
"""simple docstring"""


import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM






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

	return orig_key






def  _snake_case			(						snake_case__				:       Dict		, snake_case__				:       List[str]       ):
	for key in orig_state_dict.copy().keys():
		A  				=				orig_state_dict.pop(snake_case__       )

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

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

	return orig_state_dict






def  _snake_case			(						snake_case__				:       Any		, snake_case__				:       str		, snake_case__				:       Union[str, Any]       ):
	A  				=				torch.load(snake_case__		, map_location='cpu'       )['model_state_dict']
	A  				=				NystromformerConfig.from_json_file(snake_case__       )
	A  				=				NystromformerForMaskedLM(snake_case__       )

	A  				=				convert_checkpoint_helper(snake_case__		, snake_case__       )

	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__":
		_lowercase											=			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.'''
		)
		_lowercase											=			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


_lowercase											=			{'''configuration_vilt''': ['''VILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViltConfig''']}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			['''ViltFeatureExtractor''']
		_lowercase											=			['''ViltImageProcessor''']
		_lowercase											=			['''ViltProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_lowercase											=			[
		    '''VILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ViltForImageAndTextRetrieval''',
		    '''ViltForImagesAndTextClassification''',
		    '''ViltForTokenClassification''',
		    '''ViltForMaskedLM''',
		    '''ViltForQuestionAnswering''',
		    '''ViltLayer''',
		    '''ViltModel''',
		    '''ViltPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_vilt import VILT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViltConfig

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_vilt import ViltFeatureExtractor
				from .image_processing_vilt import ViltImageProcessor
				from .processing_vilt import ViltProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_vilt import (
				    VILT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ViltForImageAndTextRetrieval,
				    ViltForImagesAndTextClassification,
				    ViltForMaskedLM,
				    ViltForQuestionAnswering,
				    ViltForTokenClassification,
				    ViltLayer,
				    ViltModel,
				    ViltPreTrainedModel,
				)


else:
		import sys

		_lowercase											=			_LazyModule(__name__, globals()['''__file__'''], _import_structure)