




"""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)

__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 					UpperCamelCase__       (						lowercase__					:       Union[str, Any]=False				):
		# 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						:  Dict        =			min(2    ,       get_gpu_count()				) if distributed else 1
		snake_case						:  Dict        =			os.environ.get("DS_TEST_PORT"    ,       lowercase__				)
		return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()



def 					UpperCamelCase__       (						):
		snake_case						:  List[Any]        =			F'''{FIXTURE_DIRECTORY}/tests_samples'''
		snake_case						:  Dict        =			F'''{data_dir_samples}/wmt_en_ro'''
		snake_case						:  Tuple        =			F'''{data_dir_samples}/xsum'''
		snake_case						:  Dict        =			"\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						:  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",
		    ],
		}

		snake_case						:  Tuple        =			F'''{ROOT_DIRECTORY}/examples/pytorch'''

		snake_case						:  Dict        =			{
		    "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
        ''',
		}

		snake_case						:  Optional[Any]        =			get_launcher(distributed=lowercase__				)

		snake_case						:  Any        =			{}
		for task, args in tasks.items():
				snake_case						:  Optional[int]        =			args.split()
				for model in tasksamodels[task]:
						snake_case						:  Tuple        =			globals()[F'''{model.upper().replace('-'    ,       '_'				)}_TINY''']
						snake_case						:  Tuple        =			F'''--model_name_or_path {model_name}'''.split()
						snake_case						:  List[Any]        =			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 					UpperCamelCase__       (						lowercase__					:       int    ,       lowercase__					:       int    ,       lowercase__					:       Tuple				):
		# 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						:  List[str]        =			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
__A							=     list(itertools.product(stages, task_cmds.keys()))
@slow
@require_deepspeed
@require_torch_gpu
class      lowerCamelCase__      (					lowerCamelCase_     ):


			def 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







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

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

					snake_case						:  str        =			self.get_auto_remove_tmp_dir()
					snake_case						:  List[Any]        =			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 							lowerCamelCase_			(       self			,	SCREAMING_SNAKE_CASE			,	SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case     ,			snake_case						:  List[Any]        =			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
					snake_case						:  Union[str, Any]        =			F'''{output_dir}/checkpoint-1'''
					snake_case						:  str        =			F'''{chkpt_dir}/out.bin'''
					snake_case						:  Union[str, 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(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







"""simple docstring"""

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A							=     logging.get_logger(__name__)

__A							=     {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}
class      lowerCamelCase__      (					lowerCamelCase_     ):
			a__	: Optional[int]        =       """vilt"""


			def __init__(       self			,	SCREAMING_SNAKE_CASE=30_522			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=2			,	SCREAMING_SNAKE_CASE=40			,	SCREAMING_SNAKE_CASE=768			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=12			,	SCREAMING_SNAKE_CASE=3_072			,	SCREAMING_SNAKE_CASE="gelu"			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.0			,	SCREAMING_SNAKE_CASE=0.02			,	SCREAMING_SNAKE_CASE=1E-12			,	SCREAMING_SNAKE_CASE=384			,	SCREAMING_SNAKE_CASE=32			,	SCREAMING_SNAKE_CASE=3			,	SCREAMING_SNAKE_CASE=True			,	SCREAMING_SNAKE_CASE=-1			,	SCREAMING_SNAKE_CASE=False			,	SCREAMING_SNAKE_CASE=-1			,	**SCREAMING_SNAKE_CASE			,	):



					"""simple docstring"""







					super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE			,	**SCREAMING_SNAKE_CASE							)

					snake_case						:  str        =			vocab_size
					snake_case						:  Optional[Any]        =			type_vocab_size
					snake_case						:  List[str]        =			modality_type_vocab_size
					snake_case						:  Optional[Any]        =			max_position_embeddings

					snake_case						:  Tuple        =			hidden_size
					snake_case						:  List[str]        =			num_hidden_layers
					snake_case						:  int        =			num_attention_heads
					snake_case						:  Optional[int]        =			intermediate_size
					snake_case						:  List[Any]        =			hidden_act
					snake_case						:  List[str]        =			hidden_dropout_prob
					snake_case						:  Union[str, Any]        =			attention_probs_dropout_prob
					snake_case						:  Any        =			initializer_range
					snake_case						:  List[Any]        =			layer_norm_eps

					snake_case						:  Any        =			image_size
					snake_case						:  str        =			patch_size
					snake_case						:  Optional[int]        =			num_channels
					snake_case						:  List[str]        =			qkv_bias
					snake_case						:  Optional[int]        =			max_image_length
					snake_case						:  Any        =			num_images

