

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		=logging.get_logger(__name__)

a		=[
    ("""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		=[
    """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    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__			)  ->     List[str]:
	__lowerCamelCase				:				int					       = torch.load(lowerCamelCase__	, map_location='cpu'			)
	return sd






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=rename_keys_prefix			)  ->     List[Any]:
	__lowerCamelCase				:				Tuple					       = OrderedDict()
	__lowerCamelCase				:				int					       = 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
		__lowerCamelCase				:				List[Any]					       = key
		for name_pair in rename_keys_prefix:
			__lowerCamelCase				:				Optional[Any]					       = new_key.replace(name_pair[0]	, name_pair[1]			)
		__lowerCamelCase				:				Dict					       = d[key]
		if key == "bert.cls.predictions.decoder.weight":
			# Old bert code didn't have `decoder.bias`, but was added separately
			__lowerCamelCase				:				str					       = new_d['cls.predictions.bias']
	return new_d






@torch.no_grad()
def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     Tuple:
	assert (
	    checkpoint_path.split('/'			)[-1] in ACCEPTABLE_CHECKPOINTS
	), F"The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}."

	# Get Config
	if "pre" in checkpoint_path:
		__lowerCamelCase				:				Optional[Any]					       = 'pretraining'
		if "vcr" in checkpoint_path:
			__lowerCamelCase				:				Any					       = {'visual_embedding_dim': 5_1_2}
		elif "vqa_advanced" in checkpoint_path:
			__lowerCamelCase				:				List[str]					       = {'visual_embedding_dim': 2_0_4_8}
		elif "vqa" in checkpoint_path:
			__lowerCamelCase				:				Tuple					       = {'visual_embedding_dim': 2_0_4_8}
		elif "nlvr" in checkpoint_path:
			__lowerCamelCase				:				List[Any]					       = {'visual_embedding_dim': 1_0_2_4}
		else:
			raise NotImplementedError(F"No implementation found for `{checkpoint_path}`."			)
	else:
		if "vcr" in checkpoint_path:
			__lowerCamelCase				:				Tuple					       = {'visual_embedding_dim': 5_1_2}
			__lowerCamelCase				:				Any					       = 'multichoice'
		elif "vqa_advanced" in checkpoint_path:
			__lowerCamelCase				:				Tuple					       = {'visual_embedding_dim': 2_0_4_8}
			__lowerCamelCase				:				List[Any]					       = 'vqa_advanced'
		elif "vqa" in checkpoint_path:
			__lowerCamelCase				:				List[Any]					       = {'visual_embedding_dim': 2_0_4_8, 'num_labels': 3_1_2_9}
			__lowerCamelCase				:				Optional[int]					       = 'vqa'
		elif "nlvr" in checkpoint_path:
			__lowerCamelCase				:				Optional[Any]					       = {
			    'visual_embedding_dim': 1_0_2_4,
			    'num_labels': 2,
			}
			__lowerCamelCase				:				Any					       = 'nlvr'

	__lowerCamelCase				:				Any					       = VisualBertConfig(**lowerCamelCase__			)

	# Load State Dict
	__lowerCamelCase				:				Dict					       = load_state_dict(lowerCamelCase__			)

	__lowerCamelCase				:				Optional[Any]					       = get_new_dict(lowerCamelCase__	, lowerCamelCase__			)

	if model_type == "pretraining":
		__lowerCamelCase				:				Union[str, Any]					       = VisualBertForPreTraining(lowerCamelCase__			)
	elif model_type == "vqa":
		__lowerCamelCase				:				str					       = VisualBertForQuestionAnswering(lowerCamelCase__			)
	elif model_type == "nlvr":
		__lowerCamelCase				:				Dict					       = VisualBertForVisualReasoning(lowerCamelCase__			)
	elif model_type == "multichoice":
		__lowerCamelCase				:				Union[str, Any]					       = VisualBertForMultipleChoice(lowerCamelCase__			)

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


if __name__ == "__main__":
					a		=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		=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


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
    '''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
    '''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
    '''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Dict     		=		"mobilenet_v2"






   def __init__(		self						,				__A=3						,				__A=224						,				__A=1.0						,				__A=8						,				__A=8						,				__A=6						,				__A=32						,				__A=True						,				__A=True						,				__A="relu6"						,				__A=True						,				__A=0.8						,				__A=0.02						,				__A=0.001						,				__A=255						,				**__A						,				):






     """simple docstring"""





     super().__init__(**__A )

     if depth_multiplier <= 0:
       raise ValueError("depth_multiplier must be greater than zero." )

     lowerCamelCase :					str    =				num_channels
     lowerCamelCase :					Any    =				image_size
     lowerCamelCase :					Union[str, Any]    =				depth_multiplier
     lowerCamelCase :					Tuple    =				depth_divisible_by
     lowerCamelCase :					Dict    =				min_depth
     lowerCamelCase :					Dict    =				expand_ratio
     lowerCamelCase :					Optional[Any]    =				output_stride
     lowerCamelCase :					int    =				first_layer_is_expansion
     lowerCamelCase :					Union[str, Any]    =				finegrained_output
     lowerCamelCase :					Optional[Any]    =				hidden_act
     lowerCamelCase :					Optional[Any]    =				tf_padding
     lowerCamelCase :					Optional[Any]    =				classifier_dropout_prob
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					str    =				layer_norm_eps
     lowerCamelCase :					Optional[Any]    =				semantic_loss_ignore_index



class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      Union[str, Any]     		=		version.parse("1.11"    )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return OrderedDict([("pixel_values", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     if self.task == "image-classification":
       return OrderedDict([("logits", {0: "batch"})] )
     else:
       return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )






   @property
   def _snake_case    (		self ):






     """simple docstring"""





     return 1e-4



