






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 __A					(   __lowerCAmelCase=False			)->						str:





						"""simple docstring"""



						_UpperCAmelCase             =    min(2				,	get_gpu_count()			) if distributed else 1
						_UpperCAmelCase             =    os.environ.get('DS_TEST_PORT'				,	__lowerCAmelCase			)
						return F"""deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}""".split()


def __A					(   )->						str:





						"""simple docstring"""



						_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"""
        {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
        """,
						}

						_UpperCAmelCase             =    get_launcher(distributed=__lowerCAmelCase			)

						_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


_a   			=						make_task_cmds()

_a   			=						'''zero2'''
_a   			=						'''zero3'''

_a   			=						[ZEROa, ZEROa]


def __A					(   __lowerCAmelCase				,	__lowerCAmelCase				,	__lowerCAmelCase			)->						Dict:





						"""simple docstring"""



						_UpperCAmelCase             =    parameterized.to_safe_name('_'.join(str(__lowerCAmelCase			) 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				(   snake_case__):





	"""simple docstring"""






	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase					):
							"""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(UpperCAmelCase ,					name_func=UpperCAmelCase					)
	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase					):
							"""simple docstring"""







							_UpperCAmelCase      ,				_UpperCAmelCase             =    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
							_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(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








from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


_a   			=						logging.get_logger(__name__)


if is_vision_available():
		import PIL





class __lowerCamelCase				(   snake_case__):





	"""simple docstring"""






	UpperCamelCase__			=   ["pixel_values"]

	def __init__(							self ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = PILImageResampling.BICUBIC ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = True ,					UpperCAmelCase = 1 / 255 ,					UpperCAmelCase = True ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = True ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							super().__init__(**UpperCAmelCase					)
							_UpperCAmelCase             =    size if size is not None else {'shortest_edge': 224}
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    crop_size if crop_size is not None else {'height': 224, 'width': 224}
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)

							_UpperCAmelCase             =    do_resize
							_UpperCAmelCase             =    size
							_UpperCAmelCase             =    resample
							_UpperCAmelCase             =    do_center_crop
							_UpperCAmelCase             =    crop_size
							_UpperCAmelCase             =    do_rescale
							_UpperCAmelCase             =    rescale_factor
							_UpperCAmelCase             =    do_normalize
							_UpperCAmelCase             =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
							_UpperCAmelCase             =    image_std if image_std is not None else OPENAI_CLIP_STD
							_UpperCAmelCase             =    do_convert_rgb

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = PILImageResampling.BICUBIC ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    get_resize_output_image_size(
							    UpperCAmelCase ,					size=(size['height'], size['width']) ,					default_to_square=UpperCAmelCase					)
							return resize(UpperCAmelCase ,					size=UpperCAmelCase ,					resample=UpperCAmelCase ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)
							return center_crop(UpperCAmelCase ,					size=(size['height'], size['width']) ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							return rescale(UpperCAmelCase ,					scale=UpperCAmelCase ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase ,					UpperCAmelCase = None ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							return normalize(UpperCAmelCase ,					mean=UpperCAmelCase ,					std=UpperCAmelCase ,					data_format=UpperCAmelCase ,					**UpperCAmelCase					)

	def  UpperCamelCase					(							self ,					UpperCAmelCase ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = None ,					UpperCAmelCase = ChannelDimension.FIRST ,					**UpperCAmelCase ,					):
							"""simple docstring"""







							_UpperCAmelCase             =    do_resize if do_resize is not None else self.do_resize
							_UpperCAmelCase             =    size if size is not None else self.size
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase ,					default_to_square=UpperCAmelCase					)
							_UpperCAmelCase             =    resample if resample is not None else self.resample
							_UpperCAmelCase             =    do_center_crop if do_center_crop is not None else self.do_center_crop
							_UpperCAmelCase             =    crop_size if crop_size is not None else self.crop_size
							_UpperCAmelCase             =    get_size_dict(UpperCAmelCase					)
							_UpperCAmelCase             =    do_rescale if do_rescale is not None else self.do_rescale
							_UpperCAmelCase             =    rescale_factor if rescale_factor is not None else self.rescale_factor
							_UpperCAmelCase             =    do_normalize if do_normalize is not None else self.do_normalize
							_UpperCAmelCase             =    image_mean if image_mean is not None else self.image_mean
							_UpperCAmelCase             =    image_std if image_std is not None else self.image_std
							_UpperCAmelCase             =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

							_UpperCAmelCase             =    make_list_of_images(UpperCAmelCase					)

							if not valid_images(UpperCAmelCase					):
													raise ValueError(
													    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
													    'torch.Tensor, tf.Tensor or jax.ndarray.'					)

							if do_resize and size is None:
													raise ValueError('Size must be specified if do_resize is True.'					)

							if do_center_crop and crop_size is None:
													raise ValueError('Crop size must be specified if do_center_crop is True.'					)

							if do_rescale and rescale_factor is None:
													raise ValueError('Rescale factor must be specified if do_rescale is True.'					)

							if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('Image mean and std must be specified if do_normalize is True.'					)

							# PIL RGBA images are converted to RGB
							if do_convert_rgb:
													_UpperCAmelCase             =    [convert_to_rgb(UpperCAmelCase					) for image in images]

							# All transformations expect numpy arrays.
							_UpperCAmelCase             =    [to_numpy_array(UpperCAmelCase					) for image in images]

							if do_resize:
													_UpperCAmelCase             =    [self.resize(image=UpperCAmelCase ,					size=UpperCAmelCase ,					resample=UpperCAmelCase					) for image in images]

							if do_center_crop:
													_UpperCAmelCase             =    [self.center_crop(image=UpperCAmelCase ,					size=UpperCAmelCase					) for image in images]

							if do_rescale:
													_UpperCAmelCase             =    [self.rescale(image=UpperCAmelCase ,					scale=UpperCAmelCase					) for image in images]

							if do_normalize:
													_UpperCAmelCase             =    [self.normalize(image=UpperCAmelCase ,					mean=UpperCAmelCase ,					std=UpperCAmelCase					) for image in images]

							_UpperCAmelCase             =    [to_channel_dimension_format(UpperCAmelCase ,					UpperCAmelCase					) for image in images]

							_UpperCAmelCase             =    {'pixel_values': images}
							return BatchFeature(data=UpperCAmelCase ,					tensor_type=UpperCAmelCase					)
