


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      ):






			"""simple docstring"""






			def __init__(							self      ,						snake_case      ,						snake_case=13      ,						snake_case=3      ,						snake_case=224      ,						snake_case=30      ,						snake_case=400      ,						snake_case=True      ,						snake_case=None      ,						snake_case=True      ,						snake_case=[0.5, 0.5, 0.5]      ,						snake_case=[0.5, 0.5, 0.5]      ,						):

						'''simple docstring'''


						UpperCamelCase__					=       size if size is not None else {"height": 18, "width": 18}
						UpperCamelCase__					=       parent
						UpperCamelCase__					=       batch_size
						UpperCamelCase__					=       num_channels
						UpperCamelCase__					=       image_size
						UpperCamelCase__					=       min_resolution
						UpperCamelCase__					=       max_resolution
						UpperCamelCase__					=       do_resize
						UpperCamelCase__					=       size
						UpperCamelCase__					=       do_normalize
						UpperCamelCase__					=       image_mean
						UpperCamelCase__					=       image_std







			def 				snake_case__				(							self     ):

						'''simple docstring'''


						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__	(							UpperCAmelCase					,	unittest.TestCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					Tuple    =		ViTImageProcessor if is_vision_available() else None




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       EfficientFormerImageProcessorTester(self     )




			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


						return self.image_proc_tester.prepare_image_processor_dict()




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class(**self.image_processor_dict     )
						self.assertTrue(hasattr(snake_case      ,						"image_mean"     )     )
						self.assertTrue(hasattr(snake_case      ,						"image_std"     )     )
						self.assertTrue(hasattr(snake_case      ,						"do_normalize"     )     )
						self.assertTrue(hasattr(snake_case      ,						"do_resize"     )     )
						self.assertTrue(hasattr(snake_case      ,						"size"     )     )




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						pass




			def 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class(**self.image_processor_dict     )
						# create random PIL images
						UpperCamelCase__					=       prepare_image_inputs(self.image_proc_tester      ,						equal_resolution=snake_case     )
						for image in image_inputs:
									self.assertIsInstance(snake_case      ,						Image.Image     )

						# Test not batched input
						UpperCamelCase__					=       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
						UpperCamelCase__					=       image_processor(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 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class(**self.image_processor_dict     )
						# create random numpy tensors
						UpperCamelCase__					=       prepare_image_inputs(self.image_proc_tester      ,						equal_resolution=snake_case      ,						numpify=snake_case     )
						for image in image_inputs:
									self.assertIsInstance(snake_case      ,						np.ndarray     )

						# Test not batched input
						UpperCamelCase__					=       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
						UpperCamelCase__					=       image_processor(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 				snake_case__				(							self     ):

						'''simple docstring'''


						UpperCamelCase__					=       self.image_processing_class(**self.image_processor_dict     )
						# create random PyTorch tensors
						UpperCamelCase__					=       prepare_image_inputs(self.image_proc_tester      ,						equal_resolution=snake_case      ,						torchify=snake_case     )
						for image in image_inputs:
									self.assertIsInstance(snake_case      ,						torch.Tensor     )

						# Test not batched input
						UpperCamelCase__					=       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
						UpperCamelCase__					=       image_processor(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


__UpperCamelCase    		=       logging.get_logger(__name__)

__UpperCamelCase    		=       {
    'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			_UpperCamelCase		:					int    =		'data2vec-text'




			def __init__(							self      ,						snake_case=30522      ,						snake_case=768      ,						snake_case=12      ,						snake_case=12      ,						snake_case=3072      ,						snake_case="gelu"      ,						snake_case=0.1      ,						snake_case=0.1      ,						snake_case=512      ,						snake_case=2      ,						snake_case=0.02      ,						snake_case=1E-1_2      ,						snake_case=1      ,						snake_case=0      ,						snake_case=2      ,						snake_case="absolute"      ,						snake_case=True      ,						snake_case=None      ,						**snake_case      ,						):

						'''simple docstring'''


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

						UpperCamelCase__					=       vocab_size
						UpperCamelCase__					=       hidden_size
						UpperCamelCase__					=       num_hidden_layers
						UpperCamelCase__					=       num_attention_heads
						UpperCamelCase__					=       hidden_act
						UpperCamelCase__					=       intermediate_size
						UpperCamelCase__					=       hidden_dropout_prob
						UpperCamelCase__					=       attention_probs_dropout_prob
						UpperCamelCase__					=       max_position_embeddings
						UpperCamelCase__					=       type_vocab_size
						UpperCamelCase__					=       initializer_range
						UpperCamelCase__					=       layer_norm_eps
						UpperCamelCase__					=       position_embedding_type
						UpperCamelCase__					=       use_cache
						UpperCamelCase__					=       classifier_dropout







class        lowerCamelCase__	(							UpperCAmelCase      ):






			"""simple docstring"""






			@property
			def 				snake_case__				(							self     ):

						'''simple docstring'''


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


