


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)

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

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

lowerCamelCase		=	'''t5-small'''

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


# *** To Fix ***


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

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

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

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






def   lowerCamelCase_					( _a=False ):




  """simple docstring"""






  lowerCAmelCase__     :    int					  =      min(2			,     get_gpu_count() ) if distributed else 1
  lowerCAmelCase__     :    Dict					  =      os.environ.get('''DS_TEST_PORT'''			,     _a )
  return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()






def   lowerCamelCase_					( ):




  """simple docstring"""






  lowerCAmelCase__     :    Any					  =      f'{FIXTURE_DIRECTORY}/tests_samples'
  lowerCAmelCase__     :    List[Any]					  =      f'{data_dir_samples}/wmt_en_ro'
  lowerCAmelCase__     :    List[str]					  =      f'{data_dir_samples}/xsum'
  lowerCAmelCase__     :    Tuple					  =      '''
        --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.
  lowerCAmelCase__     :    Optional[int]					  =      {
      '''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__     :    Optional[Any]					  =      f'{ROOT_DIRECTORY}/examples/pytorch'

  lowerCAmelCase__     :    int					  =      {
      '''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        ',
  }

  lowerCAmelCase__     :    Optional[int]					  =      get_launcher(distributed=_a )

  lowerCAmelCase__     :    Union[str, Any]					  =      {}
  for task, args in tasks.items():
    lowerCAmelCase__     :    Tuple					  =      args.split()
    for model in tasksamodels[task]:
      lowerCAmelCase__     :    Any					  =      globals()[f'{model.upper().replace("-"			,     "_" )}_TINY']
      lowerCAmelCase__     :    Optional[int]					  =      f'--model_name_or_path {model_name}'.split()
      lowerCAmelCase__     :    Optional[Any]					  =      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		=	make_task_cmds()

lowerCamelCase		=	'''zero2'''
lowerCamelCase		=	'''zero3'''

lowerCamelCase		=	[ZEROa, ZEROa]






def   lowerCamelCase_					( _a			,     _a			,     _a ):




  """simple docstring"""






  lowerCAmelCase__     :    int					  =      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
lowerCamelCase		=	list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class       _a							(       _lowercase):




 def 							UpperCAmelCase__(      self		:	List[str]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	List[Any]	)-> 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()}'	)

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

   lowerCAmelCase__     :    str					  =      self.get_auto_remove_tmp_dir()
   lowerCAmelCase__     :    Any					  =      F'--output_dir {output_dir}'.split()

   cmd += args_ds + args_out

   return cmd, output_dir




 @parameterized.expand(_SCREAMING_SNAKE_CASE	,      name_func=_SCREAMING_SNAKE_CASE	)
 def 							UpperCAmelCase__(      self		:	Optional[int]	,      _SCREAMING_SNAKE_CASE		:	str	,      _SCREAMING_SNAKE_CASE		:	List[str]	)-> Any:
   # testing the ability to do a run followed by recovery of full fp32 weights

   lowerCAmelCase__     :    Optional[int]					  =      self.get_task_cmd(_SCREAMING_SNAKE_CASE	,      _SCREAMING_SNAKE_CASE	)

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

   # 2. test that the fp32 weights get reconsolidated
   lowerCAmelCase__     :    Union[str, Any]					  =      F'{output_dir}/checkpoint-1'
   lowerCAmelCase__     :    str					  =      F'{chkpt_dir}/out.bin'
   lowerCAmelCase__     :    Optional[int]					  =      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(_SCREAMING_SNAKE_CASE	,      shell=_SCREAMING_SNAKE_CASE	)
   assert os.path.exists(_SCREAMING_SNAKE_CASE	), 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 unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()





class       _a							(       unittest.TestCase):




	@property
	def 							UpperCAmelCase__(      self		:	Dict	)-> int:
			torch.manual_seed(0	)
			lowerCAmelCase__     :    int					  =      UNetaDModel(
			    block_out_channels=(32, 64)	,      layers_per_block=2	,      sample_size=32	,      in_channels=3	,      out_channels=3	,      down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')	,      up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')	,      )
			return model




	def 							UpperCAmelCase__(      self		:	Union[str, Any]	)-> Dict:
			lowerCAmelCase__     :    Union[str, Any]					  =      '''cpu'''
			lowerCAmelCase__     :    List[Any]					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    Optional[Any]					  =      DDPMScheduler()

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[Any]					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    int					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    str					  =      torch.Generator(device=_SCREAMING_SNAKE_CASE	).manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	,      return_dict=_SCREAMING_SNAKE_CASE	)[0]

			lowerCAmelCase__     :    Tuple					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    List[str]					  =      image_from_tuple[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    int					  =      np.array(
			    [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04]	)

			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2
			assert np.abs(image_from_tuple_slice.flatten() - expected_slice	).max() < 1E-2




	def 							UpperCAmelCase__(      self		:	List[str]	)-> Any:
			lowerCAmelCase__     :    int					  =      self.dummy_uncond_unet
			lowerCAmelCase__     :    List[Any]					  =      DDPMScheduler(prediction_type='''sample'''	)

			lowerCAmelCase__     :    int					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    Optional[int]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    List[Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    Optional[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Union[str, Any]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      num_inference_steps=2	,      output_type='''numpy'''	)[0]

			lowerCAmelCase__     :    str					  =      image[0, -3:, -3:, -1]
			lowerCAmelCase__     :    Optional[Any]					  =      image_eps[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    Any					  =      1E-2 if torch_device != '''mps''' else 3E-2
			assert np.abs(image_slice.flatten() - image_eps_slice.flatten()	).max() < tolerance





@slow
@require_torch_gpu
class       _a							(       unittest.TestCase):




	def 							UpperCAmelCase__(      self		:	Any	)-> List[Any]:
			lowerCAmelCase__     :    str					  =      '''google/ddpm-cifar10-32'''

			lowerCAmelCase__     :    List[Any]					  =      UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE	)
			lowerCAmelCase__     :    Any					  =      DDPMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[str]					  =      DDPMPipeline(unet=_SCREAMING_SNAKE_CASE	,      scheduler=_SCREAMING_SNAKE_CASE	)
			ddpm.to(_SCREAMING_SNAKE_CASE	)
			ddpm.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE	)

			lowerCAmelCase__     :    List[Any]					  =      torch.manual_seed(0	)
			lowerCAmelCase__     :    Optional[int]					  =      ddpm(generator=_SCREAMING_SNAKE_CASE	,      output_type='''numpy'''	).images

			lowerCAmelCase__     :    List[Any]					  =      image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCAmelCase__     :    List[str]					  =      np.array([0.4200, 0.3588, 0.1939, 0.3847, 0.3382, 0.2647, 0.4155, 0.3582, 0.3385]	)
			assert np.abs(image_slice.flatten() - expected_slice	).max() < 1E-2




