"""simple docstring"""
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()
SCREAMING_SNAKE_CASE__      						=  logging.get_logger(__name__)





def    lowerCAmelCase__    (			_UpperCamelCase       :				List[Any] ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				List[Any]					)					->				Optional[Any]:





						"""simple docstring"""






						snake_case								=			os.path.abspath(_UpperCamelCase					)
						logger.info(f"""Converting TensorFlow checkpoint from {tf_path}"""					)
						# Load weights from TF model
						snake_case								=			tf.train.list_variables(_UpperCamelCase					)
						snake_case								=			[]
						snake_case								=			[]
						snake_case								=			[]
						for full_name, shape in init_vars:
												# logger.info(f"Loading TF weight {name} with shape {shape}")
												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'
																		snake_case								=			name[1:]
												# figure out how many levels deep the name is
												snake_case								=			0
												for _name in name:
																		if _name.startswith('layer_with_weights'					):
																								depth += 1
																		else:
																								break
												layer_depth.append(_UpperCamelCase					)
												# read data
												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					)					)})"""					)
						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					):
												snake_case								=			full_name.split('/'					)
												snake_case								=			model
												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'					):
																								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']					)
																														snake_case								=			getattr(_UpperCamelCase ,			'embeddings'					)
																														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					)]					)
																														snake_case								=			getattr(_UpperCamelCase ,			'encoder'					)
																														snake_case								=			getattr(_UpperCamelCase ,			'layer'					)
																														snake_case								=			pointer[layer_num - 4]
																								elif layer_num == config.num_hidden_layers + 4:
																														# pooler layer
																														trace.extend(['pooler', 'dense']					)
																														snake_case								=			getattr(_UpperCamelCase ,			'pooler'					)
																														snake_case								=			getattr(_UpperCamelCase ,			'dense'					)
																		elif m_name == "embeddings":
																								trace.append('embeddings'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'embeddings'					)
																								if layer_num == 0:
																														trace.append('word_embeddings'					)
																														snake_case								=			getattr(_UpperCamelCase ,			'word_embeddings'					)
																								elif layer_num == 1:
																														trace.append('position_embeddings'					)
																														snake_case								=			getattr(_UpperCamelCase ,			'position_embeddings'					)
																								elif layer_num == 2:
																														trace.append('token_type_embeddings'					)
																														snake_case								=			getattr(_UpperCamelCase ,			'token_type_embeddings'					)
																								else:
																														raise ValueError(f"""Unknown embedding layer with name {full_name}"""					)
																								trace.append('weight'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'weight'					)
																		elif m_name == "_attention_layer":
																								# self-attention layer
																								trace.extend(['attention', 'self']					)
																								snake_case								=			getattr(_UpperCamelCase ,			'attention'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'self'					)
																		elif m_name == "_attention_layer_norm":
																								# output attention norm
																								trace.extend(['attention', 'output', 'LayerNorm']					)
																								snake_case								=			getattr(_UpperCamelCase ,			'attention'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'output'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'LayerNorm'					)
																		elif m_name == "_attention_output_dense":
																								# output attention dense
																								trace.extend(['attention', 'output', 'dense']					)
																								snake_case								=			getattr(_UpperCamelCase ,			'attention'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'output'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'dense'					)
																		elif m_name == "_output_dense":
																								# output dense
																								trace.extend(['output', 'dense']					)
																								snake_case								=			getattr(_UpperCamelCase ,			'output'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'dense'					)
																		elif m_name == "_output_layer_norm":
																								# output dense
																								trace.extend(['output', 'LayerNorm']					)
																								snake_case								=			getattr(_UpperCamelCase ,			'output'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'LayerNorm'					)
																		elif m_name == "_key_dense":
																								# attention key
																								trace.append('key'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'key'					)
																		elif m_name == "_query_dense":
																								# attention query
																								trace.append('query'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'query'					)
																		elif m_name == "_value_dense":
																								# attention value
																								trace.append('value'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'value'					)
																		elif m_name == "_intermediate_dense":
																								# attention intermediate dense
																								trace.extend(['intermediate', 'dense']					)
																								snake_case								=			getattr(_UpperCamelCase ,			'intermediate'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'dense'					)
																		elif m_name == "_output_layer_norm":
																								# output layer norm
																								trace.append('output'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'output'					)
																		# weights & biases
																		elif m_name in ["bias", "beta"]:
																								trace.append('bias'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'bias'					)
																		elif m_name in ["kernel", "gamma"]:
																								trace.append('weight'					)
																								snake_case								=			getattr(_UpperCamelCase ,			'weight'					)
																		else:
																								logger.warning(f"""Ignored {m_name}"""					)
        # for certain layers reshape is necessary
												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					):
																		snake_case								=			array.reshape(pointer.data.shape					)
												if "kernel" in full_name:
																		snake_case								=			array.transpose()
												if pointer.shape == array.shape:
																		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       :				Any ,			_UpperCamelCase       :				Tuple ,			_UpperCamelCase       :				List[Any]					)					->				Optional[Any]:





						"""simple docstring"""






						logger.info(f"""Loading model based on config from {config_path}..."""					)
						snake_case								=			BertConfig.from_json_file(_UpperCamelCase					)
						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__":
					SCREAMING_SNAKE_CASE__      						=  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).",
					)
					SCREAMING_SNAKE_CASE__      						=  parser.parse_args()
					convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)



"""simple docstring"""
from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			path_or_paths
										snake_case								=			split if split or isinstance(lowerCAmelCase		,    lowerCAmelCase    ) else 'train'
										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""

				def __init__(				self		,    lowerCAmelCase = None		,    lowerCAmelCase = None		,    lowerCAmelCase = False		,    lowerCAmelCase = False		,    lowerCAmelCase = None		,    **lowerCAmelCase		,    ):






										"""simple docstring"""

										snake_case								=			features
										snake_case								=			cache_dir
										snake_case								=			keep_in_memory
										snake_case								=			streaming
										snake_case								=			num_proc
										snake_case								=			kwargs

				@abstractmethod
				def 		snake_case (				self    ):






										"""simple docstring"""

										pass


