




'''simple docstring'''



import re
import tempfile
from pathlib import Path

import pytest
import yaml

from datasets.utils.readme import ReadMe


# @pytest.fixture
# def example_yaml_structure():

lowerCamelCase_									=		yaml.safe_load(
    '\\nname: \"\"\nallow_empty: false\nallow_empty_text: true\nsubsections:\n  - name: \"Dataset Card for X\" # First-level markdown heading\n    allow_empty: false\n    allow_empty_text: true\n    subsections:\n      - name: \"Table of Contents\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections: null\n      - name: \"Dataset Description\"\n        allow_empty: false\n        allow_empty_text: false\n        subsections:\n          - name: \"Dataset Summary\"\n            allow_empty: false\n            allow_empty_text: false\n            subsections: null\n          - name: \"Supported Tasks and Leaderboards\"\n            allow_empty: true\n            allow_empty_text: true\n            subsections: null\n          - name: Languages\n            allow_empty: false\n            allow_empty_text: true\n            subsections: null\n'
)


lowerCamelCase_									=		{
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}


lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""


lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
#### Extra Ignored Subsection
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

lowerCamelCase_									=		{
    """name""": """root""",
    """text""": """""",
    """is_empty_text""": True,
    """subsections""": [
        {
            """name""": """Dataset Card for My Dataset""",
            """text""": """""",
            """is_empty_text""": True,
            """subsections""": [
                {"""name""": """Table of Contents""", """text""": """Some text here.""", """is_empty_text""": False, """subsections""": []},
                {
                    """name""": """Dataset Description""",
                    """text""": """Some text here.""",
                    """is_empty_text""": False,
                    """subsections""": [
                        {
                            """name""": """Dataset Summary""",
                            """text""": """Some text here.""",
                            """is_empty_text""": False,
                            """subsections""": [
                                {
                                    """name""": """Extra Ignored Subsection""",
                                    """text""": """""",
                                    """is_empty_text""": True,
                                    """subsections""": [],
                                }
                            ],
                        },
                        {
                            """name""": """Supported Tasks and Leaderboards""",
                            """text""": """""",
                            """is_empty_text""": True,
                            """subsections""": [],
                        },
                        {"""name""": """Languages""", """text""": """Language Text""", """is_empty_text""": False, """subsections""": []},
                    ],
                },
            ],
        }
    ],
}

lowerCamelCase_									=		"""\
---
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

lowerCamelCase_									=		(
    """The following issues were found for the README at `{path}`:\n-\tEmpty YAML markers are present in the README."""
)

lowerCamelCase_									=		"""\
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

lowerCamelCase_									=		(
    """The following issues were found for the README at `{path}`:\n-\tNo YAML markers are present in the README."""
)

lowerCamelCase_									=		"""\
---
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tOnly the start of YAML tags present in the README."""

lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Summary` but it is empty.\n-\tExpected some text in section `Dataset Summary` but it is empty (text in subsections are ignored)."""


lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
"""
lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Dataset Card for My Dataset` but it is empty.\n-\tSection `Dataset Card for My Dataset` expected the following subsections: `Table of Contents`, `Dataset Description`. Found 'None'."""

lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Languages
Language Text
"""

lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tSection `Dataset Description` is missing subsection: `Supported Tasks and Leaderboards`."""


lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
"""

lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tExpected some content in section `Languages` but it is empty."""

lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""
lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README."""

lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
# Dataset Card My Dataset
"""

lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tThe README has several first-level headings: `Dataset Card for My Dataset`, `Dataset Card My Dataset`. Only one heading is expected. Skipping further validation for this README."""

lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tNo first-level heading starting with `Dataset Card for` found in README. Skipping further validation for this README."""

lowerCamelCase_									=		""""""

lowerCamelCase_									=		"""The following issues were found for the README at `{path}`:\n-\tThe README has no first-level headings. One heading is expected. Skipping further validation for this README.\n-\tNo YAML markers are present in the README."""

lowerCamelCase_									=		"""\
---
language:
- zh
- en
---

# Dataset Card for My Dataset
# Dataset Card for My Dataset
## Table of Contents
Some text here.
## Dataset Description
Some text here.
### Dataset Summary
Some text here.
### Supported Tasks and Leaderboards
### Languages
Language Text
"""

lowerCamelCase_									=		"""The following issues were found while parsing the README at `{path}`:\n-\tMultiple sections with the same heading `Dataset Card for My Dataset` have been found. Please keep only one of these sections."""
@pytest.mark.parametrize(
    "readme_md, expected_dict"			,	[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					List[str]			,	__A							:					str    )					->					List[str]:
   assert ReadMe.from_string(_A			,	_A    ).to_dict() == expected_dict
@pytest.mark.parametrize(
    "readme_md, expected_error"			,	[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Dict			,	__A							:					Any    )					->					str:







   with pytest.raises(_A			,	match=re.escape(expected_error.format(path="root"    )    )    ):
      _SCREAMING_SNAKE_CASE			= ReadMe.from_string(_A			,	_A    )
      readme.validate()
@pytest.mark.parametrize(
    "readme_md, expected_error"			,	[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Dict			,	__A							:					Union[str, Any]    )					->					Tuple:







   with pytest.raises(_A			,	match=re.escape(expected_error.format(path="root"    )    )    ):
      ReadMe.from_string(_A			,	_A    )
@pytest.mark.parametrize(
    "readme_md,"			,	[
        (README_MULTIPLE_SAME_HEADING_1),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Tuple    )					->					int:
   ReadMe.from_string(_A			,	_A			,	suppress_parsing_errors=_A    )
@pytest.mark.parametrize(
    "readme_md, expected_dict"			,	[
        (README_CORRECT, CORRECT_DICT),
        (README_CORRECT_FOUR_LEVEL, CORRECT_DICT_FOUR_LEVEL),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Optional[Any]			,	__A							:					Union[str, Any]    )					->					Any:







   with tempfile.TemporaryDirectory() as tmp_dir:
      _SCREAMING_SNAKE_CASE			= Path(_A    ) / 'README.md'
      with open(_A			,	"w+"    ) as readme_file:
         readme_file.write(_A    )
      _SCREAMING_SNAKE_CASE			= ReadMe.from_readme(_A			,	_A    ).to_dict()
      assert out["name"] == path
      assert out["text"] == ""
      assert out["is_empty_text"]
      assert out["subsections"] == expected_dict["subsections"]
@pytest.mark.parametrize(
    "readme_md, expected_error"			,	[
        (README_NO_YAML, EXPECTED_ERROR_README_NO_YAML),
        (README_EMPTY_YAML, EXPECTED_ERROR_README_EMPTY_YAML),
        (README_INCORRECT_YAML, EXPECTED_ERROR_README_INCORRECT_YAML),
        (README_EMPTY, EXPECTED_ERROR_README_EMPTY),
        (README_NONE_SUBSECTION, EXPECTED_ERROR_README_NONE_SUBSECTION),
        (README_MISSING_FIRST_LEVEL, EXPECTED_ERROR_README_MISSING_FIRST_LEVEL),
        (README_MISSING_SUBSECTION, EXPECTED_ERROR_README_MISSING_SUBSECTION),
        (README_MISSING_TEXT, EXPECTED_ERROR_README_MISSING_TEXT),
        (README_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_WRONG_FIRST_LEVEL),
        (README_MULTIPLE_WRONG_FIRST_LEVEL, EXPECTED_ERROR_README_MULTIPLE_WRONG_FIRST_LEVEL),
        (README_MISSING_CONTENT, EXPECTED_ERROR_README_MISSING_CONTENT),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					int			,	__A							:					Dict    )					->					List[str]:







   with tempfile.TemporaryDirectory() as tmp_dir:
      _SCREAMING_SNAKE_CASE			= Path(_A    ) / 'README.md'
      with open(_A			,	"w+"    ) as readme_file:
         readme_file.write(_A    )
      _SCREAMING_SNAKE_CASE			= expected_error.format(path=_A    )
      with pytest.raises(_A			,	match=re.escape(_A    )    ):
         _SCREAMING_SNAKE_CASE			= ReadMe.from_readme(_A			,	_A    )
         readme.validate()
@pytest.mark.parametrize(
    "readme_md, expected_error"			,	[
        (README_MULTIPLE_SAME_HEADING_1, EXPECTED_ERROR_README_MULTIPLE_SAME_HEADING_1),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Optional[int]			,	__A							:					List[Any]    )					->					List[str]:







   with tempfile.TemporaryDirectory() as tmp_dir:
      _SCREAMING_SNAKE_CASE			= Path(_A    ) / 'README.md'
      with open(_A			,	"w+"    ) as readme_file:
         readme_file.write(_A    )
      _SCREAMING_SNAKE_CASE			= expected_error.format(path=_A    )
      with pytest.raises(_A			,	match=re.escape(_A    )    ):
         ReadMe.from_readme(_A			,	_A    )






@pytest.mark.parametrize(
    "readme_md,"			,	[
        (README_MULTIPLE_SAME_HEADING_1),
    ]			,	)
def 					SCREAMING_SNAKE_CASE_					(				__A							:					Any    )					->					List[str]:







   with tempfile.TemporaryDirectory() as tmp_dir:
      _SCREAMING_SNAKE_CASE			= Path(_A    ) / 'README.md'
      with open(_A			,	"w+"    ) as readme_file:
         readme_file.write(_A    )
      ReadMe.from_readme(_A			,	_A			,	suppress_parsing_errors=_A    )





'''simple docstring'''







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     :  Optional[Any]											=    logging.getLogger(__name__)
@dataclass
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     field(
         metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Pretrained config name or path if not the same as model_name"}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"}     ,       )
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Whether tp freeze the encoder."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Whether  to freeze the embeddings."}						)
@dataclass
class 						UpperCamelCase__       :



     """simple docstring"""






     __magic_name__           =     field(
         metadata={"help": "The input data dir. Should contain the .tsv files (or other data files) for the task."}						)
     __magic_name__           =     field(
         default="summarization"     ,       metadata={"help": "Task name, summarization (or summarization_{dataset} for pegasus) or translation"}     ,       )
     __magic_name__           =     field(
         default=1_0_2_4     ,       metadata={
             "help": (
                 "The maximum total input sequence length after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_2_8     ,       metadata={
             "help": (
                 "The maximum total sequence length for target text after tokenization. Sequences longer "
                 "than this will be truncated, sequences shorter will be padded."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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``."
             )
         }     ,       )
     __magic_name__           =     field(
         default=1_4_2     ,       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."
             )
         }     ,       )
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# training examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# validation examples. -1 means use all."}						)
     __magic_name__           =     field(default=-1     ,       metadata={"help": "# test examples. -1 means use all."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Source language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "Target language id for translation."}						)
     __magic_name__           =     field(default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "# num_beams to use for evaluation."}						)
     __magic_name__           =     field(
         default=SCREAMING_SNAKE_CASE_     ,       metadata={"help": "If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined."}     ,       )






def       lowercase					(_A ,    _A ,    _A       ):







       """simple docstring"""







       logger.info(f'***** {split} metrics *****'       )
       for key in sorted(metrics.keys()       ):
              logger.info(f'  {key} = {metrics[key]}'       )
       save_json(_A ,    os.path.join(_A ,    f'{split}_results.json'       )       )






def       lowercase					():







       """simple docstring"""







       _lowerCAmelCase     :				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.
              _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Tuple       =							parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]       )       )
       else:
              _lowerCAmelCase      ,  _lowerCAmelCase      ,  _lowerCAmelCase     :				Optional[int]       =							parser.parse_args_into_dataclasses()

       check_output_dir(_A       )

       # 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' ,    _A       )

       # 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.

       _lowerCAmelCase     :				Optional[int]       =							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 ,    )

       _lowerCAmelCase     :				Tuple       =							('encoder_layerdrop', 'decoder_layerdrop', 'dropout', 'attention_dropout')
       for p in extra_model_params:
              if getattr(_A ,    _A ,    _A       ):
                     assert hasattr(_A ,    _A       ), f'({config.__class__.__name__}) doesn\'t have a `{p}` attribute'
                     setattr(_A ,    _A ,    getattr(_A ,    _A       )       )

       _lowerCAmelCase     :				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 ,    )
       _lowerCAmelCase     :				str       =							AutoModelForSeqaSeqLM.from_pretrained(
           model_args.model_name_or_path ,    from_tf='.ckpt' in model_args.model_name_or_path ,    config=_A ,    cache_dir=model_args.cache_dir ,    )

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

       # set num_beams for evaluation
       if data_args.eval_beams is None:
              _lowerCAmelCase     :				Union[str, Any]       =							model.config.num_beams

       # set decoder_start_token_id for MBart
       if model.config.decoder_start_token_id is None and isinstance(_A ,    (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(_A ,    _A       ):
                     _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.lang_code_to_id[data_args.tgt_lang]
              else:
                     _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.convert_tokens_to_ids(data_args.tgt_lang       )

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

       _lowerCAmelCase     :				Any       =							SeqaSeqDataset

       # Get datasets
       _lowerCAmelCase     :				List[Any]       =							(
           dataset_class(
               _A ,    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
       )
       _lowerCAmelCase     :				Optional[Any]       =							(
           dataset_class(
               _A ,    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
       )
       _lowerCAmelCase     :				Union[str, Any]       =							(
           dataset_class(
               _A ,    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
       _lowerCAmelCase     :				Optional[Any]       =							(
           build_compute_metrics_fn(data_args.task ,    _A       ) if training_args.predict_with_generate else None
       )
       _lowerCAmelCase     :				int       =							SeqaSeqTrainer(
           model=_A ,    args=_A ,    data_args=_A ,    train_dataset=_A ,    eval_dataset=_A ,    data_collator=SeqaSeqDataCollator(
               _A ,    _A ,    model.config.decoder_start_token_id ,    training_args.tpu_num_cores       ) ,    compute_metrics=_A ,    tokenizer=_A ,    )

       _lowerCAmelCase     :				str       =							{}
       # Training
       if training_args.do_train:
              logger.info('*** Train ***'       )

              _lowerCAmelCase     :				Dict       =							trainer.train(
                  model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path       ) else None       )
              _lowerCAmelCase     :				Any       =							train_result.metrics
              _lowerCAmelCase     :				str       =							data_args.n_train

              trainer.save_model()  # this also saves the tokenizer

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

                     # 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 ***'       )

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

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

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

              _lowerCAmelCase     :				Any       =							trainer.predict(test_dataset=_A ,    metric_key_prefix='test'       )
              _lowerCAmelCase     :				Optional[int]       =							test_output.metrics
              _lowerCAmelCase     :				Optional[int]       =							data_args.n_test

              if trainer.is_world_process_zero():
                     _lowerCAmelCase     :				Dict       =							round(metrics['test_loss'] ,    4       )
                     handle_metrics('test' ,    _A ,    training_args.output_dir       )
                     all_metrics.update(_A       )

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

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

       return all_metrics






def       lowercase					(_A       ):







       """simple docstring"""







       main()


if __name__ == "__main__":
       main()

