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()
_lowerCamelCase					:   Any							=		logging.get_logger(__name__)





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							str  ):
					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__     :							Union[str, Any]      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							int  ):
					# 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__":
							_lowerCamelCase					:   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).''',
							)
							_lowerCamelCase					:   Tuple							=		parser.parse_args()
							convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# this script dumps information about the environment

import os
import platform
import sys


_lowerCamelCase					:   List[Any]							=		'''3'''

print('''Python version:''', sys.version)

print('''OS platform:''', platform.platform())
print('''OS architecture:''', platform.machine())

try:
							import torch

							print('''Torch version:''', torch.__version__)
							print('''Cuda available:''', torch.cuda.is_available())
							print('''Cuda version:''', torch.version.cuda)
							print('''CuDNN version:''', torch.backends.cudnn.version())
							print('''Number of GPUs available:''', torch.cuda.device_count())
except ImportError:
							print('''Torch version:''', None)

try:
							import transformers

							print('''transformers version:''', transformers.__version__)
except ImportError:
							print('''transformers version:''', None)




