


'''simple docstring'''



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     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			def get_masked_lm_array(UpperCamelCase__					):
						UpperCAmelCase__  :					List[Any]        =     f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					Union[str, Any]        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)

						if "kernel" in name:
									UpperCAmelCase__  :					List[str]        =     array.transpose()

						return torch.from_numpy(UpperCamelCase__					)

			def get_encoder_array(UpperCamelCase__					):
						UpperCAmelCase__  :					Union[str, Any]        =     f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					str        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)

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

						return torch.from_numpy(UpperCamelCase__					)

			def get_encoder_layer_array(UpperCamelCase__ ,      UpperCamelCase__					):
						UpperCAmelCase__  :					Union[str, Any]        =     f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					Optional[int]        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)

						if "kernel" in name:
									UpperCAmelCase__  :					Union[str, Any]        =     array.transpose()

						return torch.from_numpy(UpperCamelCase__					)

			def get_encoder_attention_layer_array(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
						UpperCAmelCase__  :					Optional[int]        =     f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
						UpperCAmelCase__  :					List[str]        =     tf.train.load_variable(UpperCamelCase__ ,      UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[int]        =     array.reshape(UpperCamelCase__					)

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

						return torch.from_numpy(UpperCamelCase__					)

			print(f'''Loading model based on config from {config_path}...'''					)
			UpperCAmelCase__  :					List[str]        =     BertConfig.from_json_file(UpperCamelCase__					)
			UpperCAmelCase__  :					Optional[int]        =     BertForMaskedLM(UpperCamelCase__					)

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

						# Self-attention
						UpperCAmelCase__  :					BertSelfAttention        =     layer.attention.self

						UpperCAmelCase__  :					List[Any]        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_query_dense/kernel""" ,      self_attn.query.weight.data.shape					)
						UpperCAmelCase__  :					str        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_query_dense/bias""" ,      self_attn.query.bias.data.shape					)
						UpperCAmelCase__  :					Optional[int]        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_key_dense/kernel""" ,      self_attn.key.weight.data.shape					)
						UpperCAmelCase__  :					Dict        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_key_dense/bias""" ,      self_attn.key.bias.data.shape					)
						UpperCAmelCase__  :					Optional[Any]        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_value_dense/kernel""" ,      self_attn.value.weight.data.shape					)
						UpperCAmelCase__  :					Optional[int]        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_value_dense/bias""" ,      self_attn.value.bias.data.shape					)

						# Self-attention Output
						UpperCAmelCase__  :					BertSelfOutput        =     layer.attention.output

						UpperCAmelCase__  :					Optional[int]        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_output_dense/kernel""" ,      self_output.dense.weight.data.shape					)
						UpperCAmelCase__  :					Dict        =     get_encoder_attention_layer_array(
						    UpperCamelCase__ ,      """_output_dense/bias""" ,      self_output.dense.bias.data.shape					)

						UpperCAmelCase__  :					List[str]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_attention_layer_norm/gamma"""					)
						UpperCAmelCase__  :					int        =     get_encoder_layer_array(UpperCamelCase__ ,      """_attention_layer_norm/beta"""					)

						# Intermediate
						UpperCAmelCase__  :					BertIntermediate        =     layer.intermediate

						UpperCAmelCase__  :					Tuple        =     get_encoder_layer_array(UpperCamelCase__ ,      """_intermediate_dense/kernel"""					)
						UpperCAmelCase__  :					Dict        =     get_encoder_layer_array(UpperCamelCase__ ,      """_intermediate_dense/bias"""					)

						# Output
						UpperCAmelCase__  :					BertOutput        =     layer.output

						UpperCAmelCase__  :					List[Any]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_dense/kernel"""					)
						UpperCAmelCase__  :					Optional[Any]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_dense/bias"""					)

						UpperCAmelCase__  :					int        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_layer_norm/gamma"""					)
						UpperCAmelCase__  :					List[str]        =     get_encoder_layer_array(UpperCamelCase__ ,      """_output_layer_norm/beta"""					)

			# Embeddings
			UpperCAmelCase__  :					Dict        =     get_encoder_array("""_position_embedding_layer/embeddings"""					)
			UpperCAmelCase__  :					Dict        =     get_encoder_array("""_type_embedding_layer/embeddings"""					)
			UpperCAmelCase__  :					Union[str, Any]        =     get_encoder_array("""_embedding_norm_layer/gamma"""					)
			UpperCAmelCase__  :					Optional[int]        =     get_encoder_array("""_embedding_norm_layer/beta"""					)

			# LM Head
			UpperCAmelCase__  :					List[str]        =     model.cls.predictions.transform

			UpperCAmelCase__  :					Dict        =     get_masked_lm_array("""dense/kernel"""					)
			UpperCAmelCase__  :					Tuple        =     get_masked_lm_array("""dense/bias"""					)

			UpperCAmelCase__  :					str        =     get_masked_lm_array("""layer_norm/gamma"""					)
			UpperCAmelCase__  :					Dict        =     get_masked_lm_array("""layer_norm/beta"""					)

			UpperCAmelCase__  :					Optional[Any]        =     get_masked_lm_array("""embedding_table"""					)

			# Pooling
			UpperCAmelCase__  :					str        =     BertPooler(config=UpperCamelCase__					)
			UpperCAmelCase__  :					BertPooler        =     get_encoder_array("""_pooler_layer/kernel"""					)
			UpperCAmelCase__  :					BertPooler        =     get_encoder_array("""_pooler_layer/bias"""					)

			# Export final model
			model.save_pretrained(UpperCamelCase__					)

			# Integration test - should load without any errors ;)
			UpperCAmelCase__  :					Optional[int]        =     BertForMaskedLM.from_pretrained(UpperCamelCase__					)
			print(new_model.eval()					)

			print("""Model conversion was done sucessfully!"""					)


if __name__ == "__main__":
		__A	=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	=parser.parse_args()
		convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



'''simple docstring'''



import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__A	=logging.get_logger(__name__)







def     _UpperCamelCase      (		UpperCamelCase__					):
			UpperCAmelCase__  :					List[str]        =     R"""\w+[.]\d+"""
			UpperCAmelCase__  :					List[Any]        =     re.findall(UpperCamelCase__ ,      UpperCamelCase__					)
			for pat in pats:
						UpperCAmelCase__  :					str        =     key.replace(UpperCamelCase__ ,      """_""".join(pat.split("""."""					)					)					)
			return key







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					):
			UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
			if (
			    any("""norm""" in str_ for str_ in pt_tuple_key					)
			    and (pt_tuple_key[-1] == "bias")
			    and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
			    and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
			):
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor
			elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
						UpperCAmelCase__  :					str        =     pt_tuple_key[:-1] + ("""scale""",)
						return renamed_pt_tuple_key, pt_tensor

			# embedding
			if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
						UpperCAmelCase__  :					Union[str, Any]        =     pt_tuple_key[:-1] + ("""embedding""",)
						return renamed_pt_tuple_key, pt_tensor

			# conv layer
			UpperCAmelCase__  :					Any        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
						UpperCAmelCase__  :					Any        =     pt_tensor.transpose(2 ,      3 ,      1 ,      0					)
						return renamed_pt_tuple_key, pt_tensor

			# linear layer
			UpperCAmelCase__  :					Optional[int]        =     pt_tuple_key[:-1] + ("""kernel""",)
			if pt_tuple_key[-1] == "weight":
						UpperCAmelCase__  :					Any        =     pt_tensor.T
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm weight
			UpperCAmelCase__  :					Optional[Any]        =     pt_tuple_key[:-1] + ("""weight""",)
			if pt_tuple_key[-1] == "gamma":
						return renamed_pt_tuple_key, pt_tensor

			# old PyTorch layer norm bias
			UpperCAmelCase__  :					Dict        =     pt_tuple_key[:-1] + ("""bias""",)
			if pt_tuple_key[-1] == "beta":
						return renamed_pt_tuple_key, pt_tensor

			return pt_tuple_key, pt_tensor







def     _UpperCamelCase      (		UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__=4_2					):
			# Step 1: Convert pytorch tensor to numpy
			UpperCAmelCase__  :					str        =     {k: v.numpy() for k, v in pt_state_dict.items()}

			# Step 2: Since the model is stateless, get random Flax params
			UpperCAmelCase__  :					str        =     flax_model.init_weights(PRNGKey(UpperCamelCase__					)					)

			UpperCAmelCase__  :					Union[str, Any]        =     flatten_dict(UpperCamelCase__					)
			UpperCAmelCase__  :					int        =     {}

			# Need to change some parameters name to match Flax names
			for pt_key, pt_tensor in pt_state_dict.items():
						UpperCAmelCase__  :					str        =     rename_key(UpperCamelCase__					)
						UpperCAmelCase__  :					Optional[Any]        =     tuple(renamed_pt_key.split("""."""					)					)

						# Correctly rename weight parameters
						UpperCAmelCase__   ,       UpperCAmelCase__  :					Tuple        =     rename_key_and_reshape_tensor(UpperCamelCase__ ,      UpperCamelCase__ ,      UpperCamelCase__					)

						if flax_key in random_flax_state_dict:
									if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
												raise ValueError(
												    f'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
												    f'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''					)

        # also add unexpected weight so that warning is thrown
						UpperCAmelCase__  :					str        =     jnp.asarray(UpperCamelCase__					)

			return unflatten_dict(UpperCamelCase__					)