
"""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()
_A     :			str  						=			logging.get_logger(__name__)

_A     :			Optional[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     :			Optional[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     __magic_name__      ( __snake_case							:			Optional[Any]    )					->							int:
       lowercase       :							int									=				torch.load(__snake_case	,					map_location="cpu"    )
       return sd


def     __magic_name__      ( __snake_case							:			Dict	,					__snake_case							:			Dict	,					__snake_case							:			Tuple=rename_keys_prefix    )					->							Tuple:
       lowercase       :							Optional[Any]									=				OrderedDict()
       lowercase       :							Optional[Any]									=				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       :							Union[str, Any]									=				d[key]
              if key == "bert.cls.predictions.decoder.weight":
                     # Old bert code didn't have `decoder.bias`, but was added separately
                     lowercase       :							Optional[int]									=				new_d["cls.predictions.bias"]
       return new_d


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

       lowercase       :							Union[str, Any]									=				VisualBertConfig(**__snake_case    )

       # Load State Dict
       lowercase       :							Dict									=				load_state_dict(__snake_case    )

       lowercase       :							Union[str, Any]									=				get_new_dict(__snake_case	,					__snake_case    )

       if model_type == "pretraining":
              lowercase       :							Optional[Any]									=				VisualBertForPreTraining(__snake_case    )
       elif model_type == "vqa":
              lowercase       :							Dict									=				VisualBertForQuestionAnswering(__snake_case    )
       elif model_type == "nlvr":
              lowercase       :							Tuple									=				VisualBertForVisualReasoning(__snake_case    )
       elif model_type == "multichoice":
              lowercase       :							Optional[Any]									=				VisualBertForMultipleChoice(__snake_case    )

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


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




"""simple docstring"""




# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and thus also resulting in a larger model due to a large vocab size.
# This gives ~3MB in total for all files.
#
# If you want a 50 times smaller than this see `fsmt-make-super-tiny-model.py`, which is slightly more complicated
#
#
# It will be used then as "stas/tiny-wmt19-en-de"

# Build
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration

_A     :			Dict  						=			"""facebook/wmt19-en-de"""
_A     :			str  						=			FSMTTokenizer.from_pretrained(mname)
# get the correct vocab sizes, etc. from the master model
_A     :			List[Any]  						=			FSMTConfig.from_pretrained(mname)
config.update(
    dict(
        d_model=4,
        encoder_layers=1,
        decoder_layers=1,
        encoder_ffn_dim=4,
        decoder_ffn_dim=4,
        encoder_attention_heads=1,
        decoder_attention_heads=1,
    )
)

_A     :			int  						=			FSMTForConditionalGeneration(config)
print(F"num of params {tiny_model.num_parameters()}")

# Test
_A     :			Dict  						=			tokenizer(["""Making tiny model"""], return_tensors="""pt""")
_A     :			List[Any]  						=			tiny_model(**batch)

print("""test output:""", len(outputs.logits[0]))

# Save
_A     :			List[Any]  						=			"""tiny-wmt19-en-de"""
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F"Generated {mname_tiny}")

# Upload
# transformers-cli upload tiny-wmt19-en-de


