


'''simple docstring'''



import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roberta import RobertaTokenizer


__UpperCAmelCase    :List[Any]				       =     logging.get_logger(__name__)

__UpperCAmelCase    :List[str]				       =     {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

__UpperCAmelCase    :List[str]				       =     {
    "vocab_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/vocab.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/vocab.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/vocab.json",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/merges.txt",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/merges.txt",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/merges.txt",
        "roberta-base-openai-detector": "https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt",
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt"
        ),
    },
    "tokenizer_file": {
        "roberta-base": "https://huggingface.co/roberta-base/resolve/main/tokenizer.json",
        "roberta-large": "https://huggingface.co/roberta-large/resolve/main/tokenizer.json",
        "roberta-large-mnli": "https://huggingface.co/roberta-large-mnli/resolve/main/tokenizer.json",
        "distilroberta-base": "https://huggingface.co/distilroberta-base/resolve/main/tokenizer.json",
        "roberta-base-openai-detector": (
            "https://huggingface.co/roberta-base-openai-detector/resolve/main/tokenizer.json"
        ),
        "roberta-large-openai-detector": (
            "https://huggingface.co/roberta-large-openai-detector/resolve/main/tokenizer.json"
        ),
    },
}

__UpperCAmelCase    :Union[str, Any]				       =     {
    "roberta-base": 5_1_2,
    "roberta-large": 5_1_2,
    "roberta-large-mnli": 5_1_2,
    "distilroberta-base": 5_1_2,
    "roberta-base-openai-detector": 5_1_2,
    "roberta-large-openai-detector": 5_1_2,
}



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				List[Any]							     =     VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE :				Optional[Any]							     =     PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE :				Tuple							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE :				List[Any]							     =     ["input_ids", "attention_mask"]
     SCREAMING_SNAKE_CASE :				Optional[int]							     =     RobertaTokenizer

     def __init__(       self  :	Union[str, Any]							,				snake_case  :	List[Any]=None							,				snake_case  :	int=None							,				snake_case  :	str=None							,				snake_case  :	int="replace"							,				snake_case  :	Tuple="<s>"							,				snake_case  :	Dict="</s>"							,				snake_case  :	int="</s>"							,				snake_case  :	List[str]="<s>"							,				snake_case  :	Tuple="<unk>"							,				snake_case  :	Optional[int]="<pad>"							,				snake_case  :	Union[str, Any]="<mask>"							,				snake_case  :	Union[str, Any]=False							,				snake_case  :	Optional[int]=True							,				**snake_case  :	Dict							,				)		->							List[str]:
          super().__init__(
              snake_case							,				snake_case							,				tokenizer_file=snake_case							,				errors=snake_case							,				bos_token=snake_case							,				eos_token=snake_case							,				sep_token=snake_case							,				cls_token=snake_case							,				unk_token=snake_case							,				pad_token=snake_case							,				mask_token=snake_case							,				add_prefix_space=snake_case							,				trim_offsets=snake_case							,				**snake_case							,				)

          __UpperCAmelCase			:				Union[str, Any]													=    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
          if pre_tok_state.get('''add_prefix_space'''							,				snake_case    ) != add_prefix_space:
               __UpperCAmelCase			:				Tuple													=    getattr(snake_case							,				pre_tok_state.pop('''type'''    )    )
               __UpperCAmelCase			:				Any													=    add_prefix_space
               __UpperCAmelCase			:				int													=    pre_tok_class(**snake_case    )

          __UpperCAmelCase			:				List[str]													=    add_prefix_space

          __UpperCAmelCase			:				Tuple													=    '''post_processor'''
          __UpperCAmelCase			:				Tuple													=    getattr(self.backend_tokenizer							,				snake_case							,				snake_case    )
          if tokenizer_component_instance:
               __UpperCAmelCase			:				Tuple													=    json.loads(tokenizer_component_instance.__getstate__()    )

               # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
               if "sep" in state:
                    __UpperCAmelCase			:				Union[str, Any]													=    tuple(state['''sep''']    )
               if "cls" in state:
                    __UpperCAmelCase			:				List[Any]													=    tuple(state['''cls''']    )

               __UpperCAmelCase			:				Tuple													=    False

               if state.get('''add_prefix_space'''							,				snake_case    ) != add_prefix_space:
                    __UpperCAmelCase			:				Optional[int]													=    add_prefix_space
                    __UpperCAmelCase			:				str													=    True

               if state.get('''trim_offsets'''							,				snake_case    ) != trim_offsets:
                    __UpperCAmelCase			:				Optional[Any]													=    trim_offsets
                    __UpperCAmelCase			:				Optional[Any]													=    True

               if changes_to_apply:
                    __UpperCAmelCase			:				str													=    getattr(snake_case							,				state.pop('''type'''    )    )
                    __UpperCAmelCase			:				List[str]													=    component_class(**snake_case    )
                    setattr(self.backend_tokenizer							,				snake_case							,				snake_case    )

     @property
     def 				lowerCamelCase__				(       self  :	int    )		->							str:
          if self._mask_token is None:
               if self.verbose:
                    logger.error('''Using mask_token, but it is not set yet.'''    )
               return None
          return str(self._mask_token    )

     @mask_token.setter
     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	Dict    )		->							List[Any]:
          __UpperCAmelCase			:				int													=    AddedToken(snake_case							,				lstrip=snake_case							,				rstrip=snake_case    ) if isinstance(snake_case							,				snake_case    ) else value
          __UpperCAmelCase			:				Dict													=    value

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				*snake_case  :	Optional[Any]							,				**snake_case  :	Any    )		->							BatchEncoding:
          __UpperCAmelCase			:				str													=    kwargs.get('''is_split_into_words'''							,				snake_case    )
          assert self.add_prefix_space or not is_split_into_words, (
              f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._batch_encode_plus(*snake_case							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				*snake_case  :	int							,				**snake_case  :	List[str]    )		->							BatchEncoding:
          __UpperCAmelCase			:				int													=    kwargs.get('''is_split_into_words'''							,				snake_case    )

          assert self.add_prefix_space or not is_split_into_words, (
              f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
              "to use it with pretokenized inputs."
          )

          return super()._encode_plus(*snake_case							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	str							,				snake_case  :	Optional[str] = None    )		->							Tuple[str]:
          __UpperCAmelCase			:				Optional[int]													=    self._tokenizer.model.save(snake_case							,				name=snake_case    )
          return tuple(snake_case    )

     def 				lowerCamelCase__				(       self  :	List[str]							,				snake_case  :	Optional[int]							,				snake_case  :	Optional[int]=None    )		->							Any:
          __UpperCAmelCase			:				Tuple													=    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
          if token_ids_a is None:
               return output

          return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	List[int]							,				snake_case  :	Optional[List[int]] = None    )		->							List[int]:
          __UpperCAmelCase			:				Optional[int]													=    [self.sep_token_id]
          __UpperCAmelCase			:				Optional[Any]													=    [self.cls_token_id]

          if token_ids_a is None:
               return len(cls + token_ids_a + sep    ) * [0]
          return len(cls + token_ids_a + sep + sep + token_ids_a + sep    ) * [0]



'''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


__UpperCAmelCase    :Any				       =     "sshleifer/mar_enro_6_3_student"



class 			a       (	_a ):


     """simple docstring"""






     def 				lowerCamelCase__				(       self  :	int    )		->							Optional[Any]:
          super().setUp()

          __UpperCAmelCase			:				Optional[Any]													=    cached_path(
              '''https://cdn-datasets.huggingface.co/translation/wmt_en_ro-tr40k-va0.5k-te0.5k.tar.gz'''							,				extract_compressed_file=snake_case							,				)
          __UpperCAmelCase			:				str													=    f'{data_cached}/wmt_en_ro-tr40k-va0.5k-te0.5k'

     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Union[str, Any]    )		->							Optional[Any]:
          MarianMTModel.from_pretrained(snake_case    )






     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[int]    )		->							int:
          __UpperCAmelCase			:				Optional[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
          __UpperCAmelCase			:				Tuple													=    (self.test_file_dir / '''train_mbart_cc25_enro.sh''').open().read().split('''finetune.py'''    )[1].strip()
          __UpperCAmelCase			:				List[Any]													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          for k, v in env_vars_to_replace.items():
               __UpperCAmelCase			:				Any													=    bash_script.replace(snake_case							,				str(snake_case    )    )
          __UpperCAmelCase			:				str													=    self.get_auto_remove_tmp_dir()

          # bash_script = bash_script.replace("--fp16 ", "")
          __UpperCAmelCase			:				Optional[Any]													=    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

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

          # Check metrics
          __UpperCAmelCase			:				Optional[int]													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				int													=    metrics['''val'''][0]
          __UpperCAmelCase			:				List[Any]													=    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}']							,				snake_case    )

          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
          __UpperCAmelCase			:				Any													=    os.listdir(snake_case    )
          __UpperCAmelCase			:				Tuple													=    [x for x in contents if x.endswith('''.ckpt'''    )][0]
          __UpperCAmelCase			:				Optional[int]													=    os.path.join(args.output_dir							,				snake_case    )
          __UpperCAmelCase			:				Optional[Any]													=    torch.load(snake_case							,				map_location='''cpu'''    )
          __UpperCAmelCase			:				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:
               __UpperCAmelCase			:				Union[str, Any]													=    {os.path.basename(snake_case    ) 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 			a       (	_a ):


     """simple docstring"""






     @timeout_decorator.timeout(600    )
     @slow
     @require_torch_gpu
     def 				lowerCamelCase__				(       self  :	Optional[Any]    )		->							Optional[Any]:
          __UpperCAmelCase			:				Any													=    f'{self.test_file_dir_str}/test_data/wmt_en_ro'
          __UpperCAmelCase			:				List[str]													=    {
              '''--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
          __UpperCAmelCase			:				str													=    (
              (self.test_file_dir / '''distil_marian_no_teacher.sh''').open().read().split('''distillation.py'''    )[1].strip()
          )
          __UpperCAmelCase			:				Dict													=    bash_script.replace('''\\\n'''							,				''''''    ).strip().replace('''"$@"'''							,				''''''    )
          __UpperCAmelCase			:				Tuple													=    bash_script.replace('''--fp16 '''							,				''' '''    )

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

               __UpperCAmelCase			:				str													=    distill_main(snake_case    )

          # Check metrics
          __UpperCAmelCase			:				int													=    load_json(model.metrics_save_path    )
          __UpperCAmelCase			:				List[str]													=    metrics['''val'''][0]
          __UpperCAmelCase			:				Tuple													=    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}']							,				snake_case    )

          # check lightning ckpt can be loaded and has a reasonable statedict
          __UpperCAmelCase			:				Tuple													=    os.listdir(snake_case    )
          __UpperCAmelCase			:				Tuple													=    [x for x in contents if x.endswith('''.ckpt'''    )][0]
          __UpperCAmelCase			:				int													=    os.path.join(args.output_dir							,				snake_case    )
          __UpperCAmelCase			:				Dict													=    torch.load(snake_case							,				map_location='''cpu'''    )
          __UpperCAmelCase			:				Dict													=    '''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:
               __UpperCAmelCase			:				int													=    {os.path.basename(snake_case    ) 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