





"""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()
lowerCAmelCase__     			=			logging.get_logger(__name__)

lowerCAmelCase__     			=			[
    ('''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'''),
]

lowerCAmelCase__     			=			[
    '''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       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		return sd


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=rename_keys_prefix    ):




		"""simple docstring"""


		UpperCamelCase   =	OrderedDict()
		UpperCamelCase   =	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
				UpperCamelCase   =	key
				for name_pair in rename_keys_prefix:
						UpperCamelCase   =	new_key.replace(name_pair[0]  ,	name_pair[1]    )
				UpperCamelCase   =	d[key]
				if key == "bert.cls.predictions.decoder.weight":
						# Old bert code didn't have `decoder.bias`, but was added separately
						UpperCamelCase   =	new_d["cls.predictions.bias"]
		return new_d


@torch.no_grad()
def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""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:
				UpperCamelCase   =	"pretraining"
				if "vcr" in checkpoint_path:
						UpperCamelCase   =	{"visual_embedding_dim": 512}
				elif "vqa_advanced" in checkpoint_path:
						UpperCamelCase   =	{"visual_embedding_dim": 2_048}
				elif "vqa" in checkpoint_path:
						UpperCamelCase   =	{"visual_embedding_dim": 2_048}
				elif "nlvr" in checkpoint_path:
						UpperCamelCase   =	{"visual_embedding_dim": 1_024}
				else:
						raise NotImplementedError(F"No implementation found for `{checkpoint_path}`."    )
		else:
				if "vcr" in checkpoint_path:
						UpperCamelCase   =	{"visual_embedding_dim": 512}
						UpperCamelCase   =	"multichoice"
				elif "vqa_advanced" in checkpoint_path:
						UpperCamelCase   =	{"visual_embedding_dim": 2_048}
						UpperCamelCase   =	"vqa_advanced"
				elif "vqa" in checkpoint_path:
						UpperCamelCase   =	{"visual_embedding_dim": 2_048, "num_labels": 3_129}
						UpperCamelCase   =	"vqa"
				elif "nlvr" in checkpoint_path:
						UpperCamelCase   =	{
						    "visual_embedding_dim": 1_024,
						    "num_labels": 2,
						}
						UpperCamelCase   =	"nlvr"

		UpperCamelCase   =	VisualBertConfig(**_SCREAMING_SNAKE_CASE    )

		# Load State Dict
		UpperCamelCase   =	load_state_dict(_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	get_new_dict(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

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

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


if __name__ == "__main__":
						lowerCAmelCase__     			=			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.''')
						lowerCAmelCase__     			=			parser.parse_args()
						convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)













"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "encoder.version",
		    "decoder.version",
		    "model.encoder.version",
		    "model.decoder.version",
		    "decoder.output_projection.weight",
		    "_float_tensor",
		    "encoder.embed_positions._float_tensor",
		    "decoder.embed_positions._float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	mam_aaa["args"] or mam_aaa["cfg"]["model"]
		UpperCamelCase   =	mam_aaa["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["encoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	MaMaaaConfig(
		    vocab_size=_SCREAMING_SNAKE_CASE  ,	max_position_embeddings=1_024  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="relu"  ,	)

		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"]
		UpperCamelCase   =	MaMaaaForConditionalGeneration(_SCREAMING_SNAKE_CASE    )
		model.model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.shared    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument('''fairseq_path''', type=str, help='''path to a model.pt on local filesystem.''')
						parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
						model.save_pretrained(args.pytorch_dump_folder_path)






