



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()
__snake_case    :		Tuple  =logging.get_logger(__name__)






def      lowerCAmelCase__		(   lowerCamelCase_		:		List[Any]	,lowerCamelCase_		:		Optional[int]	,lowerCamelCase_		:		int):





				'''simple docstring'''

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

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





				'''simple docstring'''

				logger.info(f"""Loading model based on config from {config_path}...""")
				lowerCAmelCase__					:    Optional[int]												=					BertConfig.from_json_file(lowerCamelCase_)
				lowerCAmelCase__					:    Any												=					BertModel(lowerCamelCase_)

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

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


if __name__ == "__main__":
							__snake_case    :		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).',
							)
							__snake_case    :		Tuple  =parser.parse_args()
							convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






__snake_case    :		Any  ='\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

__snake_case    :		Tuple  =[{'type': 'code', 'content': INSTALL_CONTENT}]
__snake_case    :		Tuple  ={
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}

