


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Dict:




      """simple docstring"""

      snake_case_ =   os.path.join(args.tf_model_dir      ,      '''parameters.json'''      )
      snake_case_ =   json.loads(open(SCREAMING_SNAKE_CASE      ).read()      )
      if not params:
            raise ValueError(
                f'''It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.'''      )
      if not args.output.endswith('''.pt'''      ):
            snake_case_ =   args.output + '''.pt'''
      snake_case_ =   OrderedDict()
      with tf.device('''/CPU:0'''      ):
            snake_case_ =   tf.train.load_checkpoint(args.tf_model_dir      )
            snake_case_ =   reader.get_variable_to_shape_map()
            for key_name in shapes.keys():
                  snake_case_ =   reader.get_tensor(SCREAMING_SNAKE_CASE      ).astype(np.floataa      )
                  if key_name.endswith('''/adam_m'''      ) or key_name.endswith('''/adam_v'''      ):
                        continue
                  if key_name.startswith('''pasts/'''      ):
                        if key_name.startswith('''pasts/mlp'''      ):
                              snake_case_ =   int(key_name[9]      )
                        elif key_name.startswith('''pasts/out'''      ):
                              snake_case_ =   8
                        snake_case_ =   '''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                        snake_case_ =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
                        snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name.startswith('''model/moe'''      ):
                        snake_case_ =   int(key_name[9:].split('''/'''      )[0]      )
                        if key_name.endswith('''/switch_gating/kernel'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                              snake_case_ =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/softmlp/kernel'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                              snake_case_ =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/wo/kernel'''      ) or key_name.endswith('''/wi/kernel'''      ):
                              snake_case_ =   key_name[-9:-7]
                              for i in range(16      ):
                                    snake_case_ =   '''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                                    snake_case_ =   (
                                        vnp[i].transpose([1, 0]      ).copy()
                                    )  # In Mesh-Tensorflow, it is one array, so it is divided
                                    snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name.startswith('''model/mlp'''      ):
                        snake_case_ =   int(key_name[9:].split('''/'''      )[0]      )
                        if key_name.endswith('''/p1/kernel'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                              snake_case_ =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/p1/bias'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                              snake_case_ =   vnp.copy()  # same because it is one dimensional
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/p2/kernel'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                              snake_case_ =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/p2/bias'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                              snake_case_ =   vnp.copy()  # same because it is one dimensional
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name.startswith('''model/ln'''      ):
                        snake_case_ =   int(key_name[8:].split('''/'''      )[0]      )
                        if key_name.endswith('''/b'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.norm.bias''' % player
                              snake_case_ =   vnp.copy()  # same because it is one dimensional
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/g'''      ):
                              snake_case_ =   '''model.blocks.%d.feed_forward.norm.weight''' % player
                              snake_case_ =   vnp.copy()  # same because it is one dimensional
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name.startswith('''model/att'''      ):
                        snake_case_ =   int(key_name[9:].split('''/'''      )[0]      )
                        if key_name.endswith('''/qkv/kernel'''      ):
                              snake_case_ =   vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                              snake_case_ =   state[:, 0, :, :]
                              snake_case_ =   state[:, 1, :, :]
                              snake_case_ =   state[:, 2, :, :]
                              snake_case_ =   (
                                  state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]      )
                                  .transpose([1, 0]      )
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   (
                                  state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]      )
                                  .transpose([1, 0]      )
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   (
                                  state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]      )
                                  .transpose([1, 0]      )
                                  .copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   '''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                              snake_case_ =   '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                              snake_case_ =   '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/o/kernel'''      ):
                              snake_case_ =   '''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                              snake_case_ =   (
                                  vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]      ).transpose([1, 0]      ).copy()
                              )  # Mesh-Tensorflow is a diagonal matrix
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name.startswith('''model/an'''      ):
                        snake_case_ =   int(key_name[8:].split('''/'''      )[0]      )
                        if key_name.endswith('''/b'''      ):
                              snake_case_ =   '''model.blocks.%d.self_attn.norm.bias''' % player
                              snake_case_ =   vnp.copy()  # same because it is one dimensional
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        elif key_name.endswith('''/g'''      ):
                              snake_case_ =   '''model.blocks.%d.self_attn.norm.weight''' % player
                              snake_case_ =   vnp.copy()  # same because it is one dimensional
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif (
                      key_name.startswith('''model/wte'''      )
                      or key_name.startswith('''model/wpe'''      )
                      or key_name.startswith('''model/ete'''      )
                  ):
                        snake_case_ =   {'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                            key_name[-3:]
                        ]
                        snake_case_ =   '''model.%s.weight''' % nlayer
                        snake_case_ =   vnp.copy()  # same in embedded
                        snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                        if key_name.startswith('''model/wte'''      ):
                              snake_case_ =   '''lm_head.weight'''
                              snake_case_ =   vnp.copy()  # same in embedded
                              snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name.startswith('''model/wob'''      ):
                        snake_case_ =   '''final_logits_bias'''
                        snake_case_ =   vnp.copy()  # same in embedded
                        snake_case_ =   state.reshape((1, -1)      )
                        snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name == "model/dense/kernel":
                        snake_case_ =   '''model.last_project.weight'''
                        snake_case_ =   vnp.transpose([1, 0]      ).copy()  # Mesh-Tensorflow is a diagonal matrix
                        snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
                  elif key_name == "model/dense_1/bias":
                        snake_case_ =   '''model.last_project.bias'''
                        snake_case_ =   vnp.copy()  # same because it is one dimensional
                        snake_case_ =   torch.tensor(SCREAMING_SNAKE_CASE      )
      torch.save(SCREAMING_SNAKE_CASE      ,      args.output      )


if __name__ == "__main__":
   UpperCAmelCase    =					argparse.ArgumentParser(
       description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
   )
   parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
   parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
   UpperCAmelCase    =					parser.parse_args()
   convert_tf_gptsan_to_pt(args)



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  __lowerCAmelCase					(SCREAMING_SNAKE_CASE=None      ,      SCREAMING_SNAKE_CASE=None      )->						Tuple:




      """simple docstring"""

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







@dataclass
class lowerCAmelCase_		:

 '''simple docstring'''

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Optional[Any]:




      """simple docstring"""

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






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

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







class lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase      ):
       snake_case_ =   args
       snake_case_ =   defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}      )

       with open(self.args.csv_file	,   newline=''''''      ) as csv_file:
             snake_case_ =   csv.DictReader(_UpperCAmelCase      )
             for row in reader:
                   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
                         snake_case_ =   int(row['''result''']      )
                   elif can_convert_to_float(row['''result''']      ):
                         # value is not None
                         snake_case_ =   float(row['''result''']      )





 def 		UpperCamelCase__  (			self      ):
       snake_case_ ,					snake_case_ =   plt.subplots()
       snake_case_ =   '''Time usage''' if self.args.is_time else '''Memory usage'''
       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()      ):
             snake_case_ =   sorted(set(self.result_dict[model_name]['''bsz''']      )      )
             snake_case_ =   sorted(set(self.result_dict[model_name]['''seq_len''']      )      )
             snake_case_ =   self.result_dict[model_name]['''result''']

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

             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:
                         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:
                         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	,   )

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

                   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.'''

       snake_case_ =   title_str[:-4]
       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  __lowerCAmelCase					()->						int:




      """simple docstring"""

      snake_case_ =   HfArgumentParser(SCREAMING_SNAKE_CASE      )
      snake_case_ =   parser.parse_args_into_dataclasses()[0]
      snake_case_ =   Plot(args=SCREAMING_SNAKE_CASE      )
      plot.plot()


if __name__ == "__main__":
   main()