


"""simple docstring"""


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(42)

a_											=     get_tests_dir("fixtures")
a_											=     os.path.join(dirname(get_tests_dir()))
a_											=     dirname(os.path.abspath(__file__))

# default torch.distributed port
a_											=     "10999"

a_											=     "t5-small"

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
a_											=     "hf-internal-testing/tiny-random-mt5"
a_											=     "hf-internal-testing/tiny-random-camembert"
a_											=     "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
a_											=     "hf-internal-testing/tiny-random-convbert"
a_											=     "hf-internal-testing/tiny-random-layoutlmv2"
a_											=     "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
a_											=     "hf-internal-testing/tiny-random-ctrl"
a_											=     "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
a_											=     "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
a_											=     "hf-internal-testing/tiny-random-reformer"  # multiple issues with either mlm/qa/clas

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






def   a__			(					__lowercase=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)
				_A       	=      min(2  ,      get_gpu_count() ) if distributed else 1
				_A       	=      os.environ.get("DS_TEST_PORT"  ,      __lowercase )
				return f"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()






def   a__			(					)						->						List[Any]:
				_A       	=      f"""{FIXTURE_DIRECTORY}/tests_samples"""
				_A       	=      f"""{data_dir_samples}/wmt_en_ro"""
				_A       	=      f"""{data_dir_samples}/xsum"""
				_A       	=      '''
        --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.
				_A       	=      {
				    '''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''',
				    ],
				}

				_A       	=      f"""{ROOT_DIRECTORY}/examples/pytorch"""

				_A       	=      {
				    '''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
        """,
				}

				_A       	=      get_launcher(distributed=__lowercase )

				_A       	=      {}
				for task, args in tasks.items():
								_A       	=      args.split()
								for model in tasksamodels[task]:
												_A       	=      globals()[f"""{model.upper().replace('-'  ,      '_' )}_TINY"""]
												_A       	=      f"""--model_name_or_path {model_name}""".split()
												_A       	=      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


a_											=     make_task_cmds()

a_											=     "zero2"
a_											=     "zero3"

a_											=     [ZEROa, ZEROa]






def   a__			(					__lowercase  ,      __lowercase  ,      __lowercase )						->						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
				_A       	=      parameterized.to_safe_name("_".join(str(__lowercase ) for x in param.args ) )
				return f"""{func.__name__}_{param_based_name}"""


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




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







		def 			a_			(       self   :							Optional[int]     , a__   :							Union[str, Any]     , a__   :							str		)							->    str:





						'''simple docstring'''







						if task not in task_cmds:
										raise ValueError(F"""don\'t know of task {task}, have {task_cmds.keys()}"""		)

						_A       	=      task_cmds[task]
						_A       	=      F"""--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json""".split()

						_A       	=      self.get_auto_remove_tmp_dir()
						_A       	=      F"""--output_dir {output_dir}""".split()

						cmd += args_ds + args_out

						return cmd, output_dir







		@parameterized.expand(__A     , name_func=__A		)
		def 			a_			(       self   :							Tuple     , a__   :							List[Any]     , a__   :							List[str]		)							->    Optional[Any]:





						'''simple docstring'''







						_A       	=      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
						_A       	=      F"""{output_dir}/checkpoint-1"""
						_A       	=      F"""{chkpt_dir}/out.bin"""
						_A       	=      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


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							:  Any     =				False
_lowerCamelCase							:  int     =				Config.from_pretrained("unc-nlp/frcnn-vg-finetuned")
_lowerCamelCase							:  Union[str, Any]     =				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, 2_0_4_8), dtype="float32"),
            "sizes": datasets.Sequence(length=2, feature=datasets.Value("float32")),
            "preds_per_image": datasets.Value(dtype="int32"),
        }
    )
)





class    __UpperCAmelCase	:



   def __init__(				self				:	List[str],						__A				:	int=sys.argv[1:]		):
     UpperCAmelCase			:				List[str]										=					None
     UpperCAmelCase			:				Optional[Any]										=					None
     UpperCAmelCase			:				int										=					None
     UpperCAmelCase			:				Any										=					1
     UpperCAmelCase					,		UpperCAmelCase			:				Optional[int]										=					getopt.getopt(__A,						'''i:o:b:s''',						['''inputdir=''', '''outfile=''', '''batch_size=''', '''subset_list=''']		)
     for opt, arg in opts:
       if opt in ("-i", "--inputdir"):
         UpperCAmelCase			:				Dict										=					arg
       elif opt in ("-o", "--outfile"):
         UpperCAmelCase			:				Optional[int]										=					arg
       elif opt in ("-b", "--batch_size"):
         UpperCAmelCase			:				Tuple										=					int(__A		)
       elif opt in ("-s", "--subset_list"):
         UpperCAmelCase			:				Optional[Any]										=					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:
         UpperCAmelCase			:				Optional[Any]										=					{self._vqa_file_split()[0] for x in tryload(__A		)}
     else:
       UpperCAmelCase			:				Tuple										=					None

     UpperCAmelCase			:				Tuple										=					CONFIG
     if torch.cuda.is_available():
       UpperCAmelCase			:				Optional[Any]										=					'''cuda'''
     UpperCAmelCase			:				int										=					os.path.realpath(__A		)
     UpperCAmelCase			:				str										=					os.path.realpath(__A		)
     UpperCAmelCase			:				Union[str, Any]										=					Preprocess(self.config		)
     UpperCAmelCase			:				Optional[Any]										=					GeneralizedRCNN.from_pretrained('''unc-nlp/frcnn-vg-finetuned''',						config=self.config		)
     UpperCAmelCase			:				Any										=					batch_size if batch_size != 0 else 1
     UpperCAmelCase			:				List[str]										=					DEFAULT_SCHEMA



   def 			__magic_name__       (				self				:	Union[str, Any],						__A				:	int		):
     UpperCAmelCase			:				List[Any]										=					int(file.split('''.'''		)[0].split('''_'''		)[-1]		)
     UpperCAmelCase			:				Optional[Any]										=					os.path.join(self.inputdir,						__A		)
     return (img_id, filepath)



   @property
   def 			__magic_name__       (				self				:	str		):
     UpperCAmelCase			:				Tuple										=					[]
     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:
         UpperCAmelCase			:				Tuple										=					batch
         UpperCAmelCase			:				str										=					[]
         yield list(map(__A,						zip(*__A		)		)		)

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





   def __call__(				self				:	Optional[Any]		):
     # make writer
     if not TEST:
       UpperCAmelCase			:				int										=					datasets.ArrowWriter(features=self.schema,						path=self.outputfile		)
     # do file generator
     for i, (img_ids, filepaths) in enumerate(self.file_generator		):
       UpperCAmelCase					,		UpperCAmelCase					,		UpperCAmelCase			:				int										=					self.preprocess(__A		)
       UpperCAmelCase			:				Tuple										=					self.model(
           __A,						__A,						scales_yx=__A,						padding='''max_detections''',						max_detections=self.config.MAX_DETECTIONS,						pad_value=0,						return_tensors='''np''',						location='''cpu''',						)
       UpperCAmelCase			:				List[Any]										=					output_dict.pop('''normalized_boxes'''		)
       if not TEST:
         UpperCAmelCase			:				Tuple										=					np.array(__A		)
         UpperCAmelCase			:				Union[str, Any]										=					self.schema.encode_batch(__A		)
         writer.write_batch(__A		)
       if TEST:
         break
       # finalizer the writer
     if not TEST:
       UpperCAmelCase					,		UpperCAmelCase			:				Any										=					writer.finalize()
       print(F'''Success! You wrote {num_examples} entry(s) and {num_bytes >> 2_0} mb'''		)






def      a__	(						UpperCAmelCase				:     List[Any]     )    ->		int:
  try:
    UpperCAmelCase			:				List[str]										=					json.load(UpperCAmelCase     )
    try:
      UpperCAmelCase			:				Optional[Any]										=					list(data.keys()     )
    except Exception:
      UpperCAmelCase			:				Dict										=					[d['''img_id'''] for d in data]
  except Exception:
    try:
      UpperCAmelCase			:				List[Any]										=					eval(stream.read()     )
    except Exception:
      UpperCAmelCase			:				List[Any]										=					stream.read().split('''\n'''     )
  return data


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





