




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()
UpperCamelCase								=					logging.get_logger(__name__)



def 							_A       (						lowerCAmelCase_					: str       ,    lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Optional[int]      ):
     """simple docstring"""

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

     logger.info(F'Loading model based on config from {config_path}...'      )
     lowerCAmelCase__          =			BertConfig.from_json_file(lowerCAmelCase_      )
     lowerCAmelCase__          =			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__":
     UpperCamelCase								=					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).',
     )
     UpperCamelCase								=					parser.parse_args()
     convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






import argparse
from collections import defaultdict



def 							_A       (						lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: str       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: Dict       ,    lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			F'{file}_{class_name}_{test_name}'
     done_test[_id] += 1

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     lowerCAmelCase__          =			F'class {class_name}('
     lowerCAmelCase__          =			F'{4 * " "}def {test_name}('
     lowerCAmelCase__          =			F'{8 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			F'{16 * " "}{correct_line.split()[0]}'
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			False
     lowerCAmelCase__          =			0
     lowerCAmelCase__          =			0

     lowerCAmelCase__          =			[]
     for line in lines:
          if line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and line.startswith(lowerCAmelCase_      ):
               lowerCAmelCase__          =			True
          elif in_class and in_func and (line.startswith(lowerCAmelCase_      ) or line.startswith(lowerCAmelCase_      )):
               lowerCAmelCase__          =			len(line.split(correct_line.split()[0]      )[0]      )
               count += 1

               if count == done_test[_id]:
                    lowerCAmelCase__          =			True

          if in_class and in_func and in_line:
               if ")" not in line:
                    continue
               else:
                    lowerCAmelCase__          =			True

          if in_class and in_func and in_line and insert_line:
               new_lines.append(F'{spaces * " "}{correct_line}'      )
               lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			lowerCAmelCase__          =			False
          else:
               new_lines.append(lowerCAmelCase_      )

     with open(lowerCAmelCase_       ,    "w"      ) as f:
          for line in new_lines:
               f.write(lowerCAmelCase_      )



def 							_A       (						lowerCAmelCase_					: List[str]       ,    lowerCAmelCase_					: Any=None      ):
     """simple docstring"""

     if fail is not None:
          with open(lowerCAmelCase_       ,    "r"      ) as f:
               lowerCAmelCase__          =			{l.strip() for l in f.readlines()}
     else:
          lowerCAmelCase__          =			None

     with open(lowerCAmelCase_       ,    "r"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     lowerCAmelCase__          =			defaultdict(lowerCAmelCase_      )
     for line in correct_lines:
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			line.split(";"      )
          if test_failures is None or "::".join([file, class_name, test_name]      ) in test_failures:
               overwrite_file(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument('--correct_filename', help='filename of tests with expected result')
     parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
     UpperCamelCase								=					parser.parse_args()

     main(args.correct_filename, args.fail_filename)
