


"""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()
a__       :							str        =  logging.get_logger(__name__)




def      UpperCAmelCase__ (lowerCAmelCase_						,		lowerCAmelCase_						,		lowerCAmelCase_     ):




     '''simple docstring'''


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




     '''simple docstring'''


     logger.info(f"""Loading model based on config from {config_path}..."""     )
     __SCREAMING_SNAKE_CASE		     =       BertConfig.from_json_file(_lowerCamelCase     )
     __SCREAMING_SNAKE_CASE		     =       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__":
  a__       :							Dict        =  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).''',
  )
  a__       :							Union[str, Any]        =  parser.parse_args()
  convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




"""simple docstring"""







import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter

from transformers import HfArgumentParser




def      UpperCAmelCase__ (lowerCAmelCase_=None						,		lowerCAmelCase_=None     ):




					'''simple docstring'''


					return field(default_factory=lambda: default						,		metadata=lowerCAmelCase_     )




@dataclass
class 				UpperCamelCase_			:

	"""simple docstring"""





	snake_case__       :						str					      =    field(
	    metadata={"help": "The csv file to plot."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Whether to plot along batch size or sequence length. Defaults to sequence length."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Whether the csv file has time results or memory results. Defaults to memory results."}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Disable logarithmic scale when plotting"}    ,						)
	snake_case__       :						bool					      =    field(
	    default=UpperCamelCase    ,						metadata={
	        "help": "Whether the csv file has training results or inference results. Defaults to inference results."
	    }    ,						)
	snake_case__       :						Optional[str]					      =    field(
	    default=UpperCamelCase    ,						metadata={"help": "Filename under which the plot will be saved. If unused no plot is saved."}    ,						)
	snake_case__       :						Optional[List[str]]					      =    list_field(
	    default=UpperCamelCase    ,						metadata={"help": "List of model names that are used instead of the ones in the csv file."})




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										int(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




def      UpperCAmelCase__ (lowerCAmelCase_     ):




					'''simple docstring'''


					try:
										float(lowerCAmelCase_     )
										return True
					except ValueError:
										return False




class 				UpperCamelCase_			:

	"""simple docstring"""





	def __init__(				self    :       str    ,    UpperCAmelCase__    :       Dict     ) ->			Optional[Any]:
						__SCREAMING_SNAKE_CASE		     =       args
						__SCREAMING_SNAKE_CASE		     =       defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}     )

						with open(self.args.csv_file    ,    newline=""     ) as csv_file:
											__SCREAMING_SNAKE_CASE		     =       csv.DictReader(UpperCAmelCase__     )
											for row in reader:
																__SCREAMING_SNAKE_CASE		     =       row["model"]
																self.result_dict[model_name]["bsz"].append(int(row["batch_size"]     )     )
																self.result_dict[model_name]["seq_len"].append(int(row["sequence_length"]     )     )
																if can_convert_to_int(row["result"]     ):
																					# value is not None
																					__SCREAMING_SNAKE_CASE		     =       int(row["result"]     )
																elif can_convert_to_float(row["result"]     ):
																					# value is not None
																					__SCREAMING_SNAKE_CASE		     =       float(row["result"]     )




	def 						UpperCAmelCase_   (				self    :       List[str]     ) ->			Tuple:
						__SCREAMING_SNAKE_CASE     ,						__SCREAMING_SNAKE_CASE		     =       plt.subplots()
						__SCREAMING_SNAKE_CASE		     =       "Time usage" if self.args.is_time else "Memory usage"
						__SCREAMING_SNAKE_CASE		     =       title_str + " for training" if self.args.is_train else title_str + " for inference"

						if not self.args.no_log_scale:
											# set logarithm scales
											ax.set_xscale("log"     )
											ax.set_yscale("log"     )

						for axis in [ax.xaxis, ax.yaxis]:
											axis.set_major_formatter(ScalarFormatter()     )

						for model_name_idx, model_name in enumerate(self.result_dict.keys()     ):
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["bsz"]     )     )
											__SCREAMING_SNAKE_CASE		     =       sorted(set(self.result_dict[model_name]["seq_len"]     )     )
											__SCREAMING_SNAKE_CASE		     =       self.result_dict[model_name]["result"]

											((__SCREAMING_SNAKE_CASE)     ,						(__SCREAMING_SNAKE_CASE))		     =       (
											    (batch_sizes, sequence_lengths) if self.args.plot_along_batch else (sequence_lengths, batch_sizes)
											)

											__SCREAMING_SNAKE_CASE		     =       (
											    model_name if self.args.short_model_names is None else self.args.short_model_names[model_name_idx]
											)

											for inner_loop_value in inner_loop_array:
																if self.args.plot_along_batch:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]    ,    dtype=UpperCAmelCase__    ,    )
																else:
																					__SCREAMING_SNAKE_CASE		     =       np.asarray(
																					    [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]    ,    dtype=np.floataa    ,    )

																((__SCREAMING_SNAKE_CASE)     ,						(__SCREAMING_SNAKE_CASE))		     =       (
																    ("batch_size", "len") if self.args.plot_along_batch else ("in #tokens", "bsz")
																)

																__SCREAMING_SNAKE_CASE		     =       np.asarray(UpperCAmelCase__    ,    UpperCAmelCase__     )[: len(UpperCAmelCase__     )]
																plt.scatter(
																    UpperCAmelCase__    ,    UpperCAmelCase__    ,    label=F"""{label_model_name} - {inner_loop_label}: {inner_loop_value}"""     )
																plt.plot(UpperCAmelCase__    ,    UpperCAmelCase__    ,    "--"     )

											title_str += F""" {label_model_name} vs."""

						__SCREAMING_SNAKE_CASE		     =       title_str[:-4]
						__SCREAMING_SNAKE_CASE		     =       "Time in s" if self.args.is_time else "Memory in MB"

						# plot
						plt.title(UpperCAmelCase__     )
						plt.xlabel(UpperCAmelCase__     )
						plt.ylabel(UpperCAmelCase__     )
						plt.legend()

						if self.args.figure_png_file is not None:
											plt.savefig(self.args.figure_png_file     )
						else:
											plt.show()




def      UpperCAmelCase__ ():




					'''simple docstring'''


					__SCREAMING_SNAKE_CASE		     =       HfArgumentParser(lowerCAmelCase_     )
					__SCREAMING_SNAKE_CASE		     =       parser.parse_args_into_dataclasses()[0]
					__SCREAMING_SNAKE_CASE		     =       Plot(args=lowerCAmelCase_     )
					plot.plot()


if __name__ == "__main__":
		main()
