


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)

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

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

snake_case_  :	str							  =							"t5-small"

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


# *** To Fix ***


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

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

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

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







def   lowerCamelCase(      a__=False):
				_SCREAMING_SNAKE_CASE	     =min(2		,get_gpu_count()) if distributed else 1
				_SCREAMING_SNAKE_CASE	     =os.environ.get('''DS_TEST_PORT'''		,UpperCamelCase_)
				return f"deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}".split()







def   lowerCamelCase(      ):
				_SCREAMING_SNAKE_CASE	     =f"{FIXTURE_DIRECTORY}/tests_samples"
				_SCREAMING_SNAKE_CASE	     =f"{data_dir_samples}/wmt_en_ro"
				_SCREAMING_SNAKE_CASE	     =f"{data_dir_samples}/xsum"
				_SCREAMING_SNAKE_CASE	     ="""
        --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.
				_SCREAMING_SNAKE_CASE	     ={
				    """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""",
				    ],
				}

				_SCREAMING_SNAKE_CASE	     =f"{ROOT_DIRECTORY}/examples/pytorch"

				_SCREAMING_SNAKE_CASE	     ={
				    """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        ",
				}

				_SCREAMING_SNAKE_CASE	     =get_launcher(distributed=UpperCamelCase_)

				_SCREAMING_SNAKE_CASE	     ={}
				for task, args in tasks.items():
								_SCREAMING_SNAKE_CASE	     =args.split()
								for model in tasksamodels[task]:
												_SCREAMING_SNAKE_CASE	     =globals()[f"{model.upper().replace('-'		,'_')}_TINY"]
												_SCREAMING_SNAKE_CASE	     =f"--model_name_or_path {model_name}".split()
												_SCREAMING_SNAKE_CASE	     =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


snake_case_  :	List[str]							  =							make_task_cmds()

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

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







def   lowerCamelCase(      a__		,a__		,a__):
				_SCREAMING_SNAKE_CASE	     =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
snake_case_  :	Optional[int]							  =							list(itertools.product(stages, task_cmds.keys()))


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




						def 							__UpperCamelCase (				self    :					Dict ,			_a    :					List[str] ,			_a    :					str     )							->   Any:


										"""simple docstring"""





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

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

										_SCREAMING_SNAKE_CASE	     =self.get_auto_remove_tmp_dir()
										_SCREAMING_SNAKE_CASE	     =f"--output_dir {output_dir}".split()

										cmd += args_ds + args_out

										return cmd, output_dir




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


										"""simple docstring"""





										_SCREAMING_SNAKE_CASE	     =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
										_SCREAMING_SNAKE_CASE	     =f"{output_dir}/checkpoint-1"
										_SCREAMING_SNAKE_CASE	     =f"{chkpt_dir}/out.bin"
										_SCREAMING_SNAKE_CASE	     =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 numpy as np  # noqa: E402

from ...configuration_utils import ConfigMixin, register_to_config
from ...schedulers.scheduling_utils import SchedulerMixin


try:
	import librosa  # noqa: E402

	_lowerCamelCase					:     Union[str, Any]					  =  True
	_lowerCamelCase					:     Optional[Any]					  =  ""
except Exception as e:
	_lowerCamelCase					:     int					  =  False
	_lowerCamelCase					:     List[str]					  =  (
	    F'''Cannot import librosa because {e}. Make sure to correctly install librosa to be able to install it.'''
	)


from PIL import Image  # noqa: E402
class 				__snake_case     (_a  ,		_a						):
				lowerCAmelCase__			=   "mel_config.json"



				@register_to_config
				def __init__(					self					:						Dict						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 256						,		_UpperCAmelCase					:						int = 2_2050						,		_UpperCAmelCase					:						int = 2048						,		_UpperCAmelCase					:						int = 512						,		_UpperCAmelCase					:						int = 80						,		_UpperCAmelCase					:						int = 32						,		)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			List[str]        =					hop_length
								_lowerCAmelCase			:			Optional[Any]        =					sample_rate
								_lowerCAmelCase			:			Optional[Any]        =					n_fft
								_lowerCAmelCase			:			Dict        =					top_db
								_lowerCAmelCase			:			List[Any]        =					n_iter
								self.set_resolution(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					None

								if not _librosa_can_be_imported:
												raise ValueError(_import_error					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						int						,		_UpperCAmelCase					:						int					)     ->			List[Any]:





								'''simple docstring'''




								_lowerCAmelCase			:			Dict        =					x_res
								_lowerCAmelCase			:			Union[str, Any]        =					y_res
								_lowerCAmelCase			:			Any        =					self.y_res
								_lowerCAmelCase			:			Optional[Any]        =					self.x_res * self.hop_length - 1



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						str = None						,		_UpperCAmelCase					:						np.ndarray = None					)     ->			List[str]:





								'''simple docstring'''




								if audio_file is not None:
												_lowerCAmelCase    ,			_lowerCAmelCase			:			Dict        =					librosa.load(_UpperCAmelCase						,		mono=_UpperCAmelCase						,		sr=self.sr					)
								else:
												_lowerCAmelCase			:			Any        =					raw_audio

								# Pad with silence if necessary.
								if len(self.audio					) < self.x_res * self.hop_length:
												_lowerCAmelCase			:			Optional[Any]        =					np.concatenate([self.audio, np.zeros((self.x_res * self.hop_length - len(self.audio					),)					)]					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any					)     ->			int:





								'''simple docstring'''




								return len(self.audio					) // self.slice_size



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[str]						,		_UpperCAmelCase					:						int = 0					)     ->			np.ndarray:





								'''simple docstring'''




								return self.audio[self.slice_size * slice : self.slice_size * (slice + 1)]



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]					)     ->			int:





								'''simple docstring'''




								return self.sr



				def 					SCREAMING_SNAKE_CASE				(					self					:						Dict						,		_UpperCAmelCase					:						int					)     ->			Image.Image:





								'''simple docstring'''




								_lowerCAmelCase			:			Union[str, Any]        =					librosa.feature.melspectrogram(
								    y=self.get_audio_slice(_UpperCAmelCase					)						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_mels=self.n_mels					)
								_lowerCAmelCase			:			List[Any]        =					librosa.power_to_db(_UpperCAmelCase						,		ref=np.max						,		top_db=self.top_db					)
								_lowerCAmelCase			:			List[Any]        =					(((log_S + self.top_db) * 255 / self.top_db).clip(0						,		255					) + 0.5).astype(np.uinta					)
								_lowerCAmelCase			:			Optional[Any]        =					Image.fromarray(_UpperCAmelCase					)
								return image



				def 					SCREAMING_SNAKE_CASE				(					self					:						Optional[int]						,		_UpperCAmelCase					:						Image.Image					)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[int]        =					np.frombuffer(image.tobytes()						,		dtype="""uint8"""					).reshape((image.height, image.width)					)
								_lowerCAmelCase			:			Optional[int]        =					bytedata.astype("""float"""					) * self.top_db / 255 - self.top_db
								_lowerCAmelCase			:			List[Any]        =					librosa.db_to_power(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					librosa.feature.inverse.mel_to_audio(
								    _UpperCAmelCase						,		sr=self.sr						,		n_fft=self.n_fft						,		hop_length=self.hop_length						,		n_iter=self.n_iter					)
								return audio

