






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



def     __lowercase					(    a :				Tuple )					->					Optional[Any]:
						__snake_case      :    Any  			=MobileViTConfig()

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

						if mobilevit_name.startswith('''deeplabv3_''' ):
												__snake_case      :    Union[str, Any]  			=512
												__snake_case      :    Optional[int]  			=16
												__snake_case      :    Dict  			=21
												__snake_case      :    Union[str, Any]  			='''pascal-voc-id2label.json'''
						else:
												__snake_case      :    Optional[int]  			=1_000
												__snake_case      :    List[Any]  			='''imagenet-1k-id2label.json'''

						__snake_case      :    List[Any]  			='''huggingface/label-files'''
						__snake_case      :    Any  			=json.load(open(hf_hub_download(a       ,		a       ,		repo_type='''dataset''' )       ,		'''r''' ) )
						__snake_case      :    List[Any]  			={int(a ): v for k, v in idalabel.items()}
						__snake_case      :    str  			=idalabel
						__snake_case      :    List[Any]  			={v: k for k, v in idalabel.items()}

						return config



def     __lowercase					(    a :				List[str]       ,		a :				Union[str, Any]=False )					->					Optional[int]:
						for i in range(1       ,		6 ):
												if f'''layer_{i}.''' in name:
																		__snake_case      :    List[Any]  			=name.replace(f'''layer_{i}.'''       ,		f'''encoder.layer.{i - 1}.''' )

						if "conv_1." in name:
												__snake_case      :    Union[str, Any]  			=name.replace('''conv_1.'''       ,		'''conv_stem.''' )
						if ".block." in name:
												__snake_case      :    List[Any]  			=name.replace('''.block.'''       ,		'''.''' )

						if "exp_1x1" in name:
												__snake_case      :    Union[str, Any]  			=name.replace('''exp_1x1'''       ,		'''expand_1x1''' )
						if "red_1x1" in name:
												__snake_case      :    int  			=name.replace('''red_1x1'''       ,		'''reduce_1x1''' )
						if ".local_rep.conv_3x3." in name:
												__snake_case      :    Any  			=name.replace('''.local_rep.conv_3x3.'''       ,		'''.conv_kxk.''' )
						if ".local_rep.conv_1x1." in name:
												__snake_case      :    str  			=name.replace('''.local_rep.conv_1x1.'''       ,		'''.conv_1x1.''' )
						if ".norm." in name:
												__snake_case      :    Optional[int]  			=name.replace('''.norm.'''       ,		'''.normalization.''' )
						if ".conv." in name:
												__snake_case      :    Any  			=name.replace('''.conv.'''       ,		'''.convolution.''' )
						if ".conv_proj." in name:
												__snake_case      :    Optional[int]  			=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      :    List[str]  			=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      :    str  			=name.replace(f'''.{i}.{j}.'''       ,		f'''.{i}.''' )
																								if "expand_1x1" in name:
																														__snake_case      :    List[str]  			=name.replace('''expand_1x1'''       ,		'''downsampling_layer.expand_1x1''' )
																								if "conv_3x3" in name:
																														__snake_case      :    Optional[int]  			=name.replace('''conv_3x3'''       ,		'''downsampling_layer.conv_3x3''' )
																								if "reduce_1x1" in name:
																														__snake_case      :    Dict  			=name.replace('''reduce_1x1'''       ,		'''downsampling_layer.reduce_1x1''' )

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

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

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

						if "classifier.fc." in name:
												__snake_case      :    int  			=name.replace('''classifier.fc.'''       ,		'''classifier.''' )
						elif (not base_model) and ("segmentation_head." not in name):
												__snake_case      :    List[str]  			='''mobilevit.''' + name

						return name



def     __lowercase					(    a :				Optional[Any]       ,		a :				List[str]       ,		a :				Dict=False )					->					str:
						if base_model:
												__snake_case      :    Dict  			=''''''
						else:
												__snake_case      :    List[Any]  			='''mobilevit.'''

						for key in orig_state_dict.copy().keys():
												__snake_case      :    Any  			=orig_state_dict.pop(a )

												if key[:8] == "encoder.":
																		__snake_case      :    str  			=key[8:]

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

						return orig_state_dict



def     __lowercase					(    )					->					List[str]:
						__snake_case      :    Any  			='''http://images.cocodataset.org/val2017/000000039769.jpg'''
						__snake_case      :    Dict  			=Image.open(requests.get(a       ,		stream=a ).raw )
						return im



@torch.no_grad()
def     __lowercase					(    a :				str       ,		a :				Dict       ,		a :				Optional[Any]       ,		a :				str=False )					->					int:
						__snake_case      :    Union[str, Any]  			=get_mobilevit_config(a )

						# load original state_dict
						__snake_case      :    int  			=torch.load(a       ,		map_location='''cpu''' )

						# load 🤗 model
						if mobilevit_name.startswith('''deeplabv3_''' ):
												__snake_case      :    str  			=MobileViTForSemanticSegmentation(a ).eval()
						else:
												__snake_case      :    Optional[Any]  			=MobileViTForImageClassification(a ).eval()

						__snake_case      :    Tuple  			=convert_state_dict(a       ,		a )
						model.load_state_dict(a )

						# Check outputs on an image, prepared by MobileViTImageProcessor
						__snake_case      :    Optional[Any]  			=MobileViTImageProcessor(crop_size=config.image_size       ,		size=config.image_size + 32 )
						__snake_case      :    str  			=image_processor(images=prepare_img()       ,		return_tensors='''pt''' )
						__snake_case      :    List[Any]  			=model(**a )
						__snake_case      :    Optional[Any]  			=outputs.logits

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

												if mobilevit_name == "deeplabv3_mobilevit_s":
																		__snake_case      :    Dict  			=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":
																		__snake_case      :    Optional[int]  			=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":
																		__snake_case      :    List[Any]  			=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]],
																		        [[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_6_2_4, -9.5_9_6_4], [-10.8_840, -10.8_158, -10.6_659]],
																		        [[-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]       ,		a       ,		atol=1e-4 )
						else:
												assert logits.shape == (1, 1_000)

												if mobilevit_name == "mobilevit_s":
																		__snake_case      :    Union[str, Any]  			=torch.tensor([-0.9_8_6_6, 0.2_3_9_2, -1.1_2_4_1] )
												elif mobilevit_name == "mobilevit_xs":
																		__snake_case      :    str  			=torch.tensor([-2.4_7_6_1, -0.9_3_9_9, -1.9_5_8_7] )
												elif mobilevit_name == "mobilevit_xxs":
																		__snake_case      :    str  			=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]       ,		a       ,		atol=1e-4 )

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

						if push_to_hub:
												__snake_case      :    str  			={
												    '''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      :    Union[str, Any]  			=model_mapping[mobilevit_name]
												image_processor.push_to_hub(a       ,		organization='''apple''' )
												model.push_to_hub(a       ,		organization='''apple''' )


if __name__ == "__main__":
				UpperCamelCase_	:				Optional[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_	:				Union[str, Any]										= parser.parse_args()
				convert_movilevit_checkpoint(
				    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)










"""simple docstring"""





import os
from datetime import datetime as dt

from github import Github


UpperCamelCase_	:				List[str]										= [
    """good first issue""",
    """good second issue""",
    """good difficult issue""",
    """enhancement""",
    """new pipeline/model""",
    """new scheduler""",
    """wip""",
]



def     __lowercase					(    )					->					Optional[Any]:
      __snake_case      :    List[Any]  			=Github(os.environ['''GITHUB_TOKEN'''] )
      __snake_case      :    int  			=g.get_repo('''huggingface/diffusers''' )
      __snake_case      :    Any  			=repo.get_issues(state='''open''' )

      for issue in open_issues:
            __snake_case      :    Dict  			=sorted(issue.get_comments()       ,		key=lambda a : i.created_at       ,		reverse=a )
            __snake_case      :    Dict  			=comments[0] if len(a ) > 0 else None
            if (
                last_comment is not None
                and last_comment.user.login == "github-actions[bot]"
                and (dt.utcnow() - issue.updated_at).days > 7
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Closes the issue after 7 days of inactivity since the Stalebot notification.
                  issue.edit(state='''closed''' )
            elif (
                "stale" in issue.get_labels()
                and last_comment is not None
                and last_comment.user.login != "github-actions[bot]"
            ):
                  # Opens the issue if someone other than Stalebot commented.
                  issue.edit(state='''open''' )
                  issue.remove_from_labels('''stale''' )
            elif (
                (dt.utcnow() - issue.updated_at).days > 23
                and (dt.utcnow() - issue.created_at).days >= 30
                and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
            ):
                  # Post a Stalebot notification after 23 days of inactivity.
                  issue.create_comment(
                      '''This issue has been automatically marked as stale because it has not had '''
                      '''recent activity. If you think this still needs to be addressed '''
                      '''please comment on this thread.\n\nPlease note that issues that do not follow the '''
                      '''[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) '''
                      '''are likely to be ignored.''' )
                  issue.add_to_labels('''stale''' )


if __name__ == "__main__":
    main()


