"""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)

__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					(	UpperCAmelCase=False      )       ->   Dict:
     # 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)
     snake_case_       =      min(2		,      get_gpu_count()      ) if distributed else 1
     snake_case_       =      os.environ.get('DS_TEST_PORT'		,      UpperCAmelCase      )
     return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()







def       UpperCAmelCase					(	)       ->   List[Any]:
     snake_case_       =      f'{FIXTURE_DIRECTORY}/tests_samples'
     snake_case_       =      f'{data_dir_samples}/wmt_en_ro'
     snake_case_       =      f'{data_dir_samples}/xsum'
     snake_case_       =      '\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.
     snake_case_       =      {
         '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',
         ],
     }

     snake_case_       =      f'{ROOT_DIRECTORY}/examples/pytorch'

     snake_case_       =      {
         '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        ',
     }

     snake_case_       =      get_launcher(distributed=UpperCAmelCase      )

     snake_case_       =      {}
     for task, args in tasks.items():
          snake_case_       =      args.split()
          for model in tasksamodels[task]:
               snake_case_       =      globals()[f'{model.upper().replace("-"		,      "_"      )}_TINY']
               snake_case_       =      f'--model_name_or_path {model_name}'.split()
               snake_case_       =      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					(	UpperCAmelCase		,      UpperCAmelCase		,      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
     snake_case_       =      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
__UpperCamelCase								=    list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 				UpperCamelCase      (       lowerCAmelCase__     ):







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   int:
            # 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()}')

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

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

            cmd += args_ds + args_out

            return cmd, output_dir







       @parameterized.expand(lowerCAmelCase__,							name_func=lowerCAmelCase__)
       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Dict:
            # testing the ability to do a run followed by recovery of full fp32 weights

            snake_case_ ,			snake_case_       =      self.get_task_cmd(lowerCAmelCase__,							lowerCAmelCase__)

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

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

from .utils import ExplicitEnum, is_torch_available, logging


if is_torch_available():
    import torch


__UpperCamelCase								=    logging.get_logger(__name__)




class 				UpperCamelCase      :







       def __init__(				self,							lowerCAmelCase__,							lowerCAmelCase__=21,							lowerCAmelCase__=[],							lowerCAmelCase__=None)   ->   Optional[Any]:
            snake_case_       =      model
            snake_case_       =      trace_batch_nums
            snake_case_       =      abort_after_batch_num

            # keep a LIFO buffer of frames to dump as soon as inf/nan is encountered to give context to the problem emergence
            snake_case_       =      collections.deque([],							lowerCAmelCase__)
            snake_case_       =      []
            snake_case_       =      0
            snake_case_       =      0
            snake_case_       =      False
            snake_case_       =      '                 '

            self.analyse_model()

            self.register_forward_hook()







       def 	a_		(				self,							lowerCAmelCase__=None)   ->   Optional[int]:
            if frame is not None:
                 self.expand_frame(lowerCAmelCase__)
            self.frames.append('\n'.join(self.frame))
            snake_case_       =      []  # start a new frame







       def 	a_		(				self,							lowerCAmelCase__)   ->   List[str]:
            self.frame.append(lowerCAmelCase__)







       def 	a_		(				self)   ->   List[str]:
            print('\n'.join(self.frames))
            snake_case_       =      []







       def 	a_		(				self)   ->   Tuple:
            snake_case_       =      []







       def 	a_		(				self)   ->   List[str]:
            print(f'\nDetected inf/nan during batch_number={self.batch_number}')
            print(f'Last {len(self.frames)} forward frames:')
            print(f'{"abs min":8} {"abs max":8} metadata')
            print('\n'.join(self.frames))
            print('\n\n')
            snake_case_       =      []







       def 	a_		(				self)   ->   Any:
            # extract the fully qualified module names, to be able to report at run time. e.g.:
            # encoder.block.2.layer.0.SelfAttention.o
            #
            # for shared weights only the first shared module name will be registered
            snake_case_       =      {m: name for name, m in self.model.named_modules()}
            # self.longest_module_name = max(len(v) for v in self.module_names.values())







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__)   ->   str:
            if torch.is_tensor(lowerCAmelCase__):
                 self.expand_frame(get_abs_min_max(lowerCAmelCase__,							lowerCAmelCase__))
                 if detect_overflow(lowerCAmelCase__,							lowerCAmelCase__):
                      snake_case_       =      True
            elif var is None:
                 self.expand_frame(f'{"None":>17} {ctx}')
            else:
                 self.expand_frame(f'{"not a tensor":>17} {ctx}')







       def 	a_		(				self)   ->   List[str]:
            self.expand_frame(f'\n\n{self.prefix} *** Starting batch number={self.batch_number} ***')
            self.expand_frame(f'{"abs min":8} {"abs max":8} metadata')







       def 	a_		(				self)   ->   Dict:
            self.expand_frame(f'{self.prefix} *** Finished batch number={self.batch_number-1} ***\n\n')







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)   ->   List[str]:
            self.expand_frame(f'{self.prefix} {self.module_names[module]} {module.__class__.__name__}')

            # params
            for name, p in module.named_parameters(recurse=lowerCAmelCase__):
                 self.analyse_variable(lowerCAmelCase__,							lowerCAmelCase__)

            # inputs
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 for i, x in enumerate(lowerCAmelCase__):
                      self.analyse_variable(lowerCAmelCase__,							f'input[{i}]')
            else:
                 self.analyse_variable(lowerCAmelCase__,							'input')

            # outputs
            if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                 for i, x in enumerate(lowerCAmelCase__):
                      # possibly a tuple of tuples
                      if isinstance(lowerCAmelCase__,							lowerCAmelCase__):
                           for j, y in enumerate(lowerCAmelCase__):
                                self.analyse_variable(lowerCAmelCase__,							f'output[{i}][{j}]')
                      else:
                           self.analyse_variable(lowerCAmelCase__,							f'output[{i}]')
            else:
                 self.analyse_variable(lowerCAmelCase__,							'output')

            self.save_frame()







       def 	a_		(				self)   ->   List[Any]:
            self.model.apply(self._register_forward_hook)







       def 	a_		(				self,							lowerCAmelCase__)   ->   int:
            module.register_forward_hook(self.forward_hook)







       def 	a_		(				self,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)   ->   Optional[int]:
            # - input is a tuple of packed inputs (could be non-Tensors)
            # - output could be a Tensor or a tuple of Tensors and non-Tensors

            snake_case_       =      False

            snake_case_       =      True if self.batch_number in self.trace_batch_nums else False
            if trace_mode:
                 self.reset_saved_frames()

            if self.total_calls == 0:
                 self.batch_start_frame()
            self.total_calls += 1

            # count batch numbers - the very first forward hook of the batch will be called when the
            # batch completes - i.e. it gets called very last - we know this batch has finished
            if module == self.model:
                 self.batch_number += 1
                 snake_case_       =      True

            self.create_frame(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)

            # if last_frame_of_batch:
            #     self.batch_end_frame()

            if trace_mode:
                 self.trace_frames()

            if last_frame_of_batch:
                 self.batch_start_frame()

            if self.detected_overflow and not trace_mode:
                 self.dump_saved_frames()

                 # now we can abort, as it's pointless to continue running
                 raise ValueError(
                     'DebugUnderflowOverflow: inf/nan detected, aborting as there is no point running further. '
                     'Please scroll up above this traceback to see the activation values prior to this event.')

            # abort after certain batch if requested to do so
            if self.abort_after_batch_num is not None and self.batch_number > self.abort_after_batch_num:
                 raise ValueError(
                     f'DebugUnderflowOverflow: aborting after {self.batch_number} batches due to'
                     f' `abort_after_batch_num={self.abort_after_batch_num}` arg')







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   Optional[int]:
     snake_case_       =      var.abs()
     return f'{abs_var.min():8.2e} {abs_var.max():8.2e} {ctx}'







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   Union[str, Any]:
     snake_case_       =      False
     if torch.isnan(UpperCAmelCase      ).any().item():
          snake_case_       =      True
          print(f'{ctx} has nans'      )
     if torch.isinf(UpperCAmelCase      ).any().item():
          snake_case_       =      True
          print(f'{ctx} has infs'      )

     # if needed to monitor large elements can enable the following
     if 0:  # and detected:
          snake_case_       =      var[torch.ge(var.abs()		,      100      )]
          if naaa.numel() > 0:
               print(f'{ctx}:  n100={naaa.numel()}'      )
          snake_case_       =      var[torch.ge(var.abs()		,      1000      )]
          if naaaa.numel() > 0:
               print(f'{ctx}: n1000={naaaa.numel()}'      )
          snake_case_       =      var[torch.ge(var.abs()		,      10000      )]
          if naaaaa.numel() > 0:
               print(f'{ctx}: n10000={naaaaa.numel()}'      )

     if 0:
          print(f'min={var.min():9.2e} max={var.max():9.2e}'      )

     if 0:
          print(f'min={var.min():9.2e} max={var.max():9.2e} var={var.var():9.2e} mean={var.mean():9.2e} ({ctx})'      )

     return detected




class 				UpperCamelCase      (       lowerCAmelCase__     ):
       SCREAMING_SNAKE_CASE_				=   "underflow_overflow"
       SCREAMING_SNAKE_CASE_				=   "tpu_metrics_debug"

