code: 
  
  
  '''simple docstring'''
  
  
  
  
  import argparse
  import time
  
  import torch
  import transformers
  from measures_util import end_measure, log_measures, start_measure
  from transformers import AutoConfig, AutoModelForCausalLM, AutoModelForSeqaSeqLM, AutoTokenizer
  
  from accelerate.utils import compute_module_sizes
  
  
  __lowercase  :      str      		=				{
      """gpt-j-6b""": {"""is_causal""": True, """model""": """sgugger/sharded-gpt-j-6B""", """tokenizer""": """EleutherAI/gpt-j-6B"""},
      """gpt-neox""": {"""is_causal""": True, """model""": """EleutherAI/gpt-neox-20b"""},
      """opt""": {"""is_causal""": True, """model""": """facebook/opt-30b"""},
      """T0pp""": {"""is_causal""": False, """model""": """bigscience/T0pp""", """model_revision""": """sharded"""},
  }
  
  __lowercase  :      Any      		=				[
      """Hello, my name is""",
      """Are unicorns real? Unicorns are""",
      """For the first time in several years,""",
      """My name is Julien and I am""",
      """The goal of life is""",
      """Whenever I'm sad, I like to""",
  ]
  
  
  
  
  
  def 							a_       (						)			->int:
  
  
  
  
  
  
      '''simple docstring'''
  
      __lowerCamelCase		:      Optional[int]											=					argparse.ArgumentParser(description="Run and time generations on a big model using Accelerate."    )
      parser.add_argument("model_name"							,						type=lowercase							,						default=lowercase							,						help="The name of the model to try."    )
      parser.add_argument(
          "--tokenizer_name"							,						type=lowercase							,						default=lowercase							,						help="The name of the tokenizer (if different from the model."    )
      parser.add_argument("--is_causal"							,						type=lowercase							,						default=lowercase							,						help="Whether or not the model is causal."    )
      parser.add_argument(
          "--model_revision"							,						type=lowercase							,						default=lowercase							,						help="The revision to use for the model checkpoint."    )
      parser.add_argument("--torch_dtype"							,						type=lowercase							,						default=lowercase							,						help="The dtype for the model."    )
      parser.add_argument("--disk_offload"							,						action="store_true"    )
  
      __lowerCamelCase		:      Any											=					parser.parse_args()
  
      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
          __lowerCamelCase		:      int											=					DEFAULT_MODELS[args.model_name]
          __lowerCamelCase		:      Optional[int]											=					defaults["model"]
          if args.tokenizer_name is None:
              __lowerCamelCase		:      Union[str, Any]											=					defaults.get("tokenizer"							,						args.model_name    )
          if args.is_causal is None:
              __lowerCamelCase		:      Tuple											=					defaults["is_causal"]
          if args.model_revision is None:
              __lowerCamelCase		:      Dict											=					defaults.get("model_revision"							,						"main"    )
  
      if args.is_causal is None:
          raise ValueError("Could not infer the default for `--is_causal`, pass either True or False for it."    )
      if args.tokenizer_name is None:
          __lowerCamelCase		:      str											=					args.model_name
      if args.model_revision is None:
          __lowerCamelCase		:      Any											=					"main"
  
      return args
  
  
  
  
  
  def 							a_       (						)			->Union[str, Any]:
  
  
  
  
  
  
      '''simple docstring'''
  
      transformers.utils.logging.set_verbosity_error()
      __lowerCamelCase		:      str											=					parse_args()
  
      if args.torch_dtype is None:
          __lowerCamelCase		:      List[str]											=					AutoConfig.from_pretrained(args.model_name    )
          __lowerCamelCase		:      Optional[Any]											=					getattr(lowercase							,						"torch_dtype"							,						torch.floataa    )
      else:
          __lowerCamelCase		:      Tuple											=					getattr(lowercase							,						args.torch_dtype    )
      __lowerCamelCase		:      List[str]											=					AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      __lowerCamelCase		:      int											=					{
          "torch_dtype": torch_dtype,
          "revision": args.model_revision,
      }
      if args.disk_offload:
          __lowerCamelCase		:      int											=					"tmp_offload"
          __lowerCamelCase		:      Dict											=					True
  
      __lowerCamelCase		:      Tuple											=					start_measure()
      __lowerCamelCase		:      Any											=					model_cls.from_pretrained(args.model_name							,						device_map="auto"							,						**lowercase    )
      __lowerCamelCase		:      Optional[int]											=					end_measure(lowercase    )
      log_measures(lowercase							,						"Model loading"    )
  
      __lowerCamelCase		:      List[Any]											=					compute_module_sizes(lowercase    )
      __lowerCamelCase		:      Tuple											=					{v: 0 for v in model.hf_device_map.values()}
      for module, device in model.hf_device_map.items():
          device_size[device] += module_sizes[module]
      __lowerCamelCase		:      int											=					"\n".join([F'- {device}: {size // 2**20}MiB' for device, size in device_size.items()]    )
      print(F'\nTheoretical use:\n{message}'    )
  
      __lowerCamelCase		:      str											=					AutoTokenizer.from_pretrained(args.tokenizer_name    )
  
      __lowerCamelCase		:      int											=					start_measure()
      __lowerCamelCase		:      Union[str, Any]											=					[]
      __lowerCamelCase		:      Dict											=					[]
      __lowerCamelCase		:      Dict											=					[]
      for prompt in PROMPTS:
          __lowerCamelCase		:      Dict											=					tokenizer(lowercase							,						return_tensors="pt"    ).to(0    )
          __lowerCamelCase		:      List[str]											=					inputs["input_ids"][0].tolist()
          __lowerCamelCase		:      Tuple											=					time.time()
          __lowerCamelCase		:      List[str]											=					model.generate(inputs["input_ids"]    )
          __lowerCamelCase		:      Optional[Any]											=					time.time()
          __lowerCamelCase		:      Optional[Any]											=					outputs[0].tolist()
          __lowerCamelCase		:      str											=					len(lowercase    ) if outputs[: len(lowercase    )] != tokens else len(lowercase    ) - len(lowercase    )
          __lowerCamelCase		:      Any											=					after_generate - before_generate
  
          __lowerCamelCase		:      Union[str, Any]											=					tokenizer.decode(lowercase							,						skip_special_tokens=lowercase    )
          texts_outs.append(lowercase    )
          generation_times.append(lowercase    )
          gen_tokens.append(lowercase    )
          print(F'Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'    )
  
      __lowerCamelCase		:      Dict											=					end_measure(lowercase    )
      log_measures(lowercase							,						"Model generation"    )
  
      __lowerCamelCase		:      Any											=					[gen / tok for gen, tok in zip(lowercase							,						lowercase    )]
      __lowerCamelCase		:      Any											=					sum(lowercase    ) / len(lowercase    )
      print(F'Average time of generation per token: {avg_gen:.2f}s'    )
      print(F'First generation (avg time per token): {generation_times_per_token[0]:.2f}s'    )
      __lowerCamelCase		:      int											=					sum(generation_times_per_token[1:]    ) / (len(lowercase    ) - 1)
      print(F'Average time of generation per token (excluding the first): {avg_gen:.2f}s'    )
  
  
  if __name__ == "__main__":
      main()
  
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  
  import unittest
  
  from transformers import ErnieMTokenizer
  from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
  
  from ...test_tokenization_common import TokenizerTesterMixin
  
  
  __lowercase  :      Any      		=				get_tests_dir("""fixtures/spiece.model""")
  
  
  
  
  
  @require_sentencepiece
  @require_tokenizers
  class _snake_case   ( a__    ,   unittest.TestCase   ):
  							"""simple docstring"""
  
  
  
  
  
  							__a												=     ErnieMTokenizer
  							__a												=     False
  							__a												=     True
  							__a												=     False
  							__a												=     False
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Union[str, Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().setUp()
  
  											# We have a SentencePiece fixture for testing
  											__lowerCamelCase		:      Optional[int]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  											tokenizer.save_pretrained(self.tmpdirname	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Optional[int]											=					"this is a test"
  											__lowerCamelCase		:      List[str]											=					"this is a test"
  											return input_text, output_text
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Any											=					"<pad>"
  											__lowerCamelCase		:      Any											=					0
  
  											self.assertEqual(self.get_tokenizer()._convert_token_to_id(__A	)			,				__A	)
  											self.assertEqual(self.get_tokenizer()._convert_id_to_token(__A	)			,				__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					list(self.get_tokenizer().get_vocab().keys()	)
  
  											self.assertEqual(vocab_keys[0]			,				"<pad>"	)
  											self.assertEqual(vocab_keys[1]			,				"<unk>"	)
  											self.assertEqual(vocab_keys[-1]			,				"▁eloquent"	)
  											self.assertEqual(len(__A	)			,				30000	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Dict:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											self.assertEqual(self.get_tokenizer().vocab_size			,				30000	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if not self.test_rust_tokenizer:
  															return
  
  											__lowerCamelCase		:      Union[str, Any]											=					self.get_tokenizer()
  											__lowerCamelCase		:      Optional[Any]											=					self.get_rust_tokenizer()
  
  											__lowerCamelCase		:      Dict											=					"I was born in 92000, and this is falsé."
  
  											__lowerCamelCase		:      str											=					tokenizer.tokenize(__A	)
  											__lowerCamelCase		:      str											=					rust_tokenizer.tokenize(__A	)
  											self.assertListEqual(__A			,				__A	)
  
  											__lowerCamelCase		:      Tuple											=					tokenizer.encode(__A			,				add_special_tokens=__A	)
  											__lowerCamelCase		:      Tuple											=					rust_tokenizer.encode(__A			,				add_special_tokens=__A	)
  											self.assertListEqual(__A			,				__A	)
  
  											__lowerCamelCase		:      Optional[int]											=					self.get_rust_tokenizer()
  											__lowerCamelCase		:      Dict											=					tokenizer.encode(__A	)
  											__lowerCamelCase		:      List[str]											=					rust_tokenizer.encode(__A	)
  											self.assertListEqual(__A			,				__A	)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Optional[int]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					ErnieMTokenizer(__A			,				do_lower_case=__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  
  											__lowerCamelCase		:      Optional[Any]											=					tokenizer.tokenize("This is a test"	)
  											self.assertListEqual(__A			,				["▁this", "▁is", "▁a", "▁test"]	)
  
  											self.assertListEqual(tokenizer.convert_tokens_to_ids(__A	)			,				[48, 25, 21, 1289]	)
  
  											__lowerCamelCase		:      Any											=					tokenizer.tokenize("I was born in 92000, and this is falsé."	)
  											# ErnieMTokenizer(paddlenlp implementation) outputs '9' instead of '_9' so to mimic that '_9' is changed to '9'
  											self.assertListEqual(
  											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "é", "."]	)
  											__lowerCamelCase		:      Optional[int]											=					tokenizer.convert_tokens_to_ids(__A	)
  											self.assertListEqual(__A			,				[31, 23, 386, 19, 518, 3050, 15, 17, 48, 25, 8256, 18, 1, 9]	)
  
  											__lowerCamelCase		:      Dict											=					tokenizer.convert_ids_to_tokens(__A	)
  											self.assertListEqual(
  											    __A			,				["▁i", "▁was", "▁born", "▁in", "9", "2000", ",", "▁and", "▁this", "▁is", "▁fal", "s", "<unk>", "."]			,				)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->Tuple:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Union[str, Any]											=					ErnieMTokenizer(__A			,				unk_token="<unk>"			,				pad_token="<pad>"	)
  
  											__lowerCamelCase		:      int											=					tokenizer.encode("sequence builders"	)
  											__lowerCamelCase		:      List[Any]											=					tokenizer.encode("multi-sequence build"	)
  
  											__lowerCamelCase		:      Tuple											=					tokenizer.build_inputs_with_special_tokens(__A	)
  											__lowerCamelCase		:      Optional[int]											=					tokenizer.build_inputs_with_special_tokens(__A			,				__A	)
  
  											assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
  											assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + [
  											    tokenizer.sep_token_id
  											] + text_a + [tokenizer.sep_token_id]
  
  
  
  
  
  
  
  							@slow
  							def 		__SCREAMING_SNAKE_CASE(   self	)     ->int:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											__lowerCamelCase		:      Tuple											=					{"input_ids": [[0, 11062, 82772, 7, 15, 82772, 538, 51529, 237, 17198, 1290, 206, 9, 215175, 1314, 136, 17198, 1290, 206, 9, 56359, 42, 122009, 9, 16466, 16, 87344, 4537, 9, 4717, 78381, 6, 159958, 7, 15, 24480, 618, 4, 527, 22693, 9, 304, 4, 2777, 24480, 9874, 4, 43523, 594, 4, 803, 18392, 33189, 18, 4, 43523, 24447, 5, 5, 5, 16, 100, 24955, 83658, 9626, 144057, 15, 839, 22335, 16, 136, 24955, 83658, 83479, 15, 39102, 724, 16, 678, 645, 6460, 1328, 4589, 42, 122009, 115774, 23, 3559, 1328, 46876, 7, 136, 53894, 1940, 42227, 41159, 17721, 823, 425, 4, 27512, 98722, 206, 136, 5531, 4970, 919, 17336, 5, 2], [0, 20080, 618, 83, 82775, 47, 479, 9, 1517, 73, 53894, 333, 80581, 110117, 18811, 5256, 1295, 51, 152526, 297, 7986, 390, 124416, 538, 35431, 214, 98, 15044, 25737, 136, 7108, 43701, 23, 756, 135355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 63773, 119455, 6, 147797, 88203, 7, 645, 70, 21, 3285, 10269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}
  											# fmt: on
  
  											self.tokenizer_integration_test_util(
  											    expected_encoding=__A			,				model_name="susnato/ernie-m-base_pytorch"			,				sequences=[
  											        "Transformers (formerly known as pytorch-transformers and pytorch-pretrained-bert) provides "
  											        "general-purpose architectures (BERT, GPT-2, RoBERTa, XLM, DistilBert, XLNet...) for Natural "
  											        "Language Understanding (NLU) and Natural Language Generation (NLG) with over32+ pretrained "
  											        "models in100+ languages and deep interoperability between Jax, PyTorch and TensorFlow.",
  											        "BERT is designed to pre-train deep bidirectional representations from unlabeled text by jointly "
  											        "conditioning on both left and right context in all layers.",
  											        "The quick brown fox jumps over the lazy dog.",
  											    ]			,				)
  
  
  
  
  
  