




from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowercase__   :    List[str] 					=      logging.get_logger(__name__)

lowercase__   :    int 					=      {
    "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/config.json",
    # See all LED models at https://huggingface.co/models?filter=led
}




class     a__ (      UpperCamelCase__   ):
     a	:    int	      =			"""led"""
     a	:    Dict	      =			{
         """num_attention_heads""": """encoder_attention_heads""",
         """hidden_size""": """d_model""",
         """attention_probs_dropout_prob""": """attention_dropout""",
         """initializer_range""": """init_std""",
     }







     def __init__(			self						,	A=50265						,	A=16384						,	A=1024						,	A=12						,	A=4096						,	A=16						,	A=12						,	A=4096						,	A=16						,	A=0.0						,	A=0.0						,	A=True						,	A=True						,	A="gelu"						,	A=1024						,	A=0.1						,	A=0.0						,	A=0.0						,	A=0.0_2						,	A=2						,	A=0.0						,	A=1						,	A=0						,	A=2						,	A = 512						,	**A						,	)						->					Tuple:



          '''simple docstring'''



          a											=   vocab_size
          a											=   max_encoder_position_embeddings
          a											=   max_decoder_position_embeddings
          a											=   d_model
          a											=   encoder_ffn_dim
          a											=   encoder_layers
          a											=   encoder_attention_heads
          a											=   decoder_ffn_dim
          a											=   decoder_layers
          a											=   decoder_attention_heads
          a											=   dropout
          a											=   attention_dropout
          a											=   activation_dropout
          a											=   activation_function
          a											=   init_std
          a											=   encoder_layerdrop
          a											=   decoder_layerdrop
          a											=   classifier_dropout
          a											=   use_cache
          a											=   encoder_layers
          a											=   attention_window

          super().__init__(
              pad_token_id=A						,	bos_token_id=A						,	eos_token_id=A						,	is_encoder_decoder=A						,	decoder_start_token_id=A						,	**A						,	)







import unittest

from transformers.image_processing_utils import get_size_dict




class     a__ (      unittest.TestCase   ):







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



          a											=   {"wrong_key": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"height": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          a											=   {"width": 224, "shortest_edge": 224}
          with self.assertRaises(A				):
               get_size_dict(A				)

          # Test a dict with the correct keys is returned as is
          a											=   {"height": 224, "width": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	A				)

          a											=   {"shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          a											=   {"longest_edge": 224, "shortest_edge": 224}
          a											=   get_size_dict(A				)
          self.assertEqual(A						,	{"longest_edge": 224, "shortest_edge": 224}				)

          # Test a single int value which  represents (size, size)
          a											=   get_size_dict(224				)
          self.assertEqual(A						,	{"height": 224, "width": 224}				)

          # Test a single int value which represents the shortest edge
          a											=   get_size_dict(224						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224}				)

          # Test a tuple of ints which represents (height, width)
          a											=   get_size_dict((150, 200)				)
          self.assertEqual(A						,	{"height": 150, "width": 200}				)

          # Test a tuple of ints which represents (width, height)
          a											=   get_size_dict((150, 200)						,	height_width_order=A				)
          self.assertEqual(A						,	{"height": 200, "width": 150}				)

          # Test an int representing the shortest edge and max_size which represents the longest edge
          a											=   get_size_dict(224						,	max_size=256						,	default_to_square=A				)
          self.assertEqual(A						,	{"shortest_edge": 224, "longest_edge": 256}				)

          # Test int with default_to_square=True and max_size fails
          with self.assertRaises(A				):
               get_size_dict(224						,	max_size=256						,	default_to_square=A				)

