


from collections import OrderedDict
from typing import List, Mapping

from packaging import version

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


SCREAMING_SNAKE_CASE						:Optional[Any]			=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE						:Dict			=  {
    '''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}


class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							'efficientnet'



  def __init__(  self							:							List[str]     ,					_lowerCAmelCase							:							int = 3     ,					_lowerCAmelCase							:							int = 6_0_0     ,					_lowerCAmelCase							:							float = 2.0     ,					_lowerCAmelCase							:							float = 3.1     ,					_lowerCAmelCase							:							int = 8     ,					_lowerCAmelCase							:							List[int] = [3, 3, 5, 3, 5, 5, 3]     ,					_lowerCAmelCase							:							List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2]     ,					_lowerCAmelCase							:							List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0]     ,					_lowerCAmelCase							:							List[int] = []     ,					_lowerCAmelCase							:							List[int] = [1, 2, 2, 2, 1, 2, 1]     ,					_lowerCAmelCase							:							List[int] = [1, 2, 2, 3, 3, 4, 1]     ,					_lowerCAmelCase							:							List[int] = [1, 6, 6, 6, 6, 6, 6]     ,					_lowerCAmelCase							:							float = 0.25     ,					_lowerCAmelCase							:							str = "swish"     ,					_lowerCAmelCase							:							int = 2_5_6_0     ,					_lowerCAmelCase							:							str = "mean"     ,					_lowerCAmelCase							:							float = 0.02     ,					_lowerCAmelCase							:							float = 0.001     ,					_lowerCAmelCase							:							float = 0.99     ,					_lowerCAmelCase							:							float = 0.5     ,					_lowerCAmelCase							:							float = 0.2     ,					**_lowerCAmelCase							:							Union[str, Any]     ,					) ->			Dict:




     """simple docstring"""
     super().__init__(**_lowerCAmelCase	)

     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       width_coefficient
     snake_case_            =       depth_coefficient
     snake_case_            =       depth_divisor
     snake_case_            =       kernel_sizes
     snake_case_            =       in_channels
     snake_case_            =       out_channels
     snake_case_            =       depthwise_padding
     snake_case_            =       strides
     snake_case_            =       num_block_repeats
     snake_case_            =       expand_ratios
     snake_case_            =       squeeze_expansion_ratio
     snake_case_            =       hidden_act
     snake_case_            =       hidden_dim
     snake_case_            =       pooling_type
     snake_case_            =       initializer_range
     snake_case_            =       batch_norm_eps
     snake_case_            =       batch_norm_momentum
     snake_case_            =       dropout_rate
     snake_case_            =       drop_connect_rate
     snake_case_            =       sum(_lowerCAmelCase	) * 4






class      __lowerCAmelCase  (			a       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							version.parse('1.11'       )



  @property
  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			Mapping[str, Mapping[int, str]]:




     """simple docstring"""
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]	)



  @property
  def        lowerCAmelCase__	(  self							:							Dict	) ->			float:




     """simple docstring"""
     return 1e-5








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      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  def __init__(  self							:							Union[str, Any]     ,					_lowerCAmelCase							:							List[Any]     ,					_lowerCAmelCase							:							List[Any]=7     ,					_lowerCAmelCase							:							Any=3     ,					_lowerCAmelCase							:							Dict=1_8     ,					_lowerCAmelCase							:							Tuple=3_0     ,					_lowerCAmelCase							:							Optional[int]=4_0_0     ,					_lowerCAmelCase							:							Optional[int]=True     ,					_lowerCAmelCase							:							Any=None     ,					_lowerCAmelCase							:							List[Any]=True     ,					) ->			Dict:




     """simple docstring"""
     snake_case_            =       size if size is not None else {"height": 1_8, "width": 1_8}
     snake_case_            =       parent
     snake_case_            =       batch_size
     snake_case_            =       num_channels
     snake_case_            =       image_size
     snake_case_            =       min_resolution
     snake_case_            =       max_resolution
     snake_case_            =       do_resize
     snake_case_            =       size
     snake_case_            =       do_normalize



  def        lowerCAmelCase__	(  self							:							str	) ->			Optional[int]:




     """simple docstring"""
     return {
         # here we create 2 clusters for the sake of simplicity
         "clusters": np.asarray(
             [
                 [0.8_866_443_634_033_203, 0.6_618_829_369_544_983, 0.3_891_746_401_786_804],
                 [-0.6_042_559_146_881_104, -0.02_295_008_860_528_469, 0.5_423_797_369_003_296],
             ]	),
         "do_resize": self.do_resize,
         "size": self.size,
         "do_normalize": self.do_normalize,
     }






@require_torch
@require_vision
class      __lowerCAmelCase  (			a			,	unittest.TestCase       ):






  """simple docstring"""





  _SCREAMING_SNAKE_CASE		     =							ImageGPTImageProcessor if is_vision_available() else None



  def        lowerCAmelCase__	(  self							:							Union[str, Any]	) ->			List[Any]:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessingTester(self	)



  @property
  def        lowerCAmelCase__	(  self							:							str	) ->			Union[str, Any]:




     """simple docstring"""
     return self.image_processor_tester.prepare_image_processor_dict()



  def        lowerCAmelCase__	(  self							:							List[Any]	) ->			int:




     """simple docstring"""
     snake_case_            =       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        lowerCAmelCase__	(  self							:							int	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict	)
     self.assertEqual(image_processor.size     ,					{"height": 1_8, "width": 1_8}	)

     snake_case_            =       self.image_processing_class.from_dict(self.image_processor_dict     ,					size=4_2	)
     self.assertEqual(image_processor.size     ,					{"height": 4_2, "width": 4_2}	)



  def        lowerCAmelCase__	(  self							:							Tuple	) ->			Any:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)
     snake_case_            =       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        lowerCAmelCase__	(  self							:							int	) ->			Optional[Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        snake_case_            =       os.path.join(_lowerCAmelCase     ,					"image_processor.json"	)
        image_processor_first.to_json_file(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_json_file(_lowerCAmelCase	).to_dict()

     snake_case_            =       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        lowerCAmelCase__	(  self							:							Optional[int]	) ->			Union[str, Any]:




     """simple docstring"""
     snake_case_            =       self.image_processing_class(**self.image_processor_dict	)

     with tempfile.TemporaryDirectory() as tmpdirname:
        image_processor_first.save_pretrained(_lowerCAmelCase	)
        snake_case_            =       self.image_processing_class.from_pretrained(_lowerCAmelCase	).to_dict()

     snake_case_            =       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        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     pass






def       _lowerCAmelCase    (				)->Optional[Any]:
   '''simple docstring'''






   snake_case_            =       load_dataset("hf-internal-testing/fixtures_image_utils"		,    split="test"				)

   snake_case_            =       Image.open(dataset[4]["file"]				)
   snake_case_            =       Image.open(dataset[5]["file"]				)

   snake_case_            =       [imagea, imagea]

   return images



@require_vision
@require_torch
class      __lowerCAmelCase  (			unittest.TestCase       ):






  """simple docstring"""





  @slow
  def        lowerCAmelCase__	(  self							:							List[str]	) ->			int:




     """simple docstring"""
     snake_case_            =       ImageGPTImageProcessor.from_pretrained("openai/imagegpt-small"	)

     snake_case_            =       prepare_images()

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

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(1, 1_0_2_4)	)

     snake_case_            =       [3_0_6, 1_9_1, 1_9_1]
     self.assertEqual(encoding.input_ids[0, :3].tolist()     ,					_lowerCAmelCase	)

     # test batched
     snake_case_            =       image_processing(_lowerCAmelCase     ,					return_tensors="pt"	)

     self.assertIsInstance(encoding.input_ids     ,					torch.LongTensor	)
     self.assertEqual(encoding.input_ids.shape     ,					(2, 1_0_2_4)	)

     snake_case_            =       [3_0_3, 1_3, 1_3]
     self.assertEqual(encoding.input_ids[1, -3:].tolist()     ,					_lowerCAmelCase	)




