code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import os
  import unittest
  
  from transformers import is_torch_available
  from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
  
  
  if is_torch_available():
  			import torch
  
  			from transformers import GPTaLMHeadModel
  
  
  
  
  
  
  @require_torch
  @require_sentencepiece
  @require_tokenizers
  class 							__snake_case    (				unittest.TestCase       ):
  
  
  
  
  
  	@slow
  	@unittest.skip('Model is not available.'		)
  	def      _snake_case      (		self		)			->		Tuple:
  		snake_case__  					=      'nvidia/megatron-gpt2-345m/'
  		if "MYDIR" in os.environ:
  			snake_case__  					=      os.path.join(os.environ['MYDIR']			,					SCREAMING_SNAKE_CASE_		)
  		snake_case__  					=      GPTaLMHeadModel.from_pretrained(SCREAMING_SNAKE_CASE_		)
  		model.to(SCREAMING_SNAKE_CASE_		)
  		model.half()
  
  		snake_case__  					=      torch.tensor(
  		    [[101, 7110, 1005, 1056, 2023, 1_1333, 1_7413, 1029, 102]]			,					device=SCREAMING_SNAKE_CASE_			,					dtype=torch.long			,					)
  
  		with torch.no_grad():
  			snake_case__  					=      model(SCREAMING_SNAKE_CASE_		).logits
  
  		snake_case__  					=      torch.Size((1, 9, 5_0257)		)
  		self.assertEqual(output.shape			,					SCREAMING_SNAKE_CASE_		)
  
  		snake_case__  					=      torch.tensor(
  		    [
  		        4.9_4_1_4,
  		        -0.2_9_2_0,
  		        -1.2_1_4_8,
  		        -4.0_2_7_3,
  		        -0.5_1_6_1,
  		        -5.2_1_0_9,
  		        -1.2_4_1_2,
  		        -1.8_3_0_1,
  		        -1.7_7_3_4,
  		        -4.7_1_4_8,
  		        -0.2_3_1_7,
  		        -1.0_8_1_1,
  		        -2.1_7_7_7,
  		        0.4_1_4_1,
  		        -3.7_9_6_9,
  		        -4.0_5_8_6,
  		        -2.5_3_3_2,
  		        -3.3_8_0_9,
  		        4.3_8_6_7,
  		    ]			,					device=SCREAMING_SNAKE_CASE_			,					dtype=torch.half			,					)
  
  		for i in range(19		):
  			snake_case__					,     snake_case__  					=      8 * i // 17, 2792 * i  # along the diagonal
  			snake_case__					,     snake_case__  					=      output[0, r, c], expected_diag[i]
  			snake_case__  					=      F'''row={r} col={c} computed={computed} expected={expected}'''
  			self.assertAlmostEqual(SCREAMING_SNAKE_CASE_			,					SCREAMING_SNAKE_CASE_			,					delta=1E-4			,					msg=SCREAMING_SNAKE_CASE_		)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, '''rt''') as f_p:
  			for line in f_p:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    len(tokenizer.tokenize(token))
  
  						# Token contains strange control characters like \x96 or \x95
  						# Just filter out the complete line
  						if current_subwords_len == 0:
  									continue
  
  						if (subword_len_counter + current_subwords_len) > max_len:
  									print('''''')
  									print(line)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  