
"""simple docstring"""




import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

    if is_vision_available():
        from transformers import OneFormerImageProcessor
        from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
        from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
    from PIL import Image



def 							a__ (				lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       Optional[int]="shi-labs/oneformer_demo"):






     '''simple docstring'''
     with open(hf_hub_download(lowerCAmelCase__       ,    lowerCAmelCase__       ,    repo_type="dataset")       ,    "r") as f:
          UpperCAmelCase__							: Tuple 							=						json.load(lowerCAmelCase__)
     UpperCAmelCase__							: List[str] 							=						{}
     UpperCAmelCase__							: Any 							=						[]
     UpperCAmelCase__							: str 							=						[]
     for key, info in class_info.items():
          UpperCAmelCase__							: str 							=						info["name"]
          class_names.append(info["name"])
          if info["isthing"]:
               thing_ids.append(int(lowerCAmelCase__))
     UpperCAmelCase__							: List[Any] 							=						thing_ids
     UpperCAmelCase__							: Any 							=						class_names
     return metadata







class   _lowercase						(       unittest.TestCase				):


     '''simple docstring'''





     def __init__(      self ,			__UpperCamelCase ,			__UpperCamelCase=7 ,			__UpperCamelCase=3 ,			__UpperCamelCase=30 ,			__UpperCamelCase=4_00 ,			__UpperCamelCase=None ,			__UpperCamelCase=True ,			__UpperCamelCase=True ,			__UpperCamelCase=[0.5, 0.5, 0.5] ,			__UpperCamelCase=[0.5, 0.5, 0.5] ,			__UpperCamelCase=10 ,			__UpperCamelCase=False ,			__UpperCamelCase=2_55 ,			__UpperCamelCase="shi-labs/oneformer_demo" ,			__UpperCamelCase="ade20k_panoptic.json" ,			__UpperCamelCase=10 ,			)->					int:
          UpperCAmelCase__							: Optional[Any] 							=						parent
          UpperCAmelCase__							: Union[str, Any] 							=						batch_size
          UpperCAmelCase__							: str 							=						num_channels
          UpperCAmelCase__							: Any 							=						min_resolution
          UpperCAmelCase__							: Tuple 							=						max_resolution
          UpperCAmelCase__							: List[Any] 							=						do_resize
          UpperCAmelCase__							: Optional[Any] 							=						{"shortest_edge": 32, "longest_edge": 13_33} if size is None else size
          UpperCAmelCase__							: List[str] 							=						do_normalize
          UpperCAmelCase__							: List[Any] 							=						image_mean
          UpperCAmelCase__							: int 							=						image_std
          UpperCAmelCase__							: List[str] 							=						class_info_file
          UpperCAmelCase__							: Any 							=						prepare_metadata(_a ,			_a  )
          UpperCAmelCase__							: List[str] 							=						num_text
          UpperCAmelCase__							: Optional[int] 							=						repo_path

          # for the post_process_functions
          UpperCAmelCase__							: List[Any] 							=						2
          UpperCAmelCase__							: List[Any] 							=						10
          UpperCAmelCase__							: List[str] 							=						10
          UpperCAmelCase__							: List[str] 							=						3
          UpperCAmelCase__							: Tuple 							=						4
          UpperCAmelCase__							: Any 							=						num_labels
          UpperCAmelCase__							: str 							=						do_reduce_labels
          UpperCAmelCase__							: List[Any] 							=						ignore_index



     def  lowerCAmelCase__       (      self  )->					Tuple:
          return {
              "do_resize": self.do_resize,
              "size": self.size,
              "do_normalize": self.do_normalize,
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "num_labels": self.num_labels,
              "do_reduce_labels": self.do_reduce_labels,
              "ignore_index": self.ignore_index,
              "class_info_file": self.class_info_file,
              "metadata": self.metadata,
              "num_text": self.num_text,
          }



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase ,			__UpperCamelCase=False  )->					str:
          if not batched:
               UpperCAmelCase__							: Union[str, Any] 							=						image_inputs[0]
               if isinstance(_a ,			Image.Image  ):
                    UpperCAmelCase__							,	UpperCAmelCase__							: Union[str, Any] 							=						image.size
               else:
                    UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						image.shape[1], image.shape[2]
               if w < h:
                    UpperCAmelCase__							: Union[str, Any] 							=						int(self.size["shortest_edge"] * h / w  )
                    UpperCAmelCase__							: List[str] 							=						self.size["shortest_edge"]
               elif w > h:
                    UpperCAmelCase__							: Dict 							=						self.size["shortest_edge"]
                    UpperCAmelCase__							: str 							=						int(self.size["shortest_edge"] * w / h  )
               else:
                    UpperCAmelCase__							: Optional[int] 							=						self.size["shortest_edge"]
                    UpperCAmelCase__							: Optional[Any] 							=						self.size["shortest_edge"]

          else:
               UpperCAmelCase__							: List[str] 							=						[]
               for image in image_inputs:
                    UpperCAmelCase__							,	UpperCAmelCase__							: Optional[Any] 							=						self.get_expected_values([image]  )
                    expected_values.append((expected_height, expected_width)  )
               UpperCAmelCase__							: Dict 							=						max(_a ,			key=lambda __UpperCamelCase  : item[0]  )[0]
               UpperCAmelCase__							: Optional[int] 							=						max(_a ,			key=lambda __UpperCamelCase  : item[1]  )[1]

          return expected_height, expected_width



     def  lowerCAmelCase__       (      self  )->					List[str]:
          return OneFormerForUniversalSegmentationOutput(
              # +1 for null class
              class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)  ) ,			masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)  ) ,			)







@require_torch
@require_vision
class   _lowercase						(       __SCREAMING_SNAKE_CASE	,   unittest.TestCase				):


     '''simple docstring'''





     _A								=							OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
     # only for test_image_processing_common.test_image_proc_to_json_string
     _A								=							image_processing_class



     def  lowerCAmelCase__       (      self  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						OneFormerImageProcessorTester(self  )



     @property
     def  lowerCAmelCase__       (      self  )->					Union[str, Any]:
          return self.image_processing_tester.prepare_image_processor_dict()



     def  lowerCAmelCase__       (      self  )->					str:
          UpperCAmelCase__							: List[str] 							=						self.image_processing_class(**self.image_processor_dict  )
          self.assertTrue(hasattr(_a ,			"image_mean"  )  )
          self.assertTrue(hasattr(_a ,			"image_std"  )  )
          self.assertTrue(hasattr(_a ,			"do_normalize"  )  )
          self.assertTrue(hasattr(_a ,			"do_resize"  )  )
          self.assertTrue(hasattr(_a ,			"size"  )  )
          self.assertTrue(hasattr(_a ,			"ignore_index"  )  )
          self.assertTrue(hasattr(_a ,			"class_info_file"  )  )
          self.assertTrue(hasattr(_a ,			"num_text"  )  )
          self.assertTrue(hasattr(_a ,			"repo_path"  )  )
          self.assertTrue(hasattr(_a ,			"metadata"  )  )
          self.assertTrue(hasattr(_a ,			"do_reduce_labels"  )  )



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



     def  lowerCAmelCase__       (      self  )->					Optional[Any]:
          # Initialize image_processor
          UpperCAmelCase__							: List[str] 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PIL images
          UpperCAmelCase__							: Tuple 							=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=_a  )
          for image in image_inputs:
               self.assertIsInstance(_a ,			Image.Image  )

          # Test not batched input
          UpperCAmelCase__							: int 							=						image_processor(image_inputs[0] ,			["semantic"] ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.image_processing_tester.get_expected_values(_a  )

          self.assertEqual(
              encoded_images.shape ,			(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,			)

          # Test batched
          UpperCAmelCase__							,	UpperCAmelCase__							: str 							=						self.image_processing_tester.get_expected_values(_a ,			batched=_a  )

          UpperCAmelCase__							: str 							=						image_processor(
              _a ,			["semantic"] * len(_a  ) ,			return_tensors="pt"  ).pixel_values
          self.assertEqual(
              encoded_images.shape ,			(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,			)



     def  lowerCAmelCase__       (      self  )->					Tuple:
          # Initialize image_processor
          UpperCAmelCase__							: Optional[Any] 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random numpy tensors
          UpperCAmelCase__							: int 							=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=_a ,			numpify=_a  )
          for image in image_inputs:
               self.assertIsInstance(_a ,			np.ndarray  )

          # Test not batched input
          UpperCAmelCase__							: Optional[int] 							=						image_processor(image_inputs[0] ,			["semantic"] ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: Dict 							=						self.image_processing_tester.get_expected_values(_a  )

          self.assertEqual(
              encoded_images.shape ,			(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,			)

          # Test batched
          UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						self.image_processing_tester.get_expected_values(_a ,			batched=_a  )

          UpperCAmelCase__							: str 							=						image_processor(
              _a ,			["semantic"] * len(_a  ) ,			return_tensors="pt"  ).pixel_values
          self.assertEqual(
              encoded_images.shape ,			(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,			)



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          # Initialize image_processor
          UpperCAmelCase__							: Optional[Any] 							=						self.image_processing_class(**self.image_processor_dict  )
          # create random PyTorch tensors
          UpperCAmelCase__							: Union[str, Any] 							=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=_a ,			torchify=_a  )
          for image in image_inputs:
               self.assertIsInstance(_a ,			torch.Tensor  )

          # Test not batched input
          UpperCAmelCase__							: int 							=						image_processor(image_inputs[0] ,			["semantic"] ,			return_tensors="pt"  ).pixel_values

          UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						self.image_processing_tester.get_expected_values(_a  )

          self.assertEqual(
              encoded_images.shape ,			(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,			)

          # Test batched
          UpperCAmelCase__							,	UpperCAmelCase__							: Optional[int] 							=						self.image_processing_tester.get_expected_values(_a ,			batched=_a  )

          UpperCAmelCase__							: Optional[Any] 							=						image_processor(
              _a ,			["semantic"] * len(_a  ) ,			return_tensors="pt"  ).pixel_values
          self.assertEqual(
              encoded_images.shape ,			(
                  self.image_processing_tester.batch_size,
                  self.image_processing_tester.num_channels,
                  expected_height,
                  expected_width,
              ) ,			)



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase=False ,			__UpperCamelCase=False ,			__UpperCamelCase="np"  )->					Optional[Any]:
          UpperCAmelCase__							: List[Any] 							=						self.image_processing_class(**self.image_processor_dict  )
          # prepare image and target
          UpperCAmelCase__							: List[str] 							=						self.image_processing_tester.num_labels
          UpperCAmelCase__							: List[str] 							=						None
          UpperCAmelCase__							: Optional[int] 							=						None
          UpperCAmelCase__							: List[Any] 							=						prepare_image_inputs(self.image_processing_tester ,			equal_resolution=_a  )
          if with_segmentation_maps:
               UpperCAmelCase__							: int 							=						num_labels
               if is_instance_map:
                    UpperCAmelCase__							: Optional[Any] 							=						list(range(_a  )  ) * 2
                    UpperCAmelCase__							: Union[str, Any] 							=						dict(enumerate(_a  )  )
               UpperCAmelCase__							: Union[str, Any] 							=						[
                   np.random.randint(0 ,			high * 2 ,			(img.size[1], img.size[0])  ).astype(np.uinta  ) for img in image_inputs
               ]
               if segmentation_type == "pil":
                    UpperCAmelCase__							: str 							=						[Image.fromarray(_a  ) for annotation in annotations]

          UpperCAmelCase__							: Tuple 							=						image_processor(
              _a ,			["semantic"] * len(_a  ) ,			_a ,			return_tensors="pt" ,			instance_id_to_semantic_id=_a ,			pad_and_return_pixel_mask=_a ,			)

          return inputs



     def  lowerCAmelCase__       (      self  )->					int:
          pass



     def  lowerCAmelCase__       (      self  )->					Any:
          def common(__UpperCamelCase=False ,			__UpperCamelCase=None  ):
               UpperCAmelCase__							: Union[str, Any] 							=						self.comm_get_image_processor_inputs(
                   with_segmentation_maps=_a ,			is_instance_map=_a ,			segmentation_type=_a  )

               UpperCAmelCase__							: str 							=						inputs["mask_labels"]
               UpperCAmelCase__							: List[str] 							=						inputs["class_labels"]
               UpperCAmelCase__							: Any 							=						inputs["pixel_values"]
               UpperCAmelCase__							: Dict 							=						inputs["text_inputs"]

               # check the batch_size
               for mask_label, class_label, text_input in zip(_a ,			_a ,			_a  ):
                    self.assertEqual(mask_label.shape[0] ,			class_label.shape[0]  )
                    # this ensure padding has happened
                    self.assertEqual(mask_label.shape[1:] ,			pixel_values.shape[2:]  )
                    self.assertEqual(len(_a  ) ,			self.image_processing_tester.num_text  )

          common()
          common(is_instance_map=_a  )
          common(is_instance_map=_a ,			segmentation_type="pil"  )
          common(is_instance_map=_a ,			segmentation_type="pil"  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Optional[Any] 							=						np.zeros((20, 50)  )
          UpperCAmelCase__							: Optional[int] 							=						1
          UpperCAmelCase__							: Optional[Any] 							=						1
          UpperCAmelCase__							: Optional[Any] 							=						1

          UpperCAmelCase__							: List[str] 							=						binary_mask_to_rle(_a  )
          self.assertEqual(len(_a  ) ,			4  )
          self.assertEqual(rle[0] ,			21  )
          self.assertEqual(rle[1] ,			45  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: Optional[int] 							=						self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes ,			max_seq_length=77 ,			task_seq_length=77 ,			class_info_file="ade20k_panoptic.json" ,			num_text=self.image_processing_tester.num_text ,			repo_path="shi-labs/oneformer_demo" ,			)
          UpperCAmelCase__							: int 							=						self.image_processing_tester.get_fake_oneformer_outputs()

          UpperCAmelCase__							: Tuple 							=						fature_extractor.post_process_semantic_segmentation(_a  )

          self.assertEqual(len(_a  ) ,			self.image_processing_tester.batch_size  )
          self.assertEqual(
              segmentation[0].shape ,			(
                  self.image_processing_tester.height,
                  self.image_processing_tester.width,
              ) ,			)

          UpperCAmelCase__							: Dict 							=						[(1, 4) for i in range(self.image_processing_tester.batch_size  )]
          UpperCAmelCase__							: Dict 							=						fature_extractor.post_process_semantic_segmentation(_a ,			target_sizes=_a  )

          self.assertEqual(segmentation[0].shape ,			target_sizes[0]  )



     def  lowerCAmelCase__       (      self  )->					Optional[int]:
          UpperCAmelCase__							: Union[str, Any] 							=						self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes ,			max_seq_length=77 ,			task_seq_length=77 ,			class_info_file="ade20k_panoptic.json" ,			num_text=self.image_processing_tester.num_text ,			repo_path="shi-labs/oneformer_demo" ,			)
          UpperCAmelCase__							: str 							=						self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase__							: str 							=						image_processor.post_process_instance_segmentation(_a ,			threshold=0  )

          self.assertTrue(len(_a  ) == self.image_processing_tester.batch_size  )
          for el in segmentation:
               self.assertTrue("segmentation" in el  )
               self.assertTrue("segments_info" in el  )
               self.assertEqual(type(el["segments_info"]  ) ,			_a  )
               self.assertEqual(
                   el["segmentation"].shape ,			(self.image_processing_tester.height, self.image_processing_tester.width)  )



     def  lowerCAmelCase__       (      self  )->					List[str]:
          UpperCAmelCase__							: str 							=						self.image_processing_class(
              num_labels=self.image_processing_tester.num_classes ,			max_seq_length=77 ,			task_seq_length=77 ,			class_info_file="ade20k_panoptic.json" ,			num_text=self.image_processing_tester.num_text ,			repo_path="shi-labs/oneformer_demo" ,			)
          UpperCAmelCase__							: List[str] 							=						self.image_processing_tester.get_fake_oneformer_outputs()
          UpperCAmelCase__							: Tuple 							=						image_processor.post_process_panoptic_segmentation(_a ,			threshold=0  )

          self.assertTrue(len(_a  ) == self.image_processing_tester.batch_size  )
          for el in segmentation:
               self.assertTrue("segmentation" in el  )
               self.assertTrue("segments_info" in el  )
               self.assertEqual(type(el["segments_info"]  ) ,			_a  )
               self.assertEqual(
                   el["segmentation"].shape ,			(self.image_processing_tester.height, self.image_processing_tester.width)  )





"""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,
)


A__      :	int          =    getLogger(__name__)



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str       ,    lowerCAmelCase	:       int = 8       ,    lowerCAmelCase	:       int = 1024       ,    lowerCAmelCase	:       List[Any]="val"       ,    lowerCAmelCase	:       str=None       ,    lowerCAmelCase	:       int=False       ,    lowerCAmelCase	:       Dict="summarization"       ,    lowerCAmelCase	:       int=None       ,    lowerCAmelCase	:       List[str]=1       ,    lowerCAmelCase	:       Dict = None       ,    lowerCAmelCase	:       List[str]=""       ,    **lowerCAmelCase	:       int       ,    ):






     '''simple docstring'''
     UpperCAmelCase__							: Dict 							=						str(lowerCAmelCase		)
     assert local_rank is not None
     torch.distributed.init_process_group(backend="nccl"       ,    rank=lowerCAmelCase		)

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

     UpperCAmelCase__							: List[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:
          UpperCAmelCase__							: int 							=						tokenizer.model_max_length
     if prefix is None:
          UpperCAmelCase__							: Union[str, Any] 							=						prefix or getattr(model.config       ,    "prefix"       ,    ""		) or ""
     UpperCAmelCase__							: str 							=						SeqaSeqDataset(
         lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase       ,    max_target_length=1024       ,    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.
     UpperCAmelCase__							: Union[str, Any] 							=						ds.make_sortish_sampler(lowerCAmelCase       ,    distributed=lowerCAmelCase       ,    add_extra_examples=lowerCAmelCase       ,    shuffle=lowerCAmelCase		)
     UpperCAmelCase__							: Optional[int] 							=						DataLoader(lowerCAmelCase       ,    sampler=lowerCAmelCase       ,    batch_size=lowerCAmelCase       ,    collate_fn=ds.collate_fn		)
     UpperCAmelCase__							: str 							=						[]
     for batch in tqdm(lowerCAmelCase		):
          UpperCAmelCase__							: Dict 							=						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       ,    )
          UpperCAmelCase__							: int 							=						tokenizer.batch_decode(lowerCAmelCase       ,    skip_special_tokens=lowerCAmelCase       ,    clean_up_tokenization_spaces=lowerCAmelCase		)
          UpperCAmelCase__							: int 							=						batch["ids"]
          if num_return_sequences > 1:
               UpperCAmelCase__							: 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 							a__ (				):






     '''simple docstring'''
     UpperCAmelCase__							: 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=600       ,    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"		)
     UpperCAmelCase__							: Optional[int] 							=						time.time()
     UpperCAmelCase__							,	UpperCAmelCase__							: List[Any] 							=						parser.parse_known_args()
     UpperCAmelCase__							: 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}"		)
     UpperCAmelCase__							: Dict 							=						Path(args.save_dir + "_tmp"		)
     Path(lowerCAmelCase		).mkdir(exist_ok=lowerCAmelCase		)  # this handles locking.
     UpperCAmelCase__							: List[str] 							=						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.
     UpperCAmelCase__							: List[str] 							=						{}
     if args.src_lang is not None:
          UpperCAmelCase__							: str 							=						args.src_lang
     if args.tgt_lang is not None:
          UpperCAmelCase__							: List[str] 							=						args.tgt_lang

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

          # Calculate metrics, save metrics,  and save _generations.txt
          UpperCAmelCase__							: List[Any] 							=						"translation" in args.task
          UpperCAmelCase__							: Optional[Any] 							=						calculate_bleu if calc_bleu else calculate_rouge
          UpperCAmelCase__							: Optional[Any] 							=						"bleu" if calc_bleu else "rouge"
          UpperCAmelCase__							: Dict 							=						score_fn(lowerCAmelCase       ,    lowerCAmelCase		)
          UpperCAmelCase__							: List[Any] 							=						len(lowerCAmelCase		)
          UpperCAmelCase__							: Union[str, Any] 							=						time.time() - start_time
          UpperCAmelCase__							: Optional[int] 							=						round(runtime / metrics["n_obs"]       ,    4		)
          UpperCAmelCase__							: Tuple 							=						num_replicas
          # TODO(@stas00): add whatever metadata to metrics
          UpperCAmelCase__							: Any 							=						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 							a__ (				lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     UpperCAmelCase__							: str 							=						[]
     for partial_result in partial_results:
          records.extend(lowerCAmelCase		)
     UpperCAmelCase__							: Dict 							=						sorted(lowerCAmelCase       ,    key=lambda lowerCAmelCase		: x["id"]		)
     UpperCAmelCase__							: List[str] 							=						[x["pred"] for x in records]
     return preds



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Optional[int]		):






     '''simple docstring'''
     # WAIT FOR lots of .json files
     UpperCAmelCase__							: int 							=						time.time()
     logger.info("waiting for all nodes to finish"		)
     UpperCAmelCase__							: Dict 							=						None
     while (time.time() - start_wait) < timeout:
          UpperCAmelCase__							: str 							=						list(save_dir.glob("rank_*.json"		)		)
          if len(lowerCAmelCase		) < num_replicas:
               continue
          try:
               # make sure all json files are fully saved
               UpperCAmelCase__							: Union[str, Any] 							=						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()



