

'''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 GLPNImageProcessor

class 						__magic_name__   (							unittest.TestCase ):







 def __init__(       self  , snake_case_  , snake_case_=7  , snake_case_=3  , snake_case_=18  , snake_case_=30  , snake_case_=4_00  , snake_case_=True  , snake_case_=32  , snake_case_=True  , ):
   lowercase									=parent
   lowercase									=batch_size
   lowercase									=num_channels
   lowercase									=image_size
   lowercase									=min_resolution
   lowercase									=max_resolution
   lowercase									=do_resize
   lowercase									=size_divisor
   lowercase									=do_rescale







 def 						_A(       self   ):
   return {
       "do_resize": self.do_resize,
       "size_divisor": self.size_divisor,
       "do_rescale": self.do_rescale,
   }

@require_torch
@require_vision
class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ,				unittest.TestCase ):
 UpperCamelCase__     =       GLPNImageProcessor if is_vision_available() else None







 def 						_A(       self   ):
   lowercase									=GLPNImageProcessingTester(self   )







 @property
 def 						_A(       self   ):
   return self.image_processor_tester.prepare_image_processor_dict()







 def 						_A(       self   ):
   lowercase									=self.image_processing_class(**self.image_processor_dict   )
   self.assertTrue(hasattr(snake_case_  , '''do_resize'''   )   )
   self.assertTrue(hasattr(snake_case_  , '''size_divisor'''   )   )
   self.assertTrue(hasattr(snake_case_  , '''resample'''   )   )
   self.assertTrue(hasattr(snake_case_  , '''do_rescale'''   )   )







 def 						_A(       self   ):
   pass







 def 						_A(       self   ):
   lowercase									=self.image_processing_class(**self.image_processor_dict   )
   # create random PIL images
   lowercase									=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 (GLPNImageProcessor doesn't support batching)
   lowercase									=image_processing(image_inputs[0]  , return_tensors='''pt'''   ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







 def 						_A(       self   ):
   lowercase									=self.image_processing_class(**self.image_processor_dict   )
   # create random numpy tensors
   lowercase									=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 (GLPNImageProcessor doesn't support batching)
   lowercase									=image_processing(image_inputs[0]  , return_tensors='''pt'''   ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )







 def 						_A(       self   ):
   lowercase									=self.image_processing_class(**self.image_processor_dict   )
   # create random PyTorch tensors
   lowercase									=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 (GLPNImageProcessor doesn't support batching)
   lowercase									=image_processing(image_inputs[0]  , return_tensors='''pt'''   ).pixel_values
   self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0   )
   self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0   )



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_   							=  {
    'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/config.json',
    'distilbert-base-uncased-distilled-squad': (
        'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/config.json',
    'distilbert-base-cased-distilled-squad': (
        'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/config.json'
    ),
    'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/config.json',
    'distilbert-base-multilingual-cased': (
        'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/config.json'
    ),
    'distilbert-base-uncased-finetuned-sst-2-english': (
        'https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english/resolve/main/config.json'
    ),
}





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):
  _a		     =    """distilbert"""
  _a		     =    {
      """hidden_size""": """dim""",
      """num_attention_heads""": """n_heads""",
      """num_hidden_layers""": """n_layers""",
  }




  def __init__(							self				,  lowerCAmelCase=30_522				,  lowerCAmelCase=512				,  lowerCAmelCase=False				,  lowerCAmelCase=6				,  lowerCAmelCase=12				,  lowerCAmelCase=768				,  lowerCAmelCase=4 * 768				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.1				,  lowerCAmelCase="gelu"				,  lowerCAmelCase=0.02				,  lowerCAmelCase=0.1				,  lowerCAmelCase=0.2				,  lowerCAmelCase=0				,  **lowerCAmelCase				,  )    ->					Dict:
        '''simple docstring'''






        _lowercase      			=vocab_size
        _lowercase      			=max_position_embeddings
        _lowercase      			=sinusoidal_pos_embds
        _lowercase      			=n_layers
        _lowercase      			=n_heads
        _lowercase      			=dim
        _lowercase      			=hidden_dim
        _lowercase      			=dropout
        _lowercase      			=attention_dropout
        _lowercase      			=activation
        _lowercase      			=initializer_range
        _lowercase      			=qa_dropout
        _lowercase      			=seq_classif_dropout
        super().__init__(**lowerCAmelCase				,  pad_token_id=lowerCAmelCase   )





class 					__lowerCAmelCase	(   SCREAMING_SNAKE_CASE				):




  @property
  def     A__	(							self   )    ->					Mapping[str, Mapping[int, str]]:
        '''simple docstring'''






        if self.task == "multiple-choice":
              _lowercase      			={0: 'batch', 1: 'choice', 2: 'sequence'}
        else:
              _lowercase      			={0: 'batch', 1: 'sequence'}
        return OrderedDict(
            [
                ('input_ids', dynamic_axis),
                ('attention_mask', dynamic_axis),
            ]   )


