import unittest

import numpy as np

from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision

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 DPTImageProcessor
class    A_ (       unittest.TestCase ):

  def __init__(		self :      Optional[Any]     ,							__SCREAMING_SNAKE_CASE :      int     ,							__SCREAMING_SNAKE_CASE :      Optional[int]=7     ,							__SCREAMING_SNAKE_CASE :      List[str]=3     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=18     ,							__SCREAMING_SNAKE_CASE :      Tuple=30     ,							__SCREAMING_SNAKE_CASE :      List[Any]=4_00     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      List[Any]=None     ,							__SCREAMING_SNAKE_CASE :      Any=True     ,							__SCREAMING_SNAKE_CASE :      Tuple=[0.5, 0.5, 0.5]     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=[0.5, 0.5, 0.5]     ,							):
   __a       =  size if size is not None else {"height": 18, "width": 18}
   __a       =  parent
   __a       =  batch_size
   __a       =  num_channels
   __a       =  image_size
   __a       =  min_resolution
   __a       =  max_resolution
   __a       =  do_resize
   __a       =  size
   __a       =  do_normalize
   __a       =  image_mean
   __a       =  image_std

  def 		_UpperCAmelCase   (		self :      int       ):
   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    A_ (       a_			,       unittest.TestCase ):
  _SCREAMING_SNAKE_CASE    				=			DPTImageProcessor if is_vision_available() else None

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  DPTImageProcessingTester(self       )

  @property
  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   return self.image_processor_tester.prepare_image_processor_dict()

  def 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_mean"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"image_std"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_normalize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"do_resize"       )       )
   self.assertTrue(hasattr(__SCREAMING_SNAKE_CASE     ,							"size"       )       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.image_processing_class.from_dict(self.image_processor_dict       )
   self.assertEqual(image_processor.size     ,							{"height": 18, "width": 18}       )

   __a       =  self.image_processing_class.from_dict(self.image_processor_dict     ,							size=42       )
   self.assertEqual(image_processor.size     ,							{"height": 42, "width": 42}       )

  def 		_UpperCAmelCase   (		self :      str       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PIL images
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							Image.Image       )

   # Test not batched input
   __a       =  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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)

   # Test batched
   __a       =  image_processing(__SCREAMING_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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random numpy tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							numpify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							np.ndarray       )

   # Test not batched input
   __a       =  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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)

   # Test batched
   __a       =  image_processing(__SCREAMING_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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   # Initialize image_processing
   __a       =  self.image_processing_class(**self.image_processor_dict       )
   # create random PyTorch tensors
   __a       =  prepare_image_inputs(self.image_processor_tester     ,							equal_resolution=__SCREAMING_SNAKE_CASE     ,							torchify=__SCREAMING_SNAKE_CASE       )
   for image in image_inputs:
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							torch.Tensor       )

   # Test not batched input
   __a       =  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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)

   # Test batched
   __a       =  image_processing(__SCREAMING_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.size["height"],
           self.image_processor_tester.size["width"],
       )     ,							)



from ... import PretrainedConfig


SCREAMING_SNAKE_CASE :	Any							     =		{
    """sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class    A_ (       a_ ):
  _SCREAMING_SNAKE_CASE    				=			NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
  _SCREAMING_SNAKE_CASE    				=			"""nezha"""

  def __init__(		self :      Any     ,							__SCREAMING_SNAKE_CASE :      List[str]=2_11_28     ,							__SCREAMING_SNAKE_CASE :      Dict=7_68     ,							__SCREAMING_SNAKE_CASE :      List[Any]=12     ,							__SCREAMING_SNAKE_CASE :      str=12     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=30_72     ,							__SCREAMING_SNAKE_CASE :      Any="gelu"     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      List[str]=0.1     ,							__SCREAMING_SNAKE_CASE :      int=5_12     ,							__SCREAMING_SNAKE_CASE :      Union[str, Any]=64     ,							__SCREAMING_SNAKE_CASE :      List[Any]=2     ,							__SCREAMING_SNAKE_CASE :      int=0.02     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=1E-12     ,							__SCREAMING_SNAKE_CASE :      int=0.1     ,							__SCREAMING_SNAKE_CASE :      Any=0     ,							__SCREAMING_SNAKE_CASE :      Any=2     ,							__SCREAMING_SNAKE_CASE :      Optional[Any]=3     ,							__SCREAMING_SNAKE_CASE :      List[Any]=True     ,							**__SCREAMING_SNAKE_CASE :      List[str]     ,							):
   super().__init__(pad_token_id=__SCREAMING_SNAKE_CASE     ,							bos_token_id=__SCREAMING_SNAKE_CASE     ,							eos_token_id=__SCREAMING_SNAKE_CASE     ,							**__SCREAMING_SNAKE_CASE       )

   __a       =  vocab_size
   __a       =  hidden_size
   __a       =  num_hidden_layers
   __a       =  num_attention_heads
   __a       =  hidden_act
   __a       =  intermediate_size
   __a       =  hidden_dropout_prob
   __a       =  attention_probs_dropout_prob
   __a       =  max_position_embeddings
   __a       =  max_relative_position
   __a       =  type_vocab_size
   __a       =  initializer_range
   __a       =  layer_norm_eps
   __a       =  classifier_dropout
   __a       =  use_cache


