




'''simple docstring'''

import argparse
import logging
import sys
import time

import tensorflow as tf
from datasets import load_dataset

from transformers import AutoTokenizer, TFAutoModelForSequenceClassification


if __name__ == "__main__":
		a__         =     argparse.ArgumentParser()

		# Hyperparameters sent by the client are passed as command-line arguments to the script.
		parser.add_argument('''--epochs''', type=int, default=1)
		parser.add_argument('''--per_device_train_batch_size''', type=int, default=16)
		parser.add_argument('''--per_device_eval_batch_size''', type=int, default=8)
		parser.add_argument('''--model_name_or_path''', type=str)
		parser.add_argument('''--learning_rate''', type=str, default=5E-5)
		parser.add_argument('''--do_train''', type=bool, default=True)
		parser.add_argument('''--do_eval''', type=bool, default=True)
		parser.add_argument('''--output_dir''', type=str)

		a__    , a__         =     parser.parse_known_args()

		# overwrite batch size until we have tf_glue.py
		a__         =     16
		a__         =     16

		# Set up logging
		a__         =     logging.getLogger(__name__)

		logging.basicConfig(
		    level=logging.getLevelName('''INFO'''),
		    handlers=[logging.StreamHandler(sys.stdout)],
		    format='''%(asctime)s - %(name)s - %(levelname)s - %(message)s''',
		)

		# Load model and tokenizer
		a__         =     TFAutoModelForSequenceClassification.from_pretrained(args.model_name_or_path)
		a__         =     AutoTokenizer.from_pretrained(args.model_name_or_path)

		# Load dataset
		a__    , a__         =     load_dataset('''imdb''', split=['''train''', '''test'''])
		a__         =     train_dataset.shuffle().select(range(5_000))  # smaller the size for train dataset to 5k
		a__         =     test_dataset.shuffle().select(range(500))  # smaller the size for test dataset to 500

		# Preprocess train dataset
		a__         =     train_dataset.map(
		    lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
		)
		train_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

		a__         =     {
		    x: train_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
		    for x in ['''input_ids''', '''attention_mask''']
		}
		a__         =     tf.data.Dataset.from_tensor_slices((train_features, train_dataset['''label'''])).batch(
		    args.per_device_train_batch_size
		)

		# Preprocess test dataset
		a__         =     test_dataset.map(
		    lambda e: tokenizer(e['''text'''], truncation=True, padding='''max_length'''), batched=True
		)
		test_dataset.set_format(type='''tensorflow''', columns=['''input_ids''', '''attention_mask''', '''label'''])

		a__         =     {
		    x: test_dataset[x].to_tensor(default_value=0, shape=[None, tokenizer.model_max_length])
		    for x in ['''input_ids''', '''attention_mask''']
		}
		a__         =     tf.data.Dataset.from_tensor_slices((test_features, test_dataset['''label'''])).batch(
		    args.per_device_eval_batch_size
		)

		# fine optimizer and loss
		a__         =     tf.keras.optimizers.Adam(learning_rate=args.learning_rate)
		a__         =     tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
		a__         =     [tf.keras.metrics.SparseCategoricalAccuracy()]
		model.compile(optimizer=optimizer, loss=loss, metrics=metrics)

		a__         =     time.time()
		a__         =     model.fit(tf_train_dataset, epochs=args.epochs, batch_size=args.per_device_train_batch_size)
		a__         =     time.time() - start_train_time

		logger.info('''*** Train ***''')
		logger.info(F"train_runtime = {end_train_time}")
		for key, value in train_results.history.items():
				logger.info(F"  {key} = {value}")





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)

UpperCamelCase__       				=	get_tests_dir('''fixtures''')
UpperCamelCase__       				=	os.path.join(dirname(get_tests_dir()))
UpperCamelCase__       				=	dirname(os.path.abspath(__file__))

# default torch.distributed port
UpperCamelCase__       				=	'''10999'''

UpperCamelCase__       				=	'''t5-small'''

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


# *** To Fix ***


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

# missing tokenizer files
UpperCamelCase__       				=	'''hf-internal-testing/tiny-random-convbert'''
UpperCamelCase__       				=	'''hf-internal-testing/tiny-random-layoutlmv2'''
UpperCamelCase__       				=	'''hf-internal-testing/tiny-random-hubert'''

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

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

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






def 				UpperCAmelCase__  (							_A=False			):






  """simple docstring"""


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






def 				UpperCAmelCase__  (							):






  """simple docstring"""


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

  a_		    =  get_launcher(distributed=_A			)

  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


UpperCamelCase__       				=	make_task_cmds()

UpperCamelCase__       				=	'''zero2'''
UpperCamelCase__       				=	'''zero3'''

UpperCamelCase__       				=	[ZEROa, ZEROa]






def 				UpperCAmelCase__  (							_A      ,      _A      ,      _A			):






  """simple docstring"""


  a_		    =  parameterized.to_safe_name('''_'''.join(str(_A			) for x in param.args			)			)
  return f"{func.__name__}_{param_based_name}"


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




@slow
@require_deepspeed
@require_torch_gpu
class 							__lowercase   (			a__    ):



     def 				__magic_name__		(  self   :							Any      ,		lowercase__   :							Tuple      ,		lowercase__   :							List[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()}"       )

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

       a_    , a_		    =  self.get_task_cmd(lowercase__      ,		lowercase__       )

       # 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(lowercase__      ,		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(lowercase__      ,		shell=lowercase__       )
       assert os.path.exists(lowercase__       ), 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
