





import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM





def lowerCAmelCase__(__snake_case							)				->      str:







	'''simple docstring'''







	if "model" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''model.'''       ,''''''							)
	if "norm1" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''norm1'''       ,'''attention.output.LayerNorm'''							)
	if "norm2" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''norm2'''       ,'''output.LayerNorm'''							)
	if "norm" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''norm'''       ,'''LayerNorm'''							)
	if "transformer" in orig_key:
		lowerCamelCase__								=		orig_key.split('''.'''							)[0].split('''_'''							)[-1]
		lowerCamelCase__								=		orig_key.replace(F'transformer_{layer_num}'       ,F'encoder.layer.{layer_num}'							)
	if "mha.attn" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''mha.attn'''       ,'''attention.self'''							)
	if "mha" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''mha'''       ,'''attention'''							)
	if "W_q" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''W_q'''       ,'''self.query'''							)
	if "W_k" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''W_k'''       ,'''self.key'''							)
	if "W_v" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''W_v'''       ,'''self.value'''							)
	if "ff1" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''ff1'''       ,'''intermediate.dense'''							)
	if "ff2" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''ff2'''       ,'''output.dense'''							)
	if "ff" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''ff'''       ,'''output.dense'''							)
	if "mlm_class" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''mlm.mlm_class'''       ,'''cls.predictions.decoder'''							)
	if "mlm" in orig_key:
		lowerCamelCase__								=		orig_key.replace('''mlm'''       ,'''cls.predictions.transform'''							)
	if "cls" not in orig_key:
		lowerCamelCase__								=		'''yoso.''' + orig_key

	return orig_key





def lowerCAmelCase__(__snake_case       ,__snake_case							)				->      Optional[int]:







	'''simple docstring'''







	for key in orig_state_dict.copy().keys():
		lowerCamelCase__								=		orig_state_dict.pop(__snake_case							)

		if ("pooler" in key) or ("sen_class" in key):
			continue
		else:
			lowerCamelCase__								=		val

	lowerCamelCase__								=		orig_state_dict['''cls.predictions.decoder.bias''']
	lowerCamelCase__								=		torch.arange(__snake_case							).expand((1, -1)							) + 2

	return orig_state_dict





def lowerCAmelCase__(__snake_case       ,__snake_case       ,__snake_case							)				->      Optional[Any]:







	'''simple docstring'''







	lowerCamelCase__								=		torch.load(__snake_case       ,map_location='''cpu'''							)['''model_state_dict''']
	lowerCamelCase__								=		YosoConfig.from_json_file(__snake_case							)
	lowerCamelCase__								=		YosoForMaskedLM(__snake_case							)

	lowerCamelCase__								=		convert_checkpoint_helper(config.max_position_embeddings       ,__snake_case							)

	print(model.load_state_dict(__snake_case							)							)
	model.eval()
	model.save_pretrained(__snake_case							)

	print(F'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'							)


if __name__ == "__main__":
			_a 						=      argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
			)
			parser.add_argument(
			    "--config_file",
			    default=None,
			    type=str,
			    required=True,
			    help="The json file for YOSO model config.",
			)
			parser.add_argument(
			    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
			)
			_a 						=      parser.parse_args()
			convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)










from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


_a 						=      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      ["LxmertTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "LxmertEncoder",
			    "LxmertForPreTraining",
			    "LxmertForQuestionAnswering",
			    "LxmertModel",
			    "LxmertPreTrainedModel",
			    "LxmertVisualFeatureEncoder",
			    "LxmertXLayer",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_a 						=      [
			    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFLxmertForPreTraining",
			    "TFLxmertMainLayer",
			    "TFLxmertModel",
			    "TFLxmertPreTrainedModel",
			    "TFLxmertVisualFeatureEncoder",
			]


if TYPE_CHECKING:
			from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
			from .tokenization_lxmert import LxmertTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_lxmert_fast import LxmertTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_lxmert import (
						    LxmertEncoder,
						    LxmertForPreTraining,
						    LxmertForQuestionAnswering,
						    LxmertModel,
						    LxmertPreTrainedModel,
						    LxmertVisualFeatureEncoder,
						    LxmertXLayer,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_lxmert import (
						    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFLxmertForPreTraining,
						    TFLxmertMainLayer,
						    TFLxmertModel,
						    TFLxmertPreTrainedModel,
						    TFLxmertVisualFeatureEncoder,
						)

else:
			import sys

			_a 						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



