






'''simple docstring'''







import json
import os
import unittest

from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin




class 		UpperCamelCase__    (__UpperCAmelCase   ,unittest.TestCase			):
		_UpperCamelCase				  =					BioGptTokenizer
		_UpperCamelCase				  =					False






		def 			UpperCamelCase_					(      self       ):
								super().setUp()

								# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
								lowerCamelCase__         =  [
								    """l""",
								    """o""",
								    """w""",
								    """e""",
								    """r""",
								    """s""",
								    """t""",
								    """i""",
								    """d""",
								    """n""",
								    """w</w>""",
								    """r</w>""",
								    """t</w>""",
								    """lo""",
								    """low""",
								    """er</w>""",
								    """low</w>""",
								    """lowest</w>""",
								    """newer</w>""",
								    """wider</w>""",
								    """<unk>""",
								]
								lowerCamelCase__         =  dict(zip(lowerCAmelCase_   ,range(len(lowerCAmelCase_       )       )       )       )
								lowerCamelCase__         =  ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]

								lowerCamelCase__         =  os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["""vocab_file"""]       )
								lowerCamelCase__         =  os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES["""merges_file"""]       )
								with open(self.vocab_file   ,"""w"""       ) as fp:
														fp.write(json.dumps(lowerCAmelCase_       )       )
								with open(self.merges_file   ,"""w"""       ) as fp:
														fp.write("""\n""".join(lowerCAmelCase_       )       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase       ):
								lowerCamelCase__         =  """lower newer"""
								lowerCamelCase__         =  """lower newer"""
								return input_text, output_text






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  BioGptTokenizer(self.vocab_file   ,self.merges_file       )

								lowerCamelCase__         =  """lower"""
								lowerCamelCase__         =  ["""low""", """er</w>"""]
								lowerCamelCase__         =  tokenizer.tokenize(lowerCAmelCase_       )
								self.assertListEqual(lowerCAmelCase_   ,lowerCAmelCase_       )

								lowerCamelCase__         =  tokens + ["""<unk>"""]
								lowerCamelCase__         =  [14, 15, 20]
								self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase_       )   ,lowerCAmelCase_       )






		@slow
		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  BioGptTokenizer.from_pretrained("""microsoft/biogpt"""       )

								lowerCamelCase__         =  tokenizer.encode("""sequence builders"""   ,add_special_tokens=lowerCAmelCase_       )
								lowerCamelCase__         =  tokenizer.encode("""multi-sequence build"""   ,add_special_tokens=lowerCAmelCase_       )

								lowerCamelCase__         =  tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_       )
								lowerCamelCase__         =  tokenizer.build_inputs_with_special_tokens(lowerCAmelCase_   ,lowerCAmelCase_       )

								self.assertTrue(encoded_sentence == [2] + text       )
								self.assertTrue(encoded_pair == [2] + text + [2] + text_a       )









'''simple docstring'''







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


UpperCamelCase    :      Tuple  			=		logging.get_logger(__name__)

UpperCamelCase    :      List[str]  			=		{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}




class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					'donut-swin'

		_UpperCamelCase				  =					{
		    'num_attention_heads': 'num_heads',
		    'num_hidden_layers': 'num_layers',
		}






		def __init__(      self   ,_lowerCAmelCase=2_24   ,_lowerCAmelCase=4   ,_lowerCAmelCase=3   ,_lowerCAmelCase=96   ,_lowerCAmelCase=[2, 2, 6, 2]   ,_lowerCAmelCase=[3, 6, 12, 24]   ,_lowerCAmelCase=7   ,_lowerCAmelCase=4.0   ,_lowerCAmelCase=True   ,_lowerCAmelCase=0.0   ,_lowerCAmelCase=0.0   ,_lowerCAmelCase=0.1   ,_lowerCAmelCase="gelu"   ,_lowerCAmelCase=False   ,_lowerCAmelCase=0.02   ,_lowerCAmelCase=1E-5   ,**_lowerCAmelCase   ,):
								super().__init__(**_lowerCAmelCase       )

								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  patch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  embed_dim
								lowerCamelCase__         =  depths
								lowerCamelCase__         =  len(_lowerCAmelCase       )
								lowerCamelCase__         =  num_heads
								lowerCamelCase__         =  window_size
								lowerCamelCase__         =  mlp_ratio
								lowerCamelCase__         =  qkv_bias
								lowerCamelCase__         =  hidden_dropout_prob
								lowerCamelCase__         =  attention_probs_dropout_prob
								lowerCamelCase__         =  drop_path_rate
								lowerCamelCase__         =  hidden_act
								lowerCamelCase__         =  use_absolute_embeddings
								lowerCamelCase__         =  layer_norm_eps
								lowerCamelCase__         =  initializer_range
								# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								lowerCamelCase__         =  int(embed_dim * 2 ** (len(_lowerCAmelCase       ) - 1)       )

