


import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments

import transformers
from transformers import (
    AutoConfig,
    AutoModelForSeqaSeqLM,
    AutoTokenizer,
    HfArgumentParser,
    MBartTokenizer,
    MBartTokenizerFast,
    set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
    SeqaSeqDataCollator,
    SeqaSeqDataset,
    assert_all_frozen,
    build_compute_metrics_fn,
    check_output_dir,
    freeze_embeds,
    freeze_params,
    lmap,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


__lowerCAmelCase			=							logging.getLogger(__name__)





@dataclass
class   __a      :
 __lowercase			:							str       					=			field(
     metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}				)
 __lowercase			:							Optional[str]       					=			field(
     default=lowerCamelCase__	,	metadata={'help': 'Pretrained config name or path if not the same as model_name'}				)
 __lowercase			:							Optional[str]       					=			field(
     default=lowerCamelCase__	,	metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}				)
 __lowercase			:							Optional[str]       					=			field(
     default=lowerCamelCase__	,	metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}	,	)
 __lowercase			:							bool       					=			field(default=lowerCamelCase__	,	metadata={'help': 'Whether tp freeze the encoder.'}				)
 __lowercase			:							bool       					=			field(default=lowerCamelCase__	,	metadata={'help': 'Whether  to freeze the embeddings.'}				)





@dataclass
class   __a      :
 __lowercase			:							str       					=			field(
     metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}				)
 __lowercase			:							Optional[str]       					=			field(
     default='summarization'	,	metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}	,	)
 __lowercase			:							Optional[int]       					=			field(
     default=1024	,	metadata={
         'help': (
             'The maximum total input sequence length after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )
     }	,	)
 __lowercase			:							Optional[int]       					=			field(
     default=128	,	metadata={
         'help': (
             'The maximum total sequence length for target text after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )
     }	,	)
 __lowercase			:							Optional[int]       					=			field(
     default=142	,	metadata={
         'help': (
             'The maximum total sequence length for validation target text after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded. '
             'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
             'during ``evaluate`` and ``predict``.'
         )
     }	,	)
 __lowercase			:							Optional[int]       					=			field(
     default=142	,	metadata={
         'help': (
             'The maximum total sequence length for test target text after tokenization. Sequences longer '
             'than this will be truncated, sequences shorter will be padded.'
         )
     }	,	)
 __lowercase			:							Optional[int]       					=			field(default=-1	,	metadata={'help': '# training examples. -1 means use all.'}				)
 __lowercase			:							Optional[int]       					=			field(default=-1	,	metadata={'help': '# validation examples. -1 means use all.'}				)
 __lowercase			:							Optional[int]       					=			field(default=-1	,	metadata={'help': '# test examples. -1 means use all.'}				)
 __lowercase			:							Optional[str]       					=			field(default=lowerCamelCase__	,	metadata={'help': 'Source language id for translation.'}				)
 __lowercase			:							Optional[str]       					=			field(default=lowerCamelCase__	,	metadata={'help': 'Target language id for translation.'}				)
 __lowercase			:							Optional[int]       					=			field(default=lowerCamelCase__	,	metadata={'help': '# num_beams to use for evaluation.'}				)
 __lowercase			:							bool       					=			field(
     default=lowerCamelCase__	,	metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}	,	)





def        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    List[str]:
       logger.info(f'***** {split} metrics *****'					)
       for key in sorted(metrics.keys()					):
              logger.info(f'  {key} = {metrics[key]}'					)
       save_json(__lowerCAmelCase      ,							os.path.join(__lowerCAmelCase      ,							f'{split}_results.json'					)					)




def        snake_case_					(  )  ->    Tuple:
       lowercase__:       Optional[int]        =       HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments)					)

       if len(sys.argv					) == 2 and sys.argv[1].endswith('.json'					):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              lowercase__:       Union[str, Any]        =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]					)					)
       else:
              lowercase__:       Optional[int]        =       parser.parse_args_into_dataclasses()

       check_output_dir(__lowerCAmelCase					)

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'      ,							datefmt='%m/%d/%Y %H:%M:%S'      ,							level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN      ,							)
       logger.warning(
           'Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'      ,							training_args.local_rank      ,							training_args.device      ,							training_args.n_gpu      ,							bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED					)      ,							training_args.fpaa      ,							)
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()
       # Set the verbosity to info of the Transformers logger (on main process only):
       if is_main_process(training_args.local_rank					):
              transformers.utils.logging.set_verbosity_info()
       logger.info('Training/evaluation parameters %s'      ,							__lowerCAmelCase					)

       # Set seed
       set_seed(training_args.seed					)

       # Load pretrained model and tokenizer
       #
       # Distributed training:
       # The .from_pretrained methods guarantee that only one local process can concurrently
       # download model & vocab.

       lowercase__:       List[Any]        =       AutoConfig.from_pretrained(
           model_args.config_name if model_args.config_name else model_args.model_name_or_path      ,							cache_dir=model_args.cache_dir      ,							)

       lowercase__:       List[str]        =       ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
       for p in extra_model_params:
              if getattr(__lowerCAmelCase      ,							__lowerCAmelCase      ,							__lowerCAmelCase					):
                     assert hasattr(__lowerCAmelCase      ,							__lowerCAmelCase					), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
                     setattr(__lowerCAmelCase      ,							__lowerCAmelCase      ,							getattr(__lowerCAmelCase      ,							__lowerCAmelCase					)					)

       lowercase__:       Any        =       AutoTokenizer.from_pretrained(
           model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path      ,							cache_dir=model_args.cache_dir      ,							)
       lowercase__:       Optional[int]        =       AutoModelForSeqaSeqLM.from_pretrained(
           model_args.model_name_or_path      ,							from_tf='.ckpt' in model_args.model_name_or_path      ,							config=__lowerCAmelCase      ,							cache_dir=model_args.cache_dir      ,							)

       # use task specific params
       use_task_specific_params(__lowerCAmelCase      ,							data_args.task					)

       # set num_beams for evaluation
       if data_args.eval_beams is None:
              lowercase__:       Optional[Any]        =       model.config.num_beams

       # set decoder_start_token_id for MBart
       if model.config.decoder_start_token_id is None and isinstance(__lowerCAmelCase      ,							(MBartTokenizer, MBartTokenizerFast)					):
              assert (
                  data_args.tgt_lang is not None and data_args.src_lang is not None
              ), "mBart requires --tgt_lang and --src_lang"
              if isinstance(__lowerCAmelCase      ,							__lowerCAmelCase					):
                     lowercase__:       Union[str, Any]        =       tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     lowercase__:       int        =       tokenizer.convert_tokens_to_ids(data_args.tgt_lang					)

       if model_args.freeze_embeds:
              freeze_embeds(__lowerCAmelCase					)
       if model_args.freeze_encoder:
              freeze_params(model.get_encoder()					)
              assert_all_frozen(model.get_encoder()					)

       lowercase__:       Optional[int]        =       SeqaSeqDataset

       # Get datasets
       lowercase__:       Any        =       (
           dataset_class(
               __lowerCAmelCase      ,							type_path='train'      ,							data_dir=data_args.data_dir      ,							n_obs=data_args.n_train      ,							max_target_length=data_args.max_target_length      ,							max_source_length=data_args.max_source_length      ,							prefix=model.config.prefix or ''      ,							)
           if training_args.do_train
           else None
       )
       lowercase__:       int        =       (
           dataset_class(
               __lowerCAmelCase      ,							type_path='val'      ,							data_dir=data_args.data_dir      ,							n_obs=data_args.n_val      ,							max_target_length=data_args.val_max_target_length      ,							max_source_length=data_args.max_source_length      ,							prefix=model.config.prefix or ''      ,							)
           if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
           else None
       )
       lowercase__:       Any        =       (
           dataset_class(
               __lowerCAmelCase      ,							type_path='test'      ,							data_dir=data_args.data_dir      ,							n_obs=data_args.n_test      ,							max_target_length=data_args.test_max_target_length      ,							max_source_length=data_args.max_source_length      ,							prefix=model.config.prefix or ''      ,							)
           if training_args.do_predict
           else None
       )

       # Initialize our Trainer
       lowercase__:       Union[str, Any]        =       (
           build_compute_metrics_fn(data_args.task      ,							__lowerCAmelCase					) if training_args.predict_with_generate else None
       )
       lowercase__:       Union[str, Any]        =       SeqaSeqTrainer(
           model=__lowerCAmelCase      ,							args=__lowerCAmelCase      ,							data_args=__lowerCAmelCase      ,							train_dataset=__lowerCAmelCase      ,							eval_dataset=__lowerCAmelCase      ,							data_collator=SeqaSeqDataCollator(
               __lowerCAmelCase      ,							__lowerCAmelCase      ,							model.config.decoder_start_token_id      ,							training_args.tpu_num_cores					)      ,							compute_metrics=__lowerCAmelCase      ,							tokenizer=__lowerCAmelCase      ,							)

       lowercase__:       Optional[Any]        =       {}
       # Training
       if training_args.do_train:
              logger.info('*** Train ***'					)

              lowercase__:       Union[str, Any]        =       trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path					) else None					)
              lowercase__:       Optional[Any]        =       train_result.metrics
              lowercase__:       Tuple        =       data_args.n_train

              trainer.save_model()  # this also saves the tokenizer

              if trainer.is_world_process_zero():
                     handle_metrics('train'      ,							__lowerCAmelCase      ,							training_args.output_dir					)
                     all_metrics.update(__lowerCAmelCase					)

                     # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
                     trainer.state.save_to_json(os.path.join(training_args.output_dir      ,							'trainer_state.json'					)					)

                     # For convenience, we also re-save the tokenizer to the same directory,
                     # so that you can share your model easily on huggingface.co/models =)
                     tokenizer.save_pretrained(training_args.output_dir					)

    # Evaluation
       if training_args.do_eval:
              logger.info('*** Evaluate ***'					)

              lowercase__:       Any        =       trainer.evaluate(metric_key_prefix='val'					)
              lowercase__:       Union[str, Any]        =       data_args.n_val
              lowercase__:       int        =       round(metrics['val_loss']      ,							4					)

              if trainer.is_world_process_zero():
                     handle_metrics('val'      ,							__lowerCAmelCase      ,							training_args.output_dir					)
                     all_metrics.update(__lowerCAmelCase					)

       if training_args.do_predict:
              logger.info('*** Predict ***'					)

              lowercase__:       Any        =       trainer.predict(test_dataset=__lowerCAmelCase      ,							metric_key_prefix='test'					)
              lowercase__:       Optional[Any]        =       test_output.metrics
              lowercase__:       List[str]        =       data_args.n_test

              if trainer.is_world_process_zero():
                     lowercase__:       Optional[int]        =       round(metrics['test_loss']      ,							4					)
                     handle_metrics('test'      ,							__lowerCAmelCase      ,							training_args.output_dir					)
                     all_metrics.update(__lowerCAmelCase					)

                     if training_args.predict_with_generate:
                            lowercase__:       List[str]        =       tokenizer.batch_decode(
                                test_output.predictions      ,							skip_special_tokens=__lowerCAmelCase      ,							clean_up_tokenization_spaces=__lowerCAmelCase					)
                            lowercase__:       Tuple        =       lmap(str.strip      ,							__lowerCAmelCase					)
                            write_txt_file(__lowerCAmelCase      ,							os.path.join(training_args.output_dir      ,							'test_generations.txt'					)					)

       if trainer.is_world_process_zero():
              save_json(__lowerCAmelCase      ,							os.path.join(training_args.output_dir      ,							'all_results.json'					)					)

       return all_metrics




def        snake_case_					(  snake_case					)  ->    Union[str, Any]:
       main()


if __name__ == "__main__":
  main()






import json
import os
import unittest

from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin





@require_tokenizers
class   __a      (   __UpperCamelCase	,	unittest.TestCase				):
 __lowercase			:							Optional[int]       					=			CLIPTokenizer
 __lowercase			:							str       					=			CLIPTokenizerFast
 __lowercase			:							Tuple       					=			True
 __lowercase			:							str       					=			{}
 __lowercase			:							Dict       					=			False
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        super().setUp()

        # fmt: off
        lowercase__:       str        =       ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'lo', 'l</w>', 'w</w>', 'r</w>', 't</w>', 'low</w>', 'er</w>', 'lowest</w>', 'newer</w>', 'wider', '<unk>', '<|startoftext|>', '<|endoftext|>']
        # fmt: on
        lowercase__:       List[str]        =       dict(zip(lowerCAmelCase__    ,			range(len(lowerCAmelCase__			)			)			)			)
        lowercase__:       int        =       ['#version: 0.2', 'l o', 'lo w</w>', 'e r</w>']
        lowercase__:       Optional[int]        =       {'unk_token': '<unk>'}

        lowercase__:       int        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['vocab_file']			)
        lowercase__:       Union[str, Any]        =       os.path.join(self.tmpdirname    ,			VOCAB_FILES_NAMES['merges_file']			)
        with open(self.vocab_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write(json.dumps(lowerCAmelCase__			) + '\n'			)
        with open(self.merges_file    ,			'w'    ,			encoding='utf-8'			) as fp:
               fp.write('\n'.join(lowerCAmelCase__			)			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    List[str]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizer.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			**lowerCAmelCase__			) ->    Optional[int]:




        '''simple docstring'''
        kwargs.update(self.special_tokens_map			)
        return CLIPTokenizerFast.from_pretrained(self.tmpdirname    ,			**lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self    ,			lowerCAmelCase__			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Dict        =       'lower newer'
        return input_text, output_text
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        lowercase__:       Union[str, Any]        =       CLIPTokenizer(self.vocab_file    ,			self.merges_file    ,			**self.special_tokens_map			)
        lowercase__:       Dict        =       'lower newer'
        lowercase__:       Union[str, Any]        =       ['lo', 'w', 'er</w>', 'n', 'e', 'w', 'er</w>']
        lowercase__:       Any        =       tokenizer.tokenize(lowerCAmelCase__			)
        self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

        lowercase__:       Tuple        =       tokens + [tokenizer.unk_token]
        lowercase__:       Tuple        =       [10, 2, 16, 9, 3, 2, 16, 20]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__			)    ,			lowerCAmelCase__			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       List[str]        =       self.tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)
                      lowercase__:       Optional[int]        =       self.rust_tokenizer_class.from_pretrained(lowerCAmelCase__    ,			**lowerCAmelCase__			)

                      lowercase__:       Optional[int]        =       'A\n\'ll 11p223RF☆ho!!to?\'d\'d\'\'d of a cat to-$\'\'d.'
                      lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       Dict        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on an example containing a character (Latin Small Letter A
                      # with Tilde) encoded in 2 different ways
                      lowercase__:       Dict        =       'xa\u0303y' + ' ' + 'x\xe3y'
                      lowercase__:       Tuple        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                      lowercase__:       int        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                      self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of space type
                      lowercase__:       str        =       [
                          '\u0009',  # (horizontal tab, '\t')
                          '\u000B',  # (vertical tab)
                          '\u000C',  # (form feed)
                          '\u0020',  # (space, ' ')
                          '\u200E',  # (left-to-right mark):w
                          '\u200F',  # (right-to-left mark)
                      ]
                      for unicode_seq in spaces_unicodes:
                             lowercase__:       Optional[Any]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Tuple        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)

                      # Test that the tokenization is identical on unicode of line break type
                      lowercase__:       str        =       [
                          '\u000A',  # (line feed, '\n')
                          '\r\n',  # (carriage return and line feed, '\r\n')
                          '\u000D',  # (carriage return, '\r')
                          '\r',  # (carriage return, '\r')
                          '\u000D',  # (carriage return, '\r')
                          '\u2028',  # (line separator)
                          '\u2029',  # (paragraph separator)
                          # "\u0085", # (next line)
                      ]

                      # The tokenization is not identical for the character "\u0085" (next line). The slow version using ftfy transforms
                      # it into the Horizontal Ellipsis character "…" ("\u2026") while the fast version transforms it into a
                      # space (and thus into an empty list).

                      for unicode_seq in line_break_unicodes:
                             lowercase__:       Optional[int]        =       tokenizer_s.tokenize(lowerCAmelCase__			)
                             lowercase__:       Optional[int]        =       tokenizer_r.tokenize(lowerCAmelCase__			)

                             self.assertListEqual(lowerCAmelCase__    ,			lowerCAmelCase__			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Optional[int]:




        '''simple docstring'''
        # Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space`
        for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
               with self.subTest(F'{tokenizer.__class__.__name__} ({pretrained_name})'			):
                      lowercase__:       Optional[int]        =       'hello'  # `hello` is a token in the vocabulary of `pretrained_name`
                      lowercase__:       Optional[int]        =       F'{text_of_1_token} {text_of_1_token}'

                      lowercase__:       int        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       Dict        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(0, len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(len(lowerCAmelCase__			) + 1, len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)

                      lowercase__:       Any        =       F' {text}'

                      lowercase__:       Union[str, Any]        =       self.rust_tokenizer_class.from_pretrained(
                          lowerCAmelCase__    ,			use_fast=lowerCAmelCase__    ,			)
                      lowercase__:       int        =       tokenizer_r(lowerCAmelCase__    ,			return_offsets_mapping=lowerCAmelCase__    ,			add_special_tokens=lowerCAmelCase__			)
                      self.assertEqual(encoding.offset_mapping[0]    ,			(1, 1 + len(lowerCAmelCase__			))			)
                      self.assertEqual(
                          encoding.offset_mapping[1]    ,			(1 + len(lowerCAmelCase__			) + 1, 1 + len(lowerCAmelCase__			) + 1 + len(lowerCAmelCase__			))    ,			)
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[str]:




        '''simple docstring'''
        # Test related to the breaking change introduced in transformers v4.17.0
        # We need to check that an error in raised when the user try to load a previous version of the tokenizer.
        with self.assertRaises(lowerCAmelCase__			) as context:
               self.rust_tokenizer_class.from_pretrained('robot-test/old-clip-tokenizer'			)

        self.assertTrue(
            context.exception.args[0].startswith(
                'The `backend_tokenizer` provided does not match the expected format.'			)			)
 @require_ftfy
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    List[Any]:




        '''simple docstring'''
        super().test_tokenization_python_rust_equals()
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    Tuple:




        '''simple docstring'''
        # CLIP always lower cases letters
        pass


