


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 	_lowerCAmelCase       (    _lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase )						->	Dict:


						'''simple docstring'''






						def get_masked_lm_array(_lowerCAmelCase ):
												__snake_case				=		F'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__snake_case				=		tf.train.load_variable(_lowerCAmelCase      ,	_lowerCAmelCase )

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

												return torch.from_numpy(_lowerCAmelCase )

						def get_encoder_array(_lowerCAmelCase ):
												__snake_case				=		F'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__snake_case				=		tf.train.load_variable(_lowerCAmelCase      ,	_lowerCAmelCase )

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

												return torch.from_numpy(_lowerCAmelCase )

						def get_encoder_layer_array(_lowerCAmelCase      ,	_lowerCAmelCase ):
												__snake_case				=		F'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__snake_case				=		tf.train.load_variable(_lowerCAmelCase      ,	_lowerCAmelCase )

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

												return torch.from_numpy(_lowerCAmelCase )

						def get_encoder_attention_layer_array(_lowerCAmelCase      ,	_lowerCAmelCase      ,	_lowerCAmelCase ):
												__snake_case				=		F'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
												__snake_case				=		tf.train.load_variable(_lowerCAmelCase      ,	_lowerCAmelCase )
												__snake_case				=		array.reshape(_lowerCAmelCase )

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

												return torch.from_numpy(_lowerCAmelCase )

						print(F'''Loading model based on config from {config_path}...''' )
						__snake_case				=		BertConfig.from_json_file(_lowerCAmelCase )
						__snake_case				=		BertForMaskedLM(_lowerCAmelCase )

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

												# Self-attention
												__snake_case				=		layer.attention.self

												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_query_dense/kernel"      ,	self_attn.query.weight.data.shape )
												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_query_dense/bias"      ,	self_attn.query.bias.data.shape )
												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_key_dense/kernel"      ,	self_attn.key.weight.data.shape )
												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_key_dense/bias"      ,	self_attn.key.bias.data.shape )
												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_value_dense/kernel"      ,	self_attn.value.weight.data.shape )
												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_value_dense/bias"      ,	self_attn.value.bias.data.shape )

												# Self-attention Output
												__snake_case				=		layer.attention.output

												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_output_dense/kernel"      ,	self_output.dense.weight.data.shape )
												__snake_case				=		get_encoder_attention_layer_array(
												    _lowerCAmelCase      ,	"_output_dense/bias"      ,	self_output.dense.bias.data.shape )

												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_attention_layer_norm/gamma" )
												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_attention_layer_norm/beta" )

												# Intermediate
												__snake_case				=		layer.intermediate

												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_intermediate_dense/kernel" )
												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_intermediate_dense/bias" )

												# Output
												__snake_case				=		layer.output

												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_output_dense/kernel" )
												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_output_dense/bias" )

												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_output_layer_norm/gamma" )
												__snake_case				=		get_encoder_layer_array(_lowerCAmelCase      ,	"_output_layer_norm/beta" )

						# Embeddings
						__snake_case				=		get_encoder_array("_position_embedding_layer/embeddings" )
						__snake_case				=		get_encoder_array("_type_embedding_layer/embeddings" )
						__snake_case				=		get_encoder_array("_embedding_norm_layer/gamma" )
						__snake_case				=		get_encoder_array("_embedding_norm_layer/beta" )

						# LM Head
						__snake_case				=		model.cls.predictions.transform

						__snake_case				=		get_masked_lm_array("dense/kernel" )
						__snake_case				=		get_masked_lm_array("dense/bias" )

						__snake_case				=		get_masked_lm_array("layer_norm/gamma" )
						__snake_case				=		get_masked_lm_array("layer_norm/beta" )

						__snake_case				=		get_masked_lm_array("embedding_table" )

						# Pooling
						__snake_case				=		BertPooler(config=_lowerCAmelCase )
						__snake_case				=		get_encoder_array("_pooler_layer/kernel" )
						__snake_case				=		get_encoder_array("_pooler_layer/bias" )

						# Export final model
						model.save_pretrained(_lowerCAmelCase )

						# Integration test - should load without any errors ;)
						__snake_case				=		BertForMaskedLM.from_pretrained(_lowerCAmelCase )
						print(new_model.eval() )

						print("Model conversion was done sucessfully!" )


if __name__ == "__main__":
	A							:					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.',
	)
	A							:					List[str]     						=     parser.parse_args()
	convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A							:					Union[str, Any]     						=     logging.get_logger(__name__)

A							:					Tuple     						=     {
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}






class     UpperCamelCase(  _a   ):
					snake_case_		:				List[Any]										=	"""data2vec-vision"""


					def __init__(						self	:		Any     ,    SCREAMING_SNAKE_CASE	:		List[str]=7_6_8     ,    SCREAMING_SNAKE_CASE	:		Tuple=1_2     ,    SCREAMING_SNAKE_CASE	:		int=1_2     ,    SCREAMING_SNAKE_CASE	:		List[str]=3_0_7_2     ,    SCREAMING_SNAKE_CASE	:		Tuple="gelu"     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		List[Any]=0.0     ,    SCREAMING_SNAKE_CASE	:		Any=0.02     ,    SCREAMING_SNAKE_CASE	:		List[str]=1e-1_2     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=2_2_4     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=1_6     ,    SCREAMING_SNAKE_CASE	:		str=3     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=False     ,    SCREAMING_SNAKE_CASE	:		Any=False     ,    SCREAMING_SNAKE_CASE	:		Dict=False     ,    SCREAMING_SNAKE_CASE	:		int=False     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=0.1     ,    SCREAMING_SNAKE_CASE	:		Any=True     ,    SCREAMING_SNAKE_CASE	:		List[str]=[3, 5, 7, 1_1]     ,    SCREAMING_SNAKE_CASE	:		int=[1, 2, 3, 6]     ,    SCREAMING_SNAKE_CASE	:		List[str]=True     ,    SCREAMING_SNAKE_CASE	:		Tuple=0.4     ,    SCREAMING_SNAKE_CASE	:		Optional[Any]=2_5_6     ,    SCREAMING_SNAKE_CASE	:		Tuple=1     ,    SCREAMING_SNAKE_CASE	:		Optional[int]=False     ,    SCREAMING_SNAKE_CASE	:		List[Any]=2_5_5     ,    **SCREAMING_SNAKE_CASE	:		Any     ,    )			->	List[str]:




											'''simple docstring'''

											super().__init__(**SCREAMING_SNAKE_CASE						)

											__snake_case				=		hidden_size
											__snake_case				=		num_hidden_layers
											__snake_case				=		num_attention_heads
											__snake_case				=		intermediate_size
											__snake_case				=		hidden_act
											__snake_case				=		hidden_dropout_prob
											__snake_case				=		attention_probs_dropout_prob
											__snake_case				=		initializer_range
											__snake_case				=		layer_norm_eps

											__snake_case				=		image_size
											__snake_case				=		patch_size
											__snake_case				=		num_channels
											__snake_case				=		use_mask_token
											__snake_case				=		use_absolute_position_embeddings
											__snake_case				=		use_relative_position_bias
											__snake_case				=		use_shared_relative_position_bias
											__snake_case				=		layer_scale_init_value
											__snake_case				=		drop_path_rate
											__snake_case				=		use_mean_pooling
											# decode head attributes (semantic segmentation)
											__snake_case				=		out_indices
											__snake_case				=		pool_scales
											# auxiliary head attributes (semantic segmentation)
											__snake_case				=		use_auxiliary_head
											__snake_case				=		auxiliary_loss_weight
											__snake_case				=		auxiliary_channels
											__snake_case				=		auxiliary_num_convs
											__snake_case				=		auxiliary_concat_input
											__snake_case				=		semantic_loss_ignore_index






class     UpperCamelCase(  _a   ):
					snake_case_		:				Any										=	version.parse("""1.11"""   )


					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		List[str]						)			->	Mapping[str, Mapping[int, str]]:




											'''simple docstring'''

											return OrderedDict(
											    [
											        ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
											    ]						)






					@property
					def 							SCREAMING_SNAKE_CASE_  (						self	:		Union[str, Any]						)			->	float:




											'''simple docstring'''

											return 1e-4





