




'''simple docstring'''


import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

from filelock import FileLock

from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available


lowerCAmelCase_      :  Optional[int]				     =  logging.getLogger(__name__)







@dataclass
class      SCREAMING_SNAKE_CASE    :
      __magic_name__  :       str
      __magic_name__  :       List[str]
      __magic_name__  :       Optional[List[str]]







@dataclass
class      SCREAMING_SNAKE_CASE    :
      __magic_name__  :       List[int]
      __magic_name__  :       List[int]
      __magic_name__  :       Optional[List[int]]   							=					None
      __magic_name__  :       Optional[List[int]]   							=					None







class      SCREAMING_SNAKE_CASE    (						snake_case_				):
      __magic_name__  :       int   							=					'''train'''
      __magic_name__  :       Dict   							=					'''dev'''
      __magic_name__  :       Tuple   							=					'''test'''







class      SCREAMING_SNAKE_CASE    :






      @staticmethod
      def 							lowercase_						( lowercase__   :  List[str]						,						lowercase__   :  Union[Split, str]       ):


           '''simple docstring'''

           raise NotImplementedError






      @staticmethod
      def 							lowercase_						( lowercase__   :  str       ):


           '''simple docstring'''

           raise NotImplementedError






      @staticmethod
      def 							lowercase_						( lowercase__   :  List[InputExample]						,						lowercase__   :  List[str]						,						lowercase__   :  int						,						lowercase__   :  PreTrainedTokenizer						,						lowercase__   :  Tuple=False						,						lowercase__   :  Any="[CLS]"						,						lowercase__   :  str=1						,						lowercase__   :  Dict="[SEP]"						,						lowercase__   :  str=False						,						lowercase__   :  Union[str, Any]=False						,						lowercase__   :  str=0						,						lowercase__   :  Optional[Any]=0						,						lowercase__   :  Dict=-100						,						lowercase__   :  Any=0						,						lowercase__   :  Optional[int]=True						,						):


           '''simple docstring'''

           a_     :							int     						=  {label: i for i, label in enumerate(lowercase__       )}

           a_     :							Optional[Any]     						=  []
           for ex_index, example in enumerate(lowercase__       ):
                if ex_index % 1_0000 == 0:
                     logger.info("""Writing example %d of %d"""						,						lowercase__						,						len(lowercase__       )       )

                a_     :							List[Any]     						=  []
                a_     :							str     						=  []
                for word, label in zip(example.words						,						example.labels       ):
                     a_     :							Dict     						=  tokenizer.tokenize(lowercase__       )

                     # bert-base-multilingual-cased sometimes output "nothing ([]) when calling tokenize with just a space.
                     if len(lowercase__       ) > 0:
                          tokens.extend(lowercase__       )
                          # Use the real label id for the first token of the word, and padding ids for the remaining tokens
                          label_ids.extend([label_map[label]] + [pad_token_label_id] * (len(lowercase__       ) - 1)       )

            # Account for [CLS] and [SEP] with "- 2" and with "- 3" for RoBERTa.
                a_     :							str     						=  tokenizer.num_special_tokens_to_add()
                if len(lowercase__       ) > max_seq_length - special_tokens_count:
                     a_     :							Union[str, Any]     						=  tokens[: (max_seq_length - special_tokens_count)]
                     a_     :							Any     						=  label_ids[: (max_seq_length - special_tokens_count)]

                # The convention in BERT is:
                # (a) For sequence pairs:
                #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
                #  type_ids:   0   0  0    0    0     0       0   0   1  1  1  1   1   1
                # (b) For single sequences:
                #  tokens:   [CLS] the dog is hairy . [SEP]
                #  type_ids:   0   0   0   0  0     0   0
                #
                # Where "type_ids" are used to indicate whether this is the first
                # sequence or the second sequence. The embedding vectors for `type=0` and
                # `type=1` were learned during pre-training and are added to the wordpiece
                # embedding vector (and position vector). This is not *strictly* necessary
                # since the [SEP] token unambiguously separates the sequences, but it makes
                # it easier for the model to learn the concept of sequences.
                #
                # For classification tasks, the first vector (corresponding to [CLS]) is
                # used as the "sentence vector". Note that this only makes sense because
                # the entire model is fine-tuned.
                tokens += [sep_token]
                label_ids += [pad_token_label_id]
                if sep_token_extra:
                     # roberta uses an extra separator b/w pairs of sentences
                     tokens += [sep_token]
                     label_ids += [pad_token_label_id]
                a_     :							Dict     						=  [sequence_a_segment_id] * len(lowercase__       )

                if cls_token_at_end:
                     tokens += [cls_token]
                     label_ids += [pad_token_label_id]
                     segment_ids += [cls_token_segment_id]
                else:
                     a_     :							Any     						=  [cls_token] + tokens
                     a_     :							str     						=  [pad_token_label_id] + label_ids
                     a_     :							Any     						=  [cls_token_segment_id] + segment_ids

                a_     :							Union[str, Any]     						=  tokenizer.convert_tokens_to_ids(lowercase__       )

                # The mask has 1 for real tokens and 0 for padding tokens. Only real
                # tokens are attended to.
                a_     :							Union[str, Any]     						=  [1 if mask_padding_with_zero else 0] * len(lowercase__       )

                # Zero-pad up to the sequence length.
                a_     :							Tuple     						=  max_seq_length - len(lowercase__       )
                if pad_on_left:
                     a_     :							List[Any]     						=  ([pad_token] * padding_length) + input_ids
                     a_     :							Tuple     						=  ([0 if mask_padding_with_zero else 1] * padding_length) + input_mask
                     a_     :							Optional[Any]     						=  ([pad_token_segment_id] * padding_length) + segment_ids
                     a_     :							List[str]     						=  ([pad_token_label_id] * padding_length) + label_ids
                else:
                     input_ids += [pad_token] * padding_length
                     input_mask += [0 if mask_padding_with_zero else 1] * padding_length
                     segment_ids += [pad_token_segment_id] * padding_length
                     label_ids += [pad_token_label_id] * padding_length

                assert len(lowercase__       ) == max_seq_length
                assert len(lowercase__       ) == max_seq_length
                assert len(lowercase__       ) == max_seq_length
                assert len(lowercase__       ) == max_seq_length

                if ex_index < 5:
                     logger.info("""*** Example ***"""       )
                     logger.info("""guid: %s"""						,						example.guid       )
                     logger.info("""tokens: %s"""						,						""" """.join([str(lowercase__       ) for x in tokens]       )       )
                     logger.info("""input_ids: %s"""						,						""" """.join([str(lowercase__       ) for x in input_ids]       )       )
                     logger.info("""input_mask: %s"""						,						""" """.join([str(lowercase__       ) for x in input_mask]       )       )
                     logger.info("""segment_ids: %s"""						,						""" """.join([str(lowercase__       ) for x in segment_ids]       )       )
                     logger.info("""label_ids: %s"""						,						""" """.join([str(lowercase__       ) for x in label_ids]       )       )

                if "token_type_ids" not in tokenizer.model_input_names:
                     a_     :							Dict     						=  None

                features.append(
                    InputFeatures(
                        input_ids=lowercase__						,						attention_mask=lowercase__						,						token_type_ids=lowercase__						,						label_ids=lowercase__       )       )
           return features


if is_torch_available():
 import torch
 from torch import nn
 from torch.utils.data import Dataset







 class      SCREAMING_SNAKE_CASE    (						snake_case_				):
       __magic_name__  :       List[InputFeatures]
       __magic_name__  :       int   							=					nn.CrossEntropyLoss().ignore_index






       def __init__( self   :  List[Any]						,						lowercase__   :  TokenClassificationTask						,						lowercase__   :  str						,						lowercase__   :  PreTrainedTokenizer						,						lowercase__   :  List[str]						,						lowercase__   :  str						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  int=False						,						lowercase__   :  Split = Split.train						,						):


            '''simple docstring'''

            a_     :							Dict     						=  os.path.join(
                lowercase__						,						"""cached_{}_{}_{}""".format(mode.value						,						tokenizer.__class__.__name__						,						str(lowercase__       )       )						,						)

            # Make sure only the first process in distributed training processes the dataset,
            # and the others will use the cache.
            a_     :							str     						=  cached_features_file + """.lock"""
            with FileLock(lowercase__       ):
                 if os.path.exists(lowercase__       ) and not overwrite_cache:
                      logger.info(F"Loading features from cached file {cached_features_file}"       )
                      a_     :							Union[str, Any]     						=  torch.load(lowercase__       )
                 else:
                      logger.info(F"Creating features from dataset file at {data_dir}"       )
                      a_     :							Optional[int]     						=  token_classification_task.read_examples_from_file(lowercase__						,						lowercase__       )
                      # TODO clean up all this to leverage built-in features of tokenizers
                      a_     :							Dict     						=  token_classification_task.convert_examples_to_features(
                          lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						cls_token_at_end=bool(model_type in ["""xlnet"""]       )						,						cls_token=tokenizer.cls_token						,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0						,						sep_token=tokenizer.sep_token						,						sep_token_extra=lowercase__						,						pad_on_left=bool(tokenizer.padding_side == """left"""       )						,						pad_token=tokenizer.pad_token_id						,						pad_token_segment_id=tokenizer.pad_token_type_id						,						pad_token_label_id=self.pad_token_label_id						,						)
                      logger.info(F"Saving features into cached file {cached_features_file}"       )
                      torch.save(self.features						,						lowercase__       )






       def __len__( self   :  Optional[Any]       ):


            '''simple docstring'''

            return len(self.features       )






       def __getitem__( self   :  Dict						,						lowercase__   :  Optional[int]       ):


            '''simple docstring'''

            return self.features[i]


if is_tf_available():
 import tensorflow as tf







 class      SCREAMING_SNAKE_CASE    :
       __magic_name__  :       List[InputFeatures]
       __magic_name__  :       int   							=					-100






       def __init__( self   :  Optional[int]						,						lowercase__   :  TokenClassificationTask						,						lowercase__   :  str						,						lowercase__   :  PreTrainedTokenizer						,						lowercase__   :  List[str]						,						lowercase__   :  str						,						lowercase__   :  Optional[int] = None						,						lowercase__   :  Optional[int]=False						,						lowercase__   :  Split = Split.train						,						):


            '''simple docstring'''

            a_     :							Dict     						=  token_classification_task.read_examples_from_file(lowercase__						,						lowercase__       )
            # TODO clean up all this to leverage built-in features of tokenizers
            a_     :							Tuple     						=  token_classification_task.convert_examples_to_features(
                lowercase__						,						lowercase__						,						lowercase__						,						lowercase__						,						cls_token_at_end=bool(model_type in ["""xlnet"""]       )						,						cls_token=tokenizer.cls_token						,						cls_token_segment_id=2 if model_type in ["""xlnet"""] else 0						,						sep_token=tokenizer.sep_token						,						sep_token_extra=lowercase__						,						pad_on_left=bool(tokenizer.padding_side == """left"""       )						,						pad_token=tokenizer.pad_token_id						,						pad_token_segment_id=tokenizer.pad_token_type_id						,						pad_token_label_id=self.pad_token_label_id						,						)

            def gen():
                 for ex in self.features:
                      if ex.token_type_ids is None:
                           yield (
                               {"input_ids": ex.input_ids, "attention_mask": ex.attention_mask},
                               ex.label_ids,
                           )
                      else:
                           yield (
                               {
                                   "input_ids": ex.input_ids,
                                   "attention_mask": ex.attention_mask,
                                   "token_type_ids": ex.token_type_ids,
                               },
                               ex.label_ids,
                           )

            if "token_type_ids" not in tokenizer.model_input_names:
                 a_     :							Optional[int]     						=  tf.data.Dataset.from_generator(
                     lowercase__						,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)						,						(
                         {"""input_ids""": tf.TensorShape([None]       ), """attention_mask""": tf.TensorShape([None]       )},
                         tf.TensorShape([None]       ),
                     )						,						)
            else:
                 a_     :							List[Any]     						=  tf.data.Dataset.from_generator(
                     lowercase__						,						({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa, """token_type_ids""": tf.intaa}, tf.intaa)						,						(
                         {
                             """input_ids""": tf.TensorShape([None]       ),
                             """attention_mask""": tf.TensorShape([None]       ),
                             """token_type_ids""": tf.TensorShape([None]       ),
                         },
                         tf.TensorShape([None]       ),
                     )						,						)






       def 							lowercase_						( self   :  Union[str, Any]       ):


            '''simple docstring'''

            a_     :							Union[str, Any]     						=  self.dataset.apply(tf.data.experimental.assert_cardinality(len(self.features       )       )       )

            return self.dataset






       def __len__( self   :  Tuple       ):


            '''simple docstring'''

            return len(self.features       )






       def __getitem__( self   :  int						,						lowercase__   :  Dict       ):


            '''simple docstring'''

            return self.features[i]






'''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


lowerCAmelCase_      :  List[str]				     =  'sshleifer/mar_enro_6_3_student'







class      SCREAMING_SNAKE_CASE    (						snake_case_				):






      def 							lowercase_						( self   :  int       ):


           '''simple docstring'''

           super().setUp()

           a_     :							Optional[int]     						=  cached_path(
               """https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz"""						,						extract_compressed_file=lowercase__						,						)
           a_     :							List[Any]     						=  F"{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k"






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Union[str, Any]       ):


           '''simple docstring'''

           MarianMTModel.from_pretrained(lowercase__       )






      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Tuple       ):


           '''simple docstring'''

           a_     :							Union[str, Any]     						=  {
               """$MAX_LEN""": 64,
               """$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
           a_     :							Optional[int]     						=  (self.test_file_dir / """train_mbart_cc25_enro.sh""").open().read().split("""finetune.py"""       )[1].strip()
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           for k, v in env_vars_to_replace.items():
                a_     :							Optional[Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							Union[str, Any]     						=  self.get_auto_remove_tmp_dir()

           # bash_script = bash_script.replace("--fp16 ", "")
           a_     :							List[str]     						=  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

           a_     :							Dict     						=  ["""finetune.py"""] + bash_script.split() + args
           with patch.object(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							List[Any]     						=  argparse.ArgumentParser()
                a_     :							int     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							Dict     						=  SummarizationModule.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[str]     						=  parser.parse_args()
                a_     :							List[Any]     						=  main(lowercase__       )

           # Check metrics
           a_     :							List[str]     						=  load_json(model.metrics_save_path       )
           a_     :							Any     						=  metrics["""val"""][0]
           a_     :							int     						=  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}"]						,						lowercase__       )

           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
           a_     :							Dict     						=  os.listdir(lowercase__       )
           a_     :							Union[str, Any]     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Dict     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							Tuple     						=  """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:
                a_     :							int     						=  {os.path.basename(lowercase__       ) 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      SCREAMING_SNAKE_CASE    (						snake_case_				):






      @timeout_decorator.timeout(600       )
      @slow
      @require_torch_gpu
      def 							lowercase_						( self   :  Optional[int]       ):


           '''simple docstring'''

           a_     :							int     						=  F"{self.test_file_dir_str}/test_data/wmt_en_ro"
           a_     :							Dict     						=  {
               """--fp16_opt_level=O1""": """""",
               """$MAX_LEN""": 128,
               """$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
           a_     :							List[str]     						=  (
               (self.test_file_dir / """distil_marian_no_teacher.sh""").open().read().split("""distillation.py"""       )[1].strip()
           )
           a_     :							List[str]     						=  bash_script.replace("""\\\n"""						,						""""""       ).strip().replace("""\"$@\""""						,						""""""       )
           a_     :							Tuple     						=  bash_script.replace("""--fp16 """						,						""" """       )

           for k, v in env_vars_to_replace.items():
                a_     :							Union[str, Any]     						=  bash_script.replace(lowercase__						,						str(lowercase__       )       )
           a_     :							List[str]     						=  self.get_auto_remove_tmp_dir()
           a_     :							List[str]     						=  bash_script.replace("""--fp16"""						,						""""""       )
           a_     :							str     						=  6
           a_     :							Union[str, Any]     						=  (
               ["""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(lowercase__						,						"""argv"""						,						lowercase__       ):
                a_     :							Dict     						=  argparse.ArgumentParser()
                a_     :							Tuple     						=  pl.Trainer.add_argparse_args(lowercase__       )
                a_     :							int     						=  SummarizationDistiller.add_model_specific_args(lowercase__						,						os.getcwd()       )
                a_     :							List[Any]     						=  parser.parse_args()
                # assert args.gpus == gpus THIS BREAKS for multi_gpu

                a_     :							Union[str, Any]     						=  distill_main(lowercase__       )

           # Check metrics
           a_     :							Union[str, Any]     						=  load_json(model.metrics_save_path       )
           a_     :							List[str]     						=  metrics["""val"""][0]
           a_     :							Optional[Any]     						=  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}"]						,						lowercase__       )

           # check lightning ckpt can be loaded and has a reasonable statedict
           a_     :							Optional[int]     						=  os.listdir(lowercase__       )
           a_     :							Dict     						=  [x for x in contents if x.endswith(""".ckpt"""       )][0]
           a_     :							Optional[Any]     						=  os.path.join(args.output_dir						,						lowercase__       )
           a_     :							Optional[Any]     						=  torch.load(lowercase__						,						map_location="""cpu"""       )
           a_     :							List[str]     						=  """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:
                a_     :							int     						=  {os.path.basename(lowercase__       ) 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
