

import json
import os
from collections.abc import Mapping
from typing import Dict, List, Optional, Union

import numpy as np
import torch

from .imports import is_safetensors_available







def _a				( SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    Optional[Any]					,      SCREAMING_SNAKE_CASE	:    Tuple					,      SCREAMING_SNAKE_CASE	:    Union[str, Any]=None	)					->					Dict:

 """simple docstring"""






 __lowerCAmelCase:     Union[str, Any]     			=   None
 # Check the string instead of the dtype to be compatible with versions of PyTorch that don't have bfloat16.
 if str(weight.dtype	) == "torch.bfloat16":
  # Need to reinterpret the underlined data as int16 since NumPy does not handle bfloat16s.
  __lowerCAmelCase:     Optional[int]     			=   weight.view(torch.intaa	)
  __lowerCAmelCase:     List[str]     			=   'bfloat16'
 __lowerCAmelCase:     Dict     			=   weight.cpu().numpy()
 __lowerCAmelCase:     Dict     			=   os.path.join(SCREAMING_SNAKE_CASE					,      f'''{weight_name}.dat'''	)
 if index is not None:
  if dtype is None:
   __lowerCAmelCase:     Union[str, Any]     			=   str(array.dtype	)
  __lowerCAmelCase:     Optional[Any]     			=   {'dtype': dtype, 'shape': list(array.shape	)}
 if array.ndim == 0:
  __lowerCAmelCase:     Optional[int]     			=   array[None]
 __lowerCAmelCase:     str     			=   np.memmap(SCREAMING_SNAKE_CASE					,      dtype=array.dtype					,      mode='w+'					,      shape=array.shape	)
 __lowerCAmelCase:     List[Any]     			=   array[:]
 file_array.flush()
 return index







def _a				( SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    List[Any]	)					->					Any:

 """simple docstring"""






 __lowerCAmelCase:     Dict     			=   tuple(weight_info['shape']	)
 if shape == ():
  # NumPy memory-mapped arrays can't have 0 dims so it was saved as 1d tensor
  __lowerCAmelCase:     Tuple     			=   (1,)

 __lowerCAmelCase:     Dict     			=   weight_info['dtype']
 if dtype == "bfloat16":
  # NumPy does not support bfloat16 so this was saved as a int16
  __lowerCAmelCase:     Optional[Any]     			=   'int16'

 __lowerCAmelCase:     Tuple     			=   np.memmap(SCREAMING_SNAKE_CASE					,      dtype=SCREAMING_SNAKE_CASE					,      shape=SCREAMING_SNAKE_CASE					,      mode='r'	)

 if len(weight_info['shape']	) == 0:
  __lowerCAmelCase:     Optional[int]     			=   weight[0]
 __lowerCAmelCase:     Dict     			=   torch.tensor(SCREAMING_SNAKE_CASE	)
 if weight_info["dtype"] == "bfloat16":
  __lowerCAmelCase:     Tuple     			=   weight.view(torch.bfloataa	)

 return weight







def _a				( SCREAMING_SNAKE_CASE	:    Any					,      SCREAMING_SNAKE_CASE	:    int	)					->					List[str]:

 """simple docstring"""






 if index is None or len(SCREAMING_SNAKE_CASE	) == 0:
  # Nothing to save
  return

 __lowerCAmelCase:     List[str]     			=   os.path.join(SCREAMING_SNAKE_CASE					,      'index.json'	)
 if os.path.isfile(SCREAMING_SNAKE_CASE	):
  with open(SCREAMING_SNAKE_CASE					,      'r'					,      encoding='utf-8'	) as f:
   __lowerCAmelCase:     Tuple     			=   json.load(SCREAMING_SNAKE_CASE	)
 else:
  __lowerCAmelCase:     List[Any]     			=   {}
 current_index.update(SCREAMING_SNAKE_CASE	)

 with open(SCREAMING_SNAKE_CASE					,      'w'					,      encoding='utf-8'	) as f:
  json.dump(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      indent=2	)







def _a				( SCREAMING_SNAKE_CASE	:    Union[str, os.PathLike]					,      SCREAMING_SNAKE_CASE	:    Dict[str, torch.Tensor]	)					->					Any:

 """simple docstring"""






 os.makedirs(SCREAMING_SNAKE_CASE					,      exist_ok=SCREAMING_SNAKE_CASE	)
 __lowerCAmelCase:     Dict     			=   {}
 for name, parameter in state_dict.items():
  __lowerCAmelCase:     Tuple     			=   offload_weight(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE					,      index=SCREAMING_SNAKE_CASE	)

 # Update index
 save_offload_index(SCREAMING_SNAKE_CASE					,      SCREAMING_SNAKE_CASE	)






class 							A_				(	snake_case__      ):


     def __init__(							self    :	Tuple    ,						UpperCAmelCase    :	Mapping    ,						UpperCAmelCase    :	str      )							->			List[str]:
      __lowerCAmelCase:     Tuple     			=   dataset
      __lowerCAmelCase:     str     			=   prefix


     def __getitem__(							self    :	List[str]    ,						UpperCAmelCase    :	int      )							->			List[str]:
      return self.dataset[F'''{self.prefix}{key}''']


     def __iter__(							self    :	Union[str, Any]      )							->			Optional[int]:
      return iter([key for key in self.dataset if key.startswith(self.prefix      )]      )


     def __len__(							self    :	str      )							->			Optional[int]:
      return len(self.dataset      )






class 							A_				(	snake_case__      ):


     def __init__(							self    :	List[Any]    ,						UpperCAmelCase    :	Dict[str, torch.Tensor] = None    ,						UpperCAmelCase    :	Optional[Union[str, os.PathLike]] = None    ,						UpperCAmelCase    :	Mapping = None    ,						UpperCAmelCase    :	Any=None    ,						)							->			Dict:
      if state_dict is None and save_folder is None:
       raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'      )

      __lowerCAmelCase:     List[Any]     			=   {} if state_dict is None else state_dict
      __lowerCAmelCase:     List[Any]     			=   save_folder
      if index is None and save_folder is not None:
       with open(os.path.join(UpperCAmelCase    ,						'index.json'      )      ) as f:
        __lowerCAmelCase:     List[str]     			=   json.load(UpperCAmelCase      )
      __lowerCAmelCase:     List[Any]     			=   {} if index is None else index
      __lowerCAmelCase:     str     			=   list(self.state_dict.keys()      )
      self.all_keys.extend([key for key in self.index if key not in self.all_keys]      )
      __lowerCAmelCase:     Optional[Any]     			=   device


     def __getitem__(							self    :	str    ,						UpperCAmelCase    :	str      )							->			int:
      # State dict gets priority
      if key in self.state_dict:
       return self.state_dict[key]
      __lowerCAmelCase:     Optional[Any]     			=   self.index[key]
      if weight_info.get('safetensors_file'      ) is not None:
       if not is_safetensors_available():
        raise ImportError('These offloaded weights require the use of safetensors: `pip install safetensors`.'      )

       from safetensors import safe_open

       __lowerCAmelCase:     Dict     			=   'cpu' if self.device is None else self.device
       with safe_open(weight_info['safetensors_file']    ,						framework='pt'    ,						device=UpperCAmelCase      ) as f:
        __lowerCAmelCase:     str     			=   f.get_tensor(weight_info.get('weight_name'    ,						UpperCAmelCase      )      )

       if "dtype" in weight_info:
        return tensor.to(getattr(UpperCAmelCase    ,						weight_info['dtype']      )      )
       else:
        return tensor

      __lowerCAmelCase:     Optional[Any]     			=   os.path.join(self.save_folder    ,						F'''{key}.dat'''      )
      return load_offloaded_weight(UpperCAmelCase    ,						UpperCAmelCase      )


     def __iter__(							self    :	str      )							->			List[Any]:
      return iter(self.all_keys      )


     def __len__(							self    :	int      )							->			str:
      return len(self.all_keys      )







def _a				( SCREAMING_SNAKE_CASE	:    Dict[str, torch.Tensor]					,      SCREAMING_SNAKE_CASE	:    List[str]	)					->					List[Any]:

 """simple docstring"""






 __lowerCAmelCase:     Optional[int]     			=   {}
 for module_name in submodule_names:
  # We want to catch module_name parameter (module_name.xxx) or potentially module_name, but not any of the
  # submodules that could being like module_name (transformers.h.1 and transformers.h.10 for instance)
  result.update(
      {
          key: param
          for key, param in state_dict.items()
          if key == module_name or key.startswith(module_name + '.'	)
      }	)
 return result








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)

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

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

_a	 =     '''t5-small'''

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


# *** To Fix ***


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

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

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

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

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







def _a				( SCREAMING_SNAKE_CASE	:    List[str]=False	)					->					Union[str, Any]:

 """simple docstring"""






 __lowerCAmelCase:     Union[str, Any]     			=   min(2					,      get_gpu_count()	) if distributed else 1
 __lowerCAmelCase:     Any     			=   os.environ.get('DS_TEST_PORT'					,      SCREAMING_SNAKE_CASE	)
 return f'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()







def _a				( )					->					Optional[Any]:

 """simple docstring"""






 __lowerCAmelCase:     int     			=   f'''{FIXTURE_DIRECTORY}/tests_samples'''
 __lowerCAmelCase:     List[str]     			=   f'''{data_dir_samples}/wmt_en_ro'''
 __lowerCAmelCase:     str     			=   f'''{data_dir_samples}/xsum'''
 __lowerCAmelCase:     int     			=   '\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.
 __lowerCAmelCase:     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',
     ],
 }

 __lowerCAmelCase:     Optional[Any]     			=   f'''{ROOT_DIRECTORY}/examples/pytorch'''

 __lowerCAmelCase:     Optional[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
        ''',
 }

 __lowerCAmelCase:     List[Any]     			=   get_launcher(distributed=SCREAMING_SNAKE_CASE	)

 __lowerCAmelCase:     List[str]     			=   {}
 for task, args in tasks.items():
  __lowerCAmelCase:     Tuple     			=   args.split()
  for model in tasksamodels[task]:
   __lowerCAmelCase:     Dict     			=   globals()[f'''{model.upper().replace('-'					,      '_'	)}_TINY''']
   __lowerCAmelCase:     List[Any]     			=   f'''--model_name_or_path {model_name}'''.split()
   __lowerCAmelCase:     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


_a	 =     make_task_cmds()

_a	 =     '''zero2'''
_a	 =     '''zero3'''

_a	 =     [ZEROa, ZEROa]







def _a				( SCREAMING_SNAKE_CASE	:    Any					,      SCREAMING_SNAKE_CASE	:    List[Any]					,      SCREAMING_SNAKE_CASE	:    Optional[int]	)					->					Dict:

 """simple docstring"""






 __lowerCAmelCase:     Any     			=   parameterized.to_safe_name('_'.join(str(SCREAMING_SNAKE_CASE	) for x in param.args	)	)
 return f'''{func.__name__}_{param_based_name}'''


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






@slow
@require_deepspeed
@require_torch_gpu
class 							A_				(	snake_case__      ):


     def 						UpperCAmelCase						(							self    :	int    ,						UpperCAmelCase    :	Any    ,						UpperCAmelCase    :	Optional[Any]      )							->			List[Any]:
      # 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:     List[str]     			=   task_cmds[task]
      __lowerCAmelCase:     str     			=   F'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

      __lowerCAmelCase:     Dict     			=   self.get_auto_remove_tmp_dir()
      __lowerCAmelCase:     Optional[Any]     			=   F'''--output_dir {output_dir}'''.split()

      cmd += args_ds + args_out

      return cmd, output_dir


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

      __lowerCAmelCase			,							__lowerCAmelCase:     str     			=   self.get_task_cmd(UpperCAmelCase    ,						UpperCAmelCase      )

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

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





