code: 
  
  """simple docstring"""
  
  
  
  
  import unittest
  
  import numpy as np
  
  from transformers import (
      GPTSanJapaneseConfig,
      GPTSanJapaneseForConditionalGeneration,
      GPTSanJapaneseModel,
      GPTSanJapaneseTokenizer,
      is_torch_available,
  )
  from transformers.generation import GenerationConfig
  from transformers.testing_utils import require_torch, slow, tooslow, torch_device
  
  from ...generation.test_utils import GenerationTesterMixin
  from ...test_configuration_common import ConfigTester
  from ...test_modeling_common import ModelTesterMixin, ids_tensor
  from ...test_pipeline_mixin import PipelineTesterMixin
  
  
  
  
  
  class __snake_case		:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:Dict				,  UpperCamelCase__							:List[Any]				,  UpperCamelCase__							:List[str]=36_000				,  UpperCamelCase__							:Any=13				,  UpperCamelCase__							:List[str]=7				,  UpperCamelCase__							:Union[str, Any]=True				,  UpperCamelCase__							:int=32				,  UpperCamelCase__							:List[Any]=42				,  UpperCamelCase__							:List[Any]=5				,  UpperCamelCase__							:Union[str, Any]=2				,  UpperCamelCase__							:Dict=4				,  UpperCamelCase__							:Dict=2				,  UpperCamelCase__							:Any=32				,  UpperCamelCase__							:Dict=80				,  UpperCamelCase__							:List[Any]=33				,  UpperCamelCase__							:List[Any]=0.0				,  UpperCamelCase__							:str=1E-6				,  UpperCamelCase__							:int=100				,  UpperCamelCase__							:Any=0.0				,  ):
            _a          =				vocab_size
            _a          =				parent
            _a          =				batch_size
            _a          =				num_contexts
            # For common tests
            _a          =				self.num_contexts
            _a          =				is_training
            _a          =				hidden_size
            _a          =				num_ext_layers
            _a          =				ext_size
            _a          =				num_hidden_layers
            _a          =				num_attention_heads
            _a          =				num_experts
            _a          =				d_ff
            _a          =				d_ext
            _a          =				d_spout
            _a          =				dropout_rate
            _a          =				layer_norm_epsilon
            _a          =				expert_capacity
            _a          =				router_jitter_noise
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            return GPTSanJapaneseConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]       ):
            _a          =				ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size       )
  
            _a          =				self.get_config()
  
            return (config, input_ids)
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            _a          =				ids_tensor([self.batch_size, self.seq_length]				,  self.vocab_size       )
  
            _a          =				self.get_config()
  
            return (config, {"input_ids": input_ids})
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            return GPTSanJapaneseConfig(
                vocab_size=self.vocab_size				,  num_contexts=self.seq_length				,  d_model=self.hidden_size				,  d_ff=self.d_ff				,  d_ext=self.d_ext				,  d_spout=self.d_spout				,  num_switch_layers=self.num_hidden_layers - self.num_ext_layers				,  num_ext_layers=self.num_ext_layers				,  num_heads=self.num_attention_heads				,  num_experts=self.num_experts				,  expert_capacity=self.expert_capacity				,  dropout_rate=self.dropout_rate				,  layer_norm_epsilon=self.layer_norm_epsilon				,  router_jitter_noise=self.router_jitter_noise				,  )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:List[Any]				,  UpperCamelCase__							:Union[str, Any]				,  ):
            _a          =				GPTSanJapaneseForConditionalGeneration(config=lowercase_       )
            model.to(lowercase_       )
            model.eval()
            _a          =				model(
                input_ids=lowercase_				,  )
            self.parent.assertIsNotNone(lowercase_       )
  
  
  
  
  
  
  @require_torch
  class __snake_case		(					__UpperCAmelCase				,       __UpperCAmelCase				,       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Tuple     	=			(GPTSanJapaneseModel,) if is_torch_available() else ()
     lowerCAmelCase_				:   Union[str, Any]     	=			(
         {
             'conversational': GPTSanJapaneseForConditionalGeneration,
             'feature-extraction': GPTSanJapaneseForConditionalGeneration,
             'summarization': GPTSanJapaneseForConditionalGeneration,
             'text2text-generation': GPTSanJapaneseForConditionalGeneration,
             'translation': GPTSanJapaneseForConditionalGeneration,
         }
         if is_torch_available()
         else {}
     )
     lowerCAmelCase_				:   Tuple     	=			False
     lowerCAmelCase_				:   List[str]     	=			False
     lowerCAmelCase_				:   int     	=			False
     lowerCAmelCase_				:   Optional[int]     	=			False
     lowerCAmelCase_				:   Dict     	=			False
     lowerCAmelCase_				:   Optional[int]     	=			False
     lowerCAmelCase_				:   Any     	=			False
     lowerCAmelCase_				:   Dict     	=			False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     lowerCAmelCase_				:   List[Any]     	=			[0.8, 0.9]
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:str				,  UpperCamelCase__							:Any				,  UpperCamelCase__							:int				,  UpperCamelCase__							:Dict				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:str       ):
  
            if pipeline_test_casse_name == "SummarizationPipelineTests":
                   # TODO: fix `_reorder_cache` is not implemented for this model
                   return True
            elif pipeline_test_casse_name == "Text2TextGenerationPipelineTests":
                   # TODO: check this.
                   return True
  
            return False
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            _a          =				GPTSanJapaneseTester(self       )
            _a          =				ConfigTester(self				,  config_class=lowercase_				,  d_model=37       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            GPTSanJapaneseConfig()
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            _a          =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase_       )
  
  
  
  
     @unittest.skip(
         reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"       )
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            super().test_model_parallelism()
  
  
  
  
     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            pass
  
  
  
  
  
  
  @require_torch
  class __snake_case		(					__UpperCAmelCase				,       __UpperCAmelCase				,       unittest.TestCase      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   Any     	=			(GPTSanJapaneseForConditionalGeneration,) if is_torch_available() else ()
     lowerCAmelCase_				:   int     	=			False
     lowerCAmelCase_				:   List[Any]     	=			False
     lowerCAmelCase_				:   Optional[int]     	=			False
     lowerCAmelCase_				:   Any     	=			False
     lowerCAmelCase_				:   int     	=			False
     lowerCAmelCase_				:   Any     	=			False
     # The small GPTSAN_JAPANESE model needs higher percentages for CPU/MP tests
     lowerCAmelCase_				:   Dict     	=			[0.8, 0.9]
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Any       ):
            _a          =				GPTSanJapaneseTester(self       )
            _a          =				ConfigTester(self				,  config_class=lowercase_				,  d_model=37       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            GPTSanJapaneseConfig()
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]       ):
            _a          =				self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowercase_       )
  
  
  
  
     @unittest.skip(
         reason="skip for now as the computed `max_memory` by `model_split_percents` in the test method will be changed inside `from_pretrained`"       )
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            super().test_model_parallelism()
  
  
  
  
     @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."       )
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple       ):
            pass
  
  
  
  
     @slow
     def SCREAMING_SNAKE_CASE_      (		self							:int       ):
            _a          =				GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				tokenizer.encode("武田信玄は"				,  return_tensors="pt"       )
            _a          =				model(lowercase_       )
            _a          =				outputs.logits.detach().cpu().numpy()
            # Output of original model created with mesh-tensoflow
            _a          =				[
                # fmt: off
                [-12.037839889526367, -12.433061599731445, -14.333840370178223, -12.450345993041992, -11.1661376953125,
                -11.930137634277344, -10.659740447998047, -12.909574508666992, -13.241043090820312, -13.398579597473145,
                -11.107524871826172, -12.3685941696167, -22.97943115234375, -10.481067657470703, -12.484030723571777,
                -12.807360649108887, -14.769700050354004, -12.233579635620117, -13.428145408630371, -22.624177932739258],
                [-7.511149883270264, -8.281851768493652, -7.943127155303955, -7.55021333694458, -6.49869966506958,
                -7.586796283721924, -6.978085994720459, -7.839145183563232, -8.21964168548584, -8.695091247558594,
                -6.706910610198975, -6.6585798263549805, -19.565698623657227, -5.353842735290527, -8.350686073303223,
                -8.039388656616211, -10.856569290161133, -7.75154447555542, -8.819022178649902, -19.51532745361328],
                [-9.73066234588623, -10.223922729492188, -9.932981491088867, -11.857836723327637, -7.662626266479492,
                -11.13529109954834, -7.765097618103027, -11.472923278808594, -9.543149948120117, -11.905633926391602,
                -9.366164207458496, -11.5734281539917, -23.699003219604492, -9.429590225219727, -10.42839241027832,
                -10.585240364074707, -10.94771957397461, -11.095416069030762, -10.390240669250488, -23.769372940063477],
                [-9.728265762329102, -9.859712600708008, -10.09729290008545, -9.678522109985352, -6.879519939422607,
                -9.68487548828125, -4.2803425788879395, -10.018914222717285, -9.308445930480957, -10.63394546508789,
                -8.083646774291992, -9.06301498413086, -21.904266357421875, -8.90160846710205, -8.841876029968262,
                -11.856719970703125, -12.079398155212402, -11.233753204345703, -10.177338600158691, -21.87256622314453],
                [-9.669764518737793, -9.614198684692383, -9.814510345458984, -9.996501922607422, -11.375690460205078,
                -10.113405227661133, -10.546867370605469, -10.04369068145752, -10.907809257507324, -10.504216194152832,
                -11.129199028015137, -10.151124000549316, -21.96586799621582, -9.086349487304688, -11.730339050292969,
                -10.460667610168457, -10.298049926757812, -10.784148216247559, -10.840693473815918, -22.03152847290039],
                # fmt: on
            ]
            _a          =				np.array(lowercase_       ).flatten()
            _a          =				output_logits[0, :, :20].flatten()
  
            def check(UpperCamelCase__							:List[Any]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:str=5E-4       ):
                   return abs(a - b       ) < epsilon * max(abs(lowercase_       )				,  abs(lowercase_       )       )
  
            self.assertTrue(np.all([check(target[i]				,  predict[i]       ) for i in range(len(lowercase_       )       )]       )       )
  
  
  
  
     @slow
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]       ):
            _a          =				GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
            model.to(lowercase_       )
  
            # set deterministically
            _a          =				GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				1
  
            # use different length sentences to test batching
            _a          =				[
                '''甲斐なら武田と言うほど''',
                '''織田信長は、''',
            ]
  
            _a          =				'''left'''
            _a          =				tokenizer(lowercase_				,  return_tensors="pt"				,  padding=lowercase_       )
            _a          =				inputs['''input_ids'''].to(lowercase_       )
  
            self.assertNotEqual(inputs["attention_mask"][0].numpy().tolist()				,  inputs["attention_mask"][1].numpy().tolist()       )
  
            _a          =				model.generate(
                input_ids=lowercase_				,  attention_mask=inputs["attention_mask"].to(lowercase_       )				,  max_new_tokens=3				,  generation_config=lowercase_				,  )
  
            _a          =				tokenizer(sentences[0]				,  return_tensors="pt"       ).input_ids.to(lowercase_       )
            _a          =				model.generate(
                input_ids=lowercase_				,  max_new_tokens=3				,  generation_config=lowercase_       )
  
            _a          =				tokenizer(sentences[1]				,  return_tensors="pt"       ).input_ids.to(lowercase_       )
            _a          =				model.generate(input_ids=lowercase_				,  max_new_tokens=3				,  generation_config=lowercase_       )
  
            self.assertNotEqual(inputs_non_padded.shape				,  inputs_padded.shape       )
  
            _a          =				tokenizer.batch_decode(lowercase_				,  skip_special_tokens=lowercase_       )
            _a          =				tokenizer.decode(output_non_padded[0]				,  skip_special_tokens=lowercase_       )
            _a          =				tokenizer.decode(output_padded[0]				,  skip_special_tokens=lowercase_       )
  
            _a          =				[
                '''甲斐なら武田と言うほど甲斐の武田''',
                '''織田信長は、このような''',
            ]
            self.assertListEqual(lowercase_				,  lowercase_       )
            self.assertListEqual(lowercase_				,  [non_padded_sentence, padded_sentence]       )
  
  
  
  
     @tooslow
     def SCREAMING_SNAKE_CASE_      (		self							:str       ):
            _a          =				GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
            # Output of original model created with mesh-tensoflow
            _a          =				[
                ('''武田信玄は''', 35_675),
                ('''武田信玄は、''', 45),
                ('''武田信玄は、この''', 29),
                ('''武田信玄は、このよう''', 30_642),
                ('''武田信玄は、このような''', 35_680),
                ('''武田信玄は、このような「''', 8_640),
                ('''武田信玄は、このような「武田''', 31_617),
                ('''武田信玄は、このような「武田家''', 30_646),
                ('''武田信玄は、このような「武田家の''', 31_617),
                ('''武田信玄は、このような「武田家の家''', 31_381),
            ]
            for input, output in target:
                   _a          =				tokenizer.encode(lowercase_				,  return_tensors="pt"       )
                   _a          =				model(lowercase_       )
                   _a          =				outputs.logits.detach().cpu().numpy()[0]
                   _a          =				np.argmax(output_logits[-1]       )
                   self.assertEqual(lowercase_				,  lowercase_       )
  
  
  
  
     @slow
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            _a          =				GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
            model.to(lowercase_       )
  
            # set deterministically
            _a          =				GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				1
  
            _a          =				'''武田信玄は、'''
            _a          =				tokenizer(lowercase_				,  return_tensors="pt"       ).input_ids.to(lowercase_       )
            _a          =				tokenizer([input_text, input_text]				,  return_tensors="pt"       ).input_ids.to(lowercase_       )
  
            # spout from uniform and one-hot
            _a          =				[
                # fmt: off
                [0.87882208, 0.38426396, 0.33220248, 0.43890406, 0.16562252,
                0.04803985, 0.211572  , 0.23188473, 0.37153068, 0.7836377 ,
                0.02160172, 0.38761719, 0.75290772, 0.90198857, 0.34365777,
                0.64168169, 0.44318471, 0.14575746, 0.92562881, 0.40812148,
                0.29019122, 0.88861599, 0.65524846, 0.43563456, 0.38177187,
                0.70832965, 0.81527892, 0.68832812, 0.38833192, 0.4561522 ,
                0.14828817, 0.47248213, 0.54357335, 0.82009566, 0.1338884 ,
                0.02755417, 0.19764677, 0.2422084 , 0.04757674, 0.65409606,
                0.0824589 , 0.03304383, 0.94387689, 0.98764509, 0.82433901,
                0.27646741, 0.64907493, 0.76009406, 0.30087915, 0.17904689,
                0.41601714, 0.67046398, 0.10422822, 0.08447374, 0.07354344,
                0.61423565, 0.70284866, 0.7532333 , 0.1972038 , 0.29575659,
                0.90583886, 0.29265307, 0.50000175, 0.70407655, 0.889363  ,
                0.81904418, 0.66829128, 0.64468815, 0.56563723, 0.85601875,
                0.94924672, 0.00166762, 0.25220643, 0.74540219, 0.67993247,
                0.1549675 , 0.39385352, 0.92153607, 0.63745931, 0.27759043,
                0.84702295, 0.65904271, 0.58676614, 0.8666936 , 0.39607438,
                0.79954983, 0.42220697, 0.39650381, 0.7849864 , 0.56150201,
                0.15678925, 0.14746032, 0.34542114, 0.47026783, 0.11956489,
                0.25421435, 0.33788901, 0.68934842, 0.36424685, 0.71737898,
                0.38983449, 0.94393779, 0.39575588, 0.36616553, 0.87104665,
                0.64630203, 0.22516905, 0.88270804, 0.15031338, 0.75144345,
                0.46459025, 0.85396454, 0.86355643, 0.65139851, 0.70266061,
                0.30241389, 0.81056497, 0.88865969, 0.38773807, 0.70635849,
                0.90718459, 0.43245789, 0.28000654, 0.45935562, 0.08773519,
                0.9552151 , 0.93901511, 0.22489288], # uniform
                [1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,
                 0., 0., 0., 0., 0., 0., 0., 0.],
                # fmt: on
            ]
  
            _a          =				model.generate(
                input_ids=lowercase_				,  spout=spouts[0]				,  max_new_tokens=20				,  generation_config=lowercase_				,  )
  
            _a          =				model.generate(
                input_ids=lowercase_				,  spout=spouts[1]				,  max_new_tokens=20				,  generation_config=lowercase_				,  )
  
            _a          =				model.generate(
                input_ids=lowercase_				,  spout=lowercase_				,  max_new_tokens=20				,  generation_config=lowercase_				,  )
  
            _a          =				tokenizer.decode(outputa[0]       )
            _a          =				tokenizer.decode(outputa[0]       )
            _a          =				tokenizer.batch_decode(lowercase_       )
  
            _a          =				[
                '''武田信玄は、武田氏の滅亡後、武田氏の居城であった甲斐武田氏の居城である''',
                '''武田信玄は、武田家の滅亡を防ぐため、武田家の家臣である武田信虎を討''',
            ]
            self.assertListEqual(lowercase_				,  lowercase_       )
            self.assertListEqual(lowercase_				,  [outa_sentence, outa_sentence]       )
  
  
  
  
     @slow
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]       ):
            _a          =				GPTSanJapaneseForConditionalGeneration.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				GPTSanJapaneseTokenizer.from_pretrained("Tanrei/GPTSAN-japanese"       )
            model.to(lowercase_       )
  
            # set deterministically
            _a          =				GenerationConfig.from_pretrained("Tanrei/GPTSAN-japanese"       )
            _a          =				1
  
            _a          =				'''武田信玄'''
            _a          =				'''織田信長'''
            _a          =				'''は、'''
            _a          =				'''が、'''
            _a          =				tokenizer(lowercase_				,  prefix_text=lowercase_				,  return_tensors="pt"       )
            _a          =				tokenizer(lowercase_				,  prefix_text=lowercase_				,  return_tensors="pt"       )
            _a          =				tokenizer([[prefix_text_a, input_text_a], [prefix_text_a, input_text_a]]				,  return_tensors="pt"       )
  
            _a          =				model.generate(
                input_ids=input_tok_a.input_ids.to(lowercase_       )				,  token_type_ids=input_tok_a.token_type_ids.to(lowercase_       )				,  max_new_tokens=20				,  generation_config=lowercase_				,  )
  
            _a          =				model.generate(
                input_ids=input_tok_a.input_ids.to(lowercase_       )				,  token_type_ids=input_tok_a.token_type_ids.to(lowercase_       )				,  max_new_tokens=20				,  generation_config=lowercase_				,  )
  
            _a          =				model.generate(
                input_ids=input_tok_a.input_ids.to(lowercase_       )				,  token_type_ids=input_tok_a.token_type_ids.to(lowercase_       )				,  attention_mask=input_tok_a.attention_mask.to(lowercase_       )				,  max_new_tokens=20				,  generation_config=lowercase_				,  )
  
            _a          =				tokenizer.decode(outputa[0]       )
            _a          =				tokenizer.decode(outputa[0]       )
            _a          =				tokenizer.batch_decode(lowercase_       )
  
            _a          =				[
                '''武田信玄は、武田氏の祖である武田信虎を、その子・武田信友を擁して''',
                '''織田信長が、織田信長の妻・お市の方を妻として迎えたという逸話が残''',
            ]
            self.assertListEqual(lowercase_				,  lowercase_       )
            self.assertListEqual(lowercase_				,  [outa_sentence, outa_sentence]       )
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from __future__ import annotations
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         assert len(a					) == len(a					)
  
         _a          =				len(a					)
         _a          =				list(range(a					)					)
         _a          =				[-1] * n  # who the donor has donated to
         _a          =				[-1] * n  # who the recipient has received from
         _a          =				[0] * n
  
         while unmatched_donors:
                _a          =				unmatched_donors[0]
                _a          =				donor_pref[donor]
                _a          =				donor_preference[num_donations[donor]]
                num_donations[donor] += 1
                _a          =				recipient_pref[recipient]
                _a          =				rec_record[recipient]
  
                if prev_donor != -1:
                       if rec_preference.index(a					) > rec_preference.index(a					):
                              _a          =				donor
                              _a          =				recipient
                              unmatched_donors.append(a					)
                              unmatched_donors.remove(a					)
                else:
                       _a          =				donor
                       _a          =				recipient
                       unmatched_donors.remove(a					)
         return donor_record
  