


"""simple docstring"""






import getopt
import json
import os

# import numpy as np
import sys
from collections import OrderedDict

import datasets
import numpy as np
import torch
from modeling_frcnn import GeneralizedRCNN
from processing_image import Preprocess

from utils import Config
_lowerCamelCase  =	False
_lowerCamelCase  =	Config.from_pretrained('unc-nlp/frcnn-vg-finetuned')
_lowerCamelCase  =	datasets.Features(
    OrderedDict(
        {
            'attr_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'attr_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'boxes': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 4), dtype='float32'),
            'img_id': datasets.Value('int32'),
            'obj_ids': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'obj_probs': datasets.Sequence(length=CONFIG.MAX_DETECTIONS, feature=datasets.Value('float32')),
            'roi_features': datasets.ArrayaD((CONFIG.MAX_DETECTIONS, 2048), dtype='float32'),
            'sizes': datasets.Sequence(length=2, feature=datasets.Value('float32')),
            'preds_per_image': datasets.Value(dtype='int32'),
        }
    )
)


class       lowerCamelCase_		:





				"""simple docstring"""







				def __init__(    self							,		UpperCAmelCase__=sys.argv[1:]  ):
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					None
								SCREAMING_SNAKE_CASE__  				=					1
								SCREAMING_SNAKE_CASE__  				=					getopt.getopt(__A							,		"i:o:b:s"							,		["inputdir=", "outfile=", "batch_size=", "subset_list="]  )
								for opt, arg in opts:
												if opt in ("-i", "--inputdir"):
																SCREAMING_SNAKE_CASE__  				=					arg
												elif opt in ("-o", "--outfile"):
																SCREAMING_SNAKE_CASE__  				=					arg
												elif opt in ("-b", "--batch_size"):
																SCREAMING_SNAKE_CASE__  				=					int(__A  )
												elif opt in ("-s", "--subset_list"):
																SCREAMING_SNAKE_CASE__  				=					arg

								assert inputdir is not None  # and os.path.isdir(inputdir), f"{inputdir}"
								assert outputfile is not None and not os.path.isfile(__A  ), f'''{outputfile}'''
								if subset_list is not None:
												with open(os.path.realpath(__A  )  ) as f:
																SCREAMING_SNAKE_CASE__  				=					{self._vqa_file_split()[0] for x in tryload(__A  )}
								else:
												SCREAMING_SNAKE_CASE__  				=					None

								SCREAMING_SNAKE_CASE__  				=					CONFIG
								if torch.cuda.is_available():
												SCREAMING_SNAKE_CASE__  				=					'''cuda'''
								SCREAMING_SNAKE_CASE__  				=					os.path.realpath(__A  )
								SCREAMING_SNAKE_CASE__  				=					os.path.realpath(__A  )
								SCREAMING_SNAKE_CASE__  				=					Preprocess(self.config  )
								SCREAMING_SNAKE_CASE__  				=					GeneralizedRCNN.from_pretrained("unc-nlp/frcnn-vg-finetuned"							,		config=self.config  )
								SCREAMING_SNAKE_CASE__  				=					batch_size if batch_size != 0 else 1
								SCREAMING_SNAKE_CASE__  				=					DEFAULT_SCHEMA



				def 	lowerCAmelCase__		(    self							,		UpperCAmelCase__  ):
								SCREAMING_SNAKE_CASE__  				=					int(file.split("."  )[0].split("_"  )[-1]  )
								SCREAMING_SNAKE_CASE__  				=					os.path.join(self.inputdir							,		__A  )
								return (img_id, filepath)



				@property
				def 	lowerCAmelCase__		(    self  ):
								SCREAMING_SNAKE_CASE__  				=					[]
								for i, file in enumerate(os.listdir(self.inputdir  )  ):
												if self.subset_list is not None and i not in self.subset_list:
																continue
												batch.append(self._vqa_file_split(__A  )  )
												if len(__A  ) == self.batch:
																SCREAMING_SNAKE_CASE__  				=					batch
																SCREAMING_SNAKE_CASE__  				=					[]
																yield list(map(__A							,		zip(*__A  )  )  )

								for i in range(1  ):
												yield list(map(__A							,		zip(*__A  )  )  )






				def __call__(    self  ):
								# make writer
								if not TEST:
												SCREAMING_SNAKE_CASE__  				=					datasets.ArrowWriter(features=self.schema							,		path=self.outputfile  )
								# do file generator
								for i, (img_ids, filepaths) in enumerate(self.file_generator  ):
												SCREAMING_SNAKE_CASE__  				=					self.preprocess(__A  )
												SCREAMING_SNAKE_CASE__  				=					self.model(
												    __A							,		__A							,		scales_yx=__A							,		padding="max_detections"							,		max_detections=self.config.MAX_DETECTIONS							,		pad_value=0							,		return_tensors="np"							,		location="cpu"							,		)
												SCREAMING_SNAKE_CASE__  				=					output_dict.pop("normalized_boxes"  )
												if not TEST:
																SCREAMING_SNAKE_CASE__  				=					np.array(__A  )
																SCREAMING_SNAKE_CASE__  				=					self.schema.encode_batch(__A  )
																writer.write_batch(__A  )
												if TEST:
																break
												# finalizer the writer
								if not TEST:
												SCREAMING_SNAKE_CASE__  				=					writer.finalize()
												print(f'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 20} mb'''  )


def __lowercase  (   lowerCamelCase_	:   List[Any]	):
				try:
								SCREAMING_SNAKE_CASE__  				=					json.load(lowerCamelCase_	)
								try:
												SCREAMING_SNAKE_CASE__  				=					list(data.keys()	)
								except Exception:
												SCREAMING_SNAKE_CASE__  				=					[d['''img_id'''] for d in data]
				except Exception:
								try:
												SCREAMING_SNAKE_CASE__  				=					eval(stream.read()	)
								except Exception:
												SCREAMING_SNAKE_CASE__  				=					stream.read().split("\n"	)
				return data


if __name__ == "__main__":
	_lowerCamelCase  =	Extract(sys.argv[1:])
	extract()
	if not TEST:
		_lowerCamelCase  =	datasets.Dataset.from_file(extract.outputfile)
		# wala!
		# print(np.array(dataset[0:2]["roi_features"]).shape)



import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
     from tests.trainer.test_trainer import (  # noqa
         RegressionModelConfig,
         RegressionPreTrainedModel,
         get_regression_trainer,
     )


set_seed(4_2)

_lowerCamelCase							:  Tuple     =				get_tests_dir("fixtures")
_lowerCamelCase							:  List[Any]     =				os.path.join(dirname(get_tests_dir()))
_lowerCamelCase							:  List[Any]     =				dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase							:  Any     =				"10999"

_lowerCamelCase							:  Dict     =				"t5-small"

# *** Working Models ***
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-albert"
_lowerCamelCase							:  Any     =				"sshleifer/bart-tiny-random"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-bert"
_lowerCamelCase							:  Union[str, Any]     =				"hf-internal-testing/tiny-random-bigbird_pegasus"
_lowerCamelCase							:  Union[str, Any]     =				"hf-internal-testing/tiny-random-big_bird"
_lowerCamelCase							:  Dict     =				"hf-internal-testing/tiny-random-blenderbot"
_lowerCamelCase							:  Tuple     =				"bigscience/bigscience-small-testing"
_lowerCamelCase							:  Union[str, Any]     =				"hf-internal-testing/tiny-random-deberta"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-random-deberta-v2"
_lowerCamelCase							:  str     =				"sshleifer/tiny-distilbert-base-cased"
_lowerCamelCase							:  int     =				"hf-internal-testing/tiny-electra"
_lowerCamelCase							:  Union[str, Any]     =				"hf-internal-testing/tiny-random-flaubert"
_lowerCamelCase							:  Optional[int]     =				"stas/tiny-wmt19-en-de"
_lowerCamelCase							:  Tuple     =				"hf-internal-testing/tiny-random-funnel"
_lowerCamelCase							:  Optional[int]     =				"sshleifer/tiny-gpt2"
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-gptj"
_lowerCamelCase							:  Tuple     =				"hf-internal-testing/tiny-random-gpt_neo"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-layoutlm"
_lowerCamelCase							:  Dict     =				"hf-internal-testing/tiny-random-led"
_lowerCamelCase							:  List[Any]     =				"hf-internal-testing/tiny-random-longformer"
_lowerCamelCase							:  Tuple     =				"stas/tiny-m2m_100"  # hf tiny model is unsuitable
_lowerCamelCase							:  List[str]     =				"sshleifer/tiny-marian-en-de"
_lowerCamelCase							:  Dict     =				"sshleifer/tiny-mbart"
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-mobilebert"
_lowerCamelCase							:  int     =				"hf-internal-testing/tiny-random-mpnet"
_lowerCamelCase							:  Any     =				"stas/pegasus-cnn_dailymail-tiny-random"
_lowerCamelCase							:  Tuple     =				"hf-internal-testing/tiny-random-prophetnet"
_lowerCamelCase							:  List[Any]     =				"sshleifer/tiny-distilroberta-base"
_lowerCamelCase							:  Tuple     =				"hf-internal-testing/tiny-random-squeezebert"
_lowerCamelCase							:  Union[str, Any]     =				"patrickvonplaten/t5-tiny-random"
_lowerCamelCase							:  Union[str, Any]     =				"hf-internal-testing/tiny-random-t5-v1.1"
_lowerCamelCase							:  Dict     =				"hf-internal-testing/tiny-random-vit"
_lowerCamelCase							:  Optional[int]     =				"hf-internal-testing/tiny-xlm-roberta"
_lowerCamelCase							:  Tuple     =				"sshleifer/tiny-xlnet-base-cased"


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-mt5"
_lowerCamelCase							:  List[Any]     =				"hf-internal-testing/tiny-random-camembert"
_lowerCamelCase							:  str     =				"hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
_lowerCamelCase							:  Tuple     =				"hf-internal-testing/tiny-random-convbert"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-random-layoutlmv2"
_lowerCamelCase							:  str     =				"hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-ctrl"
_lowerCamelCase							:  Any     =				"hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
_lowerCamelCase							:  List[str]     =				"hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
_lowerCamelCase							:  List[Any]     =				"hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
_lowerCamelCase							:  Union[str, Any]     =				"hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase							:  str     =				"hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
_lowerCamelCase							:  List[Any]     =				""
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase							:  Optional[int]     =				"hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase							:  str     =				"hf-internal-testing/tiny-random-clip"
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
_lowerCamelCase							:  Optional[int]     =				"hf-internal-testing/tiny-random-speech_to_text"
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
_lowerCamelCase							:  Union[str, Any]     =				"hf-internal-testing/tiny-random-tapas"

def      a__	(						UpperCAmelCase				:     Any=False     )    ->		Optional[Any]:
  # 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
  # - it won't be able to handle that
  # 2. for now testing with just 2 gpus max (since some quality tests may give different
  # results with mode gpus because we use very little data)
  UpperCAmelCase			:				Union[str, Any]										=					min(2							,					get_gpu_count()     ) if distributed else 1
  UpperCAmelCase			:				List[Any]										=					os.environ.get('''DS_TEST_PORT'''							,					UpperCAmelCase     )
  return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()

def      a__	(						)    ->		List[Any]:
  UpperCAmelCase			:				List[str]										=					f'''{FIXTURE_DIRECTORY}/tests_samples'''
  UpperCAmelCase			:				Optional[Any]										=					f'''{data_dir_samples}/wmt_en_ro'''
  UpperCAmelCase			:				int										=					f'''{data_dir_samples}/xsum'''
  UpperCAmelCase			:				int										=					'''
        --do_train
        --max_train_samples 4
        --per_device_train_batch_size 2
        --num_train_epochs 1
        --fp16
        --report_to none
        --overwrite_output_dir
        '''.split()

  # try to cover as many models as possible once (it's enough to run on one task per model)
  # but need a tiny model for each
  #
  # should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
  UpperCAmelCase			:				str										=					{
      '''trans''': [
          '''bart''',
          '''fsmt''',
          '''m2m_100''',
          '''marian''',
          '''mbart''',
          '''t5''',
          '''t5_v1''',
          # "mt5", missing model files
      ],
      '''sum''': [
          '''pegasus''',
      ],
      '''clm''': [
          '''big_bird''',
          '''bigbird_pegasus''',
          '''blenderbot''',
          '''bloom''',
          '''gpt2''',
          '''gpt_neo''',
          '''gptj''',
          '''xlm-roberta''',
          '''prophetnet''',
          # "camembert", missing model files
      ],
      '''mlm''': [
          '''albert''',
          '''deberta''',
          '''deberta-v2''',
          '''distilbert''',
          '''electra''',
          '''flaubert''',
          '''funnel''',
          '''layoutlm''',
          # "reformer", # multiple issues with either mlm/qa/clas
      ],
      '''qa''': [
          '''led''',
          '''longformer''',
          '''mobilebert''',
          '''mpnet''',
          '''roberta''',
          '''squeezebert''',
          # "convbert", # missing tokenizer files
          # "layoutlmv2", missing model files
      ],
      '''clas''': [
          '''bert''',
          '''xlnet''',
          # "hubert", # missing tokenizer files
          # "ibert", # multiple issues with either mlm/qa/clas
          # "transfo-xl", # tokenizer issues as ctrl
          # "ctrl", # tokenizer issues
          # "openai-gpt", missing model files
          # "tapas", multiple issues
      ],
      '''img_clas''': [
          '''vit''',
      ],
  }

  UpperCAmelCase			:				int										=					f'''{ROOT_DIRECTORY}/examples/pytorch'''

  UpperCAmelCase			:				int										=					{
      '''trans''': f'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
      '''sum''': f'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
      '''clm''': f'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
      '''mlm''': f'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
      '''qa''': f'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
      '''clas''': f'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
      '''img_clas''': f'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
  }

  UpperCAmelCase			:				Dict										=					get_launcher(distributed=UpperCAmelCase     )

  UpperCAmelCase			:				Tuple										=					{}
  for task, args in tasks.items():
    UpperCAmelCase			:				str										=					args.split()
    for model in tasksamodels[task]:
      UpperCAmelCase			:				List[str]										=					globals()[f'''{model.upper().replace("-"							,					"_"     )}_TINY''']
      UpperCAmelCase			:				Tuple										=					f'''--model_name_or_path {model_name}'''.split()
      UpperCAmelCase			:				List[str]										=					launcher + args + args_model + args_main

      # # generation special case
      # if task == "gen":
      #     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
      #     args_model += f"--model_type {model}".split()
      #     cmds[f"{task}_{model}"] = launcher + args + args_model
      # else:

  return cmds


_lowerCamelCase							:  Optional[Any]     =				make_task_cmds()

_lowerCamelCase							:  Any     =				"zero2"
_lowerCamelCase							:  Optional[int]     =				"zero3"

_lowerCamelCase							:  Optional[int]     =				[ZEROa, ZEROa]

def      a__	(						UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict							,					UpperCAmelCase				:     Dict     )    ->		str:
  # customize the test name generator function as we want both params to appear in the sub-test
  # name, as by default it shows only the first param
  UpperCAmelCase			:				List[str]										=					parameterized.to_safe_name('''_'''.join(str(UpperCAmelCase     ) for x in param.args     )     )
  return f'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
_lowerCamelCase							:  Dict     =				list(itertools.product(stages, task_cmds.keys()))






@slow
@require_deepspeed
@require_torch_gpu
class    __UpperCAmelCase	(			lowerCamelCase__		):



   def 			__magic_name__       (				self				:	Optional[int],						__A				:	Union[str, Any],						__A				:	str		):
     # return a ready to run train cmd
     if task not in task_cmds:
       raise ValueError(F'''don\'t know of task {task}, have {task_cmds.keys()}'''		)

     UpperCAmelCase			:				Optional[Any]										=					task_cmds[task]
     UpperCAmelCase			:				int										=					F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

     UpperCAmelCase			:				Optional[int]										=					self.get_auto_remove_tmp_dir()
     UpperCAmelCase			:				int										=					F'''--output_dir {output_dir}'''.split()

     cmd += args_ds + args_out

     return cmd, output_dir





   @parameterized.expand(__A,						name_func=__A		)
   def 			__magic_name__       (				self				:	Tuple,						__A				:	List[Any],						__A				:	List[str]		):
     # testing the ability to do a run followed by recovery of full fp32 weights

     UpperCAmelCase					,		UpperCAmelCase			:				List[str]										=					self.get_task_cmd(__A,						__A		)

     # 1. generate the checkpoint
     cmd += "--save_steps 1".split()
     # keep for quick debug
     # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
     execute_subprocess_async(__A,						env=self.get_env()		)

     # 2. test that the fp32 weights get reconsolidated
     UpperCAmelCase			:				Tuple										=					F'''{output_dir}/checkpoint-1'''
     UpperCAmelCase			:				List[Any]										=					F'''{chkpt_dir}/out.bin'''
     UpperCAmelCase			:				List[str]										=					F'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
     # keep for quick debug
     # print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
     subprocess.check_call(__A,						shell=__A		)
     assert os.path.exists(__A		), F'''{recovered_model_path} was not found'''

     # possibly could also test that the resulting saved model is usable but given that we use
     # random models we won't know if it's any good





