

"""simple docstring"""




import unittest

from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_detectrona,
    require_pytesseract,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
     from PIL import Image

     from transformers.image_utils import load_image
else:


     class 		_A       :





           @staticmethod
           def 					_lowerCamelCase  (  *SCREAMING_SNAKE_CASE__    ,						**SCREAMING_SNAKE_CASE__		)   ->					int:
              pass






     def 	UpperCAmelCase__   (				A__   )      ->      Dict:




        """simple docstring"""
        return None


# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
SCREAMING_SNAKE_CASE_     :			Union[str, Any]	  =			(
    '''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)


@is_pipeline_test
@require_torch
@require_vision
class 		_A       (			unittest.TestCase					):
      __a								=	MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING





      @require_pytesseract
      @require_vision
      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Tuple:
         lowerCamelCase__						=			pipeline(
             "document-question-answering"    ,						model=SCREAMING_SNAKE_CASE__    ,						tokenizer=SCREAMING_SNAKE_CASE__    ,						image_processor=SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			INVOICE_URL
         lowerCamelCase__						=			list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						""		)		)		)
         lowerCamelCase__						=			"What is the placebo?"
         lowerCamelCase__						=			[
             {
                 "image": load_image(SCREAMING_SNAKE_CASE__		),
                 "question": question,
             },
             {
                 "image": image,
                 "question": question,
             },
             {
                 "image": image,
                 "question": question,
                 "word_boxes": word_boxes,
             },
         ]
         return dqa_pipeline, examples





      def 					_lowerCamelCase  (  self    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__		)   ->					Optional[int]:
         lowerCamelCase__						=			dqa_pipeline(SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(
             SCREAMING_SNAKE_CASE__    ,						[
                 [
                     {"score": ANY(SCREAMING_SNAKE_CASE__		), "answer": ANY(SCREAMING_SNAKE_CASE__		), "start": ANY(SCREAMING_SNAKE_CASE__		), "end": ANY(SCREAMING_SNAKE_CASE__		)},
                     {"score": ANY(SCREAMING_SNAKE_CASE__		), "answer": ANY(SCREAMING_SNAKE_CASE__		), "start": ANY(SCREAMING_SNAKE_CASE__		), "end": ANY(SCREAMING_SNAKE_CASE__		)},
                 ]
             ]
             * 3    ,						)





      @require_torch
      @require_detectrona
      @require_pytesseract
      def 					_lowerCamelCase  (  self		)   ->					Union[str, Any]:
         lowerCamelCase__						=			pipeline("document-question-answering"    ,						model="hf-internal-testing/tiny-random-layoutlmv2"		)
         lowerCamelCase__						=			INVOICE_URL
         lowerCamelCase__						=			"How many cats are there?"

         lowerCamelCase__						=			[
             {"score": 0.00_01, "answer": "oy 2312/2019", "start": 38, "end": 39},
             {"score": 0.00_01, "answer": "oy 2312/2019 DUE", "start": 38, "end": 40},
         ]
         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						SCREAMING_SNAKE_CASE__		)

         lowerCamelCase__						=			dqa_pipeline({"image": image, "question": question}    ,						top_k=2		)
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						SCREAMING_SNAKE_CASE__		)

         # This image does not detect ANY text in it, meaning layoutlmv2 should fail.
         # Empty answer probably
         lowerCamelCase__						=			"./tests/fixtures/tests_samples/COCO/000000039769.png"
         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(SCREAMING_SNAKE_CASE__    ,						[]		)

         # We can optionnally pass directly the words and bounding boxes
         lowerCamelCase__						=			"./tests/fixtures/tests_samples/COCO/000000039769.png"
         lowerCamelCase__						=			[]
         lowerCamelCase__						=			[]
         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						words=SCREAMING_SNAKE_CASE__    ,						boxes=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(SCREAMING_SNAKE_CASE__    ,						[]		)





      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def 					_lowerCamelCase  (  self		)   ->					str:
         lowerCamelCase__						=			pipeline(
             "document-question-answering"    ,						model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"    ,						revision="9977165"    ,						)
         lowerCamelCase__						=			INVOICE_URL
         lowerCamelCase__						=			"What is the invoice number?"

         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.99_44, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.00_09, "answer": "us-001", "start": 16, "end": 16},
             ]    ,						)

         lowerCamelCase__						=			dqa_pipeline({"image": image, "question": question}    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.99_44, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.00_09, "answer": "us-001", "start": 16, "end": 16},
             ]    ,						)

         lowerCamelCase__						=			dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 [
                     {"score": 0.99_44, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.00_09, "answer": "us-001", "start": 16, "end": 16},
                 ],
             ]
             * 2    ,						)





      @slow
      @require_torch
      @require_detectrona
      @require_pytesseract
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			pipeline(
             "document-question-answering"    ,						model="tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa"    ,						revision="9977165"    ,						max_seq_len=50    ,						)
         lowerCamelCase__						=			INVOICE_URL
         lowerCamelCase__						=			"What is the invoice number?"

         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.99_74, "answer": "1110212019", "start": 23, "end": 23},
                 {"score": 0.99_48, "answer": "us-001", "start": 16, "end": 16},
             ]    ,						)

         lowerCamelCase__						=			dqa_pipeline({"image": image, "question": question}    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.99_74, "answer": "1110212019", "start": 23, "end": 23},
                 {"score": 0.99_48, "answer": "us-001", "start": 16, "end": 16},
             ]    ,						)

         lowerCamelCase__						=			dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 [
                     {"score": 0.99_74, "answer": "1110212019", "start": 23, "end": 23},
                     {"score": 0.99_48, "answer": "us-001", "start": 16, "end": 16},
                 ]
             ]
             * 2    ,						)





      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def 					_lowerCamelCase  (  self		)   ->					Optional[int]:
         lowerCamelCase__						=			AutoTokenizer.from_pretrained(
             "impira/layoutlm-document-qa"    ,						revision="3dc6de3"    ,						add_prefix_space=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			pipeline(
             "document-question-answering"    ,						model="impira/layoutlm-document-qa"    ,						tokenizer=SCREAMING_SNAKE_CASE__    ,						revision="3dc6de3"    ,						)
         lowerCamelCase__						=			INVOICE_URL
         lowerCamelCase__						=			"What is the invoice number?"

         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.42_51, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.08_19, "answer": "1110212019", "start": 23, "end": 23},
             ]    ,						)

         lowerCamelCase__						=			dqa_pipeline({"image": image, "question": question}    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.42_51, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.08_19, "answer": "1110212019", "start": 23, "end": 23},
             ]    ,						)

         lowerCamelCase__						=			dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 [
                     {"score": 0.42_51, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.08_19, "answer": "1110212019", "start": 23, "end": 23},
                 ]
             ]
             * 2    ,						)

         lowerCamelCase__						=			list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						""		)		)		)

         # This model should also work if `image` is set to None
         lowerCamelCase__						=			dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.42_51, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.08_19, "answer": "1110212019", "start": 23, "end": 23},
             ]    ,						)





      @slow
      @require_torch
      @require_pytesseract
      @require_vision
      def 					_lowerCamelCase  (  self		)   ->					Tuple:
         lowerCamelCase__						=			AutoTokenizer.from_pretrained(
             "impira/layoutlm-document-qa"    ,						revision="3dc6de3"    ,						add_prefix_space=SCREAMING_SNAKE_CASE__		)
         lowerCamelCase__						=			pipeline(
             "document-question-answering"    ,						model="impira/layoutlm-document-qa"    ,						tokenizer=SCREAMING_SNAKE_CASE__    ,						revision="3dc6de3"    ,						max_seq_len=50    ,						)
         lowerCamelCase__						=			INVOICE_URL
         lowerCamelCase__						=			"What is the invoice number?"

         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.99_99, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.99_98, "answer": "us-001", "start": 16, "end": 16},
             ]    ,						)

         lowerCamelCase__						=			dqa_pipeline(
             [{"image": image, "question": question}, {"image": image, "question": question}]    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 [
                     {"score": 0.99_99, "answer": "us-001", "start": 16, "end": 16},
                     {"score": 0.99_98, "answer": "us-001", "start": 16, "end": 16},
                 ]
             ]
             * 2    ,						)

         lowerCamelCase__						=			list(zip(*apply_tesseract(load_image(SCREAMING_SNAKE_CASE__		)    ,						SCREAMING_SNAKE_CASE__    ,						""		)		)		)

         # This model should also work if `image` is set to None
         lowerCamelCase__						=			dqa_pipeline({"image": None, "word_boxes": word_boxes, "question": question}    ,						top_k=2		)
         self.assertEqual(
             nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[
                 {"score": 0.99_99, "answer": "us-001", "start": 16, "end": 16},
                 {"score": 0.99_98, "answer": "us-001", "start": 16, "end": 16},
             ]    ,						)





      @slow
      @require_torch
      def 					_lowerCamelCase  (  self		)   ->					int:
         lowerCamelCase__						=			pipeline(
             "document-question-answering"    ,						model="naver-clova-ix/donut-base-finetuned-docvqa"    ,						tokenizer=AutoTokenizer.from_pretrained("naver-clova-ix/donut-base-finetuned-docvqa"		)    ,						feature_extractor="naver-clova-ix/donut-base-finetuned-docvqa"    ,						)

         lowerCamelCase__						=			INVOICE_URL
         lowerCamelCase__						=			"What is the invoice number?"
         lowerCamelCase__						=			dqa_pipeline(image=SCREAMING_SNAKE_CASE__    ,						question=SCREAMING_SNAKE_CASE__    ,						top_k=2		)
         self.assertEqual(nested_simplify(SCREAMING_SNAKE_CASE__    ,						decimals=4		)    ,						[{"answer": "us-001"}]		)





      @require_tf
      @unittest.skip("Document question answering not implemented in TF"		)
      def 					_lowerCamelCase  (  self		)   ->					List[str]:
         pass







"""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,
)


SCREAMING_SNAKE_CASE_     :			Optional[Any]	  =			logging.getLogger(__name__)


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






@dataclass
class 		_A       :
      __a								=	field(
          metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'}					)
      __a								=	field(
          default='summarization'				,       metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}				,       )
      __a								=	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.'
              )
          }				,       )
      __a								=	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.'
              )
          }				,       )
      __a								=	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``.'
              )
          }				,       )
      __a								=	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.'
              )
          }				,       )
      __a								=	field(default=-1				,       metadata={'help': '# training examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# validation examples. -1 means use all.'}					)
      __a								=	field(default=-1				,       metadata={'help': '# test examples. -1 means use all.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Source language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': 'Target language id for translation.'}					)
      __a								=	field(default=__a				,       metadata={'help': '# num_beams to use for evaluation.'}					)
      __a								=	field(
          default=__a				,       metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}				,       )






def 	UpperCAmelCase__   (				A__	,    A__	,    A__   )      ->      Union[str, Any]:




   """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 	UpperCAmelCase__   (				)      ->      List[str]:




   """simple docstring"""
   # See all possible arguments in src/transformers/training_args.py
   # or by passing the --help flag to this script.
   # We now keep distinct sets of args, for a cleaner separation of concerns.

   lowerCamelCase__						=			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__						=			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
   else:
      lowerCamelCase__					,       lowerCamelCase__					,       lowerCamelCase__						=			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__						=			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__						=			("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__						=			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__						=			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__						=			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__						=			tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowerCamelCase__						=			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__						=			SeqaSeqDataset

   # Get datasets
   lowerCamelCase__						=			(
       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__						=			(
       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__						=			(
       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__						=			(
       build_compute_metrics_fn(data_args.task	,    A__   ) if training_args.predict_with_generate else None
   )
   lowerCamelCase__						=			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__						=			{}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"   )

      lowerCamelCase__						=			trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
      lowerCamelCase__						=			train_result.metrics
      lowerCamelCase__						=			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__						=			trainer.evaluate(metric_key_prefix="val"   )
      lowerCamelCase__						=			data_args.n_val
      lowerCamelCase__						=			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__						=			trainer.predict(test_dataset=A__	,    metric_key_prefix="test"   )
      lowerCamelCase__						=			test_output.metrics
      lowerCamelCase__						=			data_args.n_test

      if trainer.is_world_process_zero():
         lowerCamelCase__						=			round(metrics["test_loss"]	,    4   )
         handle_metrics("test"	,    A__	,    training_args.output_dir   )
         all_metrics.update(A__   )

         if training_args.predict_with_generate:
            lowerCamelCase__						=			tokenizer.batch_decode(
                test_output.predictions	,    skip_special_tokens=A__	,    clean_up_tokenization_spaces=A__   )
            lowerCamelCase__						=			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 	UpperCAmelCase__   (				A__   )      ->      Any:




   """simple docstring"""
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
     main()




