



"""simple docstring"""
import unittest

import numpy as np

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 PoolFormerImageProcessor
class 							_UpperCAmelCase							( unittest.TestCase      ):





     '''simple docstring'''



     def __init__(					self       ,	A       ,	A=7       ,	A=3       ,	A=3_0       ,	A=4_0_0       ,	A=True       ,	A=None       ,	A=0.9       ,	A=None       ,	A=True       ,	A=[0.5, 0.5, 0.5]       ,	A=[0.5, 0.5, 0.5]       ,	)							->					Tuple:
         _UpperCAmelCase							:  Dict											=    size if size is not None else {'''shortest_edge''': 3_0}
         _UpperCAmelCase							:  Tuple											=    crop_size if crop_size is not None else {'''height''': 3_0, '''width''': 3_0}
         _UpperCAmelCase							:  Optional[Any]											=    parent
         _UpperCAmelCase							:  Union[str, Any]											=    batch_size
         _UpperCAmelCase							:  str											=    num_channels
         _UpperCAmelCase							:  Tuple											=    min_resolution
         _UpperCAmelCase							:  Optional[Any]											=    max_resolution
         _UpperCAmelCase							:  Union[str, Any]											=    do_resize_and_center_crop
         _UpperCAmelCase							:  Any											=    size
         _UpperCAmelCase							:  Optional[int]											=    crop_pct
         _UpperCAmelCase							:  int											=    crop_size
         _UpperCAmelCase							:  Optional[int]											=    do_normalize
         _UpperCAmelCase							:  List[str]											=    image_mean
         _UpperCAmelCase							:  Tuple											=    image_std





     def  __lowerCAmelCase	(					self		)							->					int:
         return {
             "size": self.size,
             "do_resize_and_center_crop": self.do_resize_and_center_crop,
             "crop_pct": self.crop_pct,
             "crop_size": self.crop_size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
         }



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





     '''simple docstring'''



     a__									=PoolFormerImageProcessor if is_vision_available() else None





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  List[str]											=    PoolFormerImageProcessingTester(self		)





     @property
     def  __lowerCAmelCase	(					self		)							->					str:
         return self.image_processor_tester.prepare_image_processor_dict()





     def  __lowerCAmelCase	(					self		)							->					Dict:
         _UpperCAmelCase							:  List[str]											=    self.image_processing_class(**self.image_processor_dict		)
         self.assertTrue(hasattr(A       ,	'''do_resize_and_center_crop'''		)		)
         self.assertTrue(hasattr(A       ,	'''size'''		)		)
         self.assertTrue(hasattr(A       ,	'''crop_pct'''		)		)
         self.assertTrue(hasattr(A       ,	'''do_normalize'''		)		)
         self.assertTrue(hasattr(A       ,	'''image_mean'''		)		)
         self.assertTrue(hasattr(A       ,	'''image_std'''		)		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         _UpperCAmelCase							:  str											=    self.image_processing_class.from_dict(self.image_processor_dict		)
         self.assertEqual(image_processor.size       ,	{'''shortest_edge''': 3_0}		)
         self.assertEqual(image_processor.crop_size       ,	{'''height''': 3_0, '''width''': 3_0}		)

         _UpperCAmelCase							:  int											=    self.image_processing_class.from_dict(self.image_processor_dict       ,	size=4_2       ,	crop_size=8_4		)
         self.assertEqual(image_processor.size       ,	{'''shortest_edge''': 4_2}		)
         self.assertEqual(image_processor.crop_size       ,	{'''height''': 8_4, '''width''': 8_4}		)





     def  __lowerCAmelCase	(					self		)							->					Union[str, Any]:
         pass





     def  __lowerCAmelCase	(					self		)							->					Optional[Any]:
         # Initialize image_processing
         _UpperCAmelCase							:  Dict											=    self.image_processing_class(**self.image_processor_dict		)
         # create random PIL images
         _UpperCAmelCase							:  int											=    prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=A		)
         for image in image_inputs:
             self.assertIsInstance(A       ,	Image.Image		)

         # Test not batched input
         _UpperCAmelCase							:  Optional[Any]											=    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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)

         # Test batched
         _UpperCAmelCase							:  Any											=    image_processing(A       ,	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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)





     def  __lowerCAmelCase	(					self		)							->					Tuple:
         # Initialize image_processing
         _UpperCAmelCase							:  int											=    self.image_processing_class(**self.image_processor_dict		)
         # create random numpy tensors
         _UpperCAmelCase							:  int											=    prepare_image_inputs(self.image_processor_tester       ,	equal_resolution=A       ,	numpify=A		)
         for image in image_inputs:
             self.assertIsInstance(A       ,	np.ndarray		)

         # Test not batched input
         _UpperCAmelCase							:  Tuple											=    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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)

         # Test batched
         _UpperCAmelCase							:  Optional[Any]											=    image_processing(A       ,	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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)





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

         # Test not batched input
         _UpperCAmelCase							:  Dict											=    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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)

         # Test batched
         _UpperCAmelCase							:  Any											=    image_processing(A       ,	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.crop_size['''height'''],
                 self.image_processor_tester.crop_size['''width'''],
             )       ,	)







"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase    :int     =					logging.get_logger(__name__)

_lowerCAmelCase    :Union[str, Any]     =					{
    'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class 							_UpperCAmelCase							( a      ):





     '''simple docstring'''



     a__									='''mgp-str'''





     def __init__(					self       ,	A=[3_2, 1_2_8]       ,	A=4       ,	A=3       ,	A=2_7       ,	A=3_8       ,	A=5_0_2_5_7       ,	A=3_0_5_2_2       ,	A=7_6_8       ,	A=1_2       ,	A=1_2       ,	A=4.0       ,	A=True       ,	A=False       ,	A=1E-5       ,	A=0.0       ,	A=0.0       ,	A=0.0       ,	A=False       ,	A=0.02       ,	**A       ,	)							->					Union[str, Any]:
         super().__init__(**A		)

         _UpperCAmelCase							:  Any											=    image_size
         _UpperCAmelCase							:  str											=    patch_size
         _UpperCAmelCase							:  Dict											=    num_channels
         _UpperCAmelCase							:  Dict											=    max_token_length
         _UpperCAmelCase							:  Optional[Any]											=    num_character_labels
         _UpperCAmelCase							:  int											=    num_bpe_labels
         _UpperCAmelCase							:  List[str]											=    num_wordpiece_labels
         _UpperCAmelCase							:  Optional[int]											=    hidden_size
         _UpperCAmelCase							:  Any											=    num_hidden_layers
         _UpperCAmelCase							:  List[Any]											=    num_attention_heads
         _UpperCAmelCase							:  List[Any]											=    mlp_ratio
         _UpperCAmelCase							:  List[str]											=    distilled
         _UpperCAmelCase							:  Optional[int]											=    layer_norm_eps
         _UpperCAmelCase							:  str											=    drop_rate
         _UpperCAmelCase							:  List[Any]											=    qkv_bias
         _UpperCAmelCase							:  List[str]											=    attn_drop_rate
         _UpperCAmelCase							:  Dict											=    drop_path_rate
         _UpperCAmelCase							:  Union[str, Any]											=    output_aa_attentions
         _UpperCAmelCase							:  List[str]											=    initializer_range


