
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						(							snake_case__: Union[str, Any]=False ):


					'''simple docstring'''
					lowercase_					  = min(2						,       get_gpu_count() ) if distributed else 1
					lowercase_					  = 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						(							):


					'''simple docstring'''
					lowercase_					  = F'''{FIXTURE_DIRECTORY}/tests_samples'''
					lowercase_					  = F'''{data_dir_samples}/wmt_en_ro'''
					lowercase_					  = F'''{data_dir_samples}/xsum'''
					lowercase_					  = '''
        --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.
					lowercase_					  = {
					    '''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''',
					    ],
					}

					lowercase_					  = F'''{ROOT_DIRECTORY}/examples/pytorch'''

					lowercase_					  = {
					    '''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
        ''',
					}

					lowercase_					  = get_launcher(distributed=snake_case__ )

					lowercase_					  = {}
					for task, args in tasks.items():
										lowercase_					  = args.split()
										for model in tasksamodels[task]:
															lowercase_					  = globals()[F'''{model.upper().replace('-'						,       '_' )}_TINY''']
															lowercase_					  = F'''--model_name_or_path {model_name}'''.split()
															lowercase_					  = 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						(							snake_case__: Dict						,       snake_case__: Dict						,       snake_case__: Union[str, Any] ):


					'''simple docstring'''
					lowercase_					  = 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(itertools.product(stages, task_cmds.keys()))


@slow
@require_deepspeed
@require_torch_gpu
class 			lowercase__(    UpperCAmelCase							):



	"""simple docstring"""




	def 						_lowercase  (	self  :      Any	,							SCREAMING_SNAKE_CASE_  :      Union[str, Any]	,							SCREAMING_SNAKE_CASE_  :      Any	)	->					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()}'''	)

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

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

						cmd += args_ds + args_out

						return cmd, output_dir




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

						lowercase_  ,     lowercase_					  = self.get_task_cmd(SCREAMING_SNAKE_CASE_	,							SCREAMING_SNAKE_CASE_	)

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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__a        = {
    'configuration_donut_swin': ['DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'DonutSwinConfig'],
    'processing_donut': ['DonutProcessor'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'DonutSwinModel',
						    'DonutSwinPreTrainedModel',
						]

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = ['DonutFeatureExtractor']
						__a        = ['DonutImageProcessor']


if TYPE_CHECKING:
						from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
						from .processing_donut import DonutProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_donut_swin import (
												    DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
												    DonutSwinModel,
												    DonutSwinPreTrainedModel,
												)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_donut import DonutFeatureExtractor
												from .image_processing_donut import DonutImageProcessor

else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




