

'''simple docstring'''





import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase_    						=       logging.get_logger(__name__)






def 				_lowerCAmelCase			(   __magic_name__						:     List[Any]   , __magic_name__						:     List[Any]   , __magic_name__						:     List[Any]						)				->  List[Any]:
						lowercase :							List[str]    		=os.path.abspath(a__						)
						logger.info(f'''Converting TensorFlow checkpoint from {tf_path}'''						)
						# Load weights from TF model
						lowercase :							Optional[Any]    		=tf.train.list_variables(a__						)
						lowercase :							Union[str, Any]    		=[]
						lowercase :							List[str]    		=[]
						lowercase :							List[Any]    		=[]
						for full_name, shape in init_vars:
												# logger.info(f"Loading TF weight {name} with shape {shape}")
												lowercase :							List[str]    		=full_name.split('''/'''						)
												if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
																		logger.info(f'''Skipping non-model layer {full_name}'''						)
																		continue
												if "optimizer" in full_name:
																		logger.info(f'''Skipping optimization layer {full_name}'''						)
																		continue
												if name[0] == "model":
																		# ignore initial 'model'
																		lowercase :							int    		=name[1:]
												# figure out how many levels deep the name is
												lowercase :							Optional[Any]    		=0
												for _name in name:
																		if _name.startswith('''layer_with_weights'''						):
																								depth += 1
																		else:
																								break
												layer_depth.append(a__						)
												# read data
												lowercase :							Optional[Any]    		=tf.train.load_variable(a__   , a__						)
												names.append('''/'''.join(a__						)						)
												arrays.append(a__						)
						logger.info(f'''Read a total of {len(a__						):,} layers'''						)

						# Sanity check
						if len(set(a__						)						) != 1:
												raise ValueError(f'''Found layer names with different depths (layer depth {list(set(a__						)						)})'''						)
						lowercase :							Union[str, Any]    		=list(set(a__						)						)[0]
						if layer_depth != 1:
												raise ValueError(
												    '''The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP'''
												    ''' heads.'''						)

						# convert layers
						logger.info('''Converting weights...'''						)
						for full_name, array in zip(a__   , a__						):
												lowercase :							Optional[Any]    		=full_name.split('''/'''						)
												lowercase :							Any    		=model
												lowercase :							List[str]    		=[]
												for i, m_name in enumerate(a__						):
																		if m_name == ".ATTRIBUTES":
																								# variable names end with .ATTRIBUTES/VARIABLE_VALUE
																								break
																		if m_name.startswith('''layer_with_weights'''						):
																								lowercase :							Tuple    		=int(m_name.split('''-'''						)[-1]						)
																								if layer_num <= 2:
																														# embedding layers
																														# layer_num 0: word_embeddings
																														# layer_num 1: position_embeddings
																														# layer_num 2: token_type_embeddings
																														continue
																								elif layer_num == 3:
																														# embedding LayerNorm
																														trace.extend(['''embeddings''', '''LayerNorm''']						)
																														lowercase :							Any    		=getattr(a__   , '''embeddings'''						)
																														lowercase :							int    		=getattr(a__   , '''LayerNorm'''						)
																								elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
																														# encoder layers
																														trace.extend(['''encoder''', '''layer''', str(layer_num - 4						)]						)
																														lowercase :							Dict    		=getattr(a__   , '''encoder'''						)
																														lowercase :							Dict    		=getattr(a__   , '''layer'''						)
																														lowercase :							Optional[Any]    		=pointer[layer_num - 4]
																								elif layer_num == config.num_hidden_layers + 4:
																														# pooler layer
																														trace.extend(['''pooler''', '''dense''']						)
																														lowercase :							List[str]    		=getattr(a__   , '''pooler'''						)
																														lowercase :							str    		=getattr(a__   , '''dense'''						)
																		elif m_name == "embeddings":
																								trace.append('''embeddings'''						)
																								lowercase :							Optional[Any]    		=getattr(a__   , '''embeddings'''						)
																								if layer_num == 0:
																														trace.append('''word_embeddings'''						)
																														lowercase :							Any    		=getattr(a__   , '''word_embeddings'''						)
																								elif layer_num == 1:
																														trace.append('''position_embeddings'''						)
																														lowercase :							Optional[int]    		=getattr(a__   , '''position_embeddings'''						)
																								elif layer_num == 2:
																														trace.append('''token_type_embeddings'''						)
																														lowercase :							List[Any]    		=getattr(a__   , '''token_type_embeddings'''						)
																								else:
																														raise ValueError(f'''Unknown embedding layer with name {full_name}'''						)
																								trace.append('''weight'''						)
																								lowercase :							int    		=getattr(a__   , '''weight'''						)
																		elif m_name == "_attention_layer":
																								# self-attention layer
																								trace.extend(['''attention''', '''self''']						)
																								lowercase :							Union[str, Any]    		=getattr(a__   , '''attention'''						)
																								lowercase :							List[Any]    		=getattr(a__   , '''self'''						)
																		elif m_name == "_attention_layer_norm":
																								# output attention norm
																								trace.extend(['''attention''', '''output''', '''LayerNorm''']						)
																								lowercase :							List[str]    		=getattr(a__   , '''attention'''						)
																								lowercase :							int    		=getattr(a__   , '''output'''						)
																								lowercase :							List[Any]    		=getattr(a__   , '''LayerNorm'''						)
																		elif m_name == "_attention_output_dense":
																								# output attention dense
																								trace.extend(['''attention''', '''output''', '''dense''']						)
																								lowercase :							Union[str, Any]    		=getattr(a__   , '''attention'''						)
																								lowercase :							List[str]    		=getattr(a__   , '''output'''						)
																								lowercase :							Optional[Any]    		=getattr(a__   , '''dense'''						)
																		elif m_name == "_output_dense":
																								# output dense
																								trace.extend(['''output''', '''dense''']						)
																								lowercase :							List[Any]    		=getattr(a__   , '''output'''						)
																								lowercase :							int    		=getattr(a__   , '''dense'''						)
																		elif m_name == "_output_layer_norm":
																								# output dense
																								trace.extend(['''output''', '''LayerNorm''']						)
																								lowercase :							int    		=getattr(a__   , '''output'''						)
																								lowercase :							Optional[int]    		=getattr(a__   , '''LayerNorm'''						)
																		elif m_name == "_key_dense":
																								# attention key
																								trace.append('''key'''						)
																								lowercase :							Union[str, Any]    		=getattr(a__   , '''key'''						)
																		elif m_name == "_query_dense":
																								# attention query
																								trace.append('''query'''						)
																								lowercase :							str    		=getattr(a__   , '''query'''						)
																		elif m_name == "_value_dense":
																								# attention value
																								trace.append('''value'''						)
																								lowercase :							List[str]    		=getattr(a__   , '''value'''						)
																		elif m_name == "_intermediate_dense":
																								# attention intermediate dense
																								trace.extend(['''intermediate''', '''dense''']						)
																								lowercase :							str    		=getattr(a__   , '''intermediate'''						)
																								lowercase :							Union[str, Any]    		=getattr(a__   , '''dense'''						)
																		elif m_name == "_output_layer_norm":
																								# output layer norm
																								trace.append('''output'''						)
																								lowercase :							Optional[Any]    		=getattr(a__   , '''output'''						)
																		# weights & biases
																		elif m_name in ["bias", "beta"]:
																								trace.append('''bias'''						)
																								lowercase :							int    		=getattr(a__   , '''bias'''						)
																		elif m_name in ["kernel", "gamma"]:
																								trace.append('''weight'''						)
																								lowercase :							Any    		=getattr(a__   , '''weight'''						)
																		else:
																								logger.warning(f'''Ignored {m_name}'''						)
        # for certain layers reshape is necessary
												lowercase :							List[Any]    		=""".""".join(a__						)
												if re.match(R'''(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'''   , a__						) or re.match(
												    R'''(\S+)\.attention\.output\.dense\.weight'''   , a__						):
																		lowercase :							int    		=array.reshape(pointer.data.shape						)
												if "kernel" in full_name:
																		lowercase :							int    		=array.transpose()
												if pointer.shape == array.shape:
																		lowercase :							Dict    		=torch.from_numpy(a__						)
												else:
																		raise ValueError(
																		    f'''Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:'''
																		    f''' {array.shape}'''						)
												logger.info(f'''Successfully set variable {full_name} to PyTorch layer {trace}'''						)
						return model






def 				_lowerCAmelCase			(   __magic_name__						:     Tuple   , __magic_name__						:     str   , __magic_name__						:     Optional[int]						)				->  Optional[Any]:
						# Instantiate model
						logger.info(f'''Loading model based on config from {config_path}...'''						)
						lowercase :							Tuple    		=BertConfig.from_json_file(a__						)
						lowercase :							Optional[int]    		=BertModel(a__						)

						# Load weights from checkpoint
						logger.info(f'''Loading weights from checkpoint {tf_checkpoint_path}...'''						)
						load_tfa_weights_in_bert(a__   , a__   , a__						)

						# Save pytorch-model
						logger.info(f'''Saving PyTorch model to {pytorch_dump_path}...'''						)
						torch.save(model.state_dict()   , a__						)


if __name__ == "__main__":
							UpperCamelCase_    						=       argparse.ArgumentParser()
							parser.add_argument(
							    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x 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 (must include filename).""",
							)
							UpperCamelCase_    						=       parser.parse_args()
							convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)








UpperCAmelCase_							:					dict[str, float]		 =   {
    "joule": 1.0,
    "kilojoule": 1_000,
    "megajoule": 1_000_000,
    "gigajoule": 1_000_000_000,
    "wattsecond": 1.0,
    "watthour": 3_600,
    "kilowatthour": 3_600_000,
    "newtonmeter": 1.0,
    "calorie_nutr": 4_186.8,
    "kilocalorie_nutr": 4_186_800.00,
    "electronvolt": 1.6_0217_6634e-19,
    "britishthermalunit_it": 1_055.05_585,
    "footpound": 1.35_5818,
}
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   float       )					->				float:
	if to_type not in ENERGY_CONVERSION or from_type not in ENERGY_CONVERSION:
		__A :			Optional[int]		    =			(
		    f"""Incorrect 'from_type' or 'to_type' value: {from_type!r}, {to_type!r}\n"""
		    f"""Valid values are: {", ".join(a__       )}"""
		)
		raise ValueError(a__       )
	return value * ENERGY_CONVERSION[from_type] / ENERGY_CONVERSION[to_type]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





