


'''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()
UpperCAmelCase_		:   List[Any]							   =		logging.get_logger(__name__)







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Dict     		=      MobileViTConfig()

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

				if mobilevit_name.startswith("""deeplabv3_"""     ):
								_SCREAMING_SNAKE_CASE :       str     		=      512
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      16
								_SCREAMING_SNAKE_CASE :       int     		=      21
								_SCREAMING_SNAKE_CASE :       Any     		=      """pascal-voc-id2label.json"""
				else:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      1000
								_SCREAMING_SNAKE_CASE :       int     		=      """imagenet-1k-id2label.json"""

				_SCREAMING_SNAKE_CASE :       Tuple     		=      """huggingface/label-files"""
				_SCREAMING_SNAKE_CASE :       int     		=      json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      repo_type="""dataset"""     )							,      """r"""     )     )
				_SCREAMING_SNAKE_CASE :       Any     		=      {int(SCREAMING_SNAKE_CASE__     ): v for k, v in idalabel.items()}
				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      idalabel
				_SCREAMING_SNAKE_CASE :       str     		=      {v: k for k, v in idalabel.items()}

				return config







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__=False     ):







				"""simple docstring"""





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

				if "conv_1." in name:
								_SCREAMING_SNAKE_CASE :       str     		=      name.replace("""conv_1."""							,      """conv_stem."""     )
				if ".block." in name:
								_SCREAMING_SNAKE_CASE :       int     		=      name.replace(""".block."""							,      """."""     )

				if "exp_1x1" in name:
								_SCREAMING_SNAKE_CASE :       Any     		=      name.replace("""exp_1x1"""							,      """expand_1x1"""     )
				if "red_1x1" in name:
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      name.replace("""red_1x1"""							,      """reduce_1x1"""     )
				if ".local_rep.conv_3x3." in name:
								_SCREAMING_SNAKE_CASE :       str     		=      name.replace(""".local_rep.conv_3x3."""							,      """.conv_kxk."""     )
				if ".local_rep.conv_1x1." in name:
								_SCREAMING_SNAKE_CASE :       Any     		=      name.replace(""".local_rep.conv_1x1."""							,      """.conv_1x1."""     )
				if ".norm." in name:
								_SCREAMING_SNAKE_CASE :       Tuple     		=      name.replace(""".norm."""							,      """.normalization."""     )
				if ".conv." in name:
								_SCREAMING_SNAKE_CASE :       Dict     		=      name.replace(""".conv."""							,      """.convolution."""     )
				if ".conv_proj." in name:
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      name.replace(""".conv_proj."""							,      """.conv_projection."""     )

				for i in range(0							,      2     ):
								for j in range(0							,      4     ):
												if f""".{i}.{j}.""" in name:
																_SCREAMING_SNAKE_CASE :       Any     		=      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:
																_SCREAMING_SNAKE_CASE :       Any     		=      name.replace(f""".{i}.{j}."""							,      f""".{i}."""     )
																if "expand_1x1" in name:
																				_SCREAMING_SNAKE_CASE :       int     		=      name.replace("""expand_1x1"""							,      """downsampling_layer.expand_1x1"""     )
																if "conv_3x3" in name:
																				_SCREAMING_SNAKE_CASE :       Tuple     		=      name.replace("""conv_3x3"""							,      """downsampling_layer.conv_3x3"""     )
																if "reduce_1x1" in name:
																				_SCREAMING_SNAKE_CASE :       List[Any]     		=      name.replace("""reduce_1x1"""							,      """downsampling_layer.reduce_1x1"""     )

				for i in range(2							,      5     ):
								if f""".global_rep.{i}.weight""" in name:
												_SCREAMING_SNAKE_CASE :       List[Any]     		=      name.replace(f""".global_rep.{i}.weight"""							,      """.layernorm.weight"""     )
								if f""".global_rep.{i}.bias""" in name:
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      name.replace(f""".global_rep.{i}.bias"""							,      """.layernorm.bias"""     )

				if ".global_rep." in name:
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      name.replace(""".global_rep."""							,      """.transformer."""     )
				if ".pre_norm_mha.0." in name:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      name.replace(""".pre_norm_mha.0."""							,      """.layernorm_before."""     )
				if ".pre_norm_mha.1.out_proj." in name:
								_SCREAMING_SNAKE_CASE :       Dict     		=      name.replace(""".pre_norm_mha.1.out_proj."""							,      """.attention.output.dense."""     )
				if ".pre_norm_ffn.0." in name:
								_SCREAMING_SNAKE_CASE :       Dict     		=      name.replace(""".pre_norm_ffn.0."""							,      """.layernorm_after."""     )
				if ".pre_norm_ffn.1." in name:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      name.replace(""".pre_norm_ffn.1."""							,      """.intermediate.dense."""     )
				if ".pre_norm_ffn.4." in name:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      name.replace(""".pre_norm_ffn.4."""							,      """.output.dense."""     )
				if ".transformer." in name:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      name.replace(""".transformer."""							,      """.transformer.layer."""     )

				if ".aspp_layer." in name:
								_SCREAMING_SNAKE_CASE :       Any     		=      name.replace(""".aspp_layer."""							,      """."""     )
				if ".aspp_pool." in name:
								_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      name.replace(""".aspp_pool."""							,      """."""     )
				if "seg_head." in name:
								_SCREAMING_SNAKE_CASE :       Optional[int]     		=      name.replace("""seg_head."""							,      """segmentation_head."""     )
				if "segmentation_head.classifier.classifier." in name:
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      name.replace("""segmentation_head.classifier.classifier."""							,      """segmentation_head.classifier."""     )

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

				return name







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__=False     ):







				"""simple docstring"""





				if base_model:
								_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      """"""
				else:
								_SCREAMING_SNAKE_CASE :       str     		=      """mobilevit."""

				for key in orig_state_dict.copy().keys():
								_SCREAMING_SNAKE_CASE :       List[Any]     		=      orig_state_dict.pop(SCREAMING_SNAKE_CASE__     )

								if key[:8] == "encoder.":
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      key[8:]

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

				return orig_state_dict







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       List[Any]     		=      """http://images.cocodataset.org/val2017/000000039769.jpg"""
				_SCREAMING_SNAKE_CASE :       List[str]     		=      Image.open(requests.get(SCREAMING_SNAKE_CASE__							,      stream=SCREAMING_SNAKE_CASE__     ).raw     )
				return im







@torch.no_grad()
def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__=False     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       int     		=      get_mobilevit_config(SCREAMING_SNAKE_CASE__     )

				# load original state_dict
				_SCREAMING_SNAKE_CASE :       Tuple     		=      torch.load(SCREAMING_SNAKE_CASE__							,      map_location="""cpu"""     )

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

				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      convert_state_dict(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
				model.load_state_dict(SCREAMING_SNAKE_CASE__     )

				# Check outputs on an image, prepared by MobileViTImageProcessor
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      MobileViTImageProcessor(crop_size=config.image_size							,      size=config.image_size + 32     )
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      image_processor(images=prepare_img()							,      return_tensors="""pt"""     )
				_SCREAMING_SNAKE_CASE :       Dict     		=      model(**SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Tuple     		=      outputs.logits

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

								if mobilevit_name == "deeplabv3_mobilevit_s":
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      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":
												_SCREAMING_SNAKE_CASE :       Tuple     		=      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":
												_SCREAMING_SNAKE_CASE :       List[str]     		=      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]							,      SCREAMING_SNAKE_CASE__							,      atol=1E-4     )
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1]     )
								elif mobilevit_name == "mobilevit_xs":
												_SCREAMING_SNAKE_CASE :       List[Any]     		=      torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7]     )
								elif mobilevit_name == "mobilevit_xxs":
												_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      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]							,      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:
								_SCREAMING_SNAKE_CASE :       Tuple     		=      {
								    """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..."""     )
								_SCREAMING_SNAKE_CASE :       str     		=      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__":
				UpperCAmelCase_		:   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.'
				)

				UpperCAmelCase_		:   Dict							   =		parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)








'''simple docstring'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




