'''simple docstring'''




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

_lowercase		:				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"),
]

_lowercase		:				Union[str, Any]	       =		[
    "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 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]				)    ->   Union[str, Any]:
							lowercase_      :    Optional[Any]       		= torch.load(UpperCAmelCase__   ,   map_location="""cpu"""				)
							return sd




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




@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      Any   ,   UpperCAmelCase__ :      Optional[int]				)    ->   Optional[Any]:
							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_      :    List[str]       		= """pretraining"""
														if "vcr" in checkpoint_path:
																					lowercase_      :    List[Any]       		= {"""visual_embedding_dim""": 512}
														elif "vqa_advanced" in checkpoint_path:
																					lowercase_      :    Any       		= {"""visual_embedding_dim""": 2048}
														elif "vqa" in checkpoint_path:
																					lowercase_      :    str       		= {"""visual_embedding_dim""": 2048}
														elif "nlvr" in checkpoint_path:
																					lowercase_      :    List[Any]       		= {"""visual_embedding_dim""": 1024}
														else:
																					raise NotImplementedError(F'''No implementation found for `{checkpoint_path}`.'''				)
							else:
														if "vcr" in checkpoint_path:
																					lowercase_      :    Any       		= {"""visual_embedding_dim""": 512}
																					lowercase_      :    Dict       		= """multichoice"""
														elif "vqa_advanced" in checkpoint_path:
																					lowercase_      :    Optional[int]       		= {"""visual_embedding_dim""": 2048}
																					lowercase_      :    List[Any]       		= """vqa_advanced"""
														elif "vqa" in checkpoint_path:
																					lowercase_      :    int       		= {"""visual_embedding_dim""": 2048, """num_labels""": 3129}
																					lowercase_      :    Tuple       		= """vqa"""
														elif "nlvr" in checkpoint_path:
																					lowercase_      :    Any       		= {
																					    """visual_embedding_dim""": 1024,
																					    """num_labels""": 2,
																					}
																					lowercase_      :    int       		= """nlvr"""

							lowercase_      :    List[Any]       		= VisualBertConfig(**UpperCAmelCase__				)

							# Load State Dict
							lowercase_      :    str       		= load_state_dict(UpperCAmelCase__				)

							lowercase_      :    str       		= get_new_dict(UpperCAmelCase__   ,   UpperCAmelCase__				)

							if model_type == "pretraining":
														lowercase_      :    Optional[Any]       		= VisualBertForPreTraining(UpperCAmelCase__				)
							elif model_type == "vqa":
														lowercase_      :    Optional[int]       		= VisualBertForQuestionAnswering(UpperCAmelCase__				)
							elif model_type == "nlvr":
														lowercase_      :    Optional[Any]       		= VisualBertForVisualReasoning(UpperCAmelCase__				)
							elif model_type == "multichoice":
														lowercase_      :    Tuple       		= VisualBertForMultipleChoice(UpperCAmelCase__				)

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


if __name__ == "__main__":
						_lowercase		:				Tuple	       =		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.")
						_lowercase		:				str	       =		parser.parse_args()
						convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)






'''simple docstring'''




import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=_UpperCAmelCase)
class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = field(default='''image-classification''',  metadata={'''include_in_asdict_even_if_is_default''': True})
			UpperCamelCase__       = Features({'''image''': Image()})
			UpperCamelCase__       = Features({'''labels''': ClassLabel})
			UpperCamelCase__       = "image"
			UpperCamelCase__       = "labels"







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						str					):
										if self.label_column not in features:
																	raise ValueError(f'''Column {self.label_column} is not present in features.'''					)
										if not isinstance(features[self.label_column]       ,    lowercase_					):
																	raise ValueError(f'''Column {self.label_column} is not a ClassLabel.'''					)
										lowercase_      :    List[str]       		= copy.deepcopy(self					)
										lowercase_      :    List[str]       		= self.label_schema.copy()
										lowercase_      :    List[Any]       		= features[self.label_column]
										lowercase_      :    Optional[Any]       		= label_schema
										return task_template







			@property
			def 			SCREAMING_SNAKE_CASE_  (			self       :						int					):
										return {
										    self.image_column: "image",
										    self.label_column: "labels",
										}





