





'''simple docstring'''






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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


snake_case_     			=						logging.get_logger(__name__)


if is_vision_available():
     import PIL


class 	_lowercase    (				a					):
   _UpperCamelCase									= ["""pixel_values"""]


   def __init__(			self     , _UpperCAmelCase = True     , _UpperCAmelCase = None     , _UpperCAmelCase = PILImageResampling.BILINEAR     , _UpperCAmelCase = True     , _UpperCAmelCase = False     , _UpperCAmelCase = True     , _UpperCAmelCase = True     , _UpperCAmelCase = 1 / 255     , _UpperCAmelCase = True     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , **_UpperCAmelCase     , ):
       super().__init__(**_UpperCAmelCase       )

       A							:							Dict				=    size if size is not None else {'''height''': 2_560, '''width''': 1_920}
       if isinstance(_UpperCAmelCase     , (tuple, list)       ):
           # The previous feature extractor size parameter was in (width, height) format
           A							:							List[Any]				=    size[::-1]
       A							:							Optional[Any]				=    get_size_dict(_UpperCAmelCase       )

       A							:							Dict				=    do_resize
       A							:							Any				=    size
       A							:							int				=    resample
       A							:							Optional[int]				=    do_thumbnail
       A							:							Any				=    do_align_long_axis
       A							:							List[str]				=    do_pad
       A							:							Optional[Any]				=    do_rescale
       A							:							Any				=    rescale_factor
       A							:							Tuple				=    do_normalize
       A							:							Dict				=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       A							:							Dict				=    image_std if image_std is not None else IMAGENET_STANDARD_STD


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = None       ):
       A,						A							:							int				=    get_image_size(_UpperCAmelCase       )
       A,						A							:							int				=    size['''height'''], size['''width''']

       if (output_width < output_height and input_width > input_height) or (
           output_width > output_height and input_width < input_height
       ):
           A							:							Optional[int]				=    np.rotaa(_UpperCAmelCase     , 3       )

       if data_format is not None:
           A							:							Union[str, Any]				=    to_channel_dimension_format(_UpperCAmelCase     , _UpperCAmelCase       )

       return image


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = False     , _UpperCAmelCase = None     , ):
       A,						A							:							Optional[int]				=    size['''height'''], size['''width''']
       A,						A							:							Optional[Any]				=    get_image_size(_UpperCAmelCase       )

       A							:							Optional[Any]				=    output_width - input_width
       A							:							Dict				=    output_height - input_height

       if random_padding:
           A							:							List[str]				=    np.random.randint(low=0     , high=delta_height + 1       )
           A							:							List[Any]				=    np.random.randint(low=0     , high=delta_width + 1       )
       else:
           A							:							Optional[int]				=    delta_height // 2
           A							:							int				=    delta_width // 2

       A							:							Dict				=    delta_height - pad_top
       A							:							Optional[Any]				=    delta_width - pad_left

       A							:							Optional[Any]				=    ((pad_top, pad_bottom), (pad_left, pad_right))
       return pad(_UpperCAmelCase     , _UpperCAmelCase     , data_format=_UpperCAmelCase       )


   def        snake_case					(			self     , *_UpperCAmelCase     , **_UpperCAmelCase       ):
       logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''       )
       return self.pad_image(*_UpperCAmelCase     , **_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = PILImageResampling.BICUBIC     , _UpperCAmelCase = None     , **_UpperCAmelCase     , ):
       A,						A							:							List[str]				=    get_image_size(_UpperCAmelCase       )
       A,						A							:							List[str]				=    size['''height'''], size['''width''']

       # We always resize to the smallest of either the input or output size.
       A							:							Tuple				=    min(_UpperCAmelCase     , _UpperCAmelCase       )
       A							:							Dict				=    min(_UpperCAmelCase     , _UpperCAmelCase       )

       if height == input_height and width == input_width:
           return image

       if input_height > input_width:
           A							:							Any				=    int(input_width * height / input_height       )
       elif input_width > input_height:
           A							:							Union[str, Any]				=    int(input_height * width / input_width       )

       return resize(
           _UpperCAmelCase     , size=(height, width)     , resample=_UpperCAmelCase     , reducing_gap=2.0     , data_format=_UpperCAmelCase     , **_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = PILImageResampling.BICUBIC     , _UpperCAmelCase = None     , **_UpperCAmelCase     , ):
       A							:							Optional[int]				=    get_size_dict(_UpperCAmelCase       )
       A							:							int				=    min(size['''height''']     , size['''width''']       )
       A							:							Union[str, Any]				=    get_resize_output_image_size(_UpperCAmelCase     , size=_UpperCAmelCase     , default_to_square=_UpperCAmelCase       )
       A							:							str				=    resize(_UpperCAmelCase     , size=_UpperCAmelCase     , resample=_UpperCAmelCase     , data_format=_UpperCAmelCase     , **_UpperCAmelCase       )
       return resized_image


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = None     , **_UpperCAmelCase     , ):
       return rescale(_UpperCAmelCase     , scale=_UpperCAmelCase     , data_format=_UpperCAmelCase     , **_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase     , _UpperCAmelCase = None     , **_UpperCAmelCase     , ):
       return normalize(_UpperCAmelCase     , mean=_UpperCAmelCase     , std=_UpperCAmelCase     , data_format=_UpperCAmelCase     , **_UpperCAmelCase       )


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = False     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = None     , _UpperCAmelCase = ChannelDimension.FIRST     , **_UpperCAmelCase     , ):
       A							:							List[str]				=    do_resize if do_resize is not None else self.do_resize
       A							:							Dict				=    size if size is not None else self.size
       if isinstance(_UpperCAmelCase     , (tuple, list)       ):
           # Previous feature extractor had size in (width, height) format
           A							:							Tuple				=    size[::-1]
       A							:							int				=    get_size_dict(_UpperCAmelCase       )
       A							:							Any				=    resample if resample is not None else self.resample
       A							:							int				=    do_thumbnail if do_thumbnail is not None else self.do_thumbnail
       A							:							Dict				=    do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
       A							:							List[str]				=    do_pad if do_pad is not None else self.do_pad
       A							:							Tuple				=    do_rescale if do_rescale is not None else self.do_rescale
       A							:							List[Any]				=    rescale_factor if rescale_factor is not None else self.rescale_factor
       A							:							List[Any]				=    do_normalize if do_normalize is not None else self.do_normalize
       A							:							Optional[Any]				=    image_mean if image_mean is not None else self.image_mean
       A							:							str				=    image_std if image_std is not None else self.image_std

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

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

       # All transformations expect numpy arrays.
       A							:							Optional[Any]				=    [to_numpy_array(_UpperCAmelCase       ) for image in images]

       if do_align_long_axis:
           A							:							Dict				=    [self.align_long_axis(_UpperCAmelCase     , size=_UpperCAmelCase       ) for image in images]

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

       if do_thumbnail:
           A							:							int				=    [self.thumbnail(image=_UpperCAmelCase     , size=_UpperCAmelCase       ) for image in images]

       if do_pad:
           A							:							int				=    [self.pad_image(image=_UpperCAmelCase     , size=_UpperCAmelCase     , random_padding=_UpperCAmelCase       ) for image in images]

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

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

       A							:							Union[str, Any]				=    [to_channel_dimension_format(_UpperCAmelCase     , _UpperCAmelCase       ) for image in images]

       A							:							List[str]				=    {'''pixel_values''': images}
       return BatchFeature(data=_UpperCAmelCase     , tensor_type=_UpperCAmelCase       )







'''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)

snake_case_     			=						get_tests_dir("""fixtures""")
snake_case_     			=						os.path.join(dirname(get_tests_dir()))
snake_case_     			=						dirname(os.path.abspath(__file__))

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

snake_case_     			=						"""t5-small"""

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


# *** To Fix ***


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

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

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

# other issues with tiny models
snake_case_     			=						"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
snake_case_     			=						"""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_     			=						"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
snake_case_     			=						"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
snake_case_     			=						""""""
# - "luke" Entities classes - no plan to make such example
snake_case_     			=						"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
snake_case_     			=						"""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_     			=						"""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_     			=						"""hf-internal-testing/tiny-random-tapas"""
def 				_lowerCamelCase(	UpperCamelCase__     :			str=False		)		->	List[str]:
    # 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)
    A							:							int				=    min(2     ,							get_gpu_count()		) if distributed else 1
    A							:							Dict				=    os.environ.get('''DS_TEST_PORT'''     ,							UpperCamelCase__		)
    return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()
def 				_lowerCamelCase(	)		->	Any:
    A							:							Tuple				=    F'''{FIXTURE_DIRECTORY}/tests_samples'''
    A							:							int				=    F'''{data_dir_samples}/wmt_en_ro'''
    A							:							List[str]				=    F'''{data_dir_samples}/xsum'''
    A							:							int				=    '''
        --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.
    A							:							List[str]				=    {
        '''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''',
        ],
    }

    A							:							int				=    F'''{ROOT_DIRECTORY}/examples/pytorch'''

    A							:							List[str]				=    {
        '''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
        ''',
    }

    A							:							int				=    get_launcher(distributed=UpperCamelCase__		)

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


snake_case_     			=						make_task_cmds()

snake_case_     			=						"""zero2"""
snake_case_     			=						"""zero3"""

snake_case_     			=						[ZEROa, ZEROa]
def 				_lowerCamelCase(	UpperCamelCase__     :			Tuple     ,							UpperCamelCase__     :			int     ,							UpperCamelCase__     :			Optional[int]		)		->	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
    A							:							Union[str, Any]				=    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_     			=						list(itertools.product(stages, task_cmds.keys()))





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


   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # return a ready to run train cmd
       if task not in task_cmds:
           raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''       )

       A							:							List[str]				=    task_cmds[task]
       A							:							Dict				=    f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

       A							:							Optional[Any]				=    self.get_auto_remove_tmp_dir()
       A							:							Union[str, Any]				=    f'''--output_dir {output_dir}'''.split()

       cmd += args_ds + args_out

       return cmd, output_dir


   @parameterized.expand(_UpperCAmelCase     , name_func=_UpperCAmelCase       )
   def        snake_case					(			self     , _UpperCAmelCase     , _UpperCAmelCase       ):
       # testing the ability to do a run followed by recovery of full fp32 weights

       A,						A							:							List[str]				=    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
       A							:							Dict				=    f'''{output_dir}/checkpoint-1'''
       A							:							Optional[Any]				=    f'''{chkpt_dir}/out.bin'''
       A							:							Optional[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
