






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()
a_		:						Dict             =    logging.get_logger(__name__)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			SCREAMING_SNAKE_CASE						= os.path.abspath(_UpperCAmelCase)
			logger.info(F'''Converting TensorFlow checkpoint from {tf_path}''')
			# Load weights from TF model
			SCREAMING_SNAKE_CASE						= tf.train.list_variables(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= []
			SCREAMING_SNAKE_CASE						= []
			SCREAMING_SNAKE_CASE						= []
			for full_name, shape in init_vars:
						# logger.info(f"Loading TF weight {name} with shape {shape}")
						SCREAMING_SNAKE_CASE						= 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'
									SCREAMING_SNAKE_CASE						= name[1:]
						# figure out how many levels deep the name is
						SCREAMING_SNAKE_CASE						= 0
						for _name in name:
									if _name.startswith('layer_with_weights'):
												depth += 1
									else:
												break
						layer_depth.append(_UpperCAmelCase)
						# read data
						SCREAMING_SNAKE_CASE						= tf.train.load_variable(_UpperCAmelCase							,       _UpperCAmelCase)
						names.append('/'.join(_UpperCAmelCase))
						arrays.append(_UpperCAmelCase)
			logger.info(F'''Read a total of {len(_UpperCAmelCase):,} layers''')

			# Sanity check
			if len(set(_UpperCAmelCase)) != 1:
						raise ValueError(F'''Found layer names with different depths (layer depth {list(set(_UpperCAmelCase))})''')
			SCREAMING_SNAKE_CASE						= list(set(_UpperCAmelCase))[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(_UpperCAmelCase							,       _UpperCAmelCase):
						SCREAMING_SNAKE_CASE						= full_name.split('/')
						SCREAMING_SNAKE_CASE						= model
						SCREAMING_SNAKE_CASE						= []
						for i, m_name in enumerate(_UpperCAmelCase):
									if m_name == ".ATTRIBUTES":
												# variable names end with .ATTRIBUTES/VARIABLE_VALUE
												break
									if m_name.startswith('layer_with_weights'):
												SCREAMING_SNAKE_CASE						= 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'])
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'embeddings')
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'LayerNorm')
												elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
															# encoder layers
															trace.extend(['encoder', 'layer', str(layer_num - 4)])
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'encoder')
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'layer')
															SCREAMING_SNAKE_CASE						= pointer[layer_num - 4]
												elif layer_num == config.num_hidden_layers + 4:
															# pooler layer
															trace.extend(['pooler', 'dense'])
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'pooler')
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'dense')
									elif m_name == "embeddings":
												trace.append('embeddings')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'embeddings')
												if layer_num == 0:
															trace.append('word_embeddings')
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'word_embeddings')
												elif layer_num == 1:
															trace.append('position_embeddings')
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'position_embeddings')
												elif layer_num == 2:
															trace.append('token_type_embeddings')
															SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'token_type_embeddings')
												else:
															raise ValueError(F'''Unknown embedding layer with name {full_name}''')
												trace.append('weight')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'weight')
									elif m_name == "_attention_layer":
												# self-attention layer
												trace.extend(['attention', 'self'])
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'attention')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'self')
									elif m_name == "_attention_layer_norm":
												# output attention norm
												trace.extend(['attention', 'output', 'LayerNorm'])
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'attention')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'output')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'LayerNorm')
									elif m_name == "_attention_output_dense":
												# output attention dense
												trace.extend(['attention', 'output', 'dense'])
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'attention')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'output')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'dense')
									elif m_name == "_output_dense":
												# output dense
												trace.extend(['output', 'dense'])
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'output')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'dense')
									elif m_name == "_output_layer_norm":
												# output dense
												trace.extend(['output', 'LayerNorm'])
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'output')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'LayerNorm')
									elif m_name == "_key_dense":
												# attention key
												trace.append('key')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'key')
									elif m_name == "_query_dense":
												# attention query
												trace.append('query')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'query')
									elif m_name == "_value_dense":
												# attention value
												trace.append('value')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'value')
									elif m_name == "_intermediate_dense":
												# attention intermediate dense
												trace.extend(['intermediate', 'dense'])
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'intermediate')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'dense')
									elif m_name == "_output_layer_norm":
												# output layer norm
												trace.append('output')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'output')
									# weights & biases
									elif m_name in ["bias", "beta"]:
												trace.append('bias')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'bias')
									elif m_name in ["kernel", "gamma"]:
												trace.append('weight')
												SCREAMING_SNAKE_CASE						= getattr(_UpperCAmelCase							,       'weight')
									else:
												logger.warning(F'''Ignored {m_name}''')
        # for certain layers reshape is necessary
						SCREAMING_SNAKE_CASE						= '.'.join(_UpperCAmelCase)
						if re.match(R'(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)'							,       _UpperCAmelCase) or re.match(
						    R'(\S+)\.attention\.output\.dense\.weight'							,       _UpperCAmelCase):
									SCREAMING_SNAKE_CASE						= array.reshape(pointer.data.shape)
						if "kernel" in full_name:
									SCREAMING_SNAKE_CASE						= array.transpose()
						if pointer.shape == array.shape:
									SCREAMING_SNAKE_CASE						= torch.from_numpy(_UpperCAmelCase)
						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__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase):
			# Instantiate model
			logger.info(F'''Loading model based on config from {config_path}...''')
			SCREAMING_SNAKE_CASE						= BertConfig.from_json_file(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE						= BertModel(_UpperCAmelCase)

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

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


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












import unittest

from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script

@require_cpu
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						debug_launcher(test_script.main)







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						debug_launcher(test_ops.main)




