


import argparse
from collections import OrderedDict
from pathlib import Path

import torch

from transformers import (
    VisualBertConfig,
    VisualBertForMultipleChoice,
    VisualBertForPreTraining,
    VisualBertForQuestionAnswering,
    VisualBertForVisualReasoning,
)
from transformers.utils import logging


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

A			:						Union[str, Any]        =  [
    ('bert.bert', 'visual_bert'),
    ('bert.cls', 'cls'),
    ('bert.classifier', 'cls'),
    ('token_type_embeddings_visual', 'visual_token_type_embeddings'),
    ('position_embeddings_visual', 'visual_position_embeddings'),
    ('projection', 'visual_projection'),
]

A			:						Tuple        =  [
    'nlvr2_coco_pre_trained.th',
    'nlvr2_fine_tuned.th',
    'nlvr2_pre_trained.th',
    'vcr_coco_pre_train.th',
    'vcr_fine_tune.th',
    'vcr_pre_train.th',
    'vqa_coco_pre_trained.th',
    'vqa_fine_tuned.th',
    'vqa_pre_trained.th',
]






def 					UpperCamelCase   (      __magic_name__	:				Any	)  ->    Tuple:
		"""simple docstring"""




		lowercase__	     =						torch.load(__magic_name__       ,       map_location="""cpu"""	)
		return sd






def 					UpperCamelCase   (      __magic_name__	:				Tuple       ,       __magic_name__	:				str       ,       __magic_name__	:				List[Any]=rename_keys_prefix	)  ->    Any:
		"""simple docstring"""




		lowercase__	     =						OrderedDict()
		lowercase__	     =						torch.arange(config.max_position_embeddings	).expand((1, -1)	)
		# detector_d = OrderedDict()
		for key in d:
				if "detector" in key:
						# detector_d[key.replace('detector.','')] = d[key]
						continue
				lowercase__	     =						key
				for name_pair in rename_keys_prefix:
						lowercase__	     =						new_key.replace(name_pair[0]       ,       name_pair[1]	)
				lowercase__	     =						d[key]
				if key == "bert.cls.predictions.decoder.weight":
						# Old bert code didn't have `decoder.bias`, but was added separately
						lowercase__	     =						new_d["""cls.predictions.bias"""]
		return new_d






@torch.no_grad()
def 					UpperCamelCase   (      __magic_name__	:				Any       ,       __magic_name__	:				Dict	)  ->    Any:
		"""simple docstring"""




		assert (
		    checkpoint_path.split("""/"""	)[-1] in ACCEPTABLE_CHECKPOINTS
		), f'''The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'''

		# Get Config
		if "pre" in checkpoint_path:
				lowercase__	     =						"""pretraining"""
				if "vcr" in checkpoint_path:
						lowercase__	     =						{"""visual_embedding_dim""": 512}
				elif "vqa_advanced" in checkpoint_path:
						lowercase__	     =						{"""visual_embedding_dim""": 2048}
				elif "vqa" in checkpoint_path:
						lowercase__	     =						{"""visual_embedding_dim""": 2048}
				elif "nlvr" in checkpoint_path:
						lowercase__	     =						{"""visual_embedding_dim""": 1024}
				else:
						raise NotImplementedError(f'''No implementation found for `{checkpoint_path}`.'''	)
		else:
				if "vcr" in checkpoint_path:
						lowercase__	     =						{"""visual_embedding_dim""": 512}
						lowercase__	     =						"""multichoice"""
				elif "vqa_advanced" in checkpoint_path:
						lowercase__	     =						{"""visual_embedding_dim""": 2048}
						lowercase__	     =						"""vqa_advanced"""
				elif "vqa" in checkpoint_path:
						lowercase__	     =						{"""visual_embedding_dim""": 2048, """num_labels""": 3129}
						lowercase__	     =						"""vqa"""
				elif "nlvr" in checkpoint_path:
						lowercase__	     =						{
						    """visual_embedding_dim""": 1024,
						    """num_labels""": 2,
						}
						lowercase__	     =						"""nlvr"""

		lowercase__	     =						VisualBertConfig(**__magic_name__	)

		# Load State Dict
		lowercase__	     =						load_state_dict(__magic_name__	)

		lowercase__	     =						get_new_dict(__magic_name__       ,       __magic_name__	)

		if model_type == "pretraining":
				lowercase__	     =						VisualBertForPreTraining(__magic_name__	)
		elif model_type == "vqa":
				lowercase__	     =						VisualBertForQuestionAnswering(__magic_name__	)
		elif model_type == "nlvr":
				lowercase__	     =						VisualBertForVisualReasoning(__magic_name__	)
		elif model_type == "multichoice":
				lowercase__	     =						VisualBertForMultipleChoice(__magic_name__	)

		model.load_state_dict(__magic_name__	)
		# Save Checkpoints
		Path(__magic_name__	).mkdir(exist_ok=__magic_name__	)
		model.save_pretrained(__magic_name__	)


if __name__ == "__main__":
	A			:						Union[str, Any]        =  argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('orig_checkpoint_path', type=str, help='A path to .th on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', type=str, help='Path to the output PyTorch model.')
	A			:						Tuple        =  parser.parse_args()
	convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A			:						Any        =  logging.get_logger(__name__)

A			:						Tuple        =  {
    'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}



class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					'''poolformer'''

					def __init__(self       :		Dict ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		str=16 ,   _UpperCAmelCase       :		Any=16 ,   _UpperCAmelCase       :		Union[str, Any]=3 ,   _UpperCAmelCase       :		Union[str, Any]=4.0 ,   _UpperCAmelCase       :		str=[2, 2, 6, 2] ,   _UpperCAmelCase       :		int=[64, 128, 320, 512] ,   _UpperCAmelCase       :		Union[str, Any]=[7, 3, 3, 3] ,   _UpperCAmelCase       :		List[Any]=[4, 2, 2, 2] ,   _UpperCAmelCase       :		Union[str, Any]=[2, 1, 1, 1] ,   _UpperCAmelCase       :		Optional[Any]=4 ,   _UpperCAmelCase       :		Dict=0.0 ,   _UpperCAmelCase       :		Union[str, Any]="gelu" ,   _UpperCAmelCase       :		Optional[Any]=True ,   _UpperCAmelCase       :		Tuple=1E-5 ,   _UpperCAmelCase       :		Tuple=0.02 ,   **_UpperCAmelCase       :		Union[str, Any] ,   )		->  Optional[int]:




							"""simple docstring"""


							lowercase__	     =						num_channels
							lowercase__	     =						patch_size
							lowercase__	     =						stride
							lowercase__	     =						padding
							lowercase__	     =						pool_size
							lowercase__	     =						hidden_sizes
							lowercase__	     =						mlp_ratio
							lowercase__	     =						depths
							lowercase__	     =						patch_sizes
							lowercase__	     =						strides
							lowercase__	     =						num_encoder_blocks
							lowercase__	     =						drop_path_rate
							lowercase__	     =						hidden_act
							lowercase__	     =						use_layer_scale
							lowercase__	     =						layer_scale_init_value
							lowercase__	     =						initializer_range
							super().__init__(**_UpperCAmelCase						)






class 				A       (			UpperCAmelCase__				):
					'''simple docstring'''






					A__     =					version.parse('''1.11'''				)

					@property
					def     lowerCamelCase__	(self       :		List[str]						)		->  Mapping[str, Mapping[int, str]]:




							"""simple docstring"""


							return OrderedDict(
							    [
							        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
							    ]						)


					@property
					def     lowerCamelCase__	(self       :		Dict						)		->  float:




							"""simple docstring"""


							return 2E-3

