




"""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 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				)			->List[Any]:






		"""simple docstring"""


		def get_masked_lm_array(_lowerCamelCase				):
				__lowercase				:		Any												=				F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
				__lowercase				:		int												=				tf.train.load_variable(_lowerCamelCase,  _lowerCamelCase				)

				if "kernel" in name:
						__lowercase				:		Tuple												=				array.transpose()

				return torch.from_numpy(_lowerCamelCase				)

		def get_encoder_array(_lowerCamelCase				):
				__lowercase				:		Any												=				F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
				__lowercase				:		Any												=				tf.train.load_variable(_lowerCamelCase,  _lowerCamelCase				)

				if "kernel" in name:
						__lowercase				:		int												=				array.transpose()

				return torch.from_numpy(_lowerCamelCase				)

		def get_encoder_layer_array(_lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		Tuple												=				F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
				__lowercase				:		Optional[Any]												=				tf.train.load_variable(_lowerCamelCase,  _lowerCamelCase				)

				if "kernel" in name:
						__lowercase				:		Tuple												=				array.transpose()

				return torch.from_numpy(_lowerCamelCase				)

		def get_encoder_attention_layer_array(_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase				):
				__lowercase				:		Any												=				F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
				__lowercase				:		Tuple												=				tf.train.load_variable(_lowerCamelCase,  _lowerCamelCase				)
				__lowercase				:		Tuple												=				array.reshape(_lowerCamelCase				)

				if "kernel" in name:
						__lowercase				:		int												=				array.transpose()

				return torch.from_numpy(_lowerCamelCase				)

		print(F'Loading model based on config from {config_path}...'				)
		__lowercase				:		int												=				BertConfig.from_json_file(_lowerCamelCase				)
		__lowercase				:		str												=				BertForMaskedLM(_lowerCamelCase				)

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

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

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

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

				__lowercase				:		List[Any]												=				get_encoder_attention_layer_array(
				    _lowerCamelCase,  "_output_dense/kernel",  self_output.dense.weight.data.shape				)
				__lowercase				:		Optional[int]												=				get_encoder_attention_layer_array(
				    _lowerCamelCase,  "_output_dense/bias",  self_output.dense.bias.data.shape				)

				__lowercase				:		str												=				get_encoder_layer_array(_lowerCamelCase,  "_attention_layer_norm/gamma"				)
				__lowercase				:		List[str]												=				get_encoder_layer_array(_lowerCamelCase,  "_attention_layer_norm/beta"				)

				# Intermediate
				__lowercase				:		BertIntermediate												=				layer.intermediate

				__lowercase				:		List[str]												=				get_encoder_layer_array(_lowerCamelCase,  "_intermediate_dense/kernel"				)
				__lowercase				:		List[Any]												=				get_encoder_layer_array(_lowerCamelCase,  "_intermediate_dense/bias"				)

				# Output
				__lowercase				:		BertOutput												=				layer.output

				__lowercase				:		Dict												=				get_encoder_layer_array(_lowerCamelCase,  "_output_dense/kernel"				)
				__lowercase				:		Optional[Any]												=				get_encoder_layer_array(_lowerCamelCase,  "_output_dense/bias"				)

				__lowercase				:		Optional[Any]												=				get_encoder_layer_array(_lowerCamelCase,  "_output_layer_norm/gamma"				)
				__lowercase				:		str												=				get_encoder_layer_array(_lowerCamelCase,  "_output_layer_norm/beta"				)

		# Embeddings
		__lowercase				:		Union[str, Any]												=				get_encoder_array("_position_embedding_layer/embeddings"				)
		__lowercase				:		Tuple												=				get_encoder_array("_type_embedding_layer/embeddings"				)
		__lowercase				:		List[str]												=				get_encoder_array("_embedding_norm_layer/gamma"				)
		__lowercase				:		str												=				get_encoder_array("_embedding_norm_layer/beta"				)

		# LM Head
		__lowercase				:		Union[str, Any]												=				model.cls.predictions.transform

		__lowercase				:		Any												=				get_masked_lm_array("dense/kernel"				)
		__lowercase				:		Tuple												=				get_masked_lm_array("dense/bias"				)

		__lowercase				:		Optional[Any]												=				get_masked_lm_array("layer_norm/gamma"				)
		__lowercase				:		int												=				get_masked_lm_array("layer_norm/beta"				)

		__lowercase				:		str												=				get_masked_lm_array("embedding_table"				)

		# Pooling
		__lowercase				:		Dict												=				BertPooler(config=_lowerCamelCase				)
		__lowercase				:		BertPooler												=				get_encoder_array("_pooler_layer/kernel"				)
		__lowercase				:		BertPooler												=				get_encoder_array("_pooler_layer/bias"				)

		# Export final model
		model.save_pretrained(_lowerCamelCase				)

		# Integration test - should load without any errors ;)
		__lowercase				:		List[Any]												=				BertForMaskedLM.from_pretrained(_lowerCamelCase				)
		print(new_model.eval()				)

		print("Model conversion was done sucessfully!"				)


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






"""simple docstring"""







# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
import re

from ..utils import cached_file


# docstyle-ignore
__A   :  int					=      '\nHuman: <<task>>\n\nAssistant: '


__A   :  List[str]					=      'huggingface-tools/default-prompts'
__A   :  Tuple					=      {'chat': 'chat_prompt_template.txt', 'run': 'run_prompt_template.txt'}

def 						snake_case__       (					_lowerCamelCase,  _lowerCamelCase,  _lowerCamelCase="run"				)			->Optional[int]:






		"""simple docstring"""


		if prompt_or_repo_id is None:
				__lowercase				:		Any												=				DEFAULT_PROMPTS_REPO

		# prompt is considered a repo ID when it does not contain any kind of space
		if re.search("\\s",  _lowerCamelCase				) is not None:
				return prompt_or_repo_id

		__lowercase				:		Optional[Any]												=				cached_file(
		    _lowerCamelCase,  PROMPT_FILES[mode],  repo_type="dataset",  user_agent={"agent": agent_name}				)
		with open(_lowerCamelCase,  "r",  encoding="utf-8"				) as f:
				return f.read()
