


'''simple docstring'''
import inspect
import unittest

from transformers import SegformerConfig, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device

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 (
     MODEL_MAPPING,
     SegformerForImageClassification,
     SegformerForSemanticSegmentation,
     SegformerModel,
 )
 from transformers.models.segformer.modeling_segformer import SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import SegformerImageProcessor

class 						_lowerCamelCase					(    snake_case_				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[int]       =			self.config_class(**self.inputs_dict						)
  self.parent.assertTrue(hasattr(__lowercase      ,      'hidden_sizes'						)						)
  self.parent.assertTrue(hasattr(__lowercase      ,      'num_attention_heads'						)						)
  self.parent.assertTrue(hasattr(__lowercase      ,      'num_encoder_blocks'						)						)


class 						_lowerCamelCase					:




 '''simple docstring'''


 def __init__(      self      ,      __lowercase      ,      __lowercase=13      ,      __lowercase=64      ,      __lowercase=3      ,      __lowercase=4      ,      __lowercase=[2, 2, 2, 2]      ,      __lowercase=[8, 4, 2, 1]      ,      __lowercase=[16, 32, 64, 128]      ,      __lowercase=[1, 4, 8, 16]      ,      __lowercase=[1, 2, 4, 8]      ,      __lowercase=True      ,      __lowercase=True      ,      __lowercase="gelu"      ,      __lowercase=0.1      ,      __lowercase=0.1      ,      __lowercase=0.0_2      ,      __lowercase=3      ,      __lowercase=None      ,      ):

  """simple docstring"""







  __A  :		Any       =			parent
  __A  :		Tuple       =			batch_size
  __A  :		Any       =			image_size
  __A  :		str       =			num_channels
  __A  :		Optional[int]       =			num_encoder_blocks
  __A  :		int       =			sr_ratios
  __A  :		int       =			depths
  __A  :		Optional[Any]       =			hidden_sizes
  __A  :		int       =			downsampling_rates
  __A  :		str       =			num_attention_heads
  __A  :		List[str]       =			is_training
  __A  :		Any       =			use_labels
  __A  :		Dict       =			hidden_act
  __A  :		Union[str, Any]       =			hidden_dropout_prob
  __A  :		List[str]       =			attention_probs_dropout_prob
  __A  :		Optional[Any]       =			initializer_range
  __A  :		Optional[Any]       =			num_labels
  __A  :		int       =			scope


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

  __A  :		List[Any]       =			self.get_config()
  return config, pixel_values, labels


 def  snake_case__   (      self						):

  """simple docstring"""







  return SegformerConfig(
      image_size=self.image_size      ,      num_channels=self.num_channels      ,      num_encoder_blocks=self.num_encoder_blocks      ,      depths=self.depths      ,      hidden_sizes=self.hidden_sizes      ,      num_attention_heads=self.num_attention_heads      ,      hidden_act=self.hidden_act      ,      hidden_dropout_prob=self.hidden_dropout_prob      ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,      initializer_range=self.initializer_range      ,      )


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[int]       =			SegformerModel(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		str       =			model(__lowercase						)
  __A  :		Optional[int]       =			self.image_size // (self.downsampling_rates[-1] * 2)
  self.parent.assertEqual(
      result.last_hidden_state.shape      ,      (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.num_labels
  __A  :		List[str]       =			SegformerForSemanticSegmentation(__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		Dict       =			model(__lowercase						)
  self.parent.assertEqual(
      result.logits.shape      ,      (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)						)
  __A  :		int       =			model(__lowercase      ,      labels=__lowercase						)
  self.parent.assertEqual(
      result.logits.shape      ,      (self.batch_size, self.num_labels, self.image_size // 4, self.image_size // 4)						)
  self.parent.assertGreater(result.loss      ,      0.0						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  __A  :		int       =			1
  __A  :		Any       =			SegformerForSemanticSegmentation(config=__lowercase						)
  model.to(__lowercase						)
  model.eval()
  __A  :		List[Any]       =			torch.randint(0      ,      1      ,      (self.batch_size, self.image_size, self.image_size)						).to(__lowercase						)
  __A  :		Optional[Any]       =			model(__lowercase      ,      labels=__lowercase						)
  self.parent.assertGreater(result.loss      ,      0.0						)







 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			self.prepare_config_and_inputs()
  __A     ,__A     ,__A  :		int       =			config_and_inputs
  __A  :		List[str]       =			{'pixel_values': pixel_values}
  return config, inputs_dict


@require_torch
class 						_lowerCamelCase					(    snake_case_    ,		snake_case_    ,		unittest.TestCase				):




 '''simple docstring'''
 __lowercase				:		str    							=						(
     (
         SegformerModel,
         SegformerForSemanticSegmentation,
         SegformerForImageClassification,
     )
     if is_torch_available()
     else ()
 )
 __lowercase				:		int    							=						(
     {
         '''feature-extraction''': SegformerModel,
         '''image-classification''': SegformerForImageClassification,
         '''image-segmentation''': SegformerForSemanticSegmentation,
     }
     if is_torch_available()
     else {}
 )

 __lowercase				:		List[str]    							=						True
 __lowercase				:		List[str]    							=						False
 __lowercase				:		List[str]    							=						False
 __lowercase				:		Dict    							=						False


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		int       =			SegformerModelTester(self						)
  __A  :		Dict       =			SegformerConfigTester(self      ,      config_class=__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  self.config_tester.run_common_tests()


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Any       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_model(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		str       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_binary_image_segmentation(*__lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[Any]       =			self.model_tester.prepare_config_and_inputs()
  self.model_tester.create_and_check_for_image_segmentation(*__lowercase						)


 @unittest.skip('SegFormer does not use inputs_embeds'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 @unittest.skip('SegFormer does not have get_input_embeddings method and get_output_embeddings methods'						)
 def  snake_case__   (      self						):

  """simple docstring"""







  pass


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Dict       =			self.model_tester.prepare_config_and_inputs_for_common()

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

   __A  :		str       =			['pixel_values']
   self.assertListEqual(arg_names[:1]      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  __A     ,__A  :		Optional[Any]       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		int       =			True

  for model_class in self.all_model_classes:
   __A  :		Optional[int]       =			True
   __A  :		Any       =			False
   __A  :		List[Any]       =			True
   __A  :		int       =			model_class(__lowercase						)
   model.to(__lowercase						)
   model.eval()
   with torch.no_grad():
    __A  :		int       =			model(**self._prepare_for_class(__lowercase      ,      __lowercase						)						)
   __A  :		int       =			outputs.attentions

   __A  :		int       =			sum(self.model_tester.depths						)
   self.assertEqual(len(__lowercase						)      ,      __lowercase						)

   # check that output_attentions also work using config
   del inputs_dict["output_attentions"]
   __A  :		int       =			True
   __A  :		List[Any]       =			model_class(__lowercase						)
   model.to(__lowercase						)
   model.eval()
   with torch.no_grad():
    __A  :		List[str]       =			model(**self._prepare_for_class(__lowercase      ,      __lowercase						)						)
   __A  :		Tuple       =			outputs.attentions

   self.assertEqual(len(__lowercase						)      ,      __lowercase						)

   # verify the first attentions (first block, first layer)
   __A  :		Optional[int]       =			(self.model_tester.image_size // 4) ** 2
   __A  :		Tuple       =			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
   self.assertListEqual(
       list(attentions[0].shape[-3:]						)      ,      [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,      )

   # verify the last attentions (last block, last layer)
   __A  :		List[Any]       =			(self.model_tester.image_size // 32) ** 2
   __A  :		int       =			(self.model_tester.image_size // (32 * self.model_tester.sr_ratios[-1])) ** 2
   self.assertListEqual(
       list(attentions[-1].shape[-3:]						)      ,      [self.model_tester.num_attention_heads[-1], expected_seq_len, expected_reduced_seq_len]      ,      )
   __A  :		Tuple       =			len(__lowercase						)

   # Check attention is always last and order is fine
   __A  :		Tuple       =			True
   __A  :		Dict       =			True
   __A  :		int       =			model_class(__lowercase						)
   model.to(__lowercase						)
   model.eval()
   with torch.no_grad():
    __A  :		Tuple       =			model(**self._prepare_for_class(__lowercase      ,      __lowercase						)						)

   self.assertEqual(out_len + 1      ,      len(__lowercase						)						)

   __A  :		Optional[Any]       =			outputs.attentions

   self.assertEqual(len(__lowercase						)      ,      __lowercase						)
   # verify the first attentions (first block, first layer)
   __A  :		Union[str, Any]       =			(self.model_tester.image_size // 4) ** 2
   __A  :		Union[str, Any]       =			(self.model_tester.image_size // (4 * self.model_tester.sr_ratios[0])) ** 2
   self.assertListEqual(
       list(self_attentions[0].shape[-3:]						)      ,      [self.model_tester.num_attention_heads[0], expected_seq_len, expected_reduced_seq_len]      ,      )


 def  snake_case__   (      self						):

  """simple docstring"""







  def check_hidden_states_output(__lowercase      ,      __lowercase      ,      __lowercase						):
   __A  :		Tuple       =			model_class(__lowercase						)
   model.to(__lowercase						)
   model.eval()

   with torch.no_grad():
    __A  :		Any       =			model(**self._prepare_for_class(__lowercase      ,      __lowercase						)						)

   __A  :		int       =			outputs.hidden_states

   __A  :		Optional[int]       =			self.model_tester.num_encoder_blocks
   self.assertEqual(len(__lowercase						)      ,      __lowercase						)

   # verify the first hidden states (first block)
   self.assertListEqual(
       list(hidden_states[0].shape[-3:]						)      ,      [
           self.model_tester.hidden_sizes[0],
           self.model_tester.image_size // 4,
           self.model_tester.image_size // 4,
       ]      ,      )

  __A     ,__A  :		Tuple       =			self.model_tester.prepare_config_and_inputs_for_common()

  for model_class in self.all_model_classes:
   __A  :		List[Any]       =			True
   check_hidden_states_output(__lowercase      ,      __lowercase      ,      __lowercase						)

   # check that output_hidden_states also work using config
   del inputs_dict["output_hidden_states"]
   __A  :		List[Any]       =			True

   check_hidden_states_output(__lowercase      ,      __lowercase      ,      __lowercase						)


 def  snake_case__   (      self						):

  """simple docstring"""







  if not self.model_tester.is_training:
   return

  __A     ,__A  :		int       =			self.model_tester.prepare_config_and_inputs_for_common()
  __A  :		List[str]       =			True

  for model_class in self.all_model_classes:
   if model_class in get_values(__lowercase						):
    continue

   __A  :		List[str]       =			model_class(__lowercase						)
   model.to(__lowercase						)
   model.train()
   __A  :		str       =			self._prepare_for_class(__lowercase      ,      __lowercase      ,      return_labels=__lowercase						)
   __A  :		Optional[Any]       =			model(**__lowercase						).loss
   loss.backward()


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

  """simple docstring"""







  pass







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  for model_name in SEGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
   __A  :		Optional[int]       =			SegformerModel.from_pretrained(__lowercase						)
   self.assertIsNotNone(__lowercase						)


def 			_lowercase       (				):
 __A  :		List[str]       =			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'			)
 return image


@require_torch
class 						_lowerCamelCase					(    unittest.TestCase				):




 '''simple docstring'''


 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		List[str]       =			SegformerImageProcessor(
      image_scale=(512, 512)      ,      keep_ratio=__lowercase      ,      align=__lowercase      ,      do_random_crop=__lowercase						)
  __A  :		Union[str, Any]       =			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'						).to(
      __lowercase						)

  __A  :		Any       =			prepare_img()
  __A  :		int       =			image_processor(images=__lowercase      ,      return_tensors='pt'						)
  __A  :		int       =			encoded_inputs.pixel_values.to(__lowercase						)

  with torch.no_grad():
   __A  :		List[str]       =			model(__lowercase						)

  __A  :		int       =			torch.Size((1, model.config.num_labels, 128, 128)						)
  self.assertEqual(outputs.logits.shape      ,      __lowercase						)

  __A  :		Any       =			torch.tensor(
      [
          [[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
          [[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
          [[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
      ]						).to(__lowercase						)
  self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,      __lowercase      ,      atol=1E-4						)						)


 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Optional[Any]       =			SegformerImageProcessor(
      image_scale=(512, 512)      ,      keep_ratio=__lowercase      ,      align=__lowercase      ,      do_random_crop=__lowercase						)
  __A  :		List[str]       =			SegformerForSemanticSegmentation.from_pretrained(
      'nvidia/segformer-b1-finetuned-cityscapes-1024-1024'						).to(__lowercase						)

  __A  :		Dict       =			prepare_img()
  __A  :		Any       =			image_processor(images=__lowercase      ,      return_tensors='pt'						)
  __A  :		List[str]       =			encoded_inputs.pixel_values.to(__lowercase						)

  with torch.no_grad():
   __A  :		Optional[Any]       =			model(__lowercase						)

  __A  :		Union[str, Any]       =			torch.Size((1, model.config.num_labels, 128, 128)						)
  self.assertEqual(outputs.logits.shape      ,      __lowercase						)

  __A  :		int       =			torch.tensor(
      [
          [[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
          [[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
          [[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
      ]						).to(__lowercase						)
  self.assertTrue(torch.allclose(outputs.logits[0, :3, :3, :3]      ,      __lowercase      ,      atol=1E-1						)						)







 @slow
 def  snake_case__   (      self						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			SegformerImageProcessor(
      image_scale=(512, 512)      ,      keep_ratio=__lowercase      ,      align=__lowercase      ,      do_random_crop=__lowercase						)
  __A  :		str       =			SegformerForSemanticSegmentation.from_pretrained('nvidia/segformer-b0-finetuned-ade-512-512'						).to(
      __lowercase						)

  __A  :		str       =			prepare_img()
  __A  :		Dict       =			image_processor(images=__lowercase      ,      return_tensors='pt'						)
  __A  :		str       =			encoded_inputs.pixel_values.to(__lowercase						)

  with torch.no_grad():
   __A  :		Optional[int]       =			model(__lowercase						)

  __A  :		Union[str, Any]       =			outputs.logits.detach().cpu()

  __A  :		Tuple       =			image_processor.post_process_semantic_segmentation(outputs=__lowercase      ,      target_sizes=[(500, 300)]						)
  __A  :		int       =			torch.Size((500, 300)						)
  self.assertEqual(segmentation[0].shape      ,      __lowercase						)

  __A  :		List[str]       =			image_processor.post_process_semantic_segmentation(outputs=__lowercase						)
  __A  :		Tuple       =			torch.Size((128, 128)						)
  self.assertEqual(segmentation[0].shape      ,      __lowercase						)










'''simple docstring'''
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,
)


UpperCAmelCase_ :     Dict								=      getLogger(__name__)


def 			_lowercase       (				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					str,				UpperCamelCase__       :					str,				UpperCamelCase__       :					int = 8,				UpperCamelCase__       :					int = 1024,				UpperCamelCase__       :					List[Any]="val",				UpperCamelCase__       :					int=None,				UpperCamelCase__       :					str=False,				UpperCamelCase__       :					int="summarization",				UpperCamelCase__       :					List[Any]=None,				UpperCamelCase__       :					List[Any]=1,				UpperCamelCase__       :					Dict = None,				UpperCamelCase__       :					Optional[int]="",				**UpperCamelCase__       :					str,				):
 __A  :		Dict       =			str(UpperCamelCase__			)
 assert local_rank is not None
 torch.distributed.init_process_group(backend='nccl',				rank=UpperCamelCase__			)

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

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

 if max_source_length is None:
  __A  :		Union[str, Any]       =			tokenizer.model_max_length
 if prefix is None:
  __A  :		List[Any]       =			prefix or getattr(model.config,				'prefix',				''			) or ''
 __A  :		Tuple       =			SeqaSeqDataset(
     UpperCamelCase__,				UpperCamelCase__,				UpperCamelCase__,				max_target_length=1024,				type_path=UpperCamelCase__,				n_obs=UpperCamelCase__,				prefix=UpperCamelCase__,				**UpperCamelCase__,				)
 # 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  :		Any       =			ds.make_sortish_sampler(UpperCamelCase__,				distributed=UpperCamelCase__,				add_extra_examples=UpperCamelCase__,				shuffle=UpperCamelCase__			)
 __A  :		Union[str, Any]       =			DataLoader(UpperCamelCase__,				sampler=UpperCamelCase__,				batch_size=UpperCamelCase__,				collate_fn=ds.collate_fn			)
 __A  :		Tuple       =			[]
 for batch in tqdm(UpperCamelCase__			):
  __A  :		Any       =			model.generate(
      input_ids=batch['input_ids'].to(model.device			),				attention_mask=batch['attention_mask'].to(model.device			),				num_return_sequences=UpperCamelCase__,				num_beams=UpperCamelCase__,				**UpperCamelCase__,				)
  __A  :		Dict       =			tokenizer.batch_decode(UpperCamelCase__,				skip_special_tokens=UpperCamelCase__,				clean_up_tokenization_spaces=UpperCamelCase__			)
  __A  :		List[str]       =			batch['ids']
  if num_return_sequences > 1:
   __A  :		str       =			chunks(UpperCamelCase__,				UpperCamelCase__			)  # batch size chunks, each of size num_return_seq
  for i, pred in enumerate(UpperCamelCase__			):
   results.append({'pred': pred, 'id': ids[i].item()}			)
 save_json(UpperCamelCase__,				UpperCamelCase__			)
 return results, sampler.num_replicas


def 			_lowercase       (				):
 __A  :		Optional[Any]       =			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=UpperCamelCase__,				help='like cnn_dm/test.source'			)
 parser.add_argument(
     '--model_name',				type=UpperCamelCase__,				help='like facebook/bart-large-cnn,t5-base, etc.',				default='sshleifer/distilbart-xsum-12-3',				)
 parser.add_argument('--save_dir',				type=UpperCamelCase__,				help='where to save',				default='tmp_gen'			)
 parser.add_argument('--max_source_length',				type=UpperCamelCase__,				default=UpperCamelCase__			)
 parser.add_argument(
     '--type_path',				type=UpperCamelCase__,				default='test',				help='which subset to evaluate typically train/val/test'			)
 parser.add_argument('--task',				type=UpperCamelCase__,				default='summarization',				help='used for task_specific_params + metrics'			)
 parser.add_argument('--bs',				type=UpperCamelCase__,				default=8,				required=UpperCamelCase__,				help='batch size'			)
 parser.add_argument(
     '--local_rank',				type=UpperCamelCase__,				default=-1,				required=UpperCamelCase__,				help='should be passed by distributed.launch'			)

 parser.add_argument(
     '--n_obs',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__,				help='How many observations. Defaults to all.'			)
 parser.add_argument(
     '--num_return_sequences',				type=UpperCamelCase__,				default=1,				required=UpperCamelCase__,				help='How many sequences to return'			)
 parser.add_argument(
     '--sync_timeout',				type=UpperCamelCase__,				default=600,				required=UpperCamelCase__,				help='How long should master process wait for other processes to finish.',				)
 parser.add_argument('--src_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument('--tgt_lang',				type=UpperCamelCase__,				default=UpperCamelCase__,				required=UpperCamelCase__			)
 parser.add_argument(
     '--prefix',				type=UpperCamelCase__,				required=UpperCamelCase__,				default=UpperCamelCase__,				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  :		int       =			parser.parse_known_args()
 __A  :		List[str]       =			parse_numeric_n_bool_cl_kwargs(UpperCamelCase__			)
 if generate_kwargs and args.local_rank <= 0:
  print(f"""parsed the following generate kwargs: {generate_kwargs}"""			)
 __A  :		List[str]       =			Path(args.save_dir + '_tmp'			)
 Path(UpperCamelCase__			).mkdir(exist_ok=UpperCamelCase__			)  # this handles locking.
 __A  :		Optional[Any]       =			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  :		List[str]       =			{}
 if args.src_lang is not None:
  __A  :		Dict       =			args.src_lang
 if args.tgt_lang is not None:
  __A  :		Optional[Any]       =			args.tgt_lang

 Path(args.save_dir			).mkdir(exist_ok=UpperCamelCase__			)
 __A     ,__A  :		List[Any]       =			eval_data_dir(
     args.data_dir,				UpperCamelCase__,				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=UpperCamelCase__,				**UpperCamelCase__,				)

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

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


def 			_lowercase       (				UpperCamelCase__       :					List[str]			):
 __A  :		List[str]       =			[]
 for partial_result in partial_results:
  records.extend(UpperCamelCase__			)
 __A  :		List[Any]       =			sorted(UpperCamelCase__,				key=lambda UpperCamelCase__			: x["id"]			)
 __A  :		Dict       =			[x['pred'] for x in records]
 return preds


def 			_lowercase       (				UpperCamelCase__       :					Dict,				UpperCamelCase__       :					List[Any],				UpperCamelCase__       :					int			):
 # WAIT FOR lots of .json files
 __A  :		Dict       =			time.time()
 logger.info('waiting for all nodes to finish'			)
 __A  :		int       =			None
 while (time.time() - start_wait) < timeout:
  __A  :		List[Any]       =			list(save_dir.glob('rank_*.json'			)			)
  if len(UpperCamelCase__			) < num_replicas:
   continue
  try:
   # make sure all json files are fully saved
   __A  :		List[Any]       =			lmap(UpperCamelCase__,				UpperCamelCase__			)
   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()






