
"""simple docstring"""


import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_lowercase							      = logging.get_logger(__name__)

_lowercase							      = {
    '''Intel/dpt-large''': '''https://huggingface.co/Intel/dpt-large/resolve/main/config.json''',
    # See all DPT models at https://huggingface.co/models?filter=dpt
}







class  lowerCAmelCase_ (					_lowercase			):



						'''simple docstring'''


						_lowerCamelCase:			Tuple					=			'''dpt'''


						def __init__(       self   :					str     ,A_   :					Tuple=768     ,A_   :					int=12     ,A_   :					Optional[int]=12     ,A_   :					Optional[int]=3072     ,A_   :					List[str]="gelu"     ,A_   :					str=0.0     ,A_   :					int=0.0     ,A_   :					str=0.02     ,A_   :					str=1e-12     ,A_   :					str=384     ,A_   :					Dict=16     ,A_   :					Union[str, Any]=3     ,A_   :					Dict=False     ,A_   :					Any=True     ,A_   :					Optional[int]=[2, 5, 8, 11]     ,A_   :					Optional[Any]="project"     ,A_   :					Tuple=[4, 2, 1, 0.5]     ,A_   :					int=[96, 192, 384, 768]     ,A_   :					int=256     ,A_   :					str=-1     ,A_   :					Optional[int]=False     ,A_   :					Optional[int]=True     ,A_   :					Union[str, Any]=0.4     ,A_   :					Union[str, Any]=255     ,A_   :					Union[str, Any]=0.1     ,A_   :					List[str]=[1, 1024, 24, 24]     ,A_   :					List[str]=[0, 1]     ,A_   :					List[Any]=None     ,**A_   :					Tuple     ,)   ->      Union[str, Any]:
							super().__init__(**A_					)

							A        =      hidden_size
							A        =      is_hybrid

							if self.is_hybrid:
								if backbone_config is None:
									logger.info('Initializing the config with a `BiT` backbone.'					)
									A        =      {
									    'global_padding': 'same',
									    'layer_type': 'bottleneck',
									    'depths': [3, 4, 9],
									    'out_features': ['stage1', 'stage2', 'stage3'],
									    'embedding_dynamic_padding': True,
									}
									A        =      BitConfig(**A_					)
								elif isinstance(A_     ,A_					):
									logger.info('Initializing the config with a `BiT` backbone.'					)
									A        =      BitConfig(**A_					)
								elif isinstance(A_     ,A_					):
									A        =      backbone_config
								else:
									raise ValueError(
									    F'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'					)

								A        =      backbone_featmap_shape
								A        =      neck_ignore_stages

								if readout_type != "project":
									raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'					)
							else:
								A        =      None
								A        =      None
								A        =      []

							A        =      num_hidden_layers
							A        =      num_attention_heads
							A        =      intermediate_size
							A        =      hidden_act
							A        =      hidden_dropout_prob
							A        =      attention_probs_dropout_prob
							A        =      initializer_range
							A        =      layer_norm_eps
							A        =      image_size
							A        =      patch_size
							A        =      num_channels
							A        =      qkv_bias
							A        =      backbone_out_indices
							if readout_type not in ["ignore", "add", "project"]:
								raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'					)
							A        =      readout_type
							A        =      reassemble_factors
							A        =      neck_hidden_sizes
							A        =      fusion_hidden_size
							A        =      head_in_index
							A        =      use_batch_norm_in_fusion_residual
							# auxiliary head attributes (semantic segmentation)
							A        =      use_auxiliary_head
							A        =      auxiliary_loss_weight
							A        =      semantic_loss_ignore_index
							A        =      semantic_classifier_dropout




						def   _SCREAMING_SNAKE_CASE					(       self   :					Optional[int]					)   ->      str:
							A        =      copy.deepcopy(self.__dict__					)

							if output["backbone_config"] is not None:
								A        =      self.backbone_config.to_dict()

							A        =      self.__class__.model_type
							return output



"""simple docstring"""




import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel


def     lowercase			(SCREAMING_SNAKE_CASE_		:				BertModel ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				str  )   ->						Dict:
						SCREAMING_SNAKE_CASE													=					('dense.weight', 'attention.self.query', 'attention.self.key', 'attention.self.value')

						SCREAMING_SNAKE_CASE													=					(
						    ('layer.', 'layer_'),
						    ('word_embeddings.weight', 'word_embeddings'),
						    ('position_embeddings.weight', 'position_embeddings'),
						    ('token_type_embeddings.weight', 'token_type_embeddings'),
						    ('.', '/'),
						    ('LayerNorm/weight', 'LayerNorm/gamma'),
						    ('LayerNorm/bias', 'LayerNorm/beta'),
						    ('weight', 'kernel'),
						)

						if not os.path.isdir(SCREAMING_SNAKE_CASE_  ):
												os.makedirs(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					model.state_dict()

						def to_tf_var_name(SCREAMING_SNAKE_CASE_		:				str  ):
												for patt, repl in iter(SCREAMING_SNAKE_CASE_  ):
																		SCREAMING_SNAKE_CASE													=					name.replace(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
												return F'bert/{name}'

						def create_tf_var(SCREAMING_SNAKE_CASE_		:				np.ndarray ,			SCREAMING_SNAKE_CASE_		:				str ,			SCREAMING_SNAKE_CASE_		:				tf.Session  ):
												SCREAMING_SNAKE_CASE													=					tf.dtypes.as_dtype(tensor.dtype  )
												SCREAMING_SNAKE_CASE													=					tf.get_variable(dtype=SCREAMING_SNAKE_CASE_ ,			shape=tensor.shape ,			name=SCREAMING_SNAKE_CASE_ ,			initializer=tf.zeros_initializer()  )
												session.run(tf.variables_initializer([tf_var]  )  )
												session.run(SCREAMING_SNAKE_CASE_  )
												return tf_var

						tf.reset_default_graph()
						with tf.Session() as session:
												for var_name in state_dict:
																		SCREAMING_SNAKE_CASE													=					to_tf_var_name(SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					state_dict[var_name].numpy()
																		if any(x in var_name for x in tensors_to_transpose  ):
																								SCREAMING_SNAKE_CASE													=					torch_tensor.T
																		SCREAMING_SNAKE_CASE													=					create_tf_var(tensor=SCREAMING_SNAKE_CASE_ ,			name=SCREAMING_SNAKE_CASE_ ,			session=SCREAMING_SNAKE_CASE_  )
																		tf.keras.backend.set_value(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )
																		SCREAMING_SNAKE_CASE													=					session.run(SCREAMING_SNAKE_CASE_  )
																		print(F'Successfully created {tf_name}: {np.allclose(SCREAMING_SNAKE_CASE_ ,			SCREAMING_SNAKE_CASE_  )}'  )

												SCREAMING_SNAKE_CASE													=					tf.train.Saver(tf.trainable_variables()  )
												saver.save(SCREAMING_SNAKE_CASE_ ,			os.path.join(SCREAMING_SNAKE_CASE_ ,			model_name.replace('-' ,			'_'  ) + '.ckpt'  )  )


def     lowercase			(SCREAMING_SNAKE_CASE_		:				Union[str, Any]=None  )   ->						Any:
						SCREAMING_SNAKE_CASE													=					argparse.ArgumentParser()
						parser.add_argument('--model_name' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='model name e.g. bert-base-uncased'  )
						parser.add_argument(
						    '--cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			default=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory containing pytorch model'  )
						parser.add_argument('--pytorch_model_path' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='/path/to/<pytorch-model-name>.bin'  )
						parser.add_argument('--tf_cache_dir' ,			type=SCREAMING_SNAKE_CASE_ ,			required=SCREAMING_SNAKE_CASE_ ,			help='Directory in which to save tensorflow model'  )
						SCREAMING_SNAKE_CASE													=					parser.parse_args(SCREAMING_SNAKE_CASE_  )

						SCREAMING_SNAKE_CASE													=					BertModel.from_pretrained(
						    pretrained_model_name_or_path=args.model_name ,			state_dict=torch.load(args.pytorch_model_path  ) ,			cache_dir=args.cache_dir ,			)

						convert_pytorch_checkpoint_to_tf(model=SCREAMING_SNAKE_CASE_ ,			ckpt_dir=args.tf_cache_dir ,			model_name=args.model_name  )


if __name__ == "__main__":
	main()


