

from collections import OrderedDict
from typing import Mapping

from packaging import version

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


__A			:					Optional[int]						=			logging.get_logger(__name__)

__A			:					List[Any]						=			{
    'facebook/data2vec-vision-base-ft': (
        'https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json'
    ),
}





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      "data2vec-vision"





   def __init__(       self					:			Tuple     , __lowerCamelCase					:			Optional[int]=768     , __lowerCamelCase					:			Union[str, Any]=12     , __lowerCamelCase					:			int=12     , __lowerCamelCase					:			Tuple=3072     , __lowerCamelCase					:			Tuple="gelu"     , __lowerCamelCase					:			Optional[int]=0.0     , __lowerCamelCase					:			List[Any]=0.0     , __lowerCamelCase					:			Optional[int]=0.02     , __lowerCamelCase					:			List[str]=1e-12     , __lowerCamelCase					:			Union[str, Any]=224     , __lowerCamelCase					:			List[Any]=16     , __lowerCamelCase					:			str=3     , __lowerCamelCase					:			int=False     , __lowerCamelCase					:			int=False     , __lowerCamelCase					:			int=False     , __lowerCamelCase					:			str=False     , __lowerCamelCase					:			int=0.1     , __lowerCamelCase					:			Tuple=0.1     , __lowerCamelCase					:			List[str]=True     , __lowerCamelCase					:			Optional[Any]=[3, 5, 7, 11]     , __lowerCamelCase					:			Union[str, Any]=[1, 2, 3, 6]     , __lowerCamelCase					:			Tuple=True     , __lowerCamelCase					:			Optional[int]=0.4     , __lowerCamelCase					:			Dict=256     , __lowerCamelCase					:			int=1     , __lowerCamelCase					:			Union[str, Any]=False     , __lowerCamelCase					:			Tuple=255     , **__lowerCamelCase					:			List[str]     , ):
       super().__init__(**__lowerCamelCase			)

       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										=   initializer_range
       SCREAMING_SNAKE_CASE										=   layer_norm_eps

       SCREAMING_SNAKE_CASE										=   image_size
       SCREAMING_SNAKE_CASE										=   patch_size
       SCREAMING_SNAKE_CASE										=   num_channels
       SCREAMING_SNAKE_CASE										=   use_mask_token
       SCREAMING_SNAKE_CASE										=   use_absolute_position_embeddings
       SCREAMING_SNAKE_CASE										=   use_relative_position_bias
       SCREAMING_SNAKE_CASE										=   use_shared_relative_position_bias
       SCREAMING_SNAKE_CASE										=   layer_scale_init_value
       SCREAMING_SNAKE_CASE										=   drop_path_rate
       SCREAMING_SNAKE_CASE										=   use_mean_pooling
       # decode head attributes (semantic segmentation)
       SCREAMING_SNAKE_CASE										=   out_indices
       SCREAMING_SNAKE_CASE										=   pool_scales
       # auxiliary head attributes (semantic segmentation)
       SCREAMING_SNAKE_CASE										=   use_auxiliary_head
       SCREAMING_SNAKE_CASE										=   auxiliary_loss_weight
       SCREAMING_SNAKE_CASE										=   auxiliary_channels
       SCREAMING_SNAKE_CASE										=   auxiliary_num_convs
       SCREAMING_SNAKE_CASE										=   auxiliary_concat_input
       SCREAMING_SNAKE_CASE										=   semantic_loss_ignore_index





class 			_SCREAMING_SNAKE_CASE (   __snake_case		):







   '''simple docstring'''




   lowerCamelCase__				=      version.parse("1.11"		)





   @property
   def      _snake_case		(       self					:			str			):
       return OrderedDict(
           [
               ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
           ]			)





   @property
   def      _snake_case		(       self					:			List[Any]			):
       return 1e-4



"""simple docstring"""


import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import ImageGPTImageProcessor

class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   def __init__(						self ,     lowerCamelCase__ ,     lowerCamelCase__=7 ,     lowerCamelCase__=3 ,     lowerCamelCase__=18 ,     lowerCamelCase__=30 ,     lowerCamelCase__=400 ,     lowerCamelCase__=True ,     lowerCamelCase__=None ,     lowerCamelCase__=True ,     )      ->							int:
    lowercase__       :       int				=						size if size is not None else {"""height""": 18, """width""": 18}
    lowercase__       :       Optional[Any]				=						parent
    lowercase__       :       Dict				=						batch_size
    lowercase__       :       Union[str, Any]				=						num_channels
    lowercase__       :       Tuple				=						image_size
    lowercase__       :       str				=						min_resolution
    lowercase__       :       Optional[Any]				=						max_resolution
    lowercase__       :       Union[str, Any]				=						do_resize
    lowercase__       :       Dict				=						size
    lowercase__       :       Optional[Any]				=						do_normalize







   def 					UpperCAmelCase__(						self						)      ->							Any:
    return {
        # here we create 2 clusters for the sake of simplicity
        "clusters": np.asarray(
            [
                [0.8866_4436_3403_3203, 0.6618_8293_6954_4983, 0.3891_7464_0178_6804],
                [-0.6042_5591_4688_1104, -0.0_2295_0088_6052_8469, 0.5423_7973_6900_3296],
            ]						),
        "do_resize": self.do_resize,
        "size": self.size,
        "do_normalize": self.do_normalize,
    }

@require_torch
@require_vision
class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase     ,   unittest.TestCase  ):
   """simple docstring"""



   _a   :       List[Any]									=    ImageGPTImageProcessor if is_vision_available() else None


   def 					UpperCAmelCase__(						self						)      ->							Optional[int]:
    lowercase__       :       str				=						ImageGPTImageProcessingTester(self						)


   @property
   def 					UpperCAmelCase__(						self						)      ->							Optional[Any]:
    return self.image_processor_tester.prepare_image_processor_dict()


   def 					UpperCAmelCase__(						self						)      ->							int:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """clusters"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_resize"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """size"""						)						)
    self.assertTrue(hasattr(lowerCamelCase__ ,     """do_normalize"""						)						)


   def 					UpperCAmelCase__(						self						)      ->							Any:
    lowercase__       :       Optional[Any]				=						self.image_processing_class.from_dict(self.image_processor_dict						)
    self.assertEqual(image_processor.size ,     {"""height""": 18, """width""": 18}						)

    lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_dict(self.image_processor_dict ,     size=42						)
    self.assertEqual(image_processor.size ,     {"""height""": 42, """width""": 42}						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)
    lowercase__       :       Optional[Any]				=						json.loads(image_processor.to_json_string()						)
    for key, value in self.image_processor_dict.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     obj[key]						)						)
     else:
      self.assertEqual(obj[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     lowercase__       :       Optional[int]				=						os.path.join(lowerCamelCase__ ,     """image_processor.json"""						)
     image_processor_first.to_json_file(lowerCamelCase__						)
     lowercase__       :       str				=						self.image_processing_class.from_json_file(lowerCamelCase__						).to_dict()

    lowercase__       :       Optional[int]				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)


   def 					UpperCAmelCase__(						self						)      ->							List[str]:
    lowercase__       :       List[Any]				=						self.image_processing_class(**self.image_processor_dict						)

    with tempfile.TemporaryDirectory() as tmpdirname:
     image_processor_first.save_pretrained(lowerCamelCase__						)
     lowercase__       :       Union[str, Any]				=						self.image_processing_class.from_pretrained(lowerCamelCase__						).to_dict()

    lowercase__       :       Dict				=						image_processor_first.to_dict()
    for key, value in image_processor_first.items():
     if key == "clusters":
      self.assertTrue(np.array_equal(lowerCamelCase__ ,     image_processor_second[key]						)						)
     else:
      self.assertEqual(image_processor_first[key] ,     lowerCamelCase__						)







   @unittest.skip("""ImageGPT requires clusters at initialization"""						)
   def 					UpperCAmelCase__(						self						)      ->							Dict:
    pass


def    _lowerCamelCase   (     ):
 lowercase__       :       Tuple				=						load_dataset("""hf-internal-testing/fixtures_image_utils"""       ,		split="""test"""  )

 lowercase__       :       Optional[int]				=						Image.open(dataset[4]["""file"""]  )
 lowercase__       :       Union[str, Any]				=						Image.open(dataset[5]["""file"""]  )

 lowercase__       :       Optional[int]				=						[imagea, imagea]

 return images






@require_vision
@require_torch
class 				_SCREAMING_SNAKE_CASE				(							unittest.TestCase  ):
   """simple docstring"""



   @slow
   def 					UpperCAmelCase__(						self						)      ->							str:
    lowercase__       :       Optional[int]				=						ImageGPTImageProcessor.from_pretrained("""openai/imagegpt-small"""						)

    lowercase__       :       int				=						prepare_images()

    # test non-batched
    lowercase__       :       int				=						image_processing(images[0] ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (1, 1024)						)

    lowercase__       :       Any				=						[306, 191, 191]
    self.assertEqual(encoding.input_ids[0, :3].tolist() ,     lowerCamelCase__						)

    # test batched
    lowercase__       :       str				=						image_processing(lowerCamelCase__ ,     return_tensors="""pt"""						)

    self.assertIsInstance(encoding.input_ids ,     torch.LongTensor						)
    self.assertEqual(encoding.input_ids.shape ,     (2, 1024)						)

    lowercase__       :       Optional[int]				=						[303, 13, 13]
    self.assertEqual(encoding.input_ids[1, -3:].tolist() ,     lowerCamelCase__						)