
"""simple docstring"""


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






def    lowercase	(  lowerCAmelCase__	):
			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_ 		=     '''nystromformer.''' + orig_key

			return orig_key






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__	):
			for key in orig_state_dict.copy().keys():
						lowerCamelCase_ 		=     orig_state_dict.pop(lowerCAmelCase__	)

						if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
									continue
						else:
									lowerCamelCase_ 		=     val

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

			return orig_state_dict






def    lowercase	(  lowerCAmelCase__  ,lowerCAmelCase__  ,lowerCAmelCase__	):
			lowerCamelCase_ 		=     torch.load(lowerCAmelCase__  ,map_location='''cpu'''	)['''model_state_dict''']
			lowerCamelCase_ 		=     NystromformerConfig.from_json_file(lowerCAmelCase__	)
			lowerCamelCase_ 		=     NystromformerForMaskedLM(lowerCAmelCase__	)

			lowerCamelCase_ 		=     convert_checkpoint_helper(lowerCAmelCase__  ,lowerCAmelCase__	)

			model.load_state_dict(lowerCAmelCase__	)
			model.eval()
			model.save_pretrained(lowerCAmelCase__	)

			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 Nystromformer pytorch checkpoint."""
						)
						parser.add_argument(
						    """--config_file""",
						    default=None,
						    type=str,
						    required=True,
						    help="""The json file for Nystromformer 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_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A_						       =				{
    """configuration_flava""": [
        """FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """FlavaConfig""",
        """FlavaImageCodebookConfig""",
        """FlavaImageConfig""",
        """FlavaMultimodalConfig""",
        """FlavaTextConfig""",
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				["""FlavaFeatureExtractor"""]
						A_						       =				["""FlavaImageProcessor"""]
						A_						       =				["""FlavaProcessor"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						A_						       =				[
						    """FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """FlavaForPreTraining""",
						    """FlavaImageCodebook""",
						    """FlavaImageModel""",
						    """FlavaModel""",
						    """FlavaMultimodalModel""",
						    """FlavaPreTrainedModel""",
						    """FlavaTextModel""",
						]

if TYPE_CHECKING:
						from .configuration_flava import (
						    FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    FlavaConfig,
						    FlavaImageCodebookConfig,
						    FlavaImageConfig,
						    FlavaMultimodalConfig,
						    FlavaTextConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_flava import FlavaFeatureExtractor
												from .image_processing_flava import FlavaImageProcessor
												from .processing_flava import FlavaProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flava import (
												    FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FlavaForPreTraining,
												    FlavaImageCodebook,
												    FlavaImageModel,
												    FlavaModel,
												    FlavaMultimodalModel,
												    FlavaPreTrainedModel,
												    FlavaTextModel,
												)

else:
						import sys

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



