
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 			lowerCAmelCase (				unittest.TestCase			):




		def __init__(				self					,						UpperCamelCase					,						UpperCamelCase=13					,						UpperCamelCase=3					,						UpperCamelCase=224					,						UpperCamelCase=30					,						UpperCamelCase=400					,						UpperCamelCase=True					,						UpperCamelCase=None					,						UpperCamelCase=True					,						UpperCamelCase=[0.5, 0.5, 0.5]					,						UpperCamelCase=[0.5, 0.5, 0.5]					,						):
								_SCREAMING_SNAKE_CASE				= size if size is not None else {"height": 18, "width": 18}
								_SCREAMING_SNAKE_CASE				= parent
								_SCREAMING_SNAKE_CASE				= batch_size
								_SCREAMING_SNAKE_CASE				= num_channels
								_SCREAMING_SNAKE_CASE				= image_size
								_SCREAMING_SNAKE_CASE				= min_resolution
								_SCREAMING_SNAKE_CASE				= max_resolution
								_SCREAMING_SNAKE_CASE				= do_resize
								_SCREAMING_SNAKE_CASE				= size
								_SCREAMING_SNAKE_CASE				= do_normalize
								_SCREAMING_SNAKE_CASE				= image_mean
								_SCREAMING_SNAKE_CASE				= image_std




		def     lowercase       (				self      ):
								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 			lowerCAmelCase (				a__		,     unittest.TestCase			):
		a							:					Union[str, Any]       =					ViTImageProcessor if is_vision_available() else None




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= EfficientFormerImageProcessorTester(self      )




		@property
		def     lowercase       (				self      ):
								return self.image_proc_tester.prepare_image_processor_dict()




		def     lowercase       (				self      ):
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								self.assertTrue(hasattr(lowerCamelCase_					,						"image_mean"      )      )
								self.assertTrue(hasattr(lowerCamelCase_					,						"image_std"      )      )
								self.assertTrue(hasattr(lowerCamelCase_					,						"do_normalize"      )      )
								self.assertTrue(hasattr(lowerCamelCase_					,						"do_resize"      )      )
								self.assertTrue(hasattr(lowerCamelCase_					,						"size"      )      )




		def     lowercase       (				self      ):
								pass




		def     lowercase       (				self      ):
								# Initialize image_processor
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PIL images
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_proc_tester					,						equal_resolution=lowerCamelCase_      )
								for image in image_inputs:
														self.assertIsInstance(lowerCamelCase_					,						Image.Image      )

								# Test not batched input
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= image_processor(lowerCamelCase_					,						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     lowercase       (				self      ):
								# Initialize image_processor
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random numpy tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_proc_tester					,						equal_resolution=lowerCamelCase_					,						numpify=lowerCamelCase_      )
								for image in image_inputs:
														self.assertIsInstance(lowerCamelCase_					,						np.ndarray      )

								# Test not batched input
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= image_processor(lowerCamelCase_					,						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     lowercase       (				self      ):
								# Initialize image_processor
								_SCREAMING_SNAKE_CASE				= self.image_processing_class(**self.image_processor_dict      )
								# create random PyTorch tensors
								_SCREAMING_SNAKE_CASE				= prepare_image_inputs(self.image_proc_tester					,						equal_resolution=lowerCamelCase_					,						torchify=lowerCamelCase_      )
								for image in image_inputs:
														self.assertIsInstance(lowerCamelCase_					,						torch.Tensor      )

								# Test not batched input
								_SCREAMING_SNAKE_CASE				= 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
								_SCREAMING_SNAKE_CASE				= image_processor(lowerCamelCase_					,						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"],
								    )					,						)

'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

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


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

_snake_case				:   int     =				{
    """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 			lowerCAmelCase (				__UpperCAmelCase			):
		a							:					Tuple       =					"""convbert"""




		def __init__(				self					,						UpperCamelCase=30_522					,						UpperCamelCase=768					,						UpperCamelCase=12					,						UpperCamelCase=12					,						UpperCamelCase=3_072					,						UpperCamelCase="gelu"					,						UpperCamelCase=0.1					,						UpperCamelCase=0.1					,						UpperCamelCase=512					,						UpperCamelCase=2					,						UpperCamelCase=0.02					,						UpperCamelCase=1e-12					,						UpperCamelCase=1					,						UpperCamelCase=0					,						UpperCamelCase=2					,						UpperCamelCase=768					,						UpperCamelCase=2					,						UpperCamelCase=9					,						UpperCamelCase=1					,						UpperCamelCase=None					,						**UpperCamelCase					,						):
								super().__init__(
								    pad_token_id=UpperCamelCase					,						bos_token_id=UpperCamelCase					,						eos_token_id=UpperCamelCase					,						**UpperCamelCase					,						)

								_SCREAMING_SNAKE_CASE				= vocab_size
								_SCREAMING_SNAKE_CASE				= hidden_size
								_SCREAMING_SNAKE_CASE				= num_hidden_layers
								_SCREAMING_SNAKE_CASE				= num_attention_heads
								_SCREAMING_SNAKE_CASE				= intermediate_size
								_SCREAMING_SNAKE_CASE				= hidden_act
								_SCREAMING_SNAKE_CASE				= hidden_dropout_prob
								_SCREAMING_SNAKE_CASE				= attention_probs_dropout_prob
								_SCREAMING_SNAKE_CASE				= max_position_embeddings
								_SCREAMING_SNAKE_CASE				= type_vocab_size
								_SCREAMING_SNAKE_CASE				= initializer_range
								_SCREAMING_SNAKE_CASE				= layer_norm_eps
								_SCREAMING_SNAKE_CASE				= embedding_size
								_SCREAMING_SNAKE_CASE				= head_ratio
								_SCREAMING_SNAKE_CASE				= conv_kernel_size
								_SCREAMING_SNAKE_CASE				= num_groups
								_SCREAMING_SNAKE_CASE				= classifier_dropout


class 			lowerCAmelCase (				__UpperCAmelCase			):




		@property
		def     lowercase       (				self      ):
								if self.task == "multiple-choice":
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "choice", 2: "sequence"}
								else:
														_SCREAMING_SNAKE_CASE				= {0: "batch", 1: "sequence"}
								return OrderedDict(
								    [
								        ("input_ids", dynamic_axis),
								        ("attention_mask", dynamic_axis),
								        ("token_type_ids", dynamic_axis),
								    ]      )