'''simple docstring'''

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    lowerCamelCase__  (   unittest.TestCase     ):



   @property
   def  _UpperCamelCase		(    self     )				->     Optional[int]:
        """simple docstring"""





        torch.manual_seed(0     )
        _UpperCamelCase  :str       =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     )				->     Any:
        """simple docstring"""





        _UpperCamelCase  :List[str]       ="""cpu"""
        _UpperCamelCase  :Optional[Any]       =self.dummy_uncond_unet
        _UpperCamelCase  :Optional[Any]       =DDPMScheduler()

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

        _UpperCamelCase  :str       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
        _UpperCamelCase  :str       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :Optional[int]       =torch.Generator(device=lowerCAmelCase__     ).manual_seed(0     )
        _UpperCamelCase  :Dict       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""					,      return_dict=lowerCAmelCase__     )[0]

        _UpperCamelCase  :List[str]       =image[0, -3:, -3:, -1]
        _UpperCamelCase  :Optional[int]       =image_from_tuple[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Dict       =np.array(
            [9.9_56e-01, 5.7_85e-01, 4.6_75e-01, 9.9_30e-01, 0.0, 1.000, 1.1_99e-03, 2.6_48e-04, 5.1_01e-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     )				->     Optional[int]:
        """simple docstring"""





        _UpperCamelCase  :Dict       =self.dummy_uncond_unet
        _UpperCamelCase  :str       =DDPMScheduler(prediction_type="""sample"""     )

        _UpperCamelCase  :Union[str, Any]       =DDPMPipeline(unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__     )
        ddpm.to(lowerCAmelCase__     )
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :List[str]       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :str       =torch.manual_seed(0     )
        _UpperCamelCase  :int       =ddpm(generator=lowerCAmelCase__					,      num_inference_steps=2					,      output_type="""numpy"""     )[0]

        _UpperCamelCase  :Any       =image[0, -3:, -3:, -1]
        _UpperCamelCase  :List[str]       =image_eps[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Optional[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    lowerCamelCase__  (   unittest.TestCase     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """simple docstring"""





        _UpperCamelCase  :Optional[int]       ="""google/ddpm-cifar10-32"""

        _UpperCamelCase  :Optional[int]       =UNetaDModel.from_pretrained(lowerCAmelCase__     )
        _UpperCamelCase  :Any       =DDPMScheduler.from_pretrained(lowerCAmelCase__     )

        _UpperCamelCase  :Tuple       =DDPMPipeline(unet=lowerCAmelCase__					,      scheduler=lowerCAmelCase__     )
        ddpm.to(lowerCAmelCase__     )
        ddpm.set_progress_bar_config(disable=lowerCAmelCase__     )

        _UpperCamelCase  :Optional[int]       =torch.manual_seed(0     )
        _UpperCamelCase  :Optional[int]       =ddpm(generator=lowerCAmelCase__					,      output_type="""numpy"""     ).images

        _UpperCamelCase  :Any       =image[0, -3:, -3:, -1]

        assert image.shape == (1, 32, 32, 3)
        _UpperCamelCase  :Any       =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


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





