





'''simple docstring'''






import argparse
import os
import sys
from unittest.mock import patch

import pytorch_lightning as pl
import timeout_decorator
import torch
from distillation import SummarizationDistiller, distill_main
from finetune import SummarizationModule, main

from transformers import MarianMTModel
from transformers.file_utils import cached_path
from transformers.testing_utils import TestCasePlus, require_torch_gpu, slow
from utils import load_json


A__    :       Any              ='''sshleifer/mar_enro_6_3_student'''




class 			UpperCAmelCase					(      _a       ):




						def     lowercase__   (      self      :			List[Any]  )    ->				List[str]:
												super().setUp()

												_lowerCAmelCase												=							cached_path(
												    """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""      ,      extract_compressed_file=__lowerCAmelCase      ,      )
												_lowerCAmelCase												=							f"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"




						@slow
						@require_torch_gpu
						def     lowercase__   (      self      :			Any  )    ->				str:
												MarianMTModel.from_pretrained(__lowerCAmelCase  )




						@slow
						@require_torch_gpu
						def     lowercase__   (      self      :			Any  )    ->				Dict:
												_lowerCAmelCase												=							{
												    """$MAX_LEN""": 64,
												    """$BS""": 64,
												    """$GAS""": 1,
												    """$ENRO_DIR""": self.data_dir,
												    """facebook/mbart-large-cc25""": MARIAN_MODEL,
												    # "val_check_interval=0.25": "val_check_interval=1.0",
												    """--learning_rate=3e-5""": """--learning_rate 3e-4""",
												    """--num_train_epochs 6""": """--num_train_epochs 1""",
												}

												# Clean up bash script
												_lowerCAmelCase												=							(self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""  )[1].strip()
												_lowerCAmelCase												=							bash_script.replace("""\\\n"""      ,      """"""  ).strip().replace("""\"$@\""""      ,      """"""  )
												for k, v in env_vars_to_replace.items():
																		_lowerCAmelCase												=							bash_script.replace(__lowerCAmelCase      ,      str(__lowerCAmelCase  )  )
												_lowerCAmelCase												=							self.get_auto_remove_tmp_dir()

												# bash_script = bash_script.replace("--fp16 ", "")
												_lowerCAmelCase												=							f"\n            --output_dir {output_dir}\n            --tokenizer_name Helsinki-NLP/opus-mt-en-ro\n            --sortish_sampler\n            --do_predict\n            --gpus 1\n            --freeze_encoder\n            --n_train 40000\n            --n_val 500\n            --n_test 500\n            --fp16_opt_level O1\n            --num_sanity_val_steps 0\n            --eval_beams 2\n        ".split()
												# XXX: args.gpus > 1 : handle multi_gpu in the future

												_lowerCAmelCase												=							["""finetune.py"""] + bash_script.split() + args
												with patch.object(__lowerCAmelCase      ,      """argv"""      ,      __lowerCAmelCase  ):
																		_lowerCAmelCase												=							argparse.ArgumentParser()
																		_lowerCAmelCase												=							pl.Trainer.add_argparse_args(__lowerCAmelCase  )
																		_lowerCAmelCase												=							SummarizationModule.add_model_specific_args(__lowerCAmelCase      ,      os.getcwd()  )
																		_lowerCAmelCase												=							parser.parse_args()
																		_lowerCAmelCase												=							main(__lowerCAmelCase  )

												# Check metrics
												_lowerCAmelCase												=							load_json(model.metrics_save_path  )
												_lowerCAmelCase												=							metrics["""val"""][0]
												_lowerCAmelCase												=							metrics["""val"""][-1]
												self.assertEqual(len(metrics["""val"""]  )      ,      (args.max_epochs / args.val_check_interval)  )
												assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"]      ,      __lowerCAmelCase  )

												self.assertGreater(last_step_stats["""val_avg_gen_time"""]      ,      0.01  )
												# model hanging on generate. Maybe bad config was saved. (XXX: old comment/assert?)
												self.assertLessEqual(last_step_stats["""val_avg_gen_time"""]      ,      1.0  )

												# test learning requirements:

												# 1. BLEU improves over the course of training by more than 2 pts
												self.assertGreater(last_step_stats["""val_avg_bleu"""] - first_step_stats["""val_avg_bleu"""]      ,      2  )

												# 2. BLEU finishes above 17
												self.assertGreater(last_step_stats["""val_avg_bleu"""]      ,      17  )

												# 3. test BLEU and val BLEU within ~1.1 pt.
												self.assertLess(abs(metrics["""val"""][-1]["""val_avg_bleu"""] - metrics["""test"""][-1]["""test_avg_bleu"""]  )      ,      1.1  )

												# check lightning ckpt can be loaded and has a reasonable statedict
												_lowerCAmelCase												=							os.listdir(__lowerCAmelCase  )
												_lowerCAmelCase												=							[x for x in contents if x.endswith(""".ckpt"""  )][0]
												_lowerCAmelCase												=							os.path.join(args.output_dir      ,      __lowerCAmelCase  )
												_lowerCAmelCase												=							torch.load(__lowerCAmelCase      ,      map_location="""cpu"""  )
												_lowerCAmelCase												=							"""model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		_lowerCAmelCase												=							{os.path.basename(__lowerCAmelCase  ) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics["""test"""]  ) == 1




class 			UpperCAmelCase					(      _a       ):




						@timeout_decorator.timeout(6_00  )
						@slow
						@require_torch_gpu
						def     lowercase__   (      self      :			Tuple  )    ->				int:
												_lowerCAmelCase												=							f"{self.test_file_dir_str}/test_data/wmt_en_ro"
												_lowerCAmelCase												=							{
												    """--fp16_opt_level=O1""": """""",
												    """$MAX_LEN""": 1_28,
												    """$BS""": 16,
												    """$GAS""": 1,
												    """$ENRO_DIR""": data_dir,
												    """$m""": """sshleifer/student_marian_en_ro_6_1""",
												    """val_check_interval=0.25""": """val_check_interval=1.0""",
												}

												# Clean up bash script
												_lowerCAmelCase												=							(
												    (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""  )[1].strip()
												)
												_lowerCAmelCase												=							bash_script.replace("""\\\n"""      ,      """"""  ).strip().replace("""\"$@\""""      ,      """"""  )
												_lowerCAmelCase												=							bash_script.replace("""--fp16 """      ,      """ """  )

												for k, v in env_vars_to_replace.items():
																		_lowerCAmelCase												=							bash_script.replace(__lowerCAmelCase      ,      str(__lowerCAmelCase  )  )
												_lowerCAmelCase												=							self.get_auto_remove_tmp_dir()
												_lowerCAmelCase												=							bash_script.replace("""--fp16"""      ,      """"""  )
												_lowerCAmelCase												=							6
												_lowerCAmelCase												=							(
												    ["""distillation.py"""]
												    + bash_script.split()
												    + [
												        f"--output_dir={output_dir}",
												        """--gpus=1""",
												        """--learning_rate=1e-3""",
												        f"--num_train_epochs={epochs}",
												        """--warmup_steps=10""",
												        """--val_check_interval=1.0""",
												        """--do_predict""",
												    ]
												)
												with patch.object(__lowerCAmelCase      ,      """argv"""      ,      __lowerCAmelCase  ):
																		_lowerCAmelCase												=							argparse.ArgumentParser()
																		_lowerCAmelCase												=							pl.Trainer.add_argparse_args(__lowerCAmelCase  )
																		_lowerCAmelCase												=							SummarizationDistiller.add_model_specific_args(__lowerCAmelCase      ,      os.getcwd()  )
																		_lowerCAmelCase												=							parser.parse_args()
																		# assert args.gpus == gpus THIS BREAKS for multi_gpu

																		_lowerCAmelCase												=							distill_main(__lowerCAmelCase  )

												# Check metrics
												_lowerCAmelCase												=							load_json(model.metrics_save_path  )
												_lowerCAmelCase												=							metrics["""val"""][0]
												_lowerCAmelCase												=							metrics["""val"""][-1]
												assert len(metrics["""val"""]  ) >= (args.max_epochs / args.val_check_interval)  # +1 accounts for val_sanity_check

												assert last_step_stats["val_avg_gen_time"] >= 0.01

												assert first_step_stats["val_avg_bleu"] < last_step_stats["val_avg_bleu"]  # model learned nothing
												assert 1.0 >= last_step_stats["val_avg_gen_time"]  # model hanging on generate. Maybe bad config was saved.
												assert isinstance(last_step_stats[f"val_avg_{model.val_metric}"]      ,      __lowerCAmelCase  )

												# check lightning ckpt can be loaded and has a reasonable statedict
												_lowerCAmelCase												=							os.listdir(__lowerCAmelCase  )
												_lowerCAmelCase												=							[x for x in contents if x.endswith(""".ckpt"""  )][0]
												_lowerCAmelCase												=							os.path.join(args.output_dir      ,      __lowerCAmelCase  )
												_lowerCAmelCase												=							torch.load(__lowerCAmelCase      ,      map_location="""cpu"""  )
												_lowerCAmelCase												=							"""model.model.decoder.layers.0.encoder_attn_layer_norm.weight"""
												assert expected_key in ckpt["state_dict"]
												assert ckpt["state_dict"]["model.model.decoder.layers.0.encoder_attn_layer_norm.weight"].dtype == torch.floataa

												# TODO: turn on args.do_predict when PL bug fixed.
												if args.do_predict:
																		_lowerCAmelCase												=							{os.path.basename(__lowerCAmelCase  ) for p in contents}
																		assert "test_generations.txt" in contents
																		assert "test_results.txt" in contents
																		# assert len(metrics["val"]) ==  desired_n_evals
																		assert len(metrics["""test"""]  ) == 1













'''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					:
      _lowercase:      str											=       field(
          metadata={'''help''': '''The csv file to plot.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether to plot along batch size or sequence length. Defaults to sequence length.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Whether the csv file has time results or memory results. Defaults to memory results.'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={'''help''': '''Disable logarithmic scale when plotting'''}							,		)
      _lowercase:      bool											=       field(
          default=snake_case_							,		metadata={
              '''help''': '''Whether the csv file has training results or inference results. Defaults to inference results.'''
          }							,		)
      _lowercase:      Optional[str]											=       field(
          default=snake_case_							,		metadata={'''help''': '''Filename under which the plot will be saved. If unused no plot is saved.'''}							,		)
      _lowercase:      Optional[List[str]]											=       list_field(
          default=snake_case_							,		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					:




      def __init__(      self      :			List[str]      ,      __snake_case      :			Union[str, Any]  )    ->				int:
            _lowerCAmelCase												=							args
            _lowerCAmelCase												=							defaultdict(lambda: {"bsz": [], "seq_len": [], "result": {}}  )

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




      def     lowercase__   (      self      :			Dict  )    ->				str:
            _lowerCAmelCase					,							_lowerCAmelCase												=							plt.subplots()
            _lowerCAmelCase												=							"""Time usage""" if self.args.is_time else """Memory usage"""
            _lowerCAmelCase												=							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()  ):
                  _lowerCAmelCase												=							sorted(set(self.result_dict[model_name]["""bsz"""]  )  )
                  _lowerCAmelCase												=							sorted(set(self.result_dict[model_name]["""seq_len"""]  )  )
                  _lowerCAmelCase												=							self.result_dict[model_name]["""result"""]

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

                  _lowerCAmelCase												=							(
                      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:
                              _lowerCAmelCase												=							np.asarray(
                                  [results[(x, inner_loop_value)] for x in x_axis_array if (x, inner_loop_value) in results]      ,      dtype=__snake_case      ,      )
                        else:
                              _lowerCAmelCase												=							np.asarray(
                                  [results[(inner_loop_value, x)] for x in x_axis_array if (inner_loop_value, x) in results]      ,      dtype=np.floataa      ,      )

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

                        _lowerCAmelCase												=							np.asarray(__snake_case      ,      __snake_case  )[: len(__snake_case  )]
                        plt.scatter(
                            __snake_case      ,      __snake_case      ,      label=f"{label_model_name} - {inner_loop_label}: {inner_loop_value}"  )
                        plt.plot(__snake_case      ,      __snake_case      ,      """--"""  )

                  title_str += f" {label_model_name} vs."

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

            # plot
            plt.title(__snake_case  )
            plt.xlabel(__snake_case  )
            plt.ylabel(__snake_case  )
            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"""


      _lowerCAmelCase												=							HfArgumentParser(lowerCAmelCase  )
      _lowerCAmelCase												=							parser.parse_args_into_dataclasses()[0]
      _lowerCAmelCase												=							Plot(args=lowerCAmelCase  )
      plot.plot()


if __name__ == "__main__":
      main()






