




import json
import sys
import tempfile
import unittest
from pathlib import Path

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    CLIPConfig,
    CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER


sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_image_processing import CustomImageProcessor  # noqa E402







class 						lowercase							(   unittest.TestCase		):

     def 					a__	(  self				)						->							Union[str, Any]:
           _A							:     List[str]			=						0

     def 					a__	(  self				)						->							str:
           _A							:     Union[str, Any]			=						AutoImageProcessor.from_pretrained("""openai/clip-vit-base-patch32"""				)
           self.assertIsInstance(_a		,     _a				)

     def 					a__	(  self				)						->							str:
           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:     Dict			=						Path(_a				) / """preprocessor_config.json"""
                 _A							:     Union[str, Any]			=						Path(_a				) / """config.json"""
                 json.dump(
                     {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}		,     open(_a		,     """w"""				)		,     )
                 json.dump({"""model_type""": """clip"""}		,     open(_a		,     """w"""				)				)

                 _A							:     Optional[Any]			=						AutoImageProcessor.from_pretrained(_a				)
                 self.assertIsInstance(_a		,     _a				)

     def 					a__	(  self				)						->							Optional[Any]:
           # Ensure we can load the image processor from the feature extractor config
           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:     List[str]			=						Path(_a				) / """preprocessor_config.json"""
                 _A							:     str			=						Path(_a				) / """config.json"""
                 json.dump(
                     {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}		,     open(_a		,     """w"""				)		,     )
                 json.dump({"""model_type""": """clip"""}		,     open(_a		,     """w"""				)				)

                 _A							:     int			=						AutoImageProcessor.from_pretrained(_a				)
                 self.assertIsInstance(_a		,     _a				)

     def 					a__	(  self				)						->							Dict:
           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:     List[Any]			=						CLIPConfig()

                 # Create a dummy config file with image_proceesor_type
                 _A							:     Tuple			=						Path(_a				) / """preprocessor_config.json"""
                 _A							:     Any			=						Path(_a				) / """config.json"""
                 json.dump(
                     {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}		,     open(_a		,     """w"""				)		,     )
                 json.dump({"""model_type""": """clip"""}		,     open(_a		,     """w"""				)				)

                 # remove image_processor_type to make sure config.json alone is enough to load image processor locally
                 _A							:     Optional[Any]			=						AutoImageProcessor.from_pretrained(_a				).to_dict()

                 config_dict.pop("""image_processor_type"""				)
                 _A							:     Any			=						CLIPImageProcessor(**_a				)

                 # save in new folder
                 model_config.save_pretrained(_a				)
                 config.save_pretrained(_a				)

                 _A							:     int			=						AutoImageProcessor.from_pretrained(_a				)

                 # make sure private variable is not incorrectly saved
                 _A							:     Optional[Any]			=						json.loads(config.to_json_string()				)
                 self.assertTrue("""_processor_class""" not in dict_as_saved				)

           self.assertIsInstance(_a		,     _a				)

     def 					a__	(  self				)						->							Optional[Any]:
           with tempfile.TemporaryDirectory() as tmpdirname:
                 _A							:     Tuple			=						Path(_a				) / """preprocessor_config.json"""
                 json.dump(
                     {"""image_processor_type""": """CLIPImageProcessor""", """processor_class""": """CLIPProcessor"""}		,     open(_a		,     """w"""				)		,     )

                 _A							:     List[str]			=						AutoImageProcessor.from_pretrained(_a				)
                 self.assertIsInstance(_a		,     _a				)

     def 					a__	(  self				)						->							Tuple:
           with self.assertRaisesRegex(
               _a		,     """clip-base is not a local folder and is not a valid model identifier"""				):
                 _A							:     Tuple			=						AutoImageProcessor.from_pretrained("""clip-base"""				)

     def 					a__	(  self				)						->							Optional[Any]:
           with self.assertRaisesRegex(
               _a		,     R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)"""				):
                 _A							:     List[Any]			=						AutoImageProcessor.from_pretrained(_a		,     revision="""aaaaaa"""				)

     def 					a__	(  self				)						->							Optional[int]:
           with self.assertRaisesRegex(
               _a		,     """hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json."""		,     ):
                 _A							:     Optional[int]			=						AutoImageProcessor.from_pretrained("""hf-internal-testing/config-no-model"""				)

     def 					a__	(  self				)						->							Optional[Any]:
           # If remote code is not set, we will time out when asking whether to load the model.
           with self.assertRaises(_a				):
                 _A							:     str			=						AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""				)
           # If remote code is disabled, we can't load this config.
           with self.assertRaises(_a				):
                 _A							:     int			=						AutoImageProcessor.from_pretrained(
                     """hf-internal-testing/test_dynamic_image_processor"""		,     trust_remote_code=_a				)

           _A							:     Tuple			=						AutoImageProcessor.from_pretrained(
               """hf-internal-testing/test_dynamic_image_processor"""		,     trust_remote_code=_a				)
           self.assertEqual(image_processor.__class__.__name__		,     """NewImageProcessor"""				)

           # Test image processor can be reloaded.
           with tempfile.TemporaryDirectory() as tmp_dir:
                 image_processor.save_pretrained(_a				)
                 _A							:     List[Any]			=						AutoImageProcessor.from_pretrained(_a		,     trust_remote_code=_a				)
           self.assertEqual(reloaded_image_processor.__class__.__name__		,     """NewImageProcessor"""				)

     def 					a__	(  self				)						->							List[str]:
           try:
                 AutoConfig.register("""custom"""		,     _a				)
                 AutoImageProcessor.register(_a		,     _a				)
                 # Trying to register something existing in the Transformers library will raise an error
                 with self.assertRaises(_a				):
                       AutoImageProcessor.register(_a		,     _a				)

                 with tempfile.TemporaryDirectory() as tmpdirname:
                       _A							:     Dict			=						Path(_a				) / """preprocessor_config.json"""
                       _A							:     Optional[Any]			=						Path(_a				) / """config.json"""
                       json.dump(
                           {"""feature_extractor_type""": """CLIPFeatureExtractor""", """processor_class""": """CLIPProcessor"""}		,     open(_a		,     """w"""				)		,     )
                       json.dump({"""model_type""": """clip"""}		,     open(_a		,     """w"""				)				)

                       _A							:     str			=						CustomImageProcessor.from_pretrained(_a				)

                 # Now that the config is registered, it can be used as any other config with the auto-API
                 with tempfile.TemporaryDirectory() as tmp_dir:
                       image_processor.save_pretrained(_a				)
                       _A							:     List[Any]			=						AutoImageProcessor.from_pretrained(_a				)
                       self.assertIsInstance(_a		,     _a				)

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                       del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]



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







           class 						lowercase							(   UpperCamelCase__		):
                _a							 =						True

           try:
                 AutoConfig.register("""custom"""		,     _a				)
                 AutoImageProcessor.register(_a		,     _a				)
                 # If remote code is not set, the default is to use local
                 _A							:     List[str]			=						AutoImageProcessor.from_pretrained("""hf-internal-testing/test_dynamic_image_processor"""				)
                 self.assertEqual(image_processor.__class__.__name__		,     """NewImageProcessor"""				)
                 self.assertTrue(image_processor.is_local				)

                 # If remote code is disabled, we load the local one.
                 _A							:     Dict			=						AutoImageProcessor.from_pretrained(
                     """hf-internal-testing/test_dynamic_image_processor"""		,     trust_remote_code=_a				)
                 self.assertEqual(image_processor.__class__.__name__		,     """NewImageProcessor"""				)
                 self.assertTrue(image_processor.is_local				)

                 # If remote is enabled, we load from the Hub
                 _A							:     str			=						AutoImageProcessor.from_pretrained(
                     """hf-internal-testing/test_dynamic_image_processor"""		,     trust_remote_code=_a				)
                 self.assertEqual(image_processor.__class__.__name__		,     """NewImageProcessor"""				)
                 self.assertTrue(not hasattr(_a		,     """is_local"""				)				)

           finally:
                 if "custom" in CONFIG_MAPPING._extra_content:
                       del CONFIG_MAPPING._extra_content["custom"]
                 if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
                       del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]







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,
)


_snake_case										=       getLogger(__name__)







def 				lowerCAmelCase_	(     snake_case_,snake_case_,snake_case_,snake_case_ = 8,snake_case_ = 1024,snake_case_="val",snake_case_=None,snake_case_=False,snake_case_="summarization",snake_case_=None,snake_case_=1,snake_case_ = None,snake_case_="",**snake_case_,):
      _A							:     Dict			=						str(snake_case_     )
      assert local_rank is not None
      torch.distributed.init_process_group(backend="""nccl""",rank=snake_case_     )

      _A							:     Tuple			=						Path(snake_case_     )
      _A							:     List[Any]			=						save_dir.joinpath(f'''rank_{local_rank}_output.json'''     )
      torch.cuda.set_device(snake_case_     )
      _A							:     Union[str, Any]			=						AutoModelForSeqaSeqLM.from_pretrained(snake_case_     ).cuda()
      if fpaa:
            _A							:     Any			=						model.half()
      # determine if we need to increase num_beams
      use_task_specific_params(snake_case_,snake_case_     )  # update config with task specific params
      _A							:     str			=						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(snake_case_     )
      logger.info(f'''Inferred tokenizer type: {tokenizer.__class__}'''     )  # if this is wrong, check config.model_type.

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







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

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

      Path(args.save_dir     ).mkdir(exist_ok=snake_case_     )
      _A			,   _A							:     str			=						eval_data_dir(
          args.data_dir,snake_case_,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=snake_case_,**snake_case_,)

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

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







def 				lowerCAmelCase_	(     snake_case_     ):
      _A							:     Dict			=						[]
      for partial_result in partial_results:
            records.extend(snake_case_     )
      _A							:     Optional[Any]			=						sorted(snake_case_,key=lambda snake_case_     : x["id"]     )
      _A							:     List[str]			=						[x["""pred"""] for x in records]
      return preds







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

