"""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)

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

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

UpperCamelCase_    :    List[str]											=						"""t5-small"""

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


# *** To Fix ***


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

# missing tokenizer files
UpperCamelCase_    :    Any											=						"""hf-internal-testing/tiny-random-convbert"""
UpperCamelCase_    :    int											=						"""hf-internal-testing/tiny-random-layoutlmv2"""
UpperCamelCase_    :    Dict											=						"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
UpperCamelCase_    :    Tuple											=						"""hf-internal-testing/tiny-random-ctrl"""
UpperCamelCase_    :    Tuple											=						"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

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


def 					a__ (						UpperCAmelCase__    :     Union[str, Any]=False     )->		Union[str, Any]:
						# 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)
						UpperCAmelCase__   		=						min(2						,     get_gpu_count()     ) if distributed else 1
						UpperCAmelCase__   		=						os.environ.get("""DS_TEST_PORT"""						,     lowercase__     )
						return f'deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'.split()


def 					a__ (						)->		int:
						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'\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        ',
						}

						UpperCAmelCase__   		=						get_launcher(distributed=lowercase__     )

						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


UpperCamelCase_    :    List[str]											=						make_task_cmds()

UpperCamelCase_    :    Tuple											=						"""zero2"""
UpperCamelCase_    :    Any											=						"""zero3"""

UpperCamelCase_    :    List[str]											=						[ZEROa, ZEROa]


def 					a__ (						UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     int						,     UpperCAmelCase__    :     Tuple     )->		Union[str, Any]:
						# 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
						UpperCAmelCase__   		=						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
UpperCamelCase_    :    str											=						list(itertools.product(stages, task_cmds.keys()))







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

				def     A	(       self    :      str    ,      _lowercase    :      Dict    ,      _lowercase    :      Optional[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(_lowercase    ,      name_func=_lowercase       )
				def     A	(       self    :      Union[str, Any]    ,      _lowercase    :      Union[str, Any]    ,      _lowercase    :      List[str]       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						self.get_task_cmd(_lowercase    ,      _lowercase       )

										# 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(_lowercase    ,      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(_lowercase    ,      shell=_lowercase       )
										assert os.path.exists(_lowercase       ), 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"""







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

						UpperCamelCase_    :    Union[str, Any]											=						True
						UpperCamelCase_    :    int											=						""""""
except Exception as e:
						UpperCamelCase_    :    Any											=						False
						UpperCamelCase_    :    Any											=						(
						    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      _lowercase						(				__lowerCamelCase , __lowerCamelCase					):
				lowerCamelCase__									=    'mel_config.json'

				@register_to_config
				def __init__(       self    :      int    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_5_6    ,      _lowercase    :      int = 2_2_0_5_0    ,      _lowercase    :      int = 2_0_4_8    ,      _lowercase    :      int = 5_1_2    ,      _lowercase    :      int = 8_0    ,      _lowercase    :      int = 3_2    ,      ):


										'''simple docstring'''
										UpperCAmelCase__   		=						hop_length
										UpperCAmelCase__   		=						sample_rate
										UpperCAmelCase__   		=						n_fft
										UpperCAmelCase__   		=						top_db
										UpperCAmelCase__   		=						n_iter
										self.set_resolution(_lowercase    ,      _lowercase       )
										UpperCAmelCase__   		=						None

										if not _librosa_can_be_imported:
																raise ValueError(_import_error       )

				def     A	(       self    :      Any    ,      _lowercase    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						x_res
										UpperCAmelCase__   		=						y_res
										UpperCAmelCase__   		=						self.y_res
										UpperCAmelCase__   		=						self.x_res * self.hop_length - 1

				def     A	(       self    :      Dict    ,      _lowercase    :      str = None    ,      _lowercase    :      np.ndarray = None       ):


										'''simple docstring'''
										if audio_file is not None:
																UpperCAmelCase__		, UpperCAmelCase__   		=						librosa.load(_lowercase    ,      mono=_lowercase    ,      sr=self.sr       )
										else:
																UpperCAmelCase__   		=						raw_audio

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

				def     A	(       self    :      int       ):


										'''simple docstring'''
										return len(self.audio       ) // self.slice_size

				def     A	(       self    :      Optional[int]    ,      _lowercase    :      int = 0       ):


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

				def     A	(       self    :      Dict       ):


										'''simple docstring'''
										return self.sr

				def     A	(       self    :      int    ,      _lowercase    :      int       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						librosa.feature.melspectrogram(
										    y=self.get_audio_slice(_lowercase       )    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_mels=self.n_mels       )
										UpperCAmelCase__   		=						librosa.power_to_db(_lowercase    ,      ref=np.max    ,      top_db=self.top_db       )
										UpperCAmelCase__   		=						(((log_S + self.top_db) * 2_5_5 / self.top_db).clip(0    ,      2_5_5       ) + 0.5).astype(np.uinta       )
										UpperCAmelCase__   		=						Image.fromarray(_lowercase       )
										return image

				def     A	(       self    :      Dict    ,      _lowercase    :      Image.Image       ):


										'''simple docstring'''
										UpperCAmelCase__   		=						np.frombuffer(image.tobytes()    ,      dtype="""uint8"""       ).reshape((image.height, image.width)       )
										UpperCAmelCase__   		=						bytedata.astype("""float"""       ) * self.top_db / 2_5_5 - self.top_db
										UpperCAmelCase__   		=						librosa.db_to_power(_lowercase       )
										UpperCAmelCase__   		=						librosa.feature.inverse.mel_to_audio(
										    _lowercase    ,      sr=self.sr    ,      n_fft=self.n_fft    ,      hop_length=self.hop_length    ,      n_iter=self.n_iter       )
										return audio

