




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)

__a			:	Optional[int]							     =		get_tests_dir("""fixtures""")
__a			:	Union[str, Any]							     =		os.path.join(dirname(get_tests_dir()))
__a			:	str							     =		dirname(os.path.abspath(__file__))

# default torch.distributed port
__a			:	str							     =		"""10999"""

__a			:	Union[str, Any]							     =		"""t5-small"""

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
__a			:	Optional[Any]							     =		"""hf-internal-testing/tiny-random-mt5"""
__a			:	Dict							     =		"""hf-internal-testing/tiny-random-camembert"""
__a			:	Optional[int]							     =		"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
__a			:	Tuple							     =		"""hf-internal-testing/tiny-random-convbert"""
__a			:	List[str]							     =		"""hf-internal-testing/tiny-random-layoutlmv2"""
__a			:	Dict							     =		"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
__a			:	Tuple							     =		"""hf-internal-testing/tiny-random-ctrl"""
__a			:	Union[str, Any]							     =		"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

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




def  a_		(							__snake_case=False					)	->			int:

			'''simple docstring'''


			UpperCamelCase_     				= min(2     ,		get_gpu_count()					) if distributed else 1
			UpperCamelCase_     				= os.environ.get('DS_TEST_PORT'     ,		__snake_case					)
			return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()




def  a_		(							)	->			str:

			'''simple docstring'''


			UpperCamelCase_     				= F'''{FIXTURE_DIRECTORY}/tests_samples'''
			UpperCamelCase_     				= F'''{data_dir_samples}/wmt_en_ro'''
			UpperCamelCase_     				= F'''{data_dir_samples}/xsum'''
			UpperCamelCase_     				= '\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.
			UpperCamelCase_     				= {
			    '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_     				= F'''{ROOT_DIRECTORY}/examples/pytorch'''

			UpperCamelCase_     				= {
			    '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_     				= get_launcher(distributed=__snake_case					)

			UpperCamelCase_     				= {}
			for task, args in tasks.items():
						UpperCamelCase_     				= args.split()
						for model in tasksamodels[task]:
									UpperCamelCase_     				= globals()[F'''{model.upper().replace('-'     ,		'_'					)}_TINY''']
									UpperCamelCase_     				= F'''--model_name_or_path {model_name}'''.split()
									UpperCamelCase_     				= 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			:	Optional[Any]							     =		make_task_cmds()

__a			:	Dict							     =		"""zero2"""
__a			:	Dict							     =		"""zero3"""

__a			:	Union[str, Any]							     =		[ZEROa, ZEROa]




def  a_		(							__snake_case     ,		__snake_case     ,		__snake_case					)	->			str:

			'''simple docstring'''


			UpperCamelCase_     				= parameterized.to_safe_name('_'.join(str(__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[str]							     =		list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class     A					(				lowerCamelCase_       ):







							def        lowercase__							(    self   :	Dict   ,     __UpperCAmelCase   :	List[str]   ,     __UpperCAmelCase   :	str				)		->					Any:

										"""simple docstring"""



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

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

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

										cmd += args_ds + args_out

										return cmd, output_dir







							@parameterized.expand(__UpperCAmelCase   ,     name_func=__UpperCAmelCase				)
							def        lowercase__							(    self   :	Optional[int]   ,     __UpperCAmelCase   :	Tuple   ,     __UpperCAmelCase   :	int				)		->					Dict:

										"""simple docstring"""



										UpperCamelCase_				,   UpperCamelCase_     				= 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
										UpperCamelCase_     				= f'''{output_dir}/checkpoint-1'''
										UpperCamelCase_     				= f'''{chkpt_dir}/out.bin'''
										UpperCamelCase_     				= 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






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_		(							__snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case=None					)	->			str:

			'''simple docstring'''


			UpperCamelCase_     				= 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.
						UpperCamelCase_     				= weight.view(torch.intaa					)
						UpperCamelCase_     				= 'bfloat16'
			UpperCamelCase_     				= weight.cpu().numpy()
			UpperCamelCase_     				= os.path.join(__snake_case     ,		F'''{weight_name}.dat'''					)
			if index is not None:
						if dtype is None:
									UpperCamelCase_     				= str(array.dtype					)
						UpperCamelCase_     				= {'dtype': dtype, 'shape': list(array.shape					)}
			if array.ndim == 0:
						UpperCamelCase_     				= array[None]
			UpperCamelCase_     				= np.memmap(__snake_case     ,		dtype=array.dtype     ,		mode='w+'     ,		shape=array.shape					)
			UpperCamelCase_     				= array[:]
			file_array.flush()
			return index




def  a_		(							__snake_case     ,		__snake_case					)	->			Dict:

			'''simple docstring'''


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

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

			UpperCamelCase_     				= np.memmap(__snake_case     ,		dtype=__snake_case     ,		shape=__snake_case     ,		mode='r'					)

			if len(weight_info['shape']					) == 0:
						UpperCamelCase_     				= weight[0]
			UpperCamelCase_     				= torch.tensor(__snake_case					)
			if weight_info["dtype"] == "bfloat16":
						UpperCamelCase_     				= weight.view(torch.bfloataa					)

			return weight




def  a_		(							__snake_case     ,		__snake_case					)	->			Optional[int]:

			'''simple docstring'''


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

			UpperCamelCase_     				= os.path.join(__snake_case     ,		'index.json'					)
			if os.path.isfile(__snake_case					):
						with open(__snake_case     ,		'r'     ,		encoding='utf-8'					) as f:
									UpperCamelCase_     				= json.load(__snake_case					)
			else:
						UpperCamelCase_     				= {}
			current_index.update(__snake_case					)

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




def  a_		(							__snake_case     ,		__snake_case					)	->			Tuple:

			'''simple docstring'''


			os.makedirs(__snake_case     ,		exist_ok=__snake_case					)
			UpperCamelCase_     				= {}
			for name, parameter in state_dict.items():
						UpperCamelCase_     				= offload_weight(__snake_case     ,		__snake_case     ,		__snake_case     ,		index=__snake_case					)

			# Update index
			save_offload_index(__snake_case     ,		__snake_case					)




class     A					(				lowerCamelCase_       ):







							def __init__(    self   :	int   ,     __UpperCAmelCase   :	Mapping   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= dataset
										UpperCamelCase_     				= prefix







							def __getitem__(    self   :	List[Any]   ,     __UpperCAmelCase   :	Dict				)		->					Tuple:

										"""simple docstring"""



										return self.dataset[f'''{self.prefix}{key}''']







							def __iter__(    self   :	int				)		->					List[str]:

										"""simple docstring"""



										return iter([key for key in self.dataset if key.startswith(self.prefix				)]				)







							def __len__(    self   :	Any				)		->					Optional[Any]:

										"""simple docstring"""



										return len(self.dataset				)







class     A					(				lowerCamelCase_       ):







							def __init__(    self   :	int   ,     __UpperCAmelCase   :	Dict[str, torch.Tensor] = None   ,     __UpperCAmelCase   :	Optional[Union[str, os.PathLike]] = None   ,     __UpperCAmelCase   :	Mapping = None   ,     __UpperCAmelCase   :	Optional[int]=None   ,     )		->					List[str]:

										"""simple docstring"""



										if state_dict is None and save_folder is None:
													raise ValueError('Need either a `state_dict` or a `save_folder` containing offloaded weights.'				)

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







							def __getitem__(    self   :	Optional[Any]   ,     __UpperCAmelCase   :	str				)		->					Tuple:

										"""simple docstring"""



										if key in self.state_dict:
													return self.state_dict[key]
										UpperCamelCase_     				= 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

													UpperCamelCase_     				= 'cpu' if self.device is None else self.device
													with safe_open(weight_info['safetensors_file']   ,     framework='pt'   ,     device=__UpperCAmelCase				) as f:
																UpperCamelCase_     				= 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

										UpperCamelCase_     				= os.path.join(self.save_folder   ,     f'''{key}.dat'''				)
										return load_offloaded_weight(__UpperCAmelCase   ,     __UpperCAmelCase				)







							def __iter__(    self   :	Optional[int]				)		->					Union[str, Any]:

										"""simple docstring"""



										return iter(self.all_keys				)







							def __len__(    self   :	Any				)		->					Any:

										"""simple docstring"""



										return len(self.all_keys				)







def  a_		(							__snake_case     ,		__snake_case					)	->			Any:

			'''simple docstring'''


			UpperCamelCase_     				= {}
			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
