
'''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()
__lowerCAmelCase												=	logging.get_logger(__name__)






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE				):
							_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(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, repo_type="""dataset"""				)							, """r"""				)				)
							_snake_case       =  {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
							_snake_case       =  idalabel
							_snake_case       =  {v: k for k, v in idalabel.items()}

							return config






def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
							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  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
							if base_model:
														_snake_case       =  """"""
							else:
														_snake_case       =  """mobilevit."""

							for key in orig_state_dict.copy().keys():
														_snake_case       =  orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

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






@torch.no_grad()
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE=False				):
							_snake_case       =  get_mobilevit_config(_SCREAMING_SNAKE_CASE				)

							# load original state_dict
							_snake_case       =  torch.load(_SCREAMING_SNAKE_CASE							, map_location="""cpu"""				)

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

							_snake_case       =  convert_state_dict(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)
							model.load_state_dict(_SCREAMING_SNAKE_CASE				)

							# 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(**_SCREAMING_SNAKE_CASE				)
							_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(
																					    [
																					        [[6.2065, 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":
																					_snake_case       =  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":
																					_snake_case       =  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]							, _SCREAMING_SNAKE_CASE							, atol=1E-4				)
							else:
														assert logits.shape == (1, 1000)

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

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

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

							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(_SCREAMING_SNAKE_CASE							, organization="""apple"""				)
														model.push_to_hub(_SCREAMING_SNAKE_CASE							, organization="""apple"""				)


if __name__ == "__main__":
				__lowerCAmelCase												=	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.'
				)

				__lowerCAmelCase												=	parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)

'''simple docstring'''
def  __SCREAMING_SNAKE_CASE				(  _SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				):

							if not (isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				) and isinstance(_SCREAMING_SNAKE_CASE							, _SCREAMING_SNAKE_CASE				)):
														raise ValueError("""longest_common_substring() takes two strings for inputs"""				)

							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)
							_snake_case       =  len(_SCREAMING_SNAKE_CASE				)

							_snake_case       =  [[0] * (texta_length + 1) for _ in range(texta_length + 1				)]
							_snake_case       =  0
							_snake_case       =  0

							for i in range(1							, texta_length + 1				):
														for j in range(1							, texta_length + 1				):
																					if texta[i - 1] == texta[j - 1]:
																												_snake_case       =  1 + dp[i - 1][j - 1]
																												if dp[i][j] > ans_length:
																																			_snake_case       =  i
																																			_snake_case       =  dp[i][j]

							return texta[ans_index - ans_length : ans_index]


if __name__ == "__main__":
				import doctest

				doctest.testmod()