



"""simple docstring"""







import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import SegformerImageProcessor

class       UpperCamelCase  (					unittest.TestCase				):





					"""simple docstring"""






					def __init__(					self :							Optional[int]	,      _lowerCamelCase :							List[Any]	,      _lowerCamelCase :							Union[str, Any]=7	,      _lowerCamelCase :							Any=3	,      _lowerCamelCase :							Union[str, Any]=3_0	,      _lowerCamelCase :							Optional[Any]=4_0_0	,      _lowerCamelCase :							List[Any]=True	,      _lowerCamelCase :							Optional[int]=None	,      _lowerCamelCase :							Dict=True	,      _lowerCamelCase :							Any=[0.5, 0.5, 0.5]	,      _lowerCamelCase :							Optional[Any]=[0.5, 0.5, 0.5]	,      _lowerCamelCase :							str=False	,      ):
										A__				=   size if size is not None else {'''height''': 3_0, '''width''': 3_0}
										A__				=   parent
										A__				=   batch_size
										A__				=   num_channels
										A__				=   min_resolution
										A__				=   max_resolution
										A__				=   do_resize
										A__				=   size
										A__				=   do_normalize
										A__				=   image_mean
										A__				=   image_std
										A__				=   do_reduce_labels





					def 						A__  (					self :							Union[str, Any] ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_reduce_labels": self.do_reduce_labels,
										}



def   a_	(			):
					A__				=   load_dataset('''hf-internal-testing/fixtures_ade20k'''		,							split='''test'''      )

					A__				=   Image.open(dataset[0]['''file''']      )
					A__				=   Image.open(dataset[1]['''file''']      )

					return image, map



def   a_	(			):
					A__				=   load_dataset('''hf-internal-testing/fixtures_ade20k'''		,							split='''test'''      )

					A__				=   Image.open(dataset[0]['''file''']      )
					A__				=   Image.open(dataset[1]['''file''']      )
					A__				=   Image.open(dataset[2]['''file''']      )
					A__				=   Image.open(dataset[3]['''file''']      )

					return [imagea, imagea], [mapa, mapa]

@require_torch
@require_vision
class       UpperCamelCase  (					a      , unittest.TestCase				):





					"""simple docstring"""






					_lowerCamelCase					: str				   =SegformerImageProcessor if is_vision_available() else None





					def 						A__  (					self :							Tuple ):
										A__				=   SegformerImageProcessingTester(self )





					@property
					def 						A__  (					self :							int ):
										return self.image_processor_tester.prepare_image_processor_dict()





					def 						A__  (					self :							Any ):
										A__				=   self.image_processing_class(**self.image_processor_dict )
										self.assertTrue(hasattr(_lowerCamelCase	,      '''do_resize''' ) )
										self.assertTrue(hasattr(_lowerCamelCase	,      '''size''' ) )
										self.assertTrue(hasattr(_lowerCamelCase	,      '''do_normalize''' ) )
										self.assertTrue(hasattr(_lowerCamelCase	,      '''image_mean''' ) )
										self.assertTrue(hasattr(_lowerCamelCase	,      '''image_std''' ) )
										self.assertTrue(hasattr(_lowerCamelCase	,      '''do_reduce_labels''' ) )





					def 						A__  (					self :							Optional[Any] ):
										A__				=   self.image_processing_class.from_dict(self.image_processor_dict )
										self.assertEqual(image_processor.size	,      {'''height''': 3_0, '''width''': 3_0} )
										self.assertEqual(image_processor.do_reduce_labels	,      _lowerCamelCase )

										A__				=   self.image_processing_class.from_dict(self.image_processor_dict	,      size=4_2	,      reduce_labels=_lowerCamelCase )
										self.assertEqual(image_processor.size	,      {'''height''': 4_2, '''width''': 4_2} )
										self.assertEqual(image_processor.do_reduce_labels	,      _lowerCamelCase )





					def 						A__  (					self :							List[Any] ):
										pass





					def 						A__  (					self :							int ):
										# Initialize image_processing
										A__				=   self.image_processing_class(**self.image_processor_dict )
										# create random PIL images
										A__				=   prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_lowerCamelCase )
										for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase	,      Image.Image )

										# Test not batched input
										A__				=   image_processing(image_inputs[0]	,      return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape	,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )

										# Test batched
										A__				=   image_processing(_lowerCamelCase	,      return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape	,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )





					def 						A__  (					self :							List[Any] ):
										# Initialize image_processing
										A__				=   self.image_processing_class(**self.image_processor_dict )
										# create random numpy tensors
										A__				=   prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_lowerCamelCase	,      numpify=_lowerCamelCase )
										for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase	,      np.ndarray )

										# Test not batched input
										A__				=   image_processing(image_inputs[0]	,      return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape	,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )

										# Test batched
										A__				=   image_processing(_lowerCamelCase	,      return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape	,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )





					def 						A__  (					self :							List[Any] ):
										# Initialize image_processing
										A__				=   self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										A__				=   prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_lowerCamelCase	,      torchify=_lowerCamelCase )
										for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase	,      torch.Tensor )

										# Test not batched input
										A__				=   image_processing(image_inputs[0]	,      return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape	,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )

										# Test batched
										A__				=   image_processing(_lowerCamelCase	,      return_tensors='''pt''' ).pixel_values
										self.assertEqual(
										    encoded_images.shape	,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )





					def 						A__  (					self :							Optional[int] ):
										# Initialize image_processing
										A__				=   self.image_processing_class(**self.image_processor_dict )
										# create random PyTorch tensors
										A__				=   prepare_image_inputs(self.image_processor_tester	,      equal_resolution=_lowerCamelCase	,      torchify=_lowerCamelCase )
										A__				=   []
										for image in image_inputs:
															self.assertIsInstance(_lowerCamelCase	,      torch.Tensor )
															maps.append(torch.zeros(image.shape[-2:] ).long() )

										# Test not batched input
										A__				=   image_processing(image_inputs[0]	,      maps[0]	,      return_tensors='''pt''' )
										self.assertEqual(
										    encoding['''pixel_values'''].shape	,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(
										    encoding['''labels'''].shape	,      (
										        1,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(encoding['''labels'''].dtype	,      torch.long )
										self.assertTrue(encoding['''labels'''].min().item() >= 0 )
										self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5 )

										# Test batched
										A__				=   image_processing(_lowerCamelCase	,      _lowerCamelCase	,      return_tensors='''pt''' )
										self.assertEqual(
										    encoding['''pixel_values'''].shape	,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(
										    encoding['''labels'''].shape	,      (
										        self.image_processor_tester.batch_size,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(encoding['''labels'''].dtype	,      torch.long )
										self.assertTrue(encoding['''labels'''].min().item() >= 0 )
										self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5 )

										# Test not batched input (PIL images)
										A__	,     A__				=   prepare_semantic_single_inputs()

										A__				=   image_processing(_lowerCamelCase	,      _lowerCamelCase	,      return_tensors='''pt''' )
										self.assertEqual(
										    encoding['''pixel_values'''].shape	,      (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(
										    encoding['''labels'''].shape	,      (
										        1,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(encoding['''labels'''].dtype	,      torch.long )
										self.assertTrue(encoding['''labels'''].min().item() >= 0 )
										self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5 )

										# Test batched input (PIL images)
										A__	,     A__				=   prepare_semantic_batch_inputs()

										A__				=   image_processing(_lowerCamelCase	,      _lowerCamelCase	,      return_tensors='''pt''' )
										self.assertEqual(
										    encoding['''pixel_values'''].shape	,      (
										        2,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(
										    encoding['''labels'''].shape	,      (
										        2,
										        self.image_processor_tester.size['''height'''],
										        self.image_processor_tester.size['''width'''],
										    )	,      )
										self.assertEqual(encoding['''labels'''].dtype	,      torch.long )
										self.assertTrue(encoding['''labels'''].min().item() >= 0 )
										self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5 )





					def 						A__  (					self :							Any ):
										# Initialize image_processing
										A__				=   self.image_processing_class(**self.image_processor_dict )

										# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
										A__	,     A__				=   prepare_semantic_single_inputs()
										A__				=   image_processing(_lowerCamelCase	,      _lowerCamelCase	,      return_tensors='''pt''' )
										self.assertTrue(encoding['''labels'''].min().item() >= 0 )
										self.assertTrue(encoding['''labels'''].max().item() <= 1_5_0 )

										A__				=   True
										A__				=   image_processing(_lowerCamelCase	,      _lowerCamelCase	,      return_tensors='''pt''' )
										self.assertTrue(encoding['''labels'''].min().item() >= 0 )
										self.assertTrue(encoding['''labels'''].max().item() <= 2_5_5 )








"""simple docstring"""







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

import run_glue_with_pabee

from transformers.testing_utils import TestCasePlus


logging.basicConfig(level=logging.DEBUG)

__snake_case	:				List[str]  					=			logging.getLogger()



def   a_	(			):
     A__				=   argparse.ArgumentParser()
     parser.add_argument('''-f'''      )
     A__				=   parser.parse_args()
     return args.f

class       UpperCamelCase  (					a				):





     """simple docstring"""






     def 						A__  (					self :							Optional[int] ):
          A__				=   logging.StreamHandler(sys.stdout )
          logger.addHandler(_lowerCamelCase )

          A__				=   self.get_auto_remove_tmp_dir()
          A__				=   F'''
            run_glue_with_pabee.py
            --model_type albert
            --model_name_or_path albert-base-v2
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --task_name mrpc
            --do_train
            --do_eval
            --per_gpu_train_batch_size=2
            --per_gpu_eval_batch_size=1
            --learning_rate=2e-5
            --max_steps=50
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            '''.split()

          with patch.object(_lowerCamelCase	,      '''argv'''	,      _lowerCamelCase ):
               A__				=   run_glue_with_pabee.main()
               for value in result.values():
                    self.assertGreaterEqual(_lowerCamelCase	,      0.75 )



