




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)

_lowerCamelCase					:     List[Any]					  =  get_tests_dir("fixtures")
_lowerCamelCase					:     str					  =  os.path.join(dirname(get_tests_dir()))
_lowerCamelCase					:     Any					  =  dirname(os.path.abspath(__file__))

# default torch.distributed port
_lowerCamelCase					:     Tuple					  =  "10999"

_lowerCamelCase					:     str					  =  "t5-small"

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


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-mt5"
_lowerCamelCase					:     Dict					  =  "hf-internal-testing/tiny-random-camembert"
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-random-openai-gpt"

# missing tokenizer files
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-convbert"
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-random-layoutlmv2"
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-hubert"

# issues with tokenizer
_lowerCamelCase					:     Optional[Any]					  =  "hf-internal-testing/tiny-random-ctrl"
_lowerCamelCase					:     Optional[int]					  =  "hf-internal-testing/tiny-random-transfo-xl"  # same as ctrl

# other issues with tiny models
_lowerCamelCase					:     str					  =  "hf-internal-testing/tiny-random-ibert"  # multiple issues with either mlm/qa/clas
_lowerCamelCase					:     Union[str, 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
_lowerCamelCase					:     Union[str, Any]					  =  "hf-internal-testing/tiny-random-dpr"
# - "dpr"  examples/research_projects/rag-end2end-retriever/
_lowerCamelCase					:     Tuple					  =  "hf-internal-testing/tiny-random-rag"
# - "rag" research_projects
_lowerCamelCase					:     Dict					  =  ""
# - "luke" Entities classes - no plan to make such example
_lowerCamelCase					:     Any					  =  "hf-internal-testing/tiny-random-lxmert"
# - "lxmert" doesn't work with run_qa.py
_lowerCamelCase					:     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
_lowerCamelCase					:     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
_lowerCamelCase					:     int					  =  "hf-internal-testing/tiny-random-tapas"




def 							_UpperCAmelCase     (UpperCamelCase_       :       Tuple=False				):
				'''simple docstring'''
				# 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)
				_lowerCAmelCase			:			Optional[Any]        =					min(2					,			get_gpu_count()				) if distributed else 1
				_lowerCAmelCase			:			Tuple        =					os.environ.get("""DS_TEST_PORT"""					,			UpperCamelCase_				)
				return F"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()




def 							_UpperCAmelCase     ():
				'''simple docstring'''
				_lowerCAmelCase			:			Optional[Any]        =					F"{FIXTURE_DIRECTORY}/tests_samples"
				_lowerCAmelCase			:			Any        =					F"{data_dir_samples}/wmt_en_ro"
				_lowerCAmelCase			:			Tuple        =					F"{data_dir_samples}/xsum"
				_lowerCAmelCase			:			str        =					"""
        --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.
				_lowerCAmelCase			:			Optional[Any]        =					{
				    """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			:			List[Any]        =					F"{ROOT_DIRECTORY}/examples/pytorch"

				_lowerCAmelCase			:			Dict        =					{
				    """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        ",
				}

				_lowerCAmelCase			:			List[Any]        =					get_launcher(distributed=UpperCamelCase_				)

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


_lowerCamelCase					:     List[str]					  =  make_task_cmds()

_lowerCamelCase					:     str					  =  "zero2"
_lowerCamelCase					:     Union[str, Any]					  =  "zero3"

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




def 							_UpperCAmelCase     (UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       str				):
				'''simple docstring'''
				# 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
				_lowerCAmelCase			:			int        =					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
_lowerCamelCase					:     Optional[int]					  =  list(itertools.product(stages, task_cmds.keys()))





@slow
@require_deepspeed
@require_torch_gpu
class 				__snake_case     (_a						):



				def 					SCREAMING_SNAKE_CASE				(					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()}"					)

								_lowerCAmelCase			:			int        =					task_cmds[task]
								_lowerCAmelCase			:			str        =					f"--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json".split()

								_lowerCAmelCase			:			Optional[int]        =					self.get_auto_remove_tmp_dir()
								_lowerCAmelCase			:			Optional[int]        =					f"--output_dir {output_dir}".split()

								cmd += args_ds + args_out

								return cmd, output_dir



				@parameterized.expand(_UpperCAmelCase						,		name_func=_UpperCAmelCase					)
				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						Union[str, Any]						,		_UpperCAmelCase					:						Union[str, Any]					)     ->			List[str]:





								'''simple docstring'''




								_lowerCAmelCase    ,			_lowerCAmelCase			:			List[Any]        =					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			:			Dict        =					f"{output_dir}/checkpoint-1"
								_lowerCAmelCase			:			int        =					f"{chkpt_dir}/out.bin"
								_lowerCAmelCase			:			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(_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







def 							_UpperCAmelCase     (UpperCamelCase_       :       int					,			UpperCamelCase_       :       Optional[int]				):
				'''simple docstring'''
				_lowerCAmelCase			:			Dict        =					len(UpperCamelCase_				)
				# Create a stack and push index of fist element to it
				_lowerCAmelCase			:			Tuple        =					[]
				st.append(0				)

				# Span value of first element is always 1
				_lowerCAmelCase			:			List[Any]        =					1

				# Calculate span values for rest of the elements
				for i in range(1					,			UpperCamelCase_				):
								# Pop elements from stack while stack is not
								# empty and top of stack is smaller than price[i]
								while len(UpperCamelCase_				) > 0 and price[st[0]] <= price[i]:
												st.pop()

								# If stack becomes empty, then price[i] is greater
								# than all elements on left of it, i.e. price[0],
								# price[1], ..price[i-1]. Else the price[i]  is
								# greater than elements after top of stack
								_lowerCAmelCase			:			Union[str, Any]        =					i + 1 if len(UpperCamelCase_				) <= 0 else (i - st[0])

								# Push this element to stack
								st.append(UpperCamelCase_				)




def 							_UpperCAmelCase     (UpperCamelCase_       :       Dict					,			UpperCamelCase_       :       Union[str, Any]				):
				'''simple docstring'''
				for i in range(0					,			UpperCamelCase_				):
								print(arr[i]					,			end=""" """				)


# Driver program to test above function
_lowerCamelCase					:     Union[str, Any]					  =  [1_0, 4, 5, 9_0, 1_2_0, 8_0]
_lowerCamelCase					:     List[Any]					  =  [0 for i in range(len(price) + 1)]

# Fill the span values in array S[]
calculation_span(price, S)





print_array(S, len(price))

