
"""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)

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

# default torch.distributed port
lowercase_						    =					"10999"

lowercase_						    =					"t5-small"

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


# *** To Fix ***


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

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

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

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

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




def 			A_			(					lowercase=False      )     ->							Any:


     """simple docstring"""







     UpperCAmelCase_						:    Dict    					=  min(2						, get_gpu_count()      ) if distributed else 1
     UpperCAmelCase_						:    str    					=  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[str]:


     """simple docstring"""







     UpperCAmelCase_						:    List[Any]    					=  f'''{FIXTURE_DIRECTORY}/tests_samples'''
     UpperCAmelCase_						:    Union[str, Any]    					=  f'''{data_dir_samples}/wmt_en_ro'''
     UpperCAmelCase_						:    int    					=  f'''{data_dir_samples}/xsum'''
     UpperCAmelCase_						:    List[str]    					=  """
        --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_						:    Dict    					=  {
         """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_						:    Optional[Any]    					=  f'''{ROOT_DIRECTORY}/examples/pytorch'''

     UpperCAmelCase_						:    List[str]    					=  {
         """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_						:    Optional[Any]    					=  get_launcher(distributed=lowercase      )

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


lowercase_						    =					make_task_cmds()

lowercase_						    =					"zero2"
lowercase_						    =					"zero3"

lowercase_						    =					[ZEROa, ZEROa]




def 			A_			(					lowercase						, lowercase						, lowercase      )     ->							List[Any]:


     """simple docstring"""







     UpperCAmelCase_						:    int    					=  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
lowercase_						    =					list(itertools.product(stages, task_cmds.keys()))



@slow
@require_deepspeed
@require_torch_gpu
class 		UpperCAmelCase_	(lowerCamelCase_						):
 """simple docstring"""




 def   a      ( self       : str     ,   a_       : Dict     ,   a_       : Dict )->					Optional[Any]:



      """simple docstring"""

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

      UpperCAmelCase_						:    Optional[int]    					=  task_cmds[task]
      UpperCAmelCase_						:    List[Any]    					=  f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

      UpperCAmelCase_						:    Tuple    					=  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   a      ( self       : Dict     ,   a_       : Dict     ,   a_       : Dict )->					List[Any]:



      """simple docstring"""

      UpperCAmelCase_					,UpperCAmelCase_						:    Any    					=  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_						:    List[Any]    					=  f'''{output_dir}/checkpoint-1'''
      UpperCAmelCase_						:    Optional[int]    					=  f'''{chkpt_dir}/out.bin'''
      UpperCAmelCase_						:    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(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



"""simple docstring"""







from __future__ import annotations



class 		UpperCAmelCase_	:
 """simple docstring"""




 def __init__( self       : Any     ,   a_       : int = 0 )->					None:



      """simple docstring"""

      UpperCAmelCase_						:    Tuple    					=  value
      UpperCAmelCase_						:    Node | None    					=  None
      UpperCAmelCase_						:    Node | None    					=  None




def 			A_			(					lowercase						, lowercase      )     ->							Node | None:


     """simple docstring"""







     if treea is None:
          return treea
     if treea is None:
          return treea

     UpperCAmelCase_						:    int    					=  treea.value + treea.value
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.left						, treea.left      )
     UpperCAmelCase_						:    List[str]    					=  merge_two_binary_trees(treea.right						, treea.right      )
     return treea




def 			A_			(					lowercase      )     ->							None:


     """simple docstring"""







     if root:
          print(root.value      )
          print_preorder(root.left      )
          print_preorder(root.right      )


if __name__ == "__main__":
       lowercase_						    =					Node(1)
       lowercase_						    =					Node(2)
       lowercase_						    =					Node(3)
       lowercase_						    =					Node(4)

       lowercase_						    =					Node(2)
       lowercase_						    =					Node(4)
       lowercase_						    =					Node(6)
       lowercase_						    =					Node(9)
       lowercase_						    =					Node(5)

       print("Tree1 is: ")
       print_preorder(treea)
       print("Tree2 is: ")
       print_preorder(treea)
       lowercase_						    =					merge_two_binary_trees(treea, treea)
       print("Merged Tree is: ")
       print_preorder(merged_tree)

