

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device


if is_torch_available():
       import torch

       from transformers import XLMProphetNetForConditionalGeneration, XLMProphetNetTokenizer







@require_torch
class 		__magic_name__     (				unittest.TestCase				):

   '''simple docstring'''




   @slow
   def 					_UpperCAmelCase					(							self			)				->       List[str]:






     """simple docstring"""






     a__    =XLMProphetNetForConditionalGeneration.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased'''			)
     model.to(UpperCamelCase_			)

     # encoder-decoder outputs
     a__    =torch.tensor([[17, 96208, 103471, 2]]			).to(UpperCamelCase_			)
     a__    =torch.tensor(
         [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(UpperCamelCase_			)
     a__    =model(
         input_ids=UpperCamelCase_,       attention_mask=UpperCamelCase_,       encoder_outputs=UpperCamelCase_,       decoder_input_ids=UpperCamelCase_			)
     a__    =output[0]
     a__    =torch.Size((1, 14, 250012)			)
     self.assertEqual(output_predited_logis.shape,       UpperCamelCase_			)
     a__    =torch.tensor(
         [[[-6.3986, -8.2391, 12.5189], [-6.3289, -8.0864, 12.6211], [-6.2418, -8.0445, 12.7968]]]			).to(UpperCamelCase_			)
     self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3],       UpperCamelCase_,       atol=1E-4			)			)

     # encoder outputs
     a__    =model.prophetnet.encoder(UpperCamelCase_			)[0]
     a__    =torch.tensor(
         [[[-1.4260, -0.7628, 0.8453], [-1.4719, -0.1391, 0.7807], [-1.7678, 0.0114, 0.4646]]]			).to(UpperCamelCase_			)
     a__    =torch.Size((1, 4, 1024)			)
     self.assertEqual(encoder_outputs.shape,       UpperCamelCase_			)
     self.assertTrue(torch.allclose(encoder_outputs[:, :3, :3],       UpperCamelCase_,       atol=1E-4			)			)

     # decoder outputs
     a__    =model.prophetnet.decoder(
         UpperCamelCase_,       encoder_hidden_states=UpperCamelCase_,       )
     a__    =decoder_outputs[1].view(1,       model.config.ngram,       14,       -1			)
     a__    =model.lm_head(UpperCamelCase_			)
     a__    =predicting_streams_logits[:, 0]
     self.assertTrue(torch.allclose(next_first_stream_logits[:, :3, :3],       UpperCamelCase_,       atol=1E-4			)			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       Tuple:






     """simple docstring"""






     a__    =XLMProphetNetForConditionalGeneration.from_pretrained(
         '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''			)
     model.to(UpperCamelCase_			)

     a__    =torch.tensor([[17, 96208, 103471, 2]]			).to(UpperCamelCase_			)
     a__    =torch.tensor(
         [[2, 250, 9953, 34, 69489, 1620, 32, 118424, 624, 210, 105, 2913, 1032, 351]]			).to(UpperCamelCase_			)
     a__    =model(
         input_ids=UpperCamelCase_,       attention_mask=UpperCamelCase_,       encoder_outputs=UpperCamelCase_,       decoder_input_ids=UpperCamelCase_			)
     a__    =output[0]
     a__    =torch.Size((1, 14, 250012)			)
     self.assertEqual(output_predited_logis.shape,       UpperCamelCase_			)
     # compare the actual values for a slice.
     a__    =torch.tensor(
         [[[-9.2253, -9.7173, -6.3529], [-7.6701, -9.0145, -1.9382], [-8.0195, -7.0004, -0.1523]]]			).to(UpperCamelCase_			)

     self.assertTrue(torch.allclose(output_predited_logis[:, :3, :3],       UpperCamelCase_,       atol=1E-4			)			)



   @slow
   def 					_UpperCAmelCase					(							self			)				->       Optional[Any]:






     """simple docstring"""






     a__    =XLMProphetNetForConditionalGeneration.from_pretrained(
         '''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''			)
     model.to(UpperCamelCase_			)
     a__    =512

     a__    =XLMProphetNetTokenizer.from_pretrained('''microsoft/xprophetnet-large-wiki100-cased-xglue-ntg'''			)

     a__    =(
         '''Microsoft Corporation intends to officially end free support for the Windows 7 operating system after'''
         ''' January 14, 2020, according to the official portal of the organization. From that day, users of this'''
         ''' system will not be able to receive security updates, which could make their computers vulnerable to'''
         ''' cyber attacks.'''
     )
     a__    =(
         '''орпорация Microsoft намерена официально прекратить бесплатную поддержку операционной системы Windows 7'''
         ''' после 14 января 2020 года, сообщается на официальном портале организации . С указанного дня пользователи'''
         ''' этой системы не смогут получать обновления безопасности, из-за чего их компьютеры могут стать уязвимыми'''
         ''' к кибератакам.'''
     )
     a__    =(
         '''根据该组织的官方门户网站，微软公司打算在2020年1月14日之后正式终止对Windows 7操作系统的免费支持。从那时起，该系统的用户将无法接收安全更新，这可能会使他们的计算机容易受到网络攻击。'''
     )

     a__    =tokenizer(
         [EN_SENTENCE, RU_SENTENCE, ZH_SENTENCE],       padding=UpperCamelCase_,       max_length=255,       return_tensors='''pt'''			).input_ids
     a__    =input_ids.to(UpperCamelCase_			)

     a__    =model.generate(
         UpperCamelCase_,       num_beams=10,       length_penalty=1.0,       no_repeat_ngram_size=3,       early_stopping=UpperCamelCase_			)
     a__    =[tokenizer.decode(UpperCamelCase_,       skip_special_tokens=UpperCamelCase_			) for g in summary_ids]
     a__    ='''Microsoft to end Windows 7 free support after January 14, 2020'''
     a__    ='''Microsoft намерена прекратить бесплатную поддержку Windows 7 после 14 января 2020 года'''
     a__    ='''微软打算终止对Windows 7操作系统的免费支持'''
     self.assertListEqual(
         [EXPECTED_TITLE_EN, EXPECTED_TITLE_RU, EXPECTED_TITLE_ZH],       UpperCamelCase_,       )

     a__    =model.generate(
         UpperCamelCase_,       num_beams=1,       length_penalty=1.0,       no_repeat_ngram_size=3,       early_stopping=UpperCamelCase_			)
     a__    =[
         tokenizer.convert_ids_to_tokens(UpperCamelCase_,       skip_special_tokens=UpperCamelCase_			) for g in summary_ids_beama
     ]
     a__    ='''▁Microsoft ▁to ▁end ▁free ▁support ▁for ▁Windows ▁7'''.split(''' '''			)
     a__    ='''▁Microsoft ▁намерен а ▁прекрати ть ▁бес плат ную ▁поддержку ▁Windows ▁7 ▁после ▁14 ▁января ▁2020 ▁года'''.split(
         ''' '''			)
     a__    ='''微软 公司 打算 终止 对 Windows ▁7 操作 系统的 免费 支持'''.split(''' '''			)
     self.assertListEqual(
         [EXPECTED_TITLE_EN_BEAM1_TOK, EXPECTED_TITLE_RU_BEAM1_TOK, EXPECTED_TITLE_ZH_BEAM1_TOK],       UpperCamelCase_,       )




"""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


snake_case__		:  Optional[Any]     					=					{
    '''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'''},
}

snake_case__		:  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   _snake_case    (    ):
      lowerCAmelCase       :						int							= argparse.ArgumentParser(description='''Run and time generations on a big model using Accelerate.'''      )
      parser.add_argument('''model_name'''			, type=_snake_case			, default=_snake_case			, help='''The name of the model to try.'''      )
      parser.add_argument(
          '''--tokenizer_name'''			, type=_snake_case			, default=_snake_case			, help='''The name of the tokenizer (if different from the model.'''      )
      parser.add_argument('''--is_causal'''			, type=_snake_case			, default=_snake_case			, help='''Whether or not the model is causal.'''      )
      parser.add_argument(
          '''--model_revision'''			, type=_snake_case			, default=_snake_case			, help='''The revision to use for the model checkpoint.'''      )
      parser.add_argument('''--torch_dtype'''			, type=_snake_case			, default=_snake_case			, help='''The dtype for the model.'''      )
      parser.add_argument('''--disk_offload'''			, action='''store_true'''      )

      lowerCAmelCase       :						Dict							= parser.parse_args()

      # Sanitize args
      if args.model_name in DEFAULT_MODELS:
            lowerCAmelCase       :						Optional[int]							= DEFAULT_MODELS[args.model_name]
            lowerCAmelCase       :						Union[str, Any]							= 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       :						List[Any]							= defaults['''is_causal''']
            if args.model_revision is None:
                  lowerCAmelCase       :						Tuple							= 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       :						Union[str, Any]							= args.model_name
      if args.model_revision is None:
            lowerCAmelCase       :						List[Any]							= '''main'''

      return args


def   _snake_case    (    ):
      transformers.utils.logging.set_verbosity_error()
      lowerCAmelCase       :						Union[str, Any]							= parse_args()

      if args.torch_dtype is None:
            lowerCAmelCase       :						List[str]							= AutoConfig.from_pretrained(args.model_name      )
            lowerCAmelCase       :						Optional[Any]							= getattr(_snake_case			, '''torch_dtype'''			, torch.floataa      )
      else:
            lowerCAmelCase       :						Optional[int]							= getattr(_snake_case			, args.torch_dtype      )
      lowerCAmelCase       :						Tuple							= AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
      lowerCAmelCase       :						str							= {
          '''torch_dtype''': torch_dtype,
          '''revision''': args.model_revision,
      }
      if args.disk_offload:
            lowerCAmelCase       :						Optional[int]							= '''tmp_offload'''
            lowerCAmelCase       :						Optional[int]							= True

      lowerCAmelCase       :						str							= start_measure()
      lowerCAmelCase       :						List[str]							= model_cls.from_pretrained(args.model_name			, device_map='''auto'''			, **_snake_case      )
      lowerCAmelCase       :						Tuple							= end_measure(_snake_case      )
      log_measures(_snake_case			, '''Model loading'''      )

      lowerCAmelCase       :						str							= compute_module_sizes(_snake_case      )
      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       :						int							= AutoTokenizer.from_pretrained(args.tokenizer_name      )

      lowerCAmelCase       :						List[str]							= start_measure()
      lowerCAmelCase       :						Union[str, Any]							= []
      lowerCAmelCase       :						int							= []
      lowerCAmelCase       :						Dict							= []
      for prompt in PROMPTS:
            lowerCAmelCase       :						str							= tokenizer(_snake_case			, return_tensors='''pt'''      ).to(0      )
            lowerCAmelCase       :						int							= inputs['''input_ids'''][0].tolist()
            lowerCAmelCase       :						Optional[Any]							= time.time()
            lowerCAmelCase       :						Optional[int]							= model.generate(inputs['''input_ids''']      )
            lowerCAmelCase       :						Any							= time.time()
            lowerCAmelCase       :						str							= outputs[0].tolist()
            lowerCAmelCase       :						int							= len(_snake_case      ) if outputs[: len(_snake_case      )] != tokens else len(_snake_case      ) - len(_snake_case      )
            lowerCAmelCase       :						Tuple							= after_generate - before_generate

            lowerCAmelCase       :						Tuple							= tokenizer.decode(_snake_case			, skip_special_tokens=_snake_case      )
            texts_outs.append(_snake_case      )
            generation_times.append(_snake_case      )
            gen_tokens.append(_snake_case      )
            print(f'''Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n'''      )

      lowerCAmelCase       :						List[Any]							= end_measure(_snake_case      )
      log_measures(_snake_case			, '''Model generation'''      )

      lowerCAmelCase       :						str							= [gen / tok for gen, tok in zip(_snake_case			, _snake_case      )]
      lowerCAmelCase       :						Any							= sum(_snake_case      ) / len(_snake_case      )
      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       :						List[Any]							= sum(generation_times_per_token[1:]      ) / (len(_snake_case      ) - 1)
      print(f'''Average time of generation per token (excluding the first): {avg_gen:.2f}s'''      )


if __name__ == "__main__":
 main()


