
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 ViTImageProcessor





class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def __init__(			self							:Optional[int]		,    lowerCAmelCase__							:Optional[int]		,    lowerCAmelCase__							:str=13		,    lowerCAmelCase__							:Any=3		,    lowerCAmelCase__							:List[str]=224		,    lowerCAmelCase__							:Tuple=30		,    lowerCAmelCase__							:Any=400		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:Tuple=None		,    lowerCAmelCase__							:int=True		,    lowerCAmelCase__							:Optional[int]=[0.5, 0.5, 0.5]		,    lowerCAmelCase__							:Union[str, Any]=[0.5, 0.5, 0.5]		,    )  ->Union[str, Any]:
										lowercase         =      size if size is not None else {"""height""": 18, """width""": 18}
										lowercase         =      parent
										lowercase         =      batch_size
										lowercase         =      num_channels
										lowercase         =      image_size
										lowercase         =      min_resolution
										lowercase         =      max_resolution
										lowercase         =      do_resize
										lowercase         =      size
										lowercase         =      do_normalize
										lowercase         =      image_mean
										lowercase         =      image_std





						def        SCREAMING_SNAKE_CASE(			self							:Optional[int]     )  ->Dict:
										return {
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_normalize": self.do_normalize,
										    "do_resize": self.do_resize,
										    "size": self.size,
										}





@require_torch
@require_vision
class   UpperCamelCase_      (      UpperCAmelCase_		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  Optional[Any]				   =					ViTImageProcessor if is_vision_available() else None





						def        SCREAMING_SNAKE_CASE(			self							:Tuple     )  ->List[Any]:
										lowercase         =      EfficientFormerImageProcessorTester(self     )





						@property
						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Optional[int]:
										return self.image_proc_tester.prepare_image_processor_dict()





						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->Optional[int]:
										lowercase         =      self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,    "image_mean"     )     )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,    "image_std"     )     )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,    "do_normalize"     )     )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,    "do_resize"     )     )
										self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_		,    "size"     )     )





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





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->int:
										lowercase         =      self.image_processing_class(**self.image_processor_dict     )
										# create random PIL images
										lowercase         =      prepare_image_inputs(self.image_proc_tester		,    equal_resolution=SCREAMING_SNAKE_CASE_     )
										for image in image_inputs:
														self.assertIsInstance(SCREAMING_SNAKE_CASE_		,    Image.Image     )

										# Test not batched input
										lowercase         =      image_processor(image_inputs[0]		,    return_tensors="pt"     ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )		,    )

										# Test batched
										lowercase         =      image_processor(SCREAMING_SNAKE_CASE_		,    return_tensors="pt"     ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )		,    )





						def        SCREAMING_SNAKE_CASE(			self							:Dict     )  ->int:
										lowercase         =      self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										lowercase         =      prepare_image_inputs(self.image_proc_tester		,    equal_resolution=SCREAMING_SNAKE_CASE_		,    numpify=SCREAMING_SNAKE_CASE_     )
										for image in image_inputs:
														self.assertIsInstance(SCREAMING_SNAKE_CASE_		,    np.ndarray     )

										# Test not batched input
										lowercase         =      image_processor(image_inputs[0]		,    return_tensors="pt"     ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )		,    )

										# Test batched
										lowercase         =      image_processor(SCREAMING_SNAKE_CASE_		,    return_tensors="pt"     ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )		,    )





						def        SCREAMING_SNAKE_CASE(			self							:str     )  ->Optional[Any]:
										lowercase         =      self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										lowercase         =      prepare_image_inputs(self.image_proc_tester		,    equal_resolution=SCREAMING_SNAKE_CASE_		,    torchify=SCREAMING_SNAKE_CASE_     )
										for image in image_inputs:
														self.assertIsInstance(SCREAMING_SNAKE_CASE_		,    torch.Tensor     )

										# Test not batched input
										lowercase         =      image_processor(image_inputs[0]		,    return_tensors="pt"     ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        1,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )		,    )

										# Test batched
										lowercase         =      image_processor(SCREAMING_SNAKE_CASE_		,    return_tensors="pt"     ).pixel_values
										self.assertEqual(
										    encoded_images.shape		,    (
										        self.image_proc_tester.batch_size,
										        self.image_proc_tester.num_channels,
										        self.image_proc_tester.size["height"],
										        self.image_proc_tester.size["width"],
										    )		,    )


from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCamelCase__     :		List[Any]        = logging.get_logger(__name__)

lowerCamelCase__     :		Union[str, Any]        = {
    """YituTech/conv-bert-base""": """https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json""",
    """YituTech/conv-bert-medium-small""": (
        """https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"""
    ),
    """YituTech/conv-bert-small""": """https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json""",
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}



class    _snake_case (						UpperCAmelCase_				):
 __lowerCAmelCase	:  Union[str, Any]	   =      'convbert'


 def __init__(       self       ,	SCREAMING_SNAKE_CASE_=3_05_22       ,	SCREAMING_SNAKE_CASE_=7_68       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=12       ,	SCREAMING_SNAKE_CASE_=30_72       ,	SCREAMING_SNAKE_CASE_="gelu"       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=0.1       ,	SCREAMING_SNAKE_CASE_=5_12       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=0.0_2       ,	SCREAMING_SNAKE_CASE_=1E-12       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=0       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=7_68       ,	SCREAMING_SNAKE_CASE_=2       ,	SCREAMING_SNAKE_CASE_=9       ,	SCREAMING_SNAKE_CASE_=1       ,	SCREAMING_SNAKE_CASE_=None       ,	**SCREAMING_SNAKE_CASE_       ,	):


   '''simple docstring'''




   super().__init__(
       pad_token_id=SCREAMING_SNAKE_CASE_       ,	bos_token_id=SCREAMING_SNAKE_CASE_       ,	eos_token_id=SCREAMING_SNAKE_CASE_       ,	**SCREAMING_SNAKE_CASE_       ,	)

   lowercase__	: Dict     		=   vocab_size
   lowercase__	: List[Any]     		=   hidden_size
   lowercase__	: Optional[Any]     		=   num_hidden_layers
   lowercase__	: Union[str, Any]     		=   num_attention_heads
   lowercase__	: List[str]     		=   intermediate_size
   lowercase__	: Optional[int]     		=   hidden_act
   lowercase__	: Tuple     		=   hidden_dropout_prob
   lowercase__	: List[str]     		=   attention_probs_dropout_prob
   lowercase__	: Tuple     		=   max_position_embeddings
   lowercase__	: Dict     		=   type_vocab_size
   lowercase__	: Union[str, Any]     		=   initializer_range
   lowercase__	: Dict     		=   layer_norm_eps
   lowercase__	: Tuple     		=   embedding_size
   lowercase__	: List[str]     		=   head_ratio
   lowercase__	: Dict     		=   conv_kernel_size
   lowercase__	: Dict     		=   num_groups
   lowercase__	: int     		=   classifier_dropout



class    _snake_case (						UpperCAmelCase_				):


 @property
 def   lowercase__  (       self):


   '''simple docstring'''




   if self.task == "multiple-choice":
     lowercase__	: Union[str, Any]     		=   {0: """batch""", 1: """choice""", 2: """sequence"""}
   else:
     lowercase__	: str     		=   {0: """batch""", 1: """sequence"""}
   return OrderedDict(
       [
           ("""input_ids""", dynamic_axis),
           ("""attention_mask""", dynamic_axis),
           ("""token_type_ids""", dynamic_axis),
       ])






