




'''simple docstring'''



import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel







class 						lowercase_       :


   """simple docstring"""

   def __init__(       self   :      str    , __lowerCamelCase   :      int    , __lowerCamelCase   :      Dict=9_9    , __lowerCamelCase   :      Any=1_3    , __lowerCamelCase   :      Tuple=7    , __lowerCamelCase   :      int=9    , __lowerCamelCase   :      List[str]=True    , __lowerCamelCase   :      Optional[Any]=True    , __lowerCamelCase   :      int=False    , __lowerCamelCase   :      str=3_2    , __lowerCamelCase   :      Dict=5    , __lowerCamelCase   :      Union[str, Any]=4    , __lowerCamelCase   :      int=3_7    , __lowerCamelCase   :      Optional[int]=8    , __lowerCamelCase   :      Any=0.1    , __lowerCamelCase   :      Tuple=0.0_0_2    , __lowerCamelCase   :      Optional[int]=1    , __lowerCamelCase   :      int=0    , __lowerCamelCase   :      str=0    , __lowerCamelCase   :      Optional[int]=None    , __lowerCamelCase   :      Optional[Any]=None    , ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= parent
      _SCREAMING_SNAKE_CASE			= batch_size
      _SCREAMING_SNAKE_CASE			= encoder_seq_length
      _SCREAMING_SNAKE_CASE			= decoder_seq_length
      # For common tests
      _SCREAMING_SNAKE_CASE			= self.decoder_seq_length
      _SCREAMING_SNAKE_CASE			= is_training
      _SCREAMING_SNAKE_CASE			= use_attention_mask
      _SCREAMING_SNAKE_CASE			= use_labels
      _SCREAMING_SNAKE_CASE			= vocab_size
      _SCREAMING_SNAKE_CASE			= hidden_size
      _SCREAMING_SNAKE_CASE			= num_hidden_layers
      _SCREAMING_SNAKE_CASE			= num_attention_heads
      _SCREAMING_SNAKE_CASE			= d_ff
      _SCREAMING_SNAKE_CASE			= relative_attention_num_buckets
      _SCREAMING_SNAKE_CASE			= dropout_rate
      _SCREAMING_SNAKE_CASE			= initializer_factor
      _SCREAMING_SNAKE_CASE			= eos_token_id
      _SCREAMING_SNAKE_CASE			= pad_token_id
      _SCREAMING_SNAKE_CASE			= decoder_start_token_id
      _SCREAMING_SNAKE_CASE			= None
      _SCREAMING_SNAKE_CASE			= decoder_layers



   def 				lowerCAmelCase_     (       self   :      Dict       ):

      """simple docstring"""
      return TaConfig.from_pretrained("google/umt5-base"       )



   def 				lowerCAmelCase_     (       self   :      Optional[int]    , __lowerCamelCase   :      Optional[int]    , __lowerCamelCase   :      List[Any]    , __lowerCamelCase   :      List[str]    , __lowerCamelCase   :      List[Any]=None    , __lowerCamelCase   :      Tuple=None    , __lowerCamelCase   :      Optional[Any]=None    , __lowerCamelCase   :      Optional[int]=None    , __lowerCamelCase   :      Optional[Any]=None    , ):

      """simple docstring"""

      if attention_mask is None:
         _SCREAMING_SNAKE_CASE			= input_ids.ne(config.pad_token_id       )
      if decoder_attention_mask is None:
         _SCREAMING_SNAKE_CASE			= decoder_input_ids.ne(config.pad_token_id       )
      if head_mask is None:
         _SCREAMING_SNAKE_CASE			= torch.ones(config.num_hidden_layers    , config.num_attention_heads    , device=snake_case__       )
      if decoder_head_mask is None:
         _SCREAMING_SNAKE_CASE			= torch.ones(config.num_decoder_layers    , config.num_attention_heads    , device=snake_case__       )
      if cross_attn_head_mask is None:
         _SCREAMING_SNAKE_CASE			= torch.ones(
             config.num_decoder_layers    , config.num_attention_heads    , device=snake_case__       )
      return {
          "input_ids": input_ids,
          "decoder_input_ids": decoder_input_ids,
          "attention_mask": attention_mask,
          "decoder_attention_mask": decoder_attention_mask,
          "head_mask": head_mask,
          "decoder_head_mask": decoder_head_mask,
          "cross_attn_head_mask": cross_attn_head_mask,
      }



   def 				lowerCAmelCase_     (       self   :      Optional[int]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= ids_tensor([self.batch_size, self.encoder_seq_length]    , self.vocab_size       )
      _SCREAMING_SNAKE_CASE			= ids_tensor([self.batch_size, self.decoder_seq_length]    , self.vocab_size       )

      # we need to clamp the input ids here to avoid having pad token in between
      # this is because for NllbMoe the position_ids are prepared such that
      # all pad tokens have pos id = 2 and rest are between 2..seq_length
      # and the seq_length here is seq_length - num_pad_tokens
      # but when using past, there is no way of knowing if the past input ids had
      # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
      # position_ids being off by num_pad_tokens in past input
      _SCREAMING_SNAKE_CASE			= input_ids.clamp(self.pad_token_id + 1       )
      _SCREAMING_SNAKE_CASE			= decoder_input_ids.clamp(self.pad_token_id + 1       )

      _SCREAMING_SNAKE_CASE			= self.get_config()
      _SCREAMING_SNAKE_CASE			= config.num_attention_heads
      _SCREAMING_SNAKE_CASE			= self.prepare_inputs_dict(snake_case__    , snake_case__    , snake_case__       )
      return config, input_dict



   def 				lowerCAmelCase_     (       self   :      Tuple       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.prepare_config_and_inputs()
      return config, inputs_dict



   def 				lowerCAmelCase_     (       self   :      Any       ):

      """simple docstring"""
      return TaConfig(
          vocab_size=1_6_6    , d_model=self.hidden_size    , d_ff=self.d_ff    , d_kv=self.hidden_size // self.num_attention_heads    , num_layers=self.num_hidden_layers    , num_decoder_layers=self.decoder_layers    , num_heads=self.num_attention_heads    , relative_attention_num_buckets=self.relative_attention_num_buckets    , dropout_rate=self.dropout_rate    , initializer_factor=self.initializer_factor    , eos_token_id=self.eos_token_id    , bos_token_id=self.pad_token_id    , pad_token_id=self.pad_token_id    , decoder_start_token_id=self.decoder_start_token_id    , )



   def 				lowerCAmelCase_     (       self   :      Optional[Any]       ):

      """simple docstring"""
      return TaConfig(
          vocab_size=self.vocab_size    , d_model=self.hidden_size    , d_ff=self.d_ff    , d_kv=self.hidden_size // self.num_attention_heads    , num_layers=self.num_hidden_layers    , num_decoder_layers=self.decoder_layers    , num_heads=self.num_attention_heads    , relative_attention_num_buckets=self.relative_attention_num_buckets    , dropout_rate=self.dropout_rate    , initializer_factor=self.initializer_factor    , eos_token_id=self.eos_token_id    , bos_token_id=self.pad_token_id    , pad_token_id=self.pad_token_id    , decoder_start_token_id=self.decoder_start_token_id    , )



   def 				lowerCAmelCase_     (       self   :      List[Any]    , __lowerCamelCase   :      Optional[int]    , __lowerCamelCase   :      Dict    , __lowerCamelCase   :      str    , __lowerCamelCase   :      Any    , __lowerCamelCase   :      List[Any]    , __lowerCamelCase   :      str    , ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= UMTaModel(config=snake_case__       )
      model.to(snake_case__       )
      model.eval()
      _SCREAMING_SNAKE_CASE			= model(
          input_ids=snake_case__    , decoder_input_ids=snake_case__    , attention_mask=snake_case__    , decoder_attention_mask=snake_case__    , )
      _SCREAMING_SNAKE_CASE			= model(input_ids=snake_case__    , decoder_input_ids=snake_case__       )
      _SCREAMING_SNAKE_CASE			= result.last_hidden_state
      _SCREAMING_SNAKE_CASE			= result.past_key_values
      _SCREAMING_SNAKE_CASE			= result.encoder_last_hidden_state

      self.parent.assertEqual(encoder_output.size()    , (self.batch_size, self.encoder_seq_length, self.hidden_size)       )
      self.parent.assertEqual(decoder_output.size()    , (self.batch_size, self.decoder_seq_length, self.hidden_size)       )
      # There should be `num_layers` key value embeddings stored in decoder_past
      self.parent.assertEqual(len(snake_case__       )    , config.num_layers       )
      # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
      self.parent.assertEqual(len(decoder_past[0]       )    , 4       )



   def 				lowerCAmelCase_     (       self   :      int    , __lowerCamelCase   :      Union[str, Any]    , __lowerCamelCase   :      Union[str, Any]    , __lowerCamelCase   :      Optional[Any]    , __lowerCamelCase   :      Dict    , __lowerCamelCase   :      Any    , __lowerCamelCase   :      List[str]    , ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= UMTaModel(config=snake_case__       ).get_decoder().to(snake_case__       ).eval()
      # first forward pass
      _SCREAMING_SNAKE_CASE			= model(snake_case__    , use_cache=snake_case__       )
      _SCREAMING_SNAKE_CASE			= model(snake_case__       )
      _SCREAMING_SNAKE_CASE			= model(snake_case__    , use_cache=snake_case__       )

      self.parent.assertTrue(len(snake_case__       ) == len(snake_case__       )       )
      self.parent.assertTrue(len(snake_case__       ) == len(snake_case__       ) + 1       )

      _SCREAMING_SNAKE_CASE			= outputs.to_tuple()

      # create hypothetical next token and extent to next_input_ids
      _SCREAMING_SNAKE_CASE			= ids_tensor((self.batch_size, 1)    , config.vocab_size       )

      # append to next input_ids and
      _SCREAMING_SNAKE_CASE			= torch.cat([input_ids, next_tokens]    , dim=-1       )

      _SCREAMING_SNAKE_CASE			= model(snake_case__       )['last_hidden_state']
      _SCREAMING_SNAKE_CASE			= model(snake_case__    , past_key_values=snake_case__       )['last_hidden_state']

      # select random slice
      _SCREAMING_SNAKE_CASE			= ids_tensor((1,)    , output_from_past.shape[-1]       ).item()
      _SCREAMING_SNAKE_CASE			= output_from_no_past[:, -1, random_slice_idx].detach()
      _SCREAMING_SNAKE_CASE			= output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      self.parent.assertTrue(torch.allclose(snake_case__    , snake_case__    , atol=1e-3       )       )



   def 				lowerCAmelCase_     (       self   :      Optional[int]    , __lowerCamelCase   :      List[str]    , __lowerCamelCase   :      List[Any]    , ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= UMTaModel(config=snake_case__       ).to(snake_case__       ).half().eval()
      _SCREAMING_SNAKE_CASE			= model(**snake_case__       )['last_hidden_state']
      self.parent.assertFalse(torch.isnan(snake_case__       ).any().item()       )







@require_torch
class 						lowercase_       (					SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_	,    SCREAMING_SNAKE_CASE_	,    unittest.TestCase  ):


   """simple docstring"""

   lowerCamelCase_							    =		(
       (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
   )
   lowerCamelCase_							    =		(UMTaForConditionalGeneration,) if is_torch_available() else ()
   lowerCamelCase_							    =		(
       {
           '''conversational''': UMTaForConditionalGeneration,
           '''feature-extraction''': UMTaModel,
           '''summarization''': UMTaForConditionalGeneration,
           '''text2text-generation''': UMTaForConditionalGeneration,
           '''translation''': UMTaForConditionalGeneration,
           '''question-answering''': UMTaForQuestionAnswering,
       }
       if is_torch_available()
       else {}
   )
   lowerCamelCase_							    =		True
   lowerCamelCase_							    =		False
   lowerCamelCase_							    =		False
   lowerCamelCase_							    =		True
   lowerCamelCase_							    =		True
   # The small UMT5 model needs higher percentages for CPU/MP tests
   lowerCamelCase_							    =		[0.8, 0.9]



   def 				lowerCAmelCase_     (       self   :      List[str]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= UMTaModelTester(self       )



   @unittest.skip("Test has a segmentation fault on torch 1.8.0"       )
   def 				lowerCAmelCase_     (       self   :      Optional[Any]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.model_tester.prepare_config_and_inputs()
      _SCREAMING_SNAKE_CASE			= UMTaModel(config_and_inputs[0]       ).to(snake_case__       )
      with tempfile.TemporaryDirectory() as tmpdirname:
         torch.onnx.export(
             snake_case__    , (config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    , F"""{tmpdirname}/t5_test.onnx"""    , export_params=snake_case__    , opset_version=9    , input_names=["input_ids", "decoder_input_ids"]    , )



   @unittest.skipIf(torch_device == "cpu"    , "Cant do half precision"       )
   def 				lowerCAmelCase_     (       self   :      List[str]       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model_fpaa_forward(*snake_case__       )



   def 				lowerCAmelCase_     (       self   :      int       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= ['encoder_attentions', 'decoder_attentions', 'cross_attentions']
      _SCREAMING_SNAKE_CASE			= self.model_tester.prepare_config_and_inputs()
      _SCREAMING_SNAKE_CASE			= config_and_inputs[0]
      _SCREAMING_SNAKE_CASE			= UMTaForConditionalGeneration(snake_case__       ).eval()
      model.to(snake_case__       )

      _SCREAMING_SNAKE_CASE			= {
          'head_mask': torch.zeros(config.num_layers    , config.num_heads    , device=snake_case__       ),
          'decoder_head_mask': torch.zeros(config.num_decoder_layers    , config.num_heads    , device=snake_case__       ),
          'cross_attn_head_mask': torch.zeros(config.num_decoder_layers    , config.num_heads    , device=snake_case__       ),
      }

      for attn_name, (name, mask) in zip(snake_case__    , head_masking.items()       ):
         _SCREAMING_SNAKE_CASE			= {name: mask}
         # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
         if name == "head_mask":
            _SCREAMING_SNAKE_CASE			= torch.ones(
                config.num_decoder_layers    , config.num_heads    , device=snake_case__       )

         _SCREAMING_SNAKE_CASE			= model.generate(
             config_and_inputs[1]["input_ids"]    , num_beams=1    , max_length=3    , output_attentions=snake_case__    , return_dict_in_generate=snake_case__    , **snake_case__    , )
         # We check the state of decoder_attentions and cross_attentions just from the last step
         _SCREAMING_SNAKE_CASE			= out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
         self.assertEqual(sum([w.sum().item() for w in attn_weights]       )    , 0.0       )



   @unittest.skip("Does not work on the tiny model as we keep hitting edge cases."       )
   def 				lowerCAmelCase_     (       self   :      int       ):

      """simple docstring"""
      pass







@require_torch
@require_sentencepiece
@require_tokenizers
class 						lowercase_       (					unittest.TestCase  ):


   """simple docstring"""

   @slow
   @unittest.skip(
       "Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged"       )
   def 				lowerCAmelCase_     (       self   :      str       ):

      """simple docstring"""
      _SCREAMING_SNAKE_CASE			= UMTaForConditionalGeneration.from_pretrained("google/umt5-small"    , return_dict=snake_case__       ).to(snake_case__       )
      _SCREAMING_SNAKE_CASE			= AutoTokenizer.from_pretrained("google/umt5-small"    , use_fast=snake_case__    , legacy=snake_case__       )
      _SCREAMING_SNAKE_CASE			= [
          'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
          'No se como puedo <extra_id_0>.',
          'This is the reason why we <extra_id_0> them.',
          'The <extra_id_0> walks in <extra_id_1>, seats',
          'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
      ]
      _SCREAMING_SNAKE_CASE			= tokenizer(snake_case__    , return_tensors="pt"    , padding=snake_case__       ).input_ids
      # fmt: off
      _SCREAMING_SNAKE_CASE			= torch.tensor(
          [
              [ 3_8_5_3_0, 2_1_0_7_0_3, 2_5_6_2_9_9, 1_4_1_0, 2_5_6_2_9_8, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   8_2_6, 3_2_1, 6_7_1, 2_5_9_2_2, 2_5_6_2_9_9, 2_7_4, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [  1_4_6_0, 3_3_9, 3_1_2, 1_9_0_1_4, 1_0_6_2_0, 7_5_8, 2_5_6_2_9_9, 2_3_5_5,2_7_4, 1, 0, 0, 0, 0, 0, 0,0, 0],
              [   5_1_7, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 3_0_1, 2_5_6_2_9_8, 2_7_5, 1_1_9_9_8_3,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
              [   3_2_0, 2_5_6_2_9_9, 1_4_8_6_9, 2_8_1, 2_2_3_4, 2_8_9, 2_2_7_5, 3_3_3,6_1_3_9_1, 2_8_9, 2_5_6_2_9_8, 5_4_3, 2_5_6_2_9_7, 1_6_8_7_1_4, 3_2_9, 2_5_6_2_9_6,2_7_4, 1],
          ]       )
      # fmt: on
      torch.testing.assert_allclose(snake_case__    , snake_case__       )

      _SCREAMING_SNAKE_CASE			= model.generate(input_ids.to(snake_case__       )       )
      _SCREAMING_SNAKE_CASE			= [
          '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
          '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
          '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
      ]
      _SCREAMING_SNAKE_CASE			= tokenizer.batch_decode(snake_case__       )
      self.assertEqual(snake_case__    , snake_case__       )





'''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()

