





import unittest

import numpy as np

from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_pytesseract_available():
  from PIL import Image

  from transformers import LayoutLMvaImageProcessor





class  lowerCAmelCase							(       unittest.TestCase ):







  def __init__(      self  :Union[str, Any]   ,							_lowercase  :Dict   ,							_lowercase  :Union[str, Any]=7   ,							_lowercase  :Dict=3   ,							_lowercase  :List[str]=18   ,							_lowercase  :Union[str, Any]=30   ,							_lowercase  :Optional[Any]=4_00   ,							_lowercase  :List[str]=True   ,							_lowercase  :List[str]=None   ,							_lowercase  :Any=True   ,							):






     '''simple docstring'''





     lowercase__			       =  size if size is not None else {"height": 18, "width": 18}
     lowercase__			       =  parent
     lowercase__			       =  batch_size
     lowercase__			       =  num_channels
     lowercase__			       =  image_size
     lowercase__			       =  min_resolution
     lowercase__			       =  max_resolution
     lowercase__			       =  do_resize
     lowercase__			       =  size
     lowercase__			       =  apply_ocr







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}





@require_torch
@require_pytesseract
class  lowerCAmelCase							(       lowercase_							,							unittest.TestCase ):
  __lowerCamelCase		=      LayoutLMvaImageProcessor if is_pytesseract_available() else None







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  LayoutLMvaImageProcessingTester(self )







  @property
  def   UpperCAmelCase					(      self  :Tuple ):






     '''simple docstring'''





     return self.image_processor_tester.prepare_image_processor_dict()







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






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     self.assertTrue(hasattr(_lowercase   ,							"do_resize" ) )
     self.assertTrue(hasattr(_lowercase   ,							"size" ) )
     self.assertTrue(hasattr(_lowercase   ,							"apply_ocr" ) )







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class.from_dict(self.image_processor_dict )
     self.assertEqual(image_processor.size   ,							{"height": 18, "width": 18} )

     lowercase__			       =  self.image_processing_class.from_dict(self.image_processor_dict   ,							size=42 )
     self.assertEqual(image_processor.size   ,							{"height": 42, "width": 42} )







  def   UpperCAmelCase					(      self  :str ):






     '''simple docstring'''





     pass







  def   UpperCAmelCase					(      self  :Optional[int] ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     # create random PIL images
     lowercase__			       =  prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_lowercase )
     for image in image_inputs:
        self.assertIsInstance(_lowercase   ,							Image.Image )

     # Test not batched input
     lowercase__			       =  image_processing(image_inputs[0]   ,							return_tensors="pt" )
     self.assertEqual(
         encoding.pixel_values.shape   ,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )   ,							)

     self.assertIsInstance(encoding.words   ,							_lowercase )
     self.assertIsInstance(encoding.boxes   ,							_lowercase )

     # Test batched
     lowercase__			       =  image_processing(_lowercase   ,							return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape   ,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )   ,							)







  def   UpperCAmelCase					(      self  :Optional[Any] ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     # create random numpy tensors
     lowercase__			       =  prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_lowercase   ,							numpify=_lowercase )
     for image in image_inputs:
        self.assertIsInstance(_lowercase   ,							np.ndarray )

     # Test not batched input
     lowercase__			       =  image_processing(image_inputs[0]   ,							return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape   ,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )   ,							)

     # Test batched
     lowercase__			       =  image_processing(_lowercase   ,							return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape   ,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )   ,							)







  def   UpperCAmelCase					(      self  :Any ):






     '''simple docstring'''





     lowercase__			       =  self.image_processing_class(**self.image_processor_dict )
     # create random PyTorch tensors
     lowercase__			       =  prepare_image_inputs(self.image_processor_tester   ,							equal_resolution=_lowercase   ,							torchify=_lowercase )
     for image in image_inputs:
        self.assertIsInstance(_lowercase   ,							torch.Tensor )

     # Test not batched input
     lowercase__			       =  image_processing(image_inputs[0]   ,							return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape   ,							(
             1,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )   ,							)

     # Test batched
     lowercase__			       =  image_processing(_lowercase   ,							return_tensors="pt" ).pixel_values
     self.assertEqual(
         encoded_images.shape   ,							(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.size["height"],
             self.image_processor_tester.size["width"],
         )   ,							)







  def   UpperCAmelCase					(      self  :List[Any] ):






     '''simple docstring'''





     lowercase__			       =  LayoutLMvaImageProcessor()

     from datasets import load_dataset

     lowercase__			       =  load_dataset("hf-internal-testing/fixtures_docvqa"   ,							split="test" )

     lowercase__			       =  Image.open(ds[0]["file"] ).convert("RGB" )

     lowercase__			       =  image_processing(_lowercase   ,							return_tensors="pt" )

     self.assertEqual(encoding.pixel_values.shape   ,							(1, 3, 2_24, 2_24) )
     self.assertEqual(len(encoding.words )   ,							len(encoding.boxes ) )

     # fmt: off
     # the words and boxes were obtained with Tesseract 4.1.1
     lowercase__			       =  [["11:14", "to", "11:39", "a.m", "11:39", "to", "11:44", "a.m.", "11:44", "a.m.", "to", "12:25", "p.m.", "12:25", "to", "12:58", "p.m.", "12:58", "to", "4:00", "p.m.", "2:00", "to", "5:00", "p.m.", "Coffee", "Break", "Coffee", "will", "be", "served", "for", "men", "and", "women", "in", "the", "lobby", "adjacent", "to", "exhibit", "area.", "Please", "move", "into", "exhibit", "area.", "(Exhibits", "Open)", "TRRF", "GENERAL", "SESSION", "(PART", "|)", "Presiding:", "Lee", "A.", "Waller", "TRRF", "Vice", "President", "“Introductory", "Remarks”", "Lee", "A.", "Waller,", "TRRF", "Vice", "Presi-", "dent", "Individual", "Interviews", "with", "TRRF", "Public", "Board", "Members", "and", "Sci-", "entific", "Advisory", "Council", "Mem-", "bers", "Conducted", "by", "TRRF", "Treasurer", "Philip", "G.", "Kuehn", "to", "get", "answers", "which", "the", "public", "refrigerated", "warehousing", "industry", "is", "looking", "for.", "Plus", "questions", "from", "the", "floor.", "Dr.", "Emil", "M.", "Mrak,", "University", "of", "Cal-", "ifornia,", "Chairman,", "TRRF", "Board;", "Sam", "R.", "Cecil,", "University", "of", "Georgia", "College", "of", "Agriculture;", "Dr.", "Stanley", "Charm,", "Tufts", "University", "School", "of", "Medicine;", "Dr.", "Robert", "H.", "Cotton,", "ITT", "Continental", "Baking", "Company;", "Dr.", "Owen", "Fennema,", "University", "of", "Wis-", "consin;", "Dr.", "Robert", "E.", "Hardenburg,", "USDA.", "Questions", "and", "Answers", "Exhibits", "Open", "Capt.", "Jack", "Stoney", "Room", "TRRF", "Scientific", "Advisory", "Council", "Meeting", "Ballroom", "Foyer"]]  # noqa: E231
     lowercase__			       =  [[[1_41, 57, 2_14, 69], [2_28, 58, 2_52, 69], [1_41, 75, 2_16, 88], [2_30, 79, 2_80, 88], [1_42, 2_60, 2_18, 2_73], [2_30, 2_61, 2_55, 2_73], [1_43, 2_79, 2_18, 2_90], [2_31, 2_82, 2_90, 2_91], [1_43, 3_42, 2_18, 3_54], [2_31, 3_45, 2_89, 3_55], [2_02, 3_62, 2_27, 3_73], [1_43, 3_79, 2_20, 3_92], [2_31, 3_82, 2_91, 3_94], [1_44, 7_14, 2_20, 7_26], [2_31, 7_15, 2_56, 7_26], [1_44, 7_32, 2_20, 7_45], [2_32, 7_36, 2_91, 7_47], [1_44, 7_69, 2_18, 7_82], [2_31, 7_70, 2_56, 7_82], [1_41, 7_88, 2_02, 8_01], [2_15, 7_91, 2_74, 8_04], [1_43, 8_26, 2_04, 8_38], [2_15, 8_26, 2_40, 8_38], [1_42, 8_44, 2_02, 8_57], [2_15, 8_47, 2_74, 8_59], [3_34, 57, 4_27, 69], [4_40, 57, 5_22, 69], [3_69, 75, 4_61, 88], [4_69, 75, 5_16, 88], [5_28, 76, 5_62, 88], [5_70, 76, 6_67, 88], [6_75, 75, 7_11, 87], [7_21, 79, 7_78, 88], [7_89, 75, 8_40, 88], [3_69, 97, 4_70, 1_07], [4_84, 94, 5_07, 1_06], [5_18, 94, 5_62, 1_07], [5_76, 94, 6_55, 1_10], [6_68, 94, 7_92, 1_09], [8_04, 95, 8_29, 1_07], [3_69, 1_13, 4_65, 1_25], [4_77, 1_16, 5_47, 1_25], [5_62, 1_13, 6_58, 1_25], [6_71, 1_16, 7_48, 1_25], [7_61, 1_13, 8_11, 1_25], [3_69, 1_31, 4_65, 1_43], [4_77, 1_33, 5_48, 1_43], [5_63, 1_30, 6_98, 1_45], [7_10, 1_30, 8_02, 1_46], [3_36, 1_71, 4_12, 1_83], [4_23, 1_71, 5_72, 1_83], [5_82, 1_70, 7_16, 1_84], [7_28, 1_71, 8_17, 1_87], [8_29, 1_71, 8_44, 1_86], [3_38, 1_97, 4_82, 2_12], [5_07, 1_96, 5_57, 2_09], [5_69, 1_96, 5_95, 2_08], [6_10, 1_96, 7_02, 2_09], [5_05, 2_14, 5_83, 2_26], [5_95, 2_14, 6_56, 2_27], [6_70, 2_15, 8_07, 2_27], [3_35, 2_59, 5_43, 2_74], [5_56, 2_59, 7_08, 2_72], [3_72, 2_79, 4_22, 2_91], [4_35, 2_79, 4_60, 2_91], [4_74, 2_79, 5_74, 2_92], [5_87, 2_78, 6_64, 2_91], [6_76, 2_78, 7_38, 2_91], [7_51, 2_79, 8_34, 2_91], [3_72, 2_98, 4_34, 3_10], [3_35, 3_41, 4_83, 3_54], [4_97, 3_41, 6_55, 3_54], [6_67, 3_41, 7_28, 3_54], [7_40, 3_41, 8_25, 3_54], [3_35, 3_60, 4_30, 3_72], [4_42, 3_60, 5_34, 3_72], [5_45, 3_59, 6_87, 3_72], [6_97, 3_60, 7_54, 3_72], [7_65, 3_60, 8_23, 3_73], [3_34, 3_78, 4_28, 3_91], [4_40, 3_78, 5_77, 3_94], [5_90, 3_78, 7_05, 3_91], [7_20, 3_78, 8_01, 3_91], [3_34, 3_97, 4_00, 4_09], [3_70, 4_16, 5_29, 4_29], [5_44, 4_16, 5_76, 4_32], [5_87, 4_16, 6_65, 4_28], [6_77, 4_16, 8_14, 4_29], [3_72, 4_35, 4_52, 4_50], [4_65, 4_34, 4_95, 4_47], [5_11, 4_34, 6_00, 4_47], [6_11, 4_36, 6_37, 4_47], [6_49, 4_36, 6_94, 4_51], [7_05, 4_38, 8_24, 4_47], [3_69, 4_53, 4_52, 4_66], [4_64, 4_54, 5_09, 4_66], [5_22, 4_53, 6_11, 4_69], [6_25, 4_53, 7_92, 4_69], [3_70, 4_72, 5_56, 4_88], [5_70, 4_72, 6_84, 4_87], [6_97, 4_72, 7_18, 4_85], [7_32, 4_72, 8_35, 4_88], [3_69, 4_90, 4_11, 5_03], [4_25, 4_90, 4_84, 5_03], [4_96, 4_90, 6_35, 5_06], [6_45, 4_90, 7_07, 5_03], [7_18, 4_91, 7_61, 5_03], [7_71, 4_90, 8_40, 5_03], [3_36, 5_10, 3_74, 5_21], [3_88, 5_10, 4_47, 5_22], [4_60, 5_10, 4_89, 5_21], [5_03, 5_10, 5_80, 5_22], [5_92, 5_09, 7_36, 5_25], [7_45, 5_09, 7_70, 5_22], [7_81, 5_09, 8_40, 5_22], [3_38, 5_28, 4_34, 5_41], [4_48, 5_28, 5_96, 5_41], [6_09, 5_27, 6_87, 5_40], [7_00, 5_28, 7_92, 5_41], [3_36, 5_46, 3_97, 5_59], [4_07, 5_46, 4_31, 5_59], [4_43, 5_46, 5_25, 5_60], [5_37, 5_46, 6_80, 5_62], [6_88, 5_46, 7_14, 5_59], [7_22, 5_46, 8_37, 5_62], [3_36, 5_65, 4_49, 5_81], [4_61, 5_65, 4_85, 5_77], [4_97, 5_65, 6_65, 5_81], [6_81, 5_65, 7_18, 5_77], [7_32, 5_65, 8_37, 5_80], [3_37, 5_84, 4_38, 5_97], [4_52, 5_83, 5_21, 5_96], [5_35, 5_84, 6_77, 5_99], [6_90, 5_83, 7_87, 5_96], [8_01, 5_83, 8_25, 5_96], [3_38, 6_02, 4_78, 6_15], [4_92, 6_02, 5_30, 6_14], [5_43, 6_02, 6_38, 6_15], [6_50, 6_02, 6_76, 6_14], [6_88, 6_02, 7_88, 6_15], [8_02, 6_02, 8_43, 6_14], [3_37, 6_21, 5_02, 6_33], [5_16, 6_21, 6_15, 6_37], [6_29, 6_21, 7_74, 6_36], [7_89, 6_21, 8_27, 6_33], [3_37, 6_39, 4_18, 6_52], [4_32, 6_40, 5_71, 6_53], [5_87, 6_39, 7_31, 6_55], [7_43, 6_39, 7_69, 6_52], [7_80, 6_39, 8_41, 6_52], [3_38, 6_58, 4_40, 6_73], [4_55, 6_58, 4_91, 6_70], [5_08, 6_58, 6_02, 6_71], [6_16, 6_58, 6_38, 6_70], [6_54, 6_58, 8_35, 6_74], [3_37, 6_77, 4_29, 6_89], [3_37, 7_14, 4_82, 7_26], [4_95, 7_14, 5_48, 7_26], [5_61, 7_14, 6_83, 7_26], [3_38, 7_70, 4_61, 7_82], [4_74, 7_69, 5_54, 7_85], [4_89, 7_88, 5_62, 8_03], [5_76, 7_88, 6_43, 8_01], [6_56, 7_87, 7_51, 8_04], [7_64, 7_88, 8_44, 8_01], [3_34, 8_25, 4_21, 8_38], [4_30, 8_24, 5_74, 8_38], [5_84, 8_24, 7_23, 8_41], [3_35, 8_44, 4_50, 8_57], [4_64, 8_43, 5_83, 8_60], [6_28, 8_62, 7_55, 8_75], [7_69, 8_61, 8_48, 8_78]]]  # noqa: E231
     # fmt: on

     self.assertListEqual(encoding.words   ,							_lowercase )
     self.assertListEqual(encoding.boxes   ,							_lowercase )

     # with apply_OCR = False
     lowercase__			       =  LayoutLMvaImageProcessor(apply_ocr=_lowercase )

     lowercase__			       =  image_processing(_lowercase   ,							return_tensors="pt" )

     self.assertEqual(encoding.pixel_values.shape   ,							(1, 3, 2_24, 2_24) )













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,
)


_snake_case           =			logging.getLogger(__name__)





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





@dataclass
class  lowerCAmelCase							:
  __lowerCamelCase		=      field(
      metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
  __lowerCamelCase		=      field(
      default='summarization'							,							metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}							,							)
  __lowerCamelCase		=      field(
      default=1_024							,							metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }							,							)
  __lowerCamelCase		=      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.'
          )
      }							,							)
  __lowerCamelCase		=      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``.'
          )
      }							,							)
  __lowerCamelCase		=      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.'
          )
      }							,							)
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# training examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# validation examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=-1							,							metadata={'help': '# test examples. -1 means use all.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Source language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': 'Target language id for translation.'} )
  __lowerCamelCase		=      field(default=lowercase_							,							metadata={'help': '# num_beams to use for evaluation.'} )
  __lowerCamelCase		=      field(
      default=lowercase_							,							metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}							,							)


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


def _A       (   ):
   # 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.

   lowercase__			       =  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__       ,							lowercase__       ,							lowercase__			       =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]	)	)
   else:
      lowercase__       ,							lowercase__       ,							lowercase__			       =  parser.parse_args_into_dataclasses()

   check_output_dir(__magic_name__	)

   # 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"  , __magic_name__	)

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

   lowercase__			       =  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__			       =  AutoModelForSeqaSeqLM.from_pretrained(
       model_args.model_name_or_path  , from_tf=".ckpt" in model_args.model_name_or_path  , config=__magic_name__  , cache_dir=model_args.cache_dir  , )

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

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

   # set decoder_start_token_id for MBart
   if model.config.decoder_start_token_id is None and isinstance(__magic_name__  , (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(__magic_name__  , __magic_name__	):
         lowercase__			       =  tokenizer.lang_code_to_id[data_args.tgt_lang]
      else:
         lowercase__			       =  tokenizer.convert_tokens_to_ids(data_args.tgt_lang	)

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

   lowercase__			       =  SeqaSeqDataset

   # Get datasets
   lowercase__			       =  (
       dataset_class(
           __magic_name__  , 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__			       =  (
       dataset_class(
           __magic_name__  , 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__			       =  (
       dataset_class(
           __magic_name__  , 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__			       =  (
       build_compute_metrics_fn(data_args.task  , __magic_name__	) if training_args.predict_with_generate else None
   )
   lowercase__			       =  SeqaSeqTrainer(
       model=__magic_name__  , args=__magic_name__  , data_args=__magic_name__  , train_dataset=__magic_name__  , eval_dataset=__magic_name__  , data_collator=SeqaSeqDataCollator(
           __magic_name__  , __magic_name__  , model.config.decoder_start_token_id  , training_args.tpu_num_cores	)  , compute_metrics=__magic_name__  , tokenizer=__magic_name__  , )

   lowercase__			       =  {}
   # Training
   if training_args.do_train:
      logger.info("*** Train ***"	)

      lowercase__			       =  trainer.train(
          model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path	) else None	)
      lowercase__			       =  train_result.metrics
      lowercase__			       =  data_args.n_train

      trainer.save_model()  # this also saves the tokenizer

      if trainer.is_world_process_zero():
         handle_metrics("train"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

         # 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__			       =  trainer.evaluate(metric_key_prefix="val"	)
      lowercase__			       =  data_args.n_val
      lowercase__			       =  round(metrics["val_loss"]  , 4	)

      if trainer.is_world_process_zero():
         handle_metrics("val"  , __magic_name__  , training_args.output_dir	)
         all_metrics.update(__magic_name__	)

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

      lowercase__			       =  trainer.predict(test_dataset=__magic_name__  , metric_key_prefix="test"	)
      lowercase__			       =  test_output.metrics
      lowercase__			       =  data_args.n_test

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

         if training_args.predict_with_generate:
            lowercase__			       =  tokenizer.batch_decode(
                test_output.predictions  , skip_special_tokens=__magic_name__  , clean_up_tokenization_spaces=__magic_name__	)
            lowercase__			       =  lmap(str.strip  , __magic_name__	)
            write_txt_file(__magic_name__  , os.path.join(training_args.output_dir  , "test_generations.txt"	)	)

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

   return all_metrics


def _A       (   __magic_name__	):
   # For xla_spawn (TPUs)
   main()


if __name__ == "__main__":
  main()






