_UpperCAmelCase		:   List[Any]										=       parser.parse_args()
if args.num_workers is None:
					_UpperCAmelCase		:   Union[str, Any]										=       multiprocessing.cpu_count()
_UpperCAmelCase		:   int										=       AutoTokenizer.from_pretrained(args.tokenizer_dir)

_UpperCAmelCase		:   Optional[int]										=       time.time()
_UpperCAmelCase		:   List[str]										=       load_dataset(args.dataset_name, split="""train""")
print(F'''Dataset loaded in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Tuple										=       time.time()
_UpperCAmelCase		:   Union[str, Any]										=       ds.map(
    tokenize,
    num_proc=args.num_workers,
    remove_columns=[
        """repo_name""",
        """path""",
        """copies""",
        """size""",
        """content""",
        """license""",
        """hash""",
        """line_mean""",
        """line_max""",
        """alpha_frac""",
        """autogenerated""",
    ],
)
print(F'''Dataset tokenized in {time.time()-t_start:.2f}s''')

_UpperCAmelCase		:   Dict										=       time.time()
ds.push_to_hub(args.tokenized_data_repo)


print(F'''Data pushed to the hub in {time.time()-t_start:.2f}s''')




