



from typing import List

from ...configuration_utils import PretrainedConfig
from ...utils import logging


snake_case    :						Tuple  =logging.get_logger(__name__)

snake_case    :						Optional[int]  ={
    'snap-research/efficientformer-l1-300': (
        'https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json'
    ),
}




class 							lowerCAmelCase__  (	UpperCamelCase						):
					__A     :    Union[str, Any]           =						'efficientformer'







					def __init__(       self      :     List[str]			,		_A      :     List[int] = [3, 2, 6, 4]			,		_A      :     List[int] = [48, 96, 224, 448]			,		_A      :     List[bool] = [True, True, True, True]			,		_A      :     int = 448			,		_A      :     int = 32			,		_A      :     int = 4			,		_A      :     int = 7			,		_A      :     int = 5			,		_A      :     int = 8			,		_A      :     int = 4			,		_A      :     float = 0.0			,		_A      :     int = 16			,		_A      :     int = 3			,		_A      :     int = 3			,		_A      :     int = 3			,		_A      :     int = 2			,		_A      :     int = 1			,		_A      :     float = 0.0			,		_A      :     int = 1			,		_A      :     bool = True			,		_A      :     bool = True			,		_A      :     float = 1e-5			,		_A      :     str = "gelu"			,		_A      :     float = 0.02			,		_A      :     float = 1e-12			,		_A      :     int = 224			,		_A      :     float = 1e-05			,		**_A      :     List[str]			,		):
											super().__init__(**_A)

											A__	:							str						    = hidden_act
											A__	:							int						    = hidden_dropout_prob
											A__	:							Dict						    = hidden_sizes
											A__	:							str						    = num_hidden_layers
											A__	:							Optional[Any]						    = num_attention_heads
											A__	:							Optional[int]						    = initializer_range
											A__	:							Union[str, Any]						    = layer_norm_eps
											A__	:							Union[str, Any]						    = patch_size
											A__	:							Dict						    = num_channels
											A__	:							Union[str, Any]						    = depths
											A__	:							Optional[int]						    = mlp_expansion_ratio
											A__	:							Optional[Any]						    = downsamples
											A__	:							str						    = dim
											A__	:							Optional[Any]						    = key_dim
											A__	:							Union[str, Any]						    = attention_ratio
											A__	:							Union[str, Any]						    = resolution
											A__	:							Any						    = pool_size
											A__	:							Optional[Any]						    = downsample_patch_size
											A__	:							List[str]						    = downsample_stride
											A__	:							List[Any]						    = downsample_pad
											A__	:							Any						    = drop_path_rate
											A__	:							Tuple						    = num_metaad_blocks
											A__	:							List[Any]						    = distillation
											A__	:							Any						    = use_layer_scale
											A__	:							Dict						    = layer_scale_init_value
											A__	:							List[str]						    = image_size
											A__	:							Optional[int]						    = batch_norm_eps




import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()
def 	snake_case__       (					__lowercase							,     __lowercase							,     __lowercase       )		->		List[Any]:







						"""simple docstring"""

						def get_masked_lm_array(__lowercase       ):
												A__	:							int						    = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_array(__lowercase       ):
												A__	:							Dict						    = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Tuple						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							str						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_layer_array(__lowercase							,     __lowercase       ):
												A__	:							int						    = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							Dict						    = tf.train.load_variable(__lowercase							,     __lowercase       )

												if "kernel" in name:
																		A__	:							Any						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						def get_encoder_attention_layer_array(__lowercase							,     __lowercase							,     __lowercase       ):
												A__	:							List[str]						    = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
												A__	:							List[Any]						    = tf.train.load_variable(__lowercase							,     __lowercase       )
												A__	:							int						    = array.reshape(__lowercase       )

												if "kernel" in name:
																		A__	:							Union[str, Any]						    = array.transpose()

												return torch.from_numpy(__lowercase       )

						print(F'Loading model based on config from {config_path}...'       )
						A__	:							Tuple						    = BertConfig.from_json_file(__lowercase       )
						A__	:							List[str]						    = BertForMaskedLM(__lowercase       )

						# Layers
						for layer_index in range(0							,     config.num_hidden_layers       ):
												A__	:							BertLayer						    = model.bert.encoder.layer[layer_index]

												# Self-attention
												A__	:							BertSelfAttention						    = layer.attention.self

												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/kernel"							,     self_attn.query.weight.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_query_dense/bias"							,     self_attn.query.bias.data.shape       )
												A__	:							int						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/kernel"							,     self_attn.key.weight.data.shape       )
												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_key_dense/bias"							,     self_attn.key.bias.data.shape       )
												A__	:							List[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/kernel"							,     self_attn.value.weight.data.shape       )
												A__	:							Optional[Any]						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_value_dense/bias"							,     self_attn.value.bias.data.shape       )

												# Self-attention Output
												A__	:							BertSelfOutput						    = layer.attention.output

												A__	:							Tuple						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/kernel"							,     self_output.dense.weight.data.shape       )
												A__	:							Any						    = get_encoder_attention_layer_array(
												    __lowercase							,     "_output_dense/bias"							,     self_output.dense.bias.data.shape       )

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/gamma"       )
												A__	:							Optional[int]						    = get_encoder_layer_array(__lowercase							,     "_attention_layer_norm/beta"       )

												# Intermediate
												A__	:							BertIntermediate						    = layer.intermediate

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/kernel"       )
												A__	:							Any						    = get_encoder_layer_array(__lowercase							,     "_intermediate_dense/bias"       )

												# Output
												A__	:							BertOutput						    = layer.output

												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_dense/kernel"       )
												A__	:							List[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_dense/bias"       )

												A__	:							Optional[Any]						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/gamma"       )
												A__	:							Dict						    = get_encoder_layer_array(__lowercase							,     "_output_layer_norm/beta"       )

						# Embeddings
						A__	:							str						    = get_encoder_array("_position_embedding_layer/embeddings"       )
						A__	:							List[str]						    = get_encoder_array("_type_embedding_layer/embeddings"       )
						A__	:							List[Any]						    = get_encoder_array("_embedding_norm_layer/gamma"       )
						A__	:							Optional[int]						    = get_encoder_array("_embedding_norm_layer/beta"       )

						# LM Head
						A__	:							Optional[Any]						    = model.cls.predictions.transform

						A__	:							int						    = get_masked_lm_array("dense/kernel"       )
						A__	:							Tuple						    = get_masked_lm_array("dense/bias"       )

						A__	:							Optional[Any]						    = get_masked_lm_array("layer_norm/gamma"       )
						A__	:							Tuple						    = get_masked_lm_array("layer_norm/beta"       )

						A__	:							Any						    = get_masked_lm_array("embedding_table"       )

						# Pooling
						A__	:							Optional[Any]						    = BertPooler(config=__lowercase       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/kernel"       )
						A__	:							BertPooler						    = get_encoder_array("_pooler_layer/bias"       )

						# Export final model
						model.save_pretrained(__lowercase       )

						# Integration test - should load without any errors ;)
						A__	:							Union[str, Any]						    = BertForMaskedLM.from_pretrained(__lowercase       )
						print(new_model.eval()       )

						print("Model conversion was done sucessfully!"       )


if __name__ == "__main__":
		snake_case    :						str  =						argparse.ArgumentParser()
		parser.add_argument(
		    '--tf_checkpoint_path', type=str, required=True, help='Path to the TensorFlow Token Dropping checkpoint path.'
		)
		parser.add_argument(
		    '--bert_config_file',
		    type=str,
		    required=True,
		    help='The config json file corresponding to the BERT model. This specifies the model architecture.',
		)
		parser.add_argument(
		    '--pytorch_dump_path',
		    type=str,
		    required=True,
		    help='Path to the output PyTorch model.',
		)
		snake_case    :						Dict  =						parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)