'''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 LevitImageProcessor
class   SCREAMING_SNAKE_CASE			(						unittest.TestCase):







		"""simple docstring"""






		def __init__(   self		,						__A		,						__A=7		,						__A=3		,						__A=18		,						__A=30		,						__A=400		,						__A=True		,						__A=None		,						__A=True		,						__A=None		,						__A=True		,						__A=[0.5, 0.5, 0.5]		,						__A=[0.5, 0.5, 0.5]		,						) ->     Optional[int]:
								_lowerCAmelCase										=size if size is not None else {'shortest_edge': 18}
								_lowerCAmelCase										=crop_size if crop_size is not None else {'height': 18, 'width': 18}
								_lowerCAmelCase										=parent
								_lowerCAmelCase										=batch_size
								_lowerCAmelCase										=num_channels
								_lowerCAmelCase										=image_size
								_lowerCAmelCase										=min_resolution
								_lowerCAmelCase										=max_resolution
								_lowerCAmelCase										=do_resize
								_lowerCAmelCase										=size
								_lowerCAmelCase										=do_center_crop
								_lowerCAmelCase										=crop_size
								_lowerCAmelCase										=do_normalize
								_lowerCAmelCase										=image_mean
								_lowerCAmelCase										=image_std






		def 		UpperCamelCase__       (   self				) ->     Dict:
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}

@require_torch
@require_vision
class   SCREAMING_SNAKE_CASE			(						__lowercase    ,			unittest.TestCase):







		"""simple docstring"""
		lowercase   :      int       =							LevitImageProcessor if is_vision_available() else None






		def 		UpperCamelCase__       (   self				) ->     Dict:
								_lowerCAmelCase										=LevitImageProcessingTester(self				)






		@property
		def 		UpperCamelCase__       (   self				) ->     int:
								return self.image_processor_tester.prepare_image_processor_dict()






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								_lowerCAmelCase										=self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(__A		,						'image_mean'				)				)
								self.assertTrue(hasattr(__A		,						'image_std'				)				)
								self.assertTrue(hasattr(__A		,						'do_normalize'				)				)
								self.assertTrue(hasattr(__A		,						'do_resize'				)				)
								self.assertTrue(hasattr(__A		,						'do_center_crop'				)				)
								self.assertTrue(hasattr(__A		,						'size'				)				)






		def 		UpperCamelCase__       (   self				) ->     Optional[int]:
								_lowerCAmelCase										=self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size		,						{'shortest_edge': 18}				)
								self.assertEqual(image_processor.crop_size		,						{'height': 18, 'width': 18}				)

								_lowerCAmelCase										=self.image_processing_class.from_dict(self.image_processor_dict		,						size=42		,						crop_size=84				)
								self.assertEqual(image_processor.size		,						{'shortest_edge': 42}				)
								self.assertEqual(image_processor.crop_size		,						{'height': 84, 'width': 84}				)






		def 		UpperCamelCase__       (   self				) ->     List[str]:
								pass






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

								# Test not batched input
								_lowerCAmelCase										=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
								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     int:
								# Initialize image_processing
								_lowerCAmelCase										=self.image_processing_class(**self.image_processor_dict				)
								# create random numpy tensors
								_lowerCAmelCase										=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
								_lowerCAmelCase										=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
								_lowerCAmelCase										=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 		UpperCamelCase__       (   self				) ->     int:
								# Initialize image_processing
								_lowerCAmelCase										=self.image_processing_class(**self.image_processor_dict				)
								# create random PyTorch tensors
								_lowerCAmelCase										=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
								_lowerCAmelCase										=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
								_lowerCAmelCase										=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 collections import OrderedDict
from typing import Mapping

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


lowercase_     		= logging.get_logger(__name__)

lowercase_     		= {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}
class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""
		lowercase   :      List[str]       =							'data2vec-text'






		def __init__(   self		,						__A=3_0522		,						__A=768		,						__A=12		,						__A=12		,						__A=3072		,						__A="gelu"		,						__A=0.1		,						__A=0.1		,						__A=512		,						__A=2		,						__A=0.02		,						__A=1E-12		,						__A=1		,						__A=0		,						__A=2		,						__A="absolute"		,						__A=True		,						__A=None		,						**__A		,						) ->     List[Any]:
								super().__init__(pad_token_id=__A		,						bos_token_id=__A		,						eos_token_id=__A		,						**__A				)

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

class   SCREAMING_SNAKE_CASE			(						__lowercase):







		"""simple docstring"""






		@property
		def 		UpperCamelCase__       (   self				) ->     Mapping[str, Mapping[int, str]]:
								if self.task == "multiple-choice":
														_lowerCAmelCase										={0: 'batch', 1: 'choice', 2: 'sequence'}
								else:
														_lowerCAmelCase										={0: 'batch', 1: 'sequence'}
								return OrderedDict(
								    [
								        ('input_ids', dynamic_axis),
								        ('attention_mask', dynamic_axis),
								    ]				)





