
"""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 (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__magic_name__									=					logging.get_logger(__name__)


class  _lowerCAmelCase    (    __A    ):
							lowercase_  :  Dict      =				['''pixel_values''']



							def __init__(   self			,    a_ = True			,    a_ = None			,    a_ = PILImageResampling.BILINEAR			,    a_ = True			,    a_ = None			,    a_ = True			,    a_ = 1 / 255			,    a_ = True			,    a_ = None			,    a_ = None			,    **a_			,    )				->				Optional[int]:
								super().__init__(**__A )
								_UpperCAmelCase			=    size if size is not None else {"shortest_edge": 256}
								_UpperCAmelCase			=    get_size_dict(__A			,    default_to_square=__A )
								_UpperCAmelCase			=    crop_size if crop_size is not None else {"height": 224, "width": 224}
								_UpperCAmelCase			=    get_size_dict(__A )
								_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 IMAGENET_STANDARD_MEAN
								_UpperCAmelCase			=    image_std if image_std is not None else IMAGENET_STANDARD_STD



							def    _a (   self			,    a_			,    a_			,    a_ = PILImageResampling.BICUBIC			,    a_ = None			,    **a_			,    )				->				Any:
								_UpperCAmelCase			=    get_size_dict(__A			,    default_to_square=__A )
								if "shortest_edge" not in size:
									raise ValueError(f"The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}" )
								_UpperCAmelCase			=    get_resize_output_image_size(__A			,    size=size["shortest_edge"]			,    default_to_square=__A )
								return resize(__A			,    size=__A			,    resample=__A			,    data_format=__A			,    **__A )



							def    _a (   self			,    a_			,    a_			,    a_ = None			,    **a_			,    )				->				Union[str, Any]:
								_UpperCAmelCase			=    get_size_dict(__A )
								return center_crop(__A			,    size=(size["height"], size["width"])			,    data_format=__A			,    **__A )



							def    _a (   self			,    a_			,    a_			,    a_ = None			,    **a_ )				->				Optional[Any]:
								return rescale(__A			,    scale=__A			,    data_format=__A			,    **__A )



							def    _a (   self			,    a_			,    a_			,    a_			,    a_ = None			,    **a_			,    )				->				Dict:
								return normalize(__A			,    mean=__A			,    std=__A			,    data_format=__A			,    **__A )



							def    _a (   self			,    a_			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = None			,    a_ = ChannelDimension.FIRST			,    **a_			,    )				->				Any:
								_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(__A			,    default_to_square=__A )
								_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(__A )
								_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			=    make_list_of_images(__A )

								if not valid_images(__A ):
									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." )

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

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

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

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

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

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

								_UpperCAmelCase			=    {"pixel_values": images}
								return BatchFeature(data=__A			,    tensor_type=__A )








import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE							    =							logging.getLogger()




def  a   ():
   __a					=  argparse.ArgumentParser()
   parser.add_argument("""-f"""   )
   __a					=  parser.parse_args()
   return args.f


class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def 	snake_case_  (	self    ):
        __a					=  logging.StreamHandler(sys.stdout    )
        logger.addHandler(__A    )


     def 	snake_case_  (	self  ,       __A    ):
        __a					=  get_gpu_count()

        if n_gpu > 1:
           pass
           # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
           # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
           # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
           # cmd = [sys.executable] + distributed_args + args
           # execute_subprocess_async(cmd, env=self.get_env())
           # XXX: test the results - need to save them first into .json file
        else:
           args.insert(0  ,       """run_glue_deebert.py"""    )
           with patch.object(__A  ,       """argv"""  ,       __A    ):
              __a					=  run_glue_deebert.main()
              for value in result.values():
                 self.assertGreaterEqual(__A  ,       0.666    )


     @slow
     @require_torch_non_multi_gpu
     def 	snake_case_  (	self    ):
        __a					=  """
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )

        __a					=  """
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
        self.run_and_check(__A    )



