





'''simple docstring'''

import inspect
import unittest

from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch

   from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
   from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import MobileViTImageProcessor
class 							__lowerCAmelCase	(       __magic_name__					):






       """simple docstring"""





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.config_class(**self.inputs_dict	)
            self.parent.assertTrue(hasattr(lowerCAmelCase__			,					'''hidden_sizes'''	)	)
            self.parent.assertTrue(hasattr(lowerCAmelCase__			,					'''neck_hidden_sizes'''	)	)
            self.parent.assertTrue(hasattr(lowerCAmelCase__			,					'''num_attention_heads'''	)	)

class 							__lowerCAmelCase	:






       """simple docstring"""





       def __init__(				self  :						str			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Optional[Any]=13			,					lowerCAmelCase__  :						Any=32			,					lowerCAmelCase__  :						Tuple=2			,					lowerCAmelCase__  :						Tuple=3			,					lowerCAmelCase__  :						List[Any]=640			,					lowerCAmelCase__  :						List[str]=4			,					lowerCAmelCase__  :						Optional[Any]="silu"			,					lowerCAmelCase__  :						Optional[int]=3			,					lowerCAmelCase__  :						Optional[int]=32			,					lowerCAmelCase__  :						List[Any]=0.1			,					lowerCAmelCase__  :						List[Any]=0.1			,					lowerCAmelCase__  :						int=0.1			,					lowerCAmelCase__  :						List[str]=0.02			,					lowerCAmelCase__  :						Any=True			,					lowerCAmelCase__  :						Optional[Any]=True			,					lowerCAmelCase__  :						str=10			,					lowerCAmelCase__  :						List[str]=None			,					)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			batch_size
            _UpperCamelCase							       =			image_size
            _UpperCamelCase							       =			patch_size
            _UpperCamelCase							       =			num_channels
            _UpperCamelCase							       =			last_hidden_size
            _UpperCamelCase							       =			num_attention_heads
            _UpperCamelCase							       =			hidden_act
            _UpperCamelCase							       =			conv_kernel_size
            _UpperCamelCase							       =			output_stride
            _UpperCamelCase							       =			hidden_dropout_prob
            _UpperCamelCase							       =			attention_probs_dropout_prob
            _UpperCamelCase							       =			classifier_dropout_prob
            _UpperCamelCase							       =			use_labels
            _UpperCamelCase							       =			is_training
            _UpperCamelCase							       =			num_labels
            _UpperCamelCase							       =			initializer_range
            _UpperCamelCase							       =			scope





       def snake_case__		(				self  :						Tuple	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]	)

            _UpperCamelCase							       =			None
            _UpperCamelCase							       =			None
            if self.use_labels:
                 _UpperCamelCase							       =			ids_tensor([self.batch_size]			,					self.num_labels	)
                 _UpperCamelCase							       =			ids_tensor([self.batch_size, self.image_size, self.image_size]			,					self.num_labels	)

            _UpperCamelCase							       =			self.get_config()

            return config, pixel_values, labels, pixel_labels





       def snake_case__		(				self  :						Union[str, Any]	)       ->	List[str]:

            '''simple docstring'''




            return MobileViTConfig(
                image_size=self.image_size			,					patch_size=self.patch_size			,					num_channels=self.num_channels			,					num_attention_heads=self.num_attention_heads			,					hidden_act=self.hidden_act			,					conv_kernel_size=self.conv_kernel_size			,					output_stride=self.output_stride			,					hidden_dropout_prob=self.hidden_dropout_prob			,					attention_probs_dropout_prob=self.attention_probs_dropout_prob			,					classifier_dropout_prob=self.classifier_dropout_prob			,					initializer_range=self.initializer_range			,					)





       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTModel(config=lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.eval()
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(
                result.last_hidden_state.shape			,					(
                    self.batch_size,
                    self.last_hidden_size,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )			,					)





       def snake_case__		(				self  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			MobileViTForImageClassification(lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.eval()
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					labels=lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.num_labels)	)





       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						int	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			MobileViTForSemanticSegmentation(lowerCAmelCase__	)
            model.to(lowerCAmelCase__	)
            model.eval()
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(
                result.logits.shape			,					(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )			,					)
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					labels=lowerCAmelCase__	)
            self.parent.assertEqual(
                result.logits.shape			,					(
                    self.batch_size,
                    self.num_labels,
                    self.image_size // self.output_stride,
                    self.image_size // self.output_stride,
                )			,					)







       def snake_case__		(				self  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.prepare_config_and_inputs()
            _UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase							       =			config_and_inputs
            _UpperCamelCase							       =			{'''pixel_values''': pixel_values}
            return config, inputs_dict

@require_torch
class 							__lowerCAmelCase	(       __magic_name__		,		__magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	List[str]									=       (
           (MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
           if is_torch_available()
           else ()
       )
       _snake_case      :	Any									=       (
           {
               'feature-extraction': MobileViTModel,
               'image-classification': MobileViTForImageClassification,
               'image-segmentation': MobileViTForSemanticSegmentation,
           }
           if is_torch_available()
           else {}
       )

       _snake_case      :	List[Any]									=       False
       _snake_case      :	Tuple									=       False
       _snake_case      :	Dict									=       False
       _snake_case      :	List[str]									=       False





       def snake_case__		(				self  :						Union[str, Any]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTModelTester(self	)
            _UpperCamelCase							       =			MobileViTConfigTester(self			,					config_class=lowerCAmelCase__			,					has_text_modality=lowerCAmelCase__	)





       def snake_case__		(				self  :						Any	)       ->	Optional[Any]:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       @unittest.skip(reason='''MobileViT does not use inputs_embeds'''	)
       def snake_case__		(				self  :						Optional[Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''MobileViT does not support input and output embeddings'''	)
       def snake_case__		(				self  :						Tuple	)       ->	List[Any]:

            '''simple docstring'''




            pass





       @unittest.skip(reason='''MobileViT does not output attentions'''	)
       def snake_case__		(				self  :						Dict	)       ->	List[str]:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						List[Any]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
                 _UpperCamelCase							       =			inspect.signature(model.forward	)
                 # signature.parameters is an OrderedDict => so arg_names order is deterministic
                 _UpperCamelCase							       =			[*signature.parameters.keys()]

                 _UpperCamelCase							       =			['''pixel_values''']
                 self.assertListEqual(arg_names[:1]			,					lowerCAmelCase__	)





       @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''	)
       def snake_case__		(				self  :						str	)       ->	Any:

            '''simple docstring'''




            pass





       def snake_case__		(				self  :						Tuple	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[int]	)       ->	List[str]:

            '''simple docstring'''




            def check_hidden_states_output(lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						Optional[int]	):
                 _UpperCamelCase							       =			model_class(lowerCAmelCase__	)
                 model.to(lowerCAmelCase__	)
                 model.eval()

                 with torch.no_grad():
                      _UpperCamelCase							       =			model(**self._prepare_for_class(lowerCAmelCase__			,					lowerCAmelCase__	)	)

                 _UpperCamelCase							       =			outputs.hidden_states

                 _UpperCamelCase							       =			5
                 self.assertEqual(len(lowerCAmelCase__	)			,					lowerCAmelCase__	)

                 # MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
                 # with the width and height being successively divided by 2.
                 _UpperCamelCase							       =			2
                 for i in range(len(lowerCAmelCase__	)	):
                      self.assertListEqual(
                          list(hidden_states[i].shape[-2:]	)			,					[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]			,					)
                      divisor *= 2

                 self.assertEqual(self.model_tester.output_stride			,					divisor // 2	)

            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

            for model_class in self.all_model_classes:
                 _UpperCamelCase							       =			True
                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 _UpperCamelCase							       =			True

                 check_hidden_states_output(lowerCAmelCase__			,					lowerCAmelCase__			,					lowerCAmelCase__	)





       def snake_case__		(				self  :						Tuple	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_image_classification(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[Any]	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase__	)







       @slow
       def snake_case__		(				self  :						str	)       ->	str:

            '''simple docstring'''




            for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			MobileViTModel.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)







def    a__							(							)						->						List[str]:





     """simple docstring"""

     _UpperCamelCase							       =			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
     return image




@require_torch
@require_vision
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @cached_property
       def snake_case__		(				self  :						Any	)       ->	List[Any]:

            '''simple docstring'''




            return MobileViTImageProcessor.from_pretrained('''apple/mobilevit-xx-small'''	) if is_vision_available() else None





       @slow
       def snake_case__		(				self  :						List[str]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTForImageClassification.from_pretrained('''apple/mobilevit-xx-small'''	).to(lowerCAmelCase__	)

            _UpperCamelCase							       =			self.default_image_processor
            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)

            # forward pass
            with torch.no_grad():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            # verify the logits
            _UpperCamelCase							       =			torch.Size((1, 1000)	)
            self.assertEqual(outputs.logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			torch.tensor([-1.9364, -1.2327, -0.4653]	).to(lowerCAmelCase__	)

            self.assertTrue(torch.allclose(outputs.logits[0, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)





       @slow
       def snake_case__		(				self  :						Optional[int]	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)
            _UpperCamelCase							       =			model.to(lowerCAmelCase__	)

            _UpperCamelCase							       =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)

            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)

            # forward pass
            with torch.no_grad():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)
            _UpperCamelCase							       =			outputs.logits

            # verify the logits
            _UpperCamelCase							       =			torch.Size((1, 21, 32, 32)	)
            self.assertEqual(logits.shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			torch.tensor(
                [
                    [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
                    [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
                    [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
                ]			,					device=lowerCAmelCase__			,					)

            self.assertTrue(torch.allclose(logits[0, :3, :3, :3]			,					lowerCAmelCase__			,					atol=1e-4	)	)







       @slow
       def snake_case__		(				self  :						Optional[Any]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			MobileViTForSemanticSegmentation.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)
            _UpperCamelCase							       =			model.to(lowerCAmelCase__	)

            _UpperCamelCase							       =			MobileViTImageProcessor.from_pretrained('''apple/deeplabv3-mobilevit-xx-small'''	)

            _UpperCamelCase							       =			prepare_img()
            _UpperCamelCase							       =			image_processor(images=lowerCAmelCase__			,					return_tensors='''pt'''	).to(lowerCAmelCase__	)

            # forward pass
            with torch.no_grad():
                 _UpperCamelCase							       =			model(**lowerCAmelCase__	)

            _UpperCamelCase							       =			outputs.logits.detach().cpu()

            _UpperCamelCase							       =			image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__			,					target_sizes=[(50, 60)]	)
            _UpperCamelCase							       =			torch.Size((50, 60)	)
            self.assertEqual(segmentation[0].shape			,					lowerCAmelCase__	)

            _UpperCamelCase							       =			image_processor.post_process_semantic_segmentation(outputs=lowerCAmelCase__	)
            _UpperCamelCase							       =			torch.Size((32, 32)	)
            self.assertEqual(segmentation[0].shape			,					lowerCAmelCase__	)







'''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,
)


__A   :    str											= logging.getLogger(__name__)






@dataclass
class   __snake_case			:







    """simple docstring"""



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







@dataclass
class   __snake_case			:







    """simple docstring"""



    lowercase  					=   field(
        metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'})
    lowercase  					=   field(
        default='summarization'      ,metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'}      ,)
    lowercase  					=   field(
        default=10_24      ,metadata={
            'help': (
                'The maximum total input sequence length after tokenization. Sequences longer '
                'than this will be truncated, sequences shorter will be padded.'
            )
        }      ,)
    lowercase  					=   field(
        default=1_28      ,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  					=   field(
        default=1_42      ,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  					=   field(
        default=1_42      ,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  					=   field(default=-1      ,metadata={'help': '# training examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# validation examples. -1 means use all.'})
    lowercase  					=   field(default=-1      ,metadata={'help': '# test examples. -1 means use all.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Source language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'Target language id for translation.'})
    lowercase  					=   field(default=_SCREAMING_SNAKE_CASE      ,metadata={'help': '# num_beams to use for evaluation.'})
    lowercase  					=   field(
        default=_SCREAMING_SNAKE_CASE      ,metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'}      ,)







def 							UpperCamelCase_     (       A__   :  Tuple				,       A__   :  List[str]				,       A__   :  str   ):
  '''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_     (       ):
  '''simple docstring'''





  lowerCAmelCase_     : Dict     =  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_     : Optional[int]     =  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
  else:
    lowerCAmelCase_,				lowerCAmelCase_,				lowerCAmelCase_     : Union[str, Any]     =  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_     : 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				,       )

  lowerCAmelCase_     : List[Any]     =  ("""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_     : Union[str, Any]     =  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_     : List[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_     : List[str]     =  tokenizer.lang_code_to_id[data_args.tgt_lang]
    else:
      lowerCAmelCase_     : int     =  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[str]     =  (
      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_     : List[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_     : List[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_     : Dict     =  (
      build_compute_metrics_fn(data_args.task				,       A__   ) if training_args.predict_with_generate else None
  )
  lowerCAmelCase_     : str     =  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_     : Dict     =  {}
  # Training
  if training_args.do_train:
    logger.info("""*** Train ***"""   )

    lowerCAmelCase_     : Optional[int]     =  trainer.train(
        model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path   ) else None   )
    lowerCAmelCase_     : Union[str, Any]     =  train_result.metrics
    lowerCAmelCase_     : Optional[int]     =  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_     : Any     =  trainer.evaluate(metric_key_prefix="""val"""   )
    lowerCAmelCase_     : Optional[int]     =  data_args.n_val
    lowerCAmelCase_     : Any     =  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_     : Union[str, Any]     =  trainer.predict(test_dataset=A__				,       metric_key_prefix="""test"""   )
    lowerCAmelCase_     : Optional[int]     =  test_output.metrics
    lowerCAmelCase_     : List[Any]     =  data_args.n_test

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

      if training_args.predict_with_generate:
        lowerCAmelCase_     : int     =  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 							UpperCamelCase_     (       A__   :  Optional[int]   ):
  '''simple docstring'''





  main()


if __name__ == "__main__":
  main()



