





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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


a__:      Any									=		logging.get_logger(__name__)


def 	UpperCamelCase__(       UpperCamelCase__			:       int ,     UpperCamelCase__			:       str  )->Tuple:
       A__											=						b.T
       A__											=						np.sum(np.square(UpperCamelCase__  ) ,     axis=1  )
       A__											=						np.sum(np.square(UpperCamelCase__  ) ,     axis=0  )
       A__											=						np.matmul(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						aa[:, None] - 2 * ab + ba[None, :]
       return d


def 	UpperCamelCase__(       UpperCamelCase__			:       Optional[int] ,     UpperCamelCase__			:       List[str]  )->Dict:
       A__											=						x.reshape(-1 ,     3  )
       A__											=						squared_euclidean_distance(UpperCamelCase__ ,     UpperCamelCase__  )
       return np.argmin(UpperCamelCase__ ,     axis=1  )




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	['''pixel_values''']



 def __init__(							self,__lowerCamelCase = None,__lowerCamelCase = True,__lowerCamelCase = None,__lowerCamelCase = PILImageResampling.BILINEAR,__lowerCamelCase = True,__lowerCamelCase = True,**__lowerCamelCase,):
        super().__init__(**__lowerCamelCase )
        A__											=						size if size is not None else {'''height''': 256, '''width''': 256}
        A__											=						get_size_dict(__lowerCamelCase )
        A__											=						np.array(__lowerCamelCase ) if clusters is not None else None
        A__											=						do_resize
        A__											=						size
        A__											=						resample
        A__											=						do_normalize
        A__											=						do_color_quantize



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase = PILImageResampling.BILINEAR,__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						get_size_dict(__lowerCamelCase )
        if "height" not in size or "width" not in size:
               raise ValueError(f"Size dictionary must contain both height and width keys. Got {size.keys()}" )
        return resize(
            __lowerCamelCase,size=(size['''height'''], size['''width''']),resample=__lowerCamelCase,data_format=__lowerCamelCase,**__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,):
        A__											=						rescale(image=__lowerCamelCase,scale=1 / 127.5,data_format=__lowerCamelCase )
        A__											=						image - 1
        return image




 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = None,__lowerCamelCase = ChannelDimension.FIRST,**__lowerCamelCase,):
        A__											=						do_resize if do_resize is not None else self.do_resize
        A__											=						size if size is not None else self.size
        A__											=						get_size_dict(__lowerCamelCase )
        A__											=						resample if resample is not None else self.resample
        A__											=						do_normalize if do_normalize is not None else self.do_normalize
        A__											=						do_color_quantize if do_color_quantize is not None else self.do_color_quantize
        A__											=						clusters if clusters is not None else self.clusters
        A__											=						np.array(__lowerCamelCase )

        A__											=						make_list_of_images(__lowerCamelCase )

        if not valid_images(__lowerCamelCase ):
               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 or resample is None:
               raise ValueError('''Size and resample must be specified if do_resize is True.''' )

        if do_color_quantize and clusters is None:
               raise ValueError('''Clusters must be specified if do_color_quantize is True.''' )

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

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

        if do_normalize:
               A__											=						[self.normalize(image=__lowerCamelCase ) for image in images]

        if do_color_quantize:
               A__											=						[to_channel_dimension_format(__lowerCamelCase,ChannelDimension.LAST ) for image in images]
               # color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
               A__											=						np.array(__lowerCamelCase )
               A__											=						color_quantize(__lowerCamelCase,__lowerCamelCase ).reshape(images.shape[:-1] )

               # flatten to (batch_size, height*width)
               A__											=						images.shape[0]
               A__											=						images.reshape(__lowerCamelCase,-1 )

               # We need to convert back to a list of images to keep consistent behaviour across processors.
               A__											=						list(__lowerCamelCase )
        else:
               A__											=						[to_channel_dimension_format(__lowerCamelCase,__lowerCamelCase ) for image in images]

        A__											=						{'''input_ids''': images}
        return BatchFeature(data=__lowerCamelCase,tensor_type=__lowerCamelCase )












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)

a__:      List[Any]									=		logging.getLogger()


def 	UpperCamelCase__(       )->Union[str, Any]:
       A__											=						argparse.ArgumentParser()
       parser.add_argument('''-f'''  )
       A__											=						parser.parse_args()
       return args.f




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):



 def 				UpperCamelCase			(							self ):
        A__											=						logging.StreamHandler(sys.stdout )
        logger.addHandler(__lowerCamelCase )



 def 				UpperCamelCase			(							self,__lowerCamelCase ):
        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(__lowerCamelCase,'''argv''',__lowerCamelCase ):
                      A__											=						run_glue_deebert.main()
                      for value in result.values():
                             self.assertGreaterEqual(__lowerCamelCase,0.666 )




 @slow
 @require_torch_non_multi_gpu
 def 				UpperCamelCase			(							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(__lowerCamelCase )

        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(__lowerCamelCase )

        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(__lowerCamelCase )





