






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






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

			return orig_key






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE						= orig_state_dict.pop(_UpperCAmelCase)

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

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

			return orig_state_dict






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= torch.load(_UpperCAmelCase							,       map_location='cpu')['model_state_dict']
			SCREAMING_SNAKE_CASE						= YosoConfig.from_json_file(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= YosoForMaskedLM(_UpperCAmelCase)

			SCREAMING_SNAKE_CASE						= convert_checkpoint_helper(config.max_position_embeddings							,       _UpperCAmelCase)

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

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


if __name__ == "__main__":
				a_		:						Optional[int]             =    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_		:						Tuple             =    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_torch_available


a_		:						List[Any]             =    {
    'configuration_bigbird_pegasus': [
        'BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BigBirdPegasusConfig',
        'BigBirdPegasusOnnxConfig',
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Dict             =    [
				    'BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'BigBirdPegasusForCausalLM',
				    'BigBirdPegasusForConditionalGeneration',
				    'BigBirdPegasusForQuestionAnswering',
				    'BigBirdPegasusForSequenceClassification',
				    'BigBirdPegasusModel',
				    'BigBirdPegasusPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_bigbird_pegasus import (
				    BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    BigBirdPegasusConfig,
				    BigBirdPegasusOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_bigbird_pegasus import (
								    BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    BigBirdPegasusForCausalLM,
								    BigBirdPegasusForConditionalGeneration,
								    BigBirdPegasusForQuestionAnswering,
								    BigBirdPegasusForSequenceClassification,
								    BigBirdPegasusModel,
								    BigBirdPegasusPreTrainedModel,
								)


else:
				import sys

				a_		:						Tuple             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




