


'''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_  :    Optional[Any]      =			get_tests_dir("""fixtures""")
a_  :    Union[str, Any]      =			os.path.join(dirname(get_tests_dir()))
a_  :    List[str]      =			dirname(os.path.abspath(__file__))

# default torch.distributed port
a_  :    Union[str, Any]      =			"""10999"""

a_  :    Union[str, Any]      =			"""t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
a_  :    Any      =			"""hf-internal-testing/tiny-random-convbert"""
a_  :    Optional[Any]      =			"""hf-internal-testing/tiny-random-layoutlmv2"""
a_  :    Optional[int]      =			"""hf-internal-testing/tiny-random-hubert"""

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

# other issues with tiny models
a_  :    Dict      =			"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
a_  :    Tuple      =			"""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_  :    int      =			"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
a_  :    Optional[int]      =			"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
a_  :    List[Any]      =			""""""
# - "luke" Entities classes - no plan to make such example
a_  :    List[Any]      =			"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
a_  :    Dict      =			"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
a_  :    Tuple      =			"""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_  :    Union[str, Any]      =			"""hf-internal-testing/tiny-random-tapas"""





def __snake_case (				UpperCAmelCase_						:						str=False		):
 # 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)
 lowerCamelCase_								=  min(2 ,    get_gpu_count()		) if distributed else 1
 lowerCamelCase_								=  os.environ.get("DS_TEST_PORT" ,    UpperCAmelCase_		)
 return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()





def __snake_case (				):
 lowerCamelCase_								=  F'''{FIXTURE_DIRECTORY}/tests_samples'''
 lowerCamelCase_								=  F'''{data_dir_samples}/wmt_en_ro'''
 lowerCamelCase_								=  F'''{data_dir_samples}/xsum'''
 lowerCamelCase_								=  "\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        ".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.
 lowerCamelCase_								=  {
     "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",
     ],
 }

 lowerCamelCase_								=  F'''{ROOT_DIRECTORY}/examples/pytorch'''

 lowerCamelCase_								=  {
     "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
        ''',
 }

 lowerCamelCase_								=  get_launcher(distributed=UpperCAmelCase_		)

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

a_  :    Dict      =			"""zero2"""
a_  :    str      =			"""zero3"""

a_  :    Any      =			[ZEROa, ZEROa]





def __snake_case (				UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						str ,    UpperCAmelCase_						:						List[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
 lowerCamelCase_								=  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
a_  :    Tuple      =			list(itertools.product(stages, task_cmds.keys()))







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





  """simple docstring"""




  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # 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()}'''					)

   lowerCamelCase_								=  task_cmds[task]
   lowerCamelCase_								=  f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

   lowerCamelCase_								=  self.get_auto_remove_tmp_dir()
   lowerCamelCase_								=  f'''--output_dir {output_dir}'''.split()

   cmd += args_ds + args_out

   return cmd, output_dir







  @parameterized.expand(UpperCamelCase      ,  name_func=UpperCamelCase					)
  def   snake_case		(   self      ,  UpperCamelCase      ,  UpperCamelCase					):







   """simple docstring"""
   # testing the ability to do a run followed by recovery of full fp32 weights

   lowerCamelCase_			,lowerCamelCase_								=  self.get_task_cmd(UpperCamelCase      ,  UpperCamelCase					)

   # 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(UpperCamelCase      ,  env=self.get_env()					)

   # 2. test that the fp32 weights get reconsolidated
   lowerCamelCase_								=  f'''{output_dir}/checkpoint-1'''
   lowerCamelCase_								=  f'''{chkpt_dir}/out.bin'''
   lowerCamelCase_								=  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(UpperCamelCase      ,  shell=UpperCamelCase					)
   assert os.path.exists(UpperCamelCase					), 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







'''simple docstring'''

import argparse
import json
import re
import string
import sys
from collections import Counter





def __snake_case (				UpperCAmelCase_						:						str		):

 def remove_articles(UpperCAmelCase_						:						Optional[Any]		):
  return re.sub(r"\b(a|an|the)\b" ,    " " ,    UpperCAmelCase_		)

 def white_space_fix(UpperCAmelCase_						:						List[str]		):
  return " ".join(text.split()		)

 def remove_punc(UpperCAmelCase_						:						Any		):
  lowerCamelCase_								=  set(string.punctuation		)
  return "".join(ch for ch in text if ch not in exclude		)

 def lower(UpperCAmelCase_						:						List[str]		):
  return text.lower()

 return white_space_fix(remove_articles(remove_punc(lower(UpperCAmelCase_		)		)		)		)





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						int		):
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  normalize_answer(UpperCAmelCase_		).split()
 lowerCamelCase_								=  Counter(UpperCAmelCase_		) & Counter(UpperCAmelCase_		)
 lowerCamelCase_								=  sum(common.values()		)
 if num_same == 0:
  return 0
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  1.0 * num_same / len(UpperCAmelCase_		)
 lowerCamelCase_								=  (2 * precision * recall) / (precision + recall)
 return fa





def __snake_case (				UpperCAmelCase_						:						Tuple ,    UpperCAmelCase_						:						List[Any]		):
 return normalize_answer(UpperCAmelCase_		) == normalize_answer(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Any ,    UpperCAmelCase_						:						Dict ,    UpperCAmelCase_						:						Union[str, Any]		):
 lowerCamelCase_								=  []
 for ground_truth in ground_truths:
  lowerCamelCase_								=  metric_fn(UpperCAmelCase_ ,    UpperCAmelCase_		)
  scores_for_ground_truths.append(UpperCAmelCase_		)
 return max(UpperCAmelCase_		)





def __snake_case (				UpperCAmelCase_						:						Optional[int] ,    UpperCAmelCase_						:						Dict		):
 lowerCamelCase_								=  lowerCamelCase_								=  lowerCamelCase_								=  0
 lowerCamelCase_								=  []
 for passage in dataset:
  for qa in passage["qas"]:
   total += 1
   if qa["id"] not in predictions:
    lowerCamelCase_								=  F'''Unanswered question {qa["id"]} will receive score 0.'''
    print(UpperCAmelCase_ ,    file=sys.stderr		)
    continue

   lowerCamelCase_								=  [x["text"] for x in qa["answers"]]
   lowerCamelCase_								=  predictions[qa["id"]]

   lowerCamelCase_								=  metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)
   if int(_exact_match		) == 1:
    correct_ids.append(qa["id"]		)
   exact_match += _exact_match

   fa += metric_max_over_ground_truths(UpperCAmelCase_ ,    UpperCAmelCase_ ,    UpperCAmelCase_		)

 lowerCamelCase_								=  exact_match / total
 lowerCamelCase_								=  fa / total

 return {"exact_match": exact_match, "f1": fa}, correct_ids


if __name__ == "__main__":
     a_  :    Optional[int]      =			"""1.0"""
     a_  :    Dict      =			argparse.ArgumentParser("""Official evaluation script for ReCoRD v1.0.""")
     parser.add_argument("""data_file""", help="""The dataset file in JSON format.""")
     parser.add_argument("""pred_file""", help="""The model prediction file in JSON format.""")
     parser.add_argument("""--output_correct_ids""", action="""store_true""", help="""Output the correctly answered query IDs.""")
     a_  :    Optional[int]      =			parser.parse_args()

     with open(args.data_file) as data_file:
          a_  :    Any      =			json.load(data_file)
          if dataset_json["version"] != expected_version:
               print(
                   f'''Evaluation expects v-{expected_version}, but got dataset with v-{dataset_json['version']}''',
                   file=sys.stderr,
               )
          a_  :    Any      =			dataset_json["""data"""]

     with open(args.pred_file) as pred_file:
          a_  :    List[str]      =			json.load(pred_file)

     a_     ,       a_  :    int      =			evaluate(dataset, predictions)

     if args.output_correct_ids:
          print(f'''Output {len(correct_ids)} correctly answered question IDs.''')
          with open("""correct_ids.json""", """w""") as f:
               json.dump(correct_ids, f)



