





from __future__ import annotations

import unittest

from transformers import LEDConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers import TFLEDForConditionalGeneration, TFLEDModel







@require_tf
class 				__lowerCAmelCase       :
      _lowercase				:					Optional[int]						=			LEDConfig
      _lowercase				:					Optional[Any]						=			{}
      _lowercase				:					Optional[int]						=			"""gelu"""







      def __init__(    self		,      lowerCAmelCase__		,      lowerCAmelCase__=1_3		,      lowerCAmelCase__=7		,      lowerCAmelCase__=True		,      lowerCAmelCase__=False		,      lowerCAmelCase__=9_9		,      lowerCAmelCase__=3_2		,      lowerCAmelCase__=2		,      lowerCAmelCase__=4		,      lowerCAmelCase__=3_7		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=0.1		,      lowerCAmelCase__=2_0		,      lowerCAmelCase__=2		,      lowerCAmelCase__=1		,      lowerCAmelCase__=0		,      lowerCAmelCase__=4		,      )   ->						int:



        '''simple docstring'''




        a__	:					int										=parent
        a__	:					int										=batch_size
        a__	:					Dict										=seq_length
        a__	:					str										=is_training
        a__	:					Optional[int]										=use_labels
        a__	:					Optional[Any]										=vocab_size
        a__	:					List[str]										=hidden_size
        a__	:					Dict										=num_hidden_layers
        a__	:					str										=num_attention_heads
        a__	:					str										=intermediate_size

        a__	:					Dict										=hidden_dropout_prob
        a__	:					Tuple										=attention_probs_dropout_prob
        a__	:					List[str]										=max_position_embeddings
        a__	:					Dict										=eos_token_id
        a__	:					Tuple										=pad_token_id
        a__	:					Optional[Any]										=bos_token_id
        a__	:					int										=attention_window

        # `ModelTesterMixin.test_attention_outputs` is expecting attention tensors to be of size
        # [num_attention_heads, encoder_seq_length, encoder_key_length], but TFLongformerSelfAttention
        # returns attention of shape [num_attention_heads, encoder_seq_length, self.attention_window + 1]
        # because its local attention only attends to `self.attention_window` and one before and one after
        a__	:					Union[str, Any]										=self.attention_window + 2

        # because of padding `encoder_seq_length`, is different from `seq_length`. Relevant for
        # the `test_attention_outputs` and `test_hidden_states_output` tests
        a__	:					List[str]										=(
            self.seq_length + (self.attention_window - self.seq_length % self.attention_window) % self.attention_window
        )







      def    _lowercase  (    self							)   ->						List[Any]:



        '''simple docstring'''




        a__	:					Any										=ids_tensor([self.batch_size, self.seq_length - 1]		,      self.vocab_size							)
        a__	:					List[Any]										=tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size							)		,      1							)
        a__	:					List[Any]										=tf.concat([input_ids, eos_tensor]		,      axis=1							)

        a__	:					Any										=ids_tensor([self.batch_size, self.seq_length]		,      self.vocab_size							)

        a__	:					Tuple										=self.config_cls(
            vocab_size=self.vocab_size		,      d_model=self.hidden_size		,      encoder_layers=self.num_hidden_layers		,      decoder_layers=self.num_hidden_layers		,      encoder_attention_heads=self.num_attention_heads		,      decoder_attention_heads=self.num_attention_heads		,      encoder_ffn_dim=self.intermediate_size		,      decoder_ffn_dim=self.intermediate_size		,      dropout=self.hidden_dropout_prob		,      attention_dropout=self.attention_probs_dropout_prob		,      max_position_embeddings=self.max_position_embeddings		,      eos_token_ids=[2]		,      bos_token_id=self.bos_token_id		,      pad_token_id=self.pad_token_id		,      decoder_start_token_id=self.pad_token_id		,      attention_window=self.attention_window		,      **self.config_updates		,      )
        a__	:					int										=prepare_led_inputs_dict(lowerCAmelCase__		,      lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					str										=tf.concat(
            [tf.zeros_like(lowerCAmelCase__							)[:, :-1], tf.ones_like(lowerCAmelCase__							)[:, -1:]]		,      axis=-1		,      )
        a__	:					Dict										=global_attention_mask
        return config, inputs_dict







      def    _lowercase  (    self		,      lowerCAmelCase__		,      lowerCAmelCase__							)   ->						Tuple:



        '''simple docstring'''




        a__	:					Any										=TFLEDModel(config=lowerCAmelCase__							).get_decoder()
        a__	:					Tuple										=inputs_dict["input_ids"]

        a__	:					Any										=input_ids[:1, :]
        a__	:					List[str]										=inputs_dict["attention_mask"][:1, :]
        a__	:					List[Any]										=1

        # first forward pass
        a__	:					Any										=model(lowerCAmelCase__		,      attention_mask=lowerCAmelCase__		,      use_cache=lowerCAmelCase__							)

        a__					,							a__	:					Optional[int]										=outputs.to_tuple()

        # create hypothetical next token and extent to next_input_ids
        a__	:					Optional[Any]										=ids_tensor((self.batch_size, 3)		,      config.vocab_size							)
        a__	:					str										=tf.cast(ids_tensor((self.batch_size, 3)		,      2							)		,      tf.inta							)

        # append to next input_ids and
        a__	:					Optional[int]										=tf.concat([input_ids, next_tokens]		,      axis=-1							)
        a__	:					List[str]										=tf.concat([attention_mask, next_attn_mask]		,      axis=-1							)

        a__	:					List[Any]										=model(lowerCAmelCase__		,      attention_mask=lowerCAmelCase__							)[0]
        a__	:					Tuple										=model(lowerCAmelCase__		,      attention_mask=lowerCAmelCase__		,      past_key_values=lowerCAmelCase__							)[0]

        self.parent.assertEqual(next_tokens.shape[1]		,      output_from_past.shape[1]							)

        # select random slice
        a__	:					Any										=int(ids_tensor((1,)		,      output_from_past.shape[-1]							)							)
        a__	:					Optional[Any]										=output_from_no_past[:, -3:, random_slice_idx]
        a__	:					int										=output_from_past[:, :, random_slice_idx]

        # test that outputs are equal for slice
        tf.debugging.assert_near(lowerCAmelCase__		,      lowerCAmelCase__		,      rtol=1E-3							)
def      _A       (		SCREAMING_SNAKE_CASE     :   Tuple       ,       SCREAMING_SNAKE_CASE     :   List[str]       ,       SCREAMING_SNAKE_CASE     :   int       ,       SCREAMING_SNAKE_CASE     :   Optional[int]=None       ,       SCREAMING_SNAKE_CASE     :   str=None       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=None       ,       SCREAMING_SNAKE_CASE     :   Optional[Any]=None       ,       ):





  """simple docstring"""



  if attention_mask is None:
    a__	:					Optional[int]										=tf.cast(tf.math.not_equal(SCREAMING_SNAKE_CASE       ,       config.pad_token_id						)       ,       tf.inta						)
  if decoder_attention_mask is None:
    a__	:					Any										=tf.concat(
        [
            tf.ones(decoder_input_ids[:, :1].shape       ,       dtype=tf.inta						),
            tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,       config.pad_token_id						)       ,       tf.inta						),
        ]       ,       axis=-1       ,       )
  if head_mask is None:
    a__	:					int										=tf.ones((config.encoder_layers, config.encoder_attention_heads)						)
  if decoder_head_mask is None:
    a__	:					Optional[int]										=tf.ones((config.decoder_layers, config.decoder_attention_heads)						)
  return {
      "input_ids": input_ids,
      "attention_mask": attention_mask,
      "decoder_input_ids": decoder_input_ids,
      "decoder_attention_mask": decoder_attention_mask,
      "head_mask": head_mask,
      "decoder_head_mask": decoder_head_mask,
  }







@require_tf
class 				__lowerCAmelCase       (    UpperCamelCase__						,				UpperCamelCase__						,				unittest.TestCase):
      _lowercase				:					int						=			(TFLEDForConditionalGeneration, TFLEDModel) if is_tf_available() else ()
      _lowercase				:					Union[str, Any]						=			(TFLEDForConditionalGeneration,) if is_tf_available() else ()
      _lowercase				:					Optional[int]						=			(
          {
              """conversational""": TFLEDForConditionalGeneration,
              """feature-extraction""": TFLEDModel,
              """summarization""": TFLEDForConditionalGeneration,
              """text2text-generation""": TFLEDForConditionalGeneration,
              """translation""": TFLEDForConditionalGeneration,
          }
          if is_tf_available()
          else {}
      )
      _lowercase				:					Union[str, Any]						=			True
      _lowercase				:					List[str]						=			False
      _lowercase				:					List[str]						=			False
      _lowercase				:					Dict						=			False







      def    _lowercase  (    self							)   ->						int:



        '''simple docstring'''




        a__	:					List[str]										=TFLEDModelTester(self							)
        a__	:					Any										=ConfigTester(self		,      config_class=lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Optional[int]:



        '''simple docstring'''




        self.config_tester.run_common_tests()







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        a__	:					Any										=self.model_tester.prepare_config_and_inputs_for_common()
        self.model_tester.check_decoder_model_past_large_inputs(*lowerCAmelCase__							)







      def    _lowercase  (    self							)   ->						Tuple:



        '''simple docstring'''




        a__					,							a__	:					Any										=self.model_tester.prepare_config_and_inputs_for_common()
        a__	:					List[str]										=tf.zeros_like(inputs_dict["attention_mask"]							)
        a__	:					Tuple										=2
        a__	:					int										=tf.where(
            tf.range(self.model_tester.seq_length							)[None, :] < num_global_attn_indices		,      1		,      inputs_dict["global_attention_mask"]		,      )

        a__	:					Dict										=True
        a__	:					List[Any]										=self.model_tester.seq_length
        a__	:					Dict										=self.model_tester.encoder_seq_length

        def check_decoder_attentions_output(lowerCAmelCase__							):
          a__	:					List[Any]										=outputs.decoder_attentions
          self.assertEqual(len(lowerCAmelCase__							)		,      self.model_tester.num_hidden_layers							)
          self.assertListEqual(
              list(decoder_attentions[0].shape[-3:]							)		,      [self.model_tester.num_attention_heads, seq_length, seq_length]		,      )

        def check_encoder_attentions_output(lowerCAmelCase__							):
          a__	:					Optional[int]										=[t.numpy() for t in outputs.encoder_attentions]
          a__	:					Any										=[t.numpy() for t in outputs.encoder_global_attentions]
          self.assertEqual(len(lowerCAmelCase__							)		,      self.model_tester.num_hidden_layers							)
          self.assertEqual(len(lowerCAmelCase__							)		,      self.model_tester.num_hidden_layers							)
          self.assertListEqual(
              list(attentions[0].shape[-3:]							)		,      [self.model_tester.num_attention_heads, seq_length, seq_length]		,      )
          self.assertListEqual(
              list(global_attentions[0].shape[-3:]							)		,      [self.model_tester.num_attention_heads, encoder_seq_length, num_global_attn_indices]		,      )

        for model_class in self.all_model_classes:
          a__	:					Optional[int]										=True
          a__	:					List[Any]										=False
          a__	:					Any										=False
          a__	:					List[str]										=model_class(lowerCAmelCase__							)
          a__	:					Optional[Any]										=model(self._prepare_for_class(lowerCAmelCase__		,      lowerCAmelCase__							)							)
          a__	:					Optional[int]										=len(lowerCAmelCase__							)
          self.assertEqual(config.output_hidden_states		,      lowerCAmelCase__							)
          check_encoder_attentions_output(lowerCAmelCase__							)

          if self.is_encoder_decoder:
            a__	:					Union[str, Any]										=model_class(lowerCAmelCase__							)
            a__	:					Dict										=model(self._prepare_for_class(lowerCAmelCase__		,      lowerCAmelCase__							)							)
            self.assertEqual(config.output_hidden_states		,      lowerCAmelCase__							)
            check_decoder_attentions_output(lowerCAmelCase__							)

          # Check that output attentions can also be changed via the config
          del inputs_dict["output_attentions"]
          a__	:					str										=True
          a__	:					Union[str, Any]										=model_class(lowerCAmelCase__							)
          a__	:					Optional[Any]										=model(self._prepare_for_class(lowerCAmelCase__		,      lowerCAmelCase__							)							)
          self.assertEqual(config.output_hidden_states		,      lowerCAmelCase__							)
          check_encoder_attentions_output(lowerCAmelCase__							)

          # Check attention is always last and order is fine
          a__	:					int										=True
          a__	:					Tuple										=True
          a__	:					Tuple										=model_class(lowerCAmelCase__							)
          a__	:					Optional[Any]										=model(self._prepare_for_class(lowerCAmelCase__		,      lowerCAmelCase__							)							)

          self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1)		,      len(lowerCAmelCase__							)							)
          self.assertEqual(model.config.output_hidden_states		,      lowerCAmelCase__							)
          check_encoder_attentions_output(lowerCAmelCase__							)







      @unittest.skip("LED keeps using potentially symbolic tensors in conditionals and breaks tracing."							)
      def    _lowercase  (    self							)   ->						Optional[Any]:



        '''simple docstring'''




        pass







      def    _lowercase  (    self							)   ->						str:



        '''simple docstring'''




        pass
def      _A       (		SCREAMING_SNAKE_CASE     :   str						):





  """simple docstring"""



  return tf.constant(SCREAMING_SNAKE_CASE       ,       dtype=tf.intaa						)


UpperCAmelCase	:	Dict		     =       1E-4







@slow
@require_tf
class 				__lowerCAmelCase       (    unittest.TestCase):







      def    _lowercase  (    self							)   ->						Dict:



        '''simple docstring'''




        a__	:					Tuple										=TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"							).led

        # change to intended input here
        a__	:					Any										=_long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]							)
        a__	:					int										=_long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]							)
        a__	:					List[str]										=prepare_led_inputs_dict(model.config		,      lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					Tuple										=model(**lowerCAmelCase__							)[0]
        a__	:					Optional[int]										=(1, 1_0_2_4, 7_6_8)
        self.assertEqual(output.shape		,      lowerCAmelCase__							)
        # change to expected output here
        a__	:					Optional[int]										=tf.convert_to_tensor(
            [[2.30_50, 2.82_79, 0.65_31], [-1.84_57, -0.14_55, -3.56_61], [-1.01_86, 0.45_86, -2.20_43]]		,      )
        tf.debugging.assert_near(output[:, :3, :3]		,      lowerCAmelCase__		,      atol=1E-3							)







      def    _lowercase  (    self							)   ->						Any:



        '''simple docstring'''




        a__	:					Tuple										=TFLEDForConditionalGeneration.from_pretrained("allenai/led-base-16384"							)

        # change to intended input here
        a__	:					Union[str, Any]										=_long_tensor([5_1_2 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]							)
        a__	:					Dict										=_long_tensor([1_2_8 * [0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9]]							)
        a__	:					int										=prepare_led_inputs_dict(model.config		,      lowerCAmelCase__		,      lowerCAmelCase__							)
        a__	:					Optional[int]										=model(**lowerCAmelCase__							)[0]
        a__	:					Any										=(1, 1_0_2_4, model.config.vocab_size)
        self.assertEqual(output.shape		,      lowerCAmelCase__							)
        # change to expected output here
        a__	:					Dict										=tf.convert_to_tensor(
            [[33.65_07, 6.45_72, 16.80_89], [5.87_39, -2.42_38, 11.29_02], [-3.21_39, -4.31_49, 4.27_83]]		,      )
        tf.debugging.assert_near(output[:, :3, :3]		,      lowerCAmelCase__		,      atol=1E-3		,      rtol=1E-3							)








import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


lowercase__       =getLogger(__name__)




def    __UpperCamelCase     (				lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				str  ,   lowerCAmelCase__							:				int = 8  ,   lowerCAmelCase__							:				int = 1_0_2_4  ,   lowerCAmelCase__							:				Union[str, Any]="val"  ,   lowerCAmelCase__							:				int=None  ,   lowerCAmelCase__							:				Any=False  ,   lowerCAmelCase__							:				Union[str, Any]="summarization"  ,   lowerCAmelCase__							:				Tuple=None  ,   lowerCAmelCase__							:				Optional[int]=1  ,   lowerCAmelCase__							:				Dict = None  ,   lowerCAmelCase__							:				int=""  ,   **lowerCAmelCase__							:				int  ,   ):
 __a :  List[Any]						=  str(lowerCAmelCase__  )
 assert local_rank is not None
 torch.distributed.init_process_group(backend='''nccl'''  ,   rank=lowerCAmelCase__  )

 __a :  Tuple						=  Path(lowerCAmelCase__  )
 __a :  Dict						=  save_dir.joinpath(f"rank_{local_rank}_output.json"  )
 torch.cuda.set_device(lowerCAmelCase__  )
 __a :  Dict						=  AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase__  ).cuda()
 if fpaa:
  __a :  str						=  model.half()
 # determine if we need to increase num_beams
 use_task_specific_params(lowerCAmelCase__  ,   lowerCAmelCase__  )  # update config with task specific params
 __a :  List[str]						=  generate_kwargs.pop('''num_beams'''  ,   model.config.num_beams  )  # AttributeError risk?
 if num_return_sequences > num_beams:
  __a :  Dict						=  num_return_sequences

 __a :  Optional[Any]						=  AutoTokenizer.from_pretrained(lowerCAmelCase__  )
 logger.info(f"Inferred tokenizer type: {tokenizer.__class__}"  )  # if this is wrong, check config.model_type.

 if max_source_length is None:
  __a :  Dict						=  tokenizer.model_max_length
 if prefix is None:
  __a :  Dict						=  prefix or getattr(model.config  ,   '''prefix'''  ,   ''''''  ) or ''''''
 __a :  List[Any]						=  SeqaSeqDataset(
     lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   max_target_length=1_0_2_4  ,   type_path=lowerCAmelCase__  ,   n_obs=lowerCAmelCase__  ,   prefix=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
 # I set shuffle=True for a more accurate progress bar.
 # If all the longest samples are first, the prog bar estimate is too high at the beginning.
 __a :  Tuple						=  ds.make_sortish_sampler(lowerCAmelCase__  ,   distributed=lowerCAmelCase__  ,   add_extra_examples=lowerCAmelCase__  ,   shuffle=lowerCAmelCase__  )
 __a :  List[Any]						=  DataLoader(lowerCAmelCase__  ,   sampler=lowerCAmelCase__  ,   batch_size=lowerCAmelCase__  ,   collate_fn=ds.collate_fn  )
 __a :  List[Any]						=  []
 for batch in tqdm(lowerCAmelCase__  ):
  __a :  Any						=  model.generate(
      input_ids=batch['''input_ids'''].to(model.device  )  ,   attention_mask=batch['''attention_mask'''].to(model.device  )  ,   num_return_sequences=lowerCAmelCase__  ,   num_beams=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )
  __a :  List[Any]						=  tokenizer.batch_decode(lowerCAmelCase__  ,   skip_special_tokens=lowerCAmelCase__  ,   clean_up_tokenization_spaces=lowerCAmelCase__  )
  __a :  int						=  batch['''ids''']
  if num_return_sequences > 1:
   __a :  List[str]						=  chunks(lowerCAmelCase__  ,   lowerCAmelCase__  )  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(lowerCAmelCase__  ):
   results.append({'''pred''': pred, '''id''': ids[i].item()}  )
 save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
 return results, sampler.num_replicas




def    __UpperCamelCase     (				):
 __a :  str						=  argparse.ArgumentParser(
     epilog='''Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate'''  )
 parser.add_argument('''--data_dir'''  ,   type=lowerCAmelCase__  ,   help='''like cnn_dm/test.source'''  )
 parser.add_argument(
     '''--model_name'''  ,   type=lowerCAmelCase__  ,   help='''like facebook/bart-large-cnn,t5-base, etc.'''  ,   default='''sshleifer/distilbart-xsum-12-3'''  ,   )
 parser.add_argument('''--save_dir'''  ,   type=lowerCAmelCase__  ,   help='''where to save'''  ,   default='''tmp_gen'''  )
 parser.add_argument('''--max_source_length'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  )
 parser.add_argument(
     '''--type_path'''  ,   type=lowerCAmelCase__  ,   default='''test'''  ,   help='''which subset to evaluate typically train/val/test'''  )
 parser.add_argument('''--task'''  ,   type=lowerCAmelCase__  ,   default='''summarization'''  ,   help='''used for task_specific_params + metrics'''  )
 parser.add_argument('''--bs'''  ,   type=lowerCAmelCase__  ,   default=8  ,   required=lowerCAmelCase__  ,   help='''batch size'''  )
 parser.add_argument(
     '''--local_rank'''  ,   type=lowerCAmelCase__  ,   default=-1  ,   required=lowerCAmelCase__  ,   help='''should be passed by distributed.launch'''  )

 parser.add_argument(
     '''--n_obs'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   help='''How many observations. Defaults to all.'''  )
 parser.add_argument(
     '''--num_return_sequences'''  ,   type=lowerCAmelCase__  ,   default=1  ,   required=lowerCAmelCase__  ,   help='''How many sequences to return'''  )
 parser.add_argument(
     '''--sync_timeout'''  ,   type=lowerCAmelCase__  ,   default=6_0_0  ,   required=lowerCAmelCase__  ,   help='''How long should master process wait for other processes to finish.'''  ,   )
 parser.add_argument('''--src_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument('''--tgt_lang'''  ,   type=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   required=lowerCAmelCase__  )
 parser.add_argument(
     '''--prefix'''  ,   type=lowerCAmelCase__  ,   required=lowerCAmelCase__  ,   default=lowerCAmelCase__  ,   help='''will be added to the begininng of src examples'''  )
 parser.add_argument('''--fp16'''  ,   action='''store_true'''  )
 parser.add_argument('''--debug'''  ,   action='''store_true'''  )
 __a :  int						=  time.time()
 __a				,       __a :  Tuple						=  parser.parse_known_args()
 __a :  Optional[int]						=  parse_numeric_n_bool_cl_kwargs(lowerCAmelCase__  )
 if generate_kwargs and args.local_rank <= 0:
  print(f"parsed the following generate kwargs: {generate_kwargs}"  )
 __a :  Union[str, Any]						=  Path(args.save_dir + '''_tmp'''  )
 Path(lowerCAmelCase__  ).mkdir(exist_ok=lowerCAmelCase__  )  # this handles locking.
 __a :  Dict						=  list(json_save_dir.glob('''rank_*.json'''  )  )
 if intermediate_files:
  raise ValueError(f"Found files at {json_save_dir} please move or remove them."  )
  # In theory, a node could finish and save before another node hits this. If this happens, we can address later.
 __a :  Optional[Any]						=  {}
 if args.src_lang is not None:
  __a :  int						=  args.src_lang
 if args.tgt_lang is not None:
  __a :  Optional[Any]						=  args.tgt_lang

 Path(args.save_dir  ).mkdir(exist_ok=lowerCAmelCase__  )
 __a				,       __a :  Tuple						=  eval_data_dir(
     args.data_dir  ,   lowerCAmelCase__  ,   args.model_name  ,   type_path=args.type_path  ,   bs=args.bs  ,   fpaa=args.fpaa  ,   task=args.task  ,   local_rank=args.local_rank  ,   n_obs=args.n_obs  ,   max_source_length=args.max_source_length  ,   num_return_sequences=args.num_return_sequences  ,   prefix=args.prefix  ,   dataset_kwargs=lowerCAmelCase__  ,   **lowerCAmelCase__  ,   )

 if args.local_rank <= 0:
  __a :  int						=  Path(args.save_dir  )
  save_dir.mkdir(exist_ok=lowerCAmelCase__  )
  __a :  List[str]						=  gather_results_from_each_node(lowerCAmelCase__  ,   lowerCAmelCase__  ,   args.sync_timeout  )
  __a :  int						=  combine_partial_results(lowerCAmelCase__  )
  if args.num_return_sequences > 1:
   __a :  List[Any]						=  save_dir.joinpath('''pseudolabel_results.json'''  )
   print(f"Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"  )
   save_json(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return
  __a :  Any						=  Path(args.data_dir  ).joinpath(args.type_path + '''.target'''  )
  with open(lowerCAmelCase__  ) as f:
   __a :  Optional[int]						=  [x.rstrip() for x in f.readlines()][: len(lowerCAmelCase__  )]

  # Calculate metrics, save metrics,  and save _generations.txt
  __a :  str						=  '''translation''' in args.task
  __a :  List[str]						=  calculate_bleu if calc_bleu else calculate_rouge
  __a :  Any						=  '''bleu''' if calc_bleu else '''rouge'''
  __a :  Dict						=  score_fn(lowerCAmelCase__  ,   lowerCAmelCase__  )
  __a :  Dict						=  len(lowerCAmelCase__  )
  __a :  str						=  time.time() - start_time
  __a :  List[str]						=  round(runtime / metrics['''n_obs''']  ,   4  )
  __a :  Tuple						=  num_replicas
  # TODO(@stas00): add whatever metadata to metrics
  __a :  Optional[int]						=  save_dir.joinpath(f"{args.type_path}_{metric_name}.json"  )
  save_json(lowerCAmelCase__  ,   lowerCAmelCase__  ,   indent=lowerCAmelCase__  )
  print(lowerCAmelCase__  )
  write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}_generations.txt"  )  )
  if args.debug:
   write_txt_file(lowerCAmelCase__  ,   save_dir.joinpath(f"{args.type_path}.target"  )  )
  else:
   shutil.rmtree(lowerCAmelCase__  )




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[int]  ):
 __a :  Optional[int]						=  []
 for partial_result in partial_results:
  records.extend(lowerCAmelCase__  )
 __a :  Tuple						=  sorted(lowerCAmelCase__  ,   key=lambda lowerCAmelCase__  : x["id"]  )
 __a :  Tuple						=  [x['''pred'''] for x in records]
 return preds




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ,   lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				Dict  ):
 # WAIT FOR lots of .json files
 __a :  Tuple						=  time.time()
 logger.info('''waiting for all nodes to finish'''  )
 __a :  Optional[int]						=  None
 while (time.time() - start_wait) < timeout:
  __a :  Optional[int]						=  list(save_dir.glob('''rank_*.json'''  )  )
  if len(lowerCAmelCase__  ) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __a :  Tuple						=  lmap(lowerCAmelCase__  ,   lowerCAmelCase__  )
   return json_data
  except JSONDecodeError:
   continue
 else:
  raise TimeoutError('''Rank 0 gave up on waiting for other processes'''  )
 # Unreachable


if __name__ == "__main__":
      # Usage for MT:
      run_generate()
