




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       a					(       unittest.TestCase      ):

					'''simple docstring'''





					def __init__(     self       :      Any ,   __snake_case       :      str ,   __snake_case       :      Optional[int]=7 ,   __snake_case       :      int=3 ,   __snake_case       :      Tuple=30 ,   __snake_case       :      Optional[Any]=4_00 ,   __snake_case       :      str=True ,   __snake_case       :      Union[str, Any]=None ,   __snake_case       :      Union[str, Any]=0.9 ,   __snake_case       :      Any=None ,   __snake_case       :      int=True ,   __snake_case       :      Union[str, Any]=[0.5, 0.5, 0.5] ,   __snake_case       :      Dict=[0.5, 0.5, 0.5] ,   ):
									UpperCAmelCase_										=					size if size is not None else {'''shortest_edge''': 30}
									UpperCAmelCase_										=					crop_size if crop_size is not None else {'''height''': 30, '''width''': 30}
									UpperCAmelCase_										=					parent
									UpperCAmelCase_										=					batch_size
									UpperCAmelCase_										=					num_channels
									UpperCAmelCase_										=					min_resolution
									UpperCAmelCase_										=					max_resolution
									UpperCAmelCase_										=					do_resize_and_center_crop
									UpperCAmelCase_										=					size
									UpperCAmelCase_										=					crop_pct
									UpperCAmelCase_										=					crop_size
									UpperCAmelCase_										=					do_normalize
									UpperCAmelCase_										=					image_mean
									UpperCAmelCase_										=					image_std




					def   lowerCamelCase_       (     self       :      Union[str, Any]    ):
									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       a					(       _A     ,       unittest.TestCase      ):

					'''simple docstring'''





					lowerCAmelCase    :	Optional[Any]       		=  PoolFormerImageProcessor if is_vision_available() else None
					def   lowerCamelCase_       (     self       :      Dict    ):
									UpperCAmelCase_										=					PoolFormerImageProcessingTester(self    )
					@property
					def   lowerCamelCase_       (     self       :      int    ):
									return self.image_processor_tester.prepare_image_processor_dict()
					def   lowerCamelCase_       (     self       :      Dict    ):
									UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
									self.assertTrue(hasattr(__snake_case ,   '''do_resize_and_center_crop'''    )    )
									self.assertTrue(hasattr(__snake_case ,   '''size'''    )    )
									self.assertTrue(hasattr(__snake_case ,   '''crop_pct'''    )    )
									self.assertTrue(hasattr(__snake_case ,   '''do_normalize'''    )    )
									self.assertTrue(hasattr(__snake_case ,   '''image_mean'''    )    )
									self.assertTrue(hasattr(__snake_case ,   '''image_std'''    )    )
					def   lowerCamelCase_       (     self       :      int    ):
									UpperCAmelCase_										=					self.image_processing_class.from_dict(self.image_processor_dict    )
									self.assertEqual(image_processor.size ,   {'''shortest_edge''': 30}    )
									self.assertEqual(image_processor.crop_size ,   {'''height''': 30, '''width''': 30}    )

									UpperCAmelCase_										=					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   lowerCamelCase_       (     self       :      Optional[Any]    ):
									pass
					def   lowerCamelCase_       (     self       :      List[str]    ):
									# Initialize image_processing
									UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
									# create random PIL images
									UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_tester ,   equal_resolution=__snake_case    )
									for image in image_inputs:
													self.assertIsInstance(__snake_case ,   Image.Image    )

									# Test not batched input
									UpperCAmelCase_										=					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_										=					image_processing(__snake_case ,   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       :      Union[str, Any]    ):
									# Initialize image_processing
									UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
									# create random numpy tensors
									UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_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_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_										=					image_processing(__snake_case ,   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       :      int    ):
									# Initialize image_processing
									UpperCAmelCase_										=					self.image_processing_class(**self.image_processor_dict    )
									# create random PyTorch tensors
									UpperCAmelCase_										=					prepare_image_inputs(self.image_processor_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_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_										=					image_processing(__snake_case ,   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'''],
									    ) ,   )











from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase      			=						logging.get_logger(__name__)

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



class       a					(       _A      ):

     '''simple docstring'''





     lowerCAmelCase    :	Optional[Any]       		=  'data2vec-text'
     def __init__(     self       :      Optional[Any] ,   __snake_case       :      Optional[int]=3_05_22 ,   __snake_case       :      List[str]=7_68 ,   __snake_case       :      Tuple=12 ,   __snake_case       :      int=12 ,   __snake_case       :      Union[str, Any]=30_72 ,   __snake_case       :      List[Any]="gelu" ,   __snake_case       :      Any=0.1 ,   __snake_case       :      Union[str, Any]=0.1 ,   __snake_case       :      Tuple=5_12 ,   __snake_case       :      str=2 ,   __snake_case       :      str=0.02 ,   __snake_case       :      List[Any]=1E-12 ,   __snake_case       :      Any=1 ,   __snake_case       :      List[Any]=0 ,   __snake_case       :      Dict=2 ,   __snake_case       :      Any="absolute" ,   __snake_case       :      Union[str, Any]=True ,   __snake_case       :      Any=None ,   **__snake_case       :      List[Any] ,   ):
         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       a					(       _A      ):

     '''simple docstring'''





     @property
     def   lowerCamelCase_       (     self       :      str    ):
         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),
             ]    )





