





"""simple docstring"""






import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
A 			=  logging.get_logger(__name__)

def     __A      (			a_						:Optional[int])		->		Any:
				print('''Loading config file...''')

				def flatten_yaml_as_dict(a_						:Optional[int]   ,       a_						:List[str]=""   ,       a_						:str="."):
								__a :						Union[str, Any]			       =  []
								for k, v in d.items():
												__a :						List[Any]			       =  parent_key + sep + k if parent_key else k
												if isinstance(a_   ,       collections.abc.MutableMapping):
																items.extend(flatten_yaml_as_dict(a_   ,       a_   ,       sep=a_).items())
												else:
																items.append((new_key, v))
								return dict(a_)

				__a :						Optional[int]			       =  argparse.Namespace()
				with open(a_   ,       '''r''') as yaml_file:
								try:
												__a :						Tuple			       =  yaml.load(a_   ,       Loader=yaml.FullLoader)

												__a :						str			       =  flatten_yaml_as_dict(a_)
												for k, v in flat_cfg.items():
																setattr(a_   ,       a_   ,       a_)
								except yaml.YAMLError as exc:
												logger.error('''Error while loading config file: {}. Error message: {}'''.format(a_   ,       str(a_)))
				return config

def     __A      (			a_						:Any   ,       a_						:Any)		->		Tuple:
				__a :						Union[str, Any]			       =  MobileViTVaConfig()

				__a :						Tuple			       =  False

				# dataset
				if task_name.startswith('''imagenet1k_'''):
								__a :						Any			       =  10_00
								if int(task_name.strip().split('''_''')[-1]) == 3_84:
												__a :						Union[str, Any]			       =  3_84
								else:
												__a :						Optional[Any]			       =  2_56
								__a :						Tuple			       =  '''imagenet-1k-id2label.json'''
				elif task_name.startswith('''imagenet21k_to_1k_'''):
								__a :						List[Any]			       =  2_10_00
								if int(task_name.strip().split('''_''')[-1]) == 3_84:
												__a :						Dict			       =  3_84
								else:
												__a :						str			       =  2_56
								__a :						Tuple			       =  '''imagenet-22k-id2label.json'''
				elif task_name.startswith('''ade20k_'''):
								__a :						Optional[Any]			       =  1_51
								__a :						Any			       =  5_12
								__a :						List[Any]			       =  '''ade20k-id2label.json'''
								__a :						Tuple			       =  True
				elif task_name.startswith('''voc_'''):
								__a :						Union[str, Any]			       =  21
								__a :						str			       =  5_12
								__a :						int			       =  '''pascal-voc-id2label.json'''
								__a :						List[str]			       =  True

				# orig_config
				__a :						List[str]			       =  load_orig_config_file(a_)
				assert getattr(a_   ,       '''model.classification.name'''   ,       -1) == "mobilevit_v2", "Invalid model"
				__a :						Any			       =  getattr(a_   ,       '''model.classification.mitv2.width_multiplier'''   ,       1.0)
				assert (
				    getattr(a_   ,       '''model.classification.mitv2.attn_norm_layer'''   ,       -1) == "layer_norm_2d"
				), "Norm layers other than layer_norm_2d is not supported"
				__a :						int			       =  getattr(a_   ,       '''model.classification.activation.name'''   ,       '''swish''')
				# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

				if is_segmentation_model:
								__a :						str			       =  getattr(a_   ,       '''model.segmentation.output_stride'''   ,       16)
								if "_deeplabv3" in task_name:
												__a :						int			       =  getattr(a_   ,       '''model.segmentation.deeplabv3.aspp_rates'''   ,       [12, 24, 36])
												__a :						Optional[int]			       =  getattr(a_   ,       '''model.segmentation.deeplabv3.aspp_out_channels'''   ,       5_12)
												__a :						List[Any]			       =  getattr(a_   ,       '''model.segmentation.deeplabv3.aspp_dropout'''   ,       0.1)

    # id2label
				__a :						List[Any]			       =  '''huggingface/label-files'''
				__a :						Any			       =  json.load(open(hf_hub_download(a_   ,       a_   ,       repo_type='''dataset''')   ,       '''r'''))
				__a :						str			       =  {int(a_): v for k, v in idalabel.items()}
				__a :						int			       =  idalabel
				__a :						List[Any]			       =  {v: k for k, v in idalabel.items()}

				return config

def     __A      (			a_						:str   ,       a_						:Optional[int]   ,       a_						:Any)		->		int:
				__a :						Tuple			       =  dct.pop(a_)
				__a :						int			       =  val

def     __A      (			a_						:str   ,       a_						:Dict=False)		->		int:
				if base_model:
								__a :						List[str]			       =  ''''''
				else:
								__a :						Union[str, Any]			       =  '''mobilevitv2.'''

				__a :						Tuple			       =  []
				for k in state_dict.keys():
								if k[:8] == "encoder.":
												__a :						Optional[int]			       =  k[8:]
								else:
												__a :						Optional[int]			       =  k

								if ".block." in k:
												__a :						int			       =  k_new.replace('''.block.'''   ,       '''.''')
								if ".conv." in k:
												__a :						int			       =  k_new.replace('''.conv.'''   ,       '''.convolution.''')
								if ".norm." in k:
												__a :						Optional[int]			       =  k_new.replace('''.norm.'''   ,       '''.normalization.''')

								if "conv_1." in k:
												__a :						str			       =  k_new.replace('''conv_1.'''   ,       F"""{model_prefix}conv_stem.""")
								for i in [1, 2]:
												if F"""layer_{i}.""" in k:
																__a :						Union[str, Any]			       =  k_new.replace(F"""layer_{i}."""   ,       F"""{model_prefix}encoder.layer.{i-1}.layer.""")
								if ".exp_1x1." in k:
												__a :						List[str]			       =  k_new.replace('''.exp_1x1.'''   ,       '''.expand_1x1.''')
								if ".red_1x1." in k:
												__a :						str			       =  k_new.replace('''.red_1x1.'''   ,       '''.reduce_1x1.''')

								for i in [3, 4, 5]:
												if F"""layer_{i}.0.""" in k:
																__a :						List[Any]			       =  k_new.replace(F"""layer_{i}.0."""   ,       F"""{model_prefix}encoder.layer.{i-1}.downsampling_layer.""")
												if F"""layer_{i}.1.local_rep.0.""" in k:
																__a :						Tuple			       =  k_new.replace(F"""layer_{i}.1.local_rep.0."""   ,       F"""{model_prefix}encoder.layer.{i-1}.conv_kxk.""")
												if F"""layer_{i}.1.local_rep.1.""" in k:
																__a :						Optional[int]			       =  k_new.replace(F"""layer_{i}.1.local_rep.1."""   ,       F"""{model_prefix}encoder.layer.{i-1}.conv_1x1.""")

								for i in [3, 4, 5]:
												if i == 3:
																__a :						int			       =  [0, 1]
												elif i == 4:
																__a :						int			       =  [0, 1, 2, 3]
												elif i == 5:
																__a :						Union[str, Any]			       =  [0, 1, 2]

												for j in j_in:
																if F"""layer_{i}.1.global_rep.{j}.""" in k:
																				__a :						Dict			       =  k_new.replace(
																				    F"""layer_{i}.1.global_rep.{j}."""   ,       F"""{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.""")
												if F"""layer_{i}.1.global_rep.{j+1}.""" in k:
																__a :						Dict			       =  k_new.replace(
																    F"""layer_{i}.1.global_rep.{j+1}."""   ,       F"""{model_prefix}encoder.layer.{i-1}.layernorm.""")

												if F"""layer_{i}.1.conv_proj.""" in k:
																__a :						int			       =  k_new.replace(F"""layer_{i}.1.conv_proj."""   ,       F"""{model_prefix}encoder.layer.{i-1}.conv_projection.""")

								if "pre_norm_attn.0." in k:
												__a :						Dict			       =  k_new.replace('''pre_norm_attn.0.'''   ,       '''layernorm_before.''')
								if "pre_norm_attn.1." in k:
												__a :						List[str]			       =  k_new.replace('''pre_norm_attn.1.'''   ,       '''attention.''')
								if "pre_norm_ffn.0." in k:
												__a :						List[Any]			       =  k_new.replace('''pre_norm_ffn.0.'''   ,       '''layernorm_after.''')
								if "pre_norm_ffn.1." in k:
												__a :						Optional[Any]			       =  k_new.replace('''pre_norm_ffn.1.'''   ,       '''ffn.conv1.''')
								if "pre_norm_ffn.3." in k:
												__a :						Tuple			       =  k_new.replace('''pre_norm_ffn.3.'''   ,       '''ffn.conv2.''')

								if "classifier.1." in k:
												__a :						Any			       =  k_new.replace('''classifier.1.'''   ,       '''classifier.''')

								if "seg_head." in k:
												__a :						Optional[int]			       =  k_new.replace('''seg_head.'''   ,       '''segmentation_head.''')
								if ".aspp_layer." in k:
												__a :						Any			       =  k_new.replace('''.aspp_layer.'''   ,       '''.''')
								if ".aspp_pool." in k:
												__a :						int			       =  k_new.replace('''.aspp_pool.'''   ,       '''.''')

								rename_keys.append((k, k_new))
				return rename_keys

def     __A      (			a_						:Tuple)		->		str:
				__a :						Any			       =  []
				for k in state_dict.keys():
								if k.startswith('''seg_head.aux_head.'''):
												keys_to_ignore.append(a_)
				for k in keys_to_ignore:
								state_dict.pop(a_   ,       a_)

def     __A      (			)		->		Tuple:
				__a :						int			       =  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
				# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
				__a :						str			       =  Image.open(requests.get(a_   ,       stream=a_).raw)
				return im

@torch.no_grad()
def     __A      (			a_						:Tuple   ,       a_						:str   ,       a_						:List[str]   ,       a_						:Optional[int])		->		str:
				__a :						List[str]			       =  get_mobilevitva_config(a_   ,       a_)

				# load original state_dict
				__a :						Optional[int]			       =  torch.load(a_   ,       map_location='''cpu''')

				# load huggingface model
				if task_name.startswith('''ade20k_''') or task_name.startswith('''voc_'''):
								__a :						Optional[Any]			       =  MobileViTVaForSemanticSegmentation(a_).eval()
								__a :						str			       =  False
				else:
								__a :						List[Any]			       =  MobileViTVaForImageClassification(a_).eval()
								__a :						Dict			       =  False

				# remove and rename some keys of load the original model
				__a :						List[str]			       =  checkpoint
				remove_unused_keys(a_)
				__a :						Optional[int]			       =  create_rename_keys(a_   ,       base_model=a_)
				for rename_key_src, rename_key_dest in rename_keys:
								rename_key(a_   ,       a_   ,       a_)

				# load modified state_dict
				model.load_state_dict(a_)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__a :						Tuple			       =  MobileViTImageProcessor(crop_size=config.image_size   ,       size=config.image_size + 32)
				__a :						Optional[Any]			       =  image_processor(images=prepare_img()   ,       return_tensors='''pt''')
				__a :						Optional[int]			       =  model(**a_)

				# verify classification model
				if task_name.startswith('''imagenet'''):
								__a :						Any			       =  outputs.logits
								__a :						Dict			       =  logits.argmax(-1).item()
								print('''Predicted class:'''   ,       model.config.idalabel[predicted_class_idx])
								if task_name.startswith('''imagenet1k_256''') and config.width_multiplier == 1.0:
												# expected_logits for base variant
												__a :						Dict			       =  torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01])
												assert torch.allclose(logits[0, :3]   ,       a_   ,       atol=1e-4)

				Path(a_).mkdir(exist_ok=a_)
				print(F"""Saving model {task_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 __name__ == "__main__":
		A 			=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--task''',
		    default='''imagenet1k_256''',
		    type=str,
		    help=(
		        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
		        '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
		    ),
		    choices=[
		        '''imagenet1k_256''',
		        '''imagenet1k_384''',
		        '''imagenet21k_to_1k_256''',
		        '''imagenet21k_to_1k_384''',
		        '''ade20k_deeplabv3''',
		        '''voc_deeplabv3''',
		    ],
		)

		parser.add_argument(
		    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
		)
		parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
		)

		A 			=  parser.parse_args()
		convert_mobilevitva_checkpoint(
		    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
		)






"""simple docstring"""






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


A 			=  logging.get_logger(__name__)

A 			=  {
    '''microsoft/git-base''': '''https://huggingface.co/microsoft/git-base/resolve/main/config.json''',
}




class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git_vision_model'''







					def __init__(						self	,			_UpperCAmelCase=768	,			_UpperCAmelCase=3072	,			_UpperCAmelCase=12	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3	,			_UpperCAmelCase=224	,			_UpperCAmelCase=16	,			_UpperCAmelCase="quick_gelu"	,			_UpperCAmelCase=1e-5	,			_UpperCAmelCase=0.0	,			_UpperCAmelCase=0.0_2	,			**_UpperCAmelCase	,			):
									super().__init__(**_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  hidden_size
									__a :						Optional[int]			       =  intermediate_size
									__a :						Tuple			       =  num_hidden_layers
									__a :						Tuple			       =  num_attention_heads
									__a :						Optional[int]			       =  num_channels
									__a :						Tuple			       =  patch_size
									__a :						Any			       =  image_size
									__a :						Optional[Any]			       =  initializer_range
									__a :						Optional[int]			       =  attention_dropout
									__a :						Any			       =  layer_norm_eps
									__a :						Dict			       =  hidden_act







					@classmethod
					def  _lowerCamelCase		(						cls	,			_UpperCAmelCase	,			**_UpperCAmelCase     ):
									cls._set_token_in_kwargs(_UpperCAmelCase     )

									__a       ,						__a :						Dict			       =  cls.get_config_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )

									# get the vision config dict if we are loading from GITConfig
									if config_dict.get('''model_type'''     ) == "git":
													__a :						int			       =  config_dict['''vision_config''']

									if "model_type" in config_dict and hasattr(cls	,			'''model_type'''     ) and config_dict["model_type"] != cls.model_type:
													logger.warning(
													    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
													    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors."""     )

									return cls.from_dict(_UpperCAmelCase	,			**_UpperCAmelCase     )






class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					__lowerCAmelCase  =  '''git'''







					def __init__(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=30522	,			_UpperCAmelCase=768	,			_UpperCAmelCase=6	,			_UpperCAmelCase=12	,			_UpperCAmelCase=3072	,			_UpperCAmelCase="gelu"	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=0.1	,			_UpperCAmelCase=1024	,			_UpperCAmelCase=0.0_2	,			_UpperCAmelCase=1e-1_2	,			_UpperCAmelCase=0	,			_UpperCAmelCase="absolute"	,			_UpperCAmelCase=True	,			_UpperCAmelCase=False	,			_UpperCAmelCase=101	,			_UpperCAmelCase=102	,			_UpperCAmelCase=None	,			**_UpperCAmelCase	,			):
									super().__init__(bos_token_id=_UpperCAmelCase	,			eos_token_id=_UpperCAmelCase	,			pad_token_id=_UpperCAmelCase	,			**_UpperCAmelCase     )

									if vision_config is None:
													__a :						Union[str, Any]			       =  {}
													logger.info('''vision_config is None. initializing the GitVisionConfig with default values.'''     )

									__a :						Union[str, Any]			       =  GitVisionConfig(**_UpperCAmelCase     )
									__a :						List[str]			       =  vocab_size
									__a :						Any			       =  hidden_size
									__a :						Dict			       =  num_hidden_layers
									__a :						Tuple			       =  num_attention_heads
									__a :						List[str]			       =  hidden_act
									__a :						Any			       =  intermediate_size
									__a :						int			       =  hidden_dropout_prob
									__a :						Dict			       =  attention_probs_dropout_prob
									__a :						Tuple			       =  max_position_embeddings
									__a :						int			       =  initializer_range
									__a :						List[str]			       =  layer_norm_eps
									__a :						Tuple			       =  position_embedding_type
									__a :						Optional[int]			       =  use_cache
									__a :						str			       =  tie_word_embeddings
									__a :						Optional[Any]			       =  num_image_with_embedding

									__a :						str			       =  bos_token_id
									__a :						Dict			       =  eos_token_id







					def  _lowerCamelCase		(						self     ):
									__a :						Optional[int]			       =  copy.deepcopy(self.__dict__     )
									__a :						int			       =  self.vision_config.to_dict()
									__a :						Tuple			       =  self.__class__.model_type
									return output