
'''simple docstring'''



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from tokenizers import processors

from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
     from .tokenization_mbartaa import MBartaaTokenizer
else:
     UpperCamelCase__ :						List[Any]    							=					None


UpperCamelCase__ :						Tuple    							=					logging.get_logger(__name__)

UpperCamelCase__ :						Optional[Any]    							=					{"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

UpperCamelCase__ :						List[str]    							=					{
    "vocab_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-50-one-to-many-mmt": (
            "https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json"
        ),
    },
}

UpperCamelCase__ :						Dict    							=					{
    "facebook/mbart-large-50-one-to-many-mmt": 1_024,
}

# fmt: off
UpperCamelCase__ :						Any    							=					["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN", "af_ZA", "az_AZ", "bn_IN", "fa_IR", "he_IL", "hr_HR", "id_ID", "ka_GE", "km_KH", "mk_MK", "ml_IN", "mn_MN", "mr_IN", "pl_PL", "ps_AF", "pt_XX", "sv_SE", "sw_KE", "ta_IN", "te_IN", "th_TH", "tl_XX", "uk_UA", "ur_PK", "xh_ZA", "gl_ES", "sl_SI"]

class 				_a			(_lowerCamelCase):





       """simple docstring"""




       SCREAMING_SNAKE_CASE =					VOCAB_FILES_NAMES
       SCREAMING_SNAKE_CASE =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       SCREAMING_SNAKE_CASE =					PRETRAINED_VOCAB_FILES_MAP
       SCREAMING_SNAKE_CASE =					['input_ids', 'attention_mask']
       SCREAMING_SNAKE_CASE =					MBartaaTokenizer

       SCREAMING_SNAKE_CASE =					[]
       SCREAMING_SNAKE_CASE =					[]






       def __init__(							self    ,							A__=None    ,							A__=None    ,							A__=None    ,							A__=None    ,							A__="</s>"    ,							A__="</s>"    ,							A__="<s>"    ,							A__="<unk>"    ,							A__="<pad>"    ,							A__="<mask>"    ,							**A__    ,							)      ->     Tuple:
          # Mask token behave like a normal word, i.e. include the space before it
          _SCREAMING_SNAKE_CASE       	=			AddedToken(A__    ,							lstrip=A__    ,							rstrip=A__ ) if isinstance(A__    ,							A__ ) else mask_token

          _SCREAMING_SNAKE_CASE       	=			kwargs.get("""additional_special_tokens"""    ,							[] )
          kwargs["additional_special_tokens"] += [
              code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
          ]

          super().__init__(
              A__    ,							src_lang=A__    ,							tgt_lang=A__    ,							tokenizer_file=A__    ,							eos_token=A__    ,							sep_token=A__    ,							cls_token=A__    ,							unk_token=A__    ,							pad_token=A__    ,							mask_token=A__    ,							**A__    ,							)

          _SCREAMING_SNAKE_CASE       	=			vocab_file
          _SCREAMING_SNAKE_CASE       	=			False if not self.vocab_file else True

          _SCREAMING_SNAKE_CASE       	=			{
              lang_code: self.convert_tokens_to_ids(A__ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
          }

          _SCREAMING_SNAKE_CASE       	=			src_lang if src_lang is not None else """en_XX"""
          _SCREAMING_SNAKE_CASE       	=			tgt_lang
          _SCREAMING_SNAKE_CASE       	=			self.lang_code_to_id[self._src_lang]
          self.set_src_lang_special_tokens(self._src_lang )






       @property
       def     UpperCamelCase			(							self )      ->     str:
          return self._src_lang






       @src_lang.setter
       def     UpperCamelCase			(							self    ,							A__ )      ->     None:
          _SCREAMING_SNAKE_CASE       	=			new_src_lang
          self.set_src_lang_special_tokens(self._src_lang )






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = None )      ->     List[int]:
          if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
          # We don't expect to process pairs, but leave the pair logic for API consistency
          return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = "en_XX"    ,							A__ = None    ,							A__ = "ro_RO"    ,							**A__    ,							)      ->     BatchEncoding:
          _SCREAMING_SNAKE_CASE       	=			src_lang
          _SCREAMING_SNAKE_CASE       	=			tgt_lang
          return super().prepare_seqaseq_batch(A__    ,							A__    ,							**A__ )






       def     UpperCamelCase			(							self )      ->     Optional[Any]:
          return self.set_src_lang_special_tokens(self.src_lang )






       def     UpperCamelCase			(							self )      ->     Any:
          return self.set_tgt_lang_special_tokens(self.tgt_lang )






       def     UpperCamelCase			(							self    ,							A__ )      ->     None:
          _SCREAMING_SNAKE_CASE       	=			self.convert_tokens_to_ids(A__ )
          _SCREAMING_SNAKE_CASE       	=			[self.cur_lang_code_id]
          _SCREAMING_SNAKE_CASE       	=			[self.eos_token_id]

          _SCREAMING_SNAKE_CASE       	=			self.convert_ids_to_tokens(self.prefix_tokens )
          _SCREAMING_SNAKE_CASE       	=			self.convert_ids_to_tokens(self.suffix_tokens )

          _SCREAMING_SNAKE_CASE       	=			processors.TemplateProcessing(
              single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,							self.prefix_tokens + self.suffix_tokens ) )    ,							)






       def     UpperCamelCase			(							self    ,							A__ )      ->     None:
          _SCREAMING_SNAKE_CASE       	=			self.convert_tokens_to_ids(A__ )
          _SCREAMING_SNAKE_CASE       	=			[self.cur_lang_code_id]
          _SCREAMING_SNAKE_CASE       	=			[self.eos_token_id]

          _SCREAMING_SNAKE_CASE       	=			self.convert_ids_to_tokens(self.prefix_tokens )
          _SCREAMING_SNAKE_CASE       	=			self.convert_ids_to_tokens(self.suffix_tokens )

          _SCREAMING_SNAKE_CASE       	=			processors.TemplateProcessing(
              single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,							pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,							special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,							self.prefix_tokens + self.suffix_tokens ) )    ,							)






       def     UpperCamelCase			(							self    ,							A__    ,							A__    ,							A__    ,							A__    ,							**A__ )      ->     Union[str, Any]:
          if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
          _SCREAMING_SNAKE_CASE       	=			src_lang
          _SCREAMING_SNAKE_CASE       	=			self(A__    ,							add_special_tokens=A__    ,							return_tensors=A__    ,							**A__ )
          _SCREAMING_SNAKE_CASE       	=			self.convert_tokens_to_ids(A__ )
          _SCREAMING_SNAKE_CASE       	=			tgt_lang_id
          return inputs






       def     UpperCamelCase			(							self    ,							A__    ,							A__ = None )      ->     Tuple[str]:
          if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer.""" )

          if not os.path.isdir(A__ ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
          _SCREAMING_SNAKE_CASE       	=			os.path.join(
              A__    ,							(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )

          if os.path.abspath(self.vocab_file ) != os.path.abspath(A__ ):
             copyfile(self.vocab_file    ,							A__ )

          return (out_vocab_file,)








'''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


UpperCamelCase__ :						Tuple    							=					{
    "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"},
}

UpperCamelCase__ :						int    							=					[
    "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 				lowerCAmelCase_		(			)       ->       str:




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

   _SCREAMING_SNAKE_CASE       	=			parser.parse_args()

   # Sanitize args
   if args.model_name in DEFAULT_MODELS:
      _SCREAMING_SNAKE_CASE       	=			DEFAULT_MODELS[args.model_name]
      _SCREAMING_SNAKE_CASE       	=			defaults["""model"""]
      if args.tokenizer_name is None:
         _SCREAMING_SNAKE_CASE       	=			defaults.get("""tokenizer"""      , args.model_name  )
      if args.is_causal is None:
         _SCREAMING_SNAKE_CASE       	=			defaults["""is_causal"""]
      if args.model_revision is None:
         _SCREAMING_SNAKE_CASE       	=			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:
      _SCREAMING_SNAKE_CASE       	=			args.model_name
   if args.model_revision is None:
      _SCREAMING_SNAKE_CASE       	=			"""main"""

   return args
def 				lowerCAmelCase_		(			)       ->       Optional[int]:




   """simple docstring"""
   transformers.utils.logging.set_verbosity_error()
   _SCREAMING_SNAKE_CASE       	=			parse_args()

   if args.torch_dtype is None:
      _SCREAMING_SNAKE_CASE       	=			AutoConfig.from_pretrained(args.model_name  )
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , """torch_dtype"""      , torch.floataa  )
   else:
      _SCREAMING_SNAKE_CASE       	=			getattr(SCREAMING_SNAKE_CASE_      , args.torch_dtype  )
   _SCREAMING_SNAKE_CASE       	=			AutoModelForCausalLM if args.is_causal else AutoModelForSeqaSeqLM
   _SCREAMING_SNAKE_CASE       	=			{
       """torch_dtype""": torch_dtype,
       """revision""": args.model_revision,
   }
   if args.disk_offload:
      _SCREAMING_SNAKE_CASE       	=			"""tmp_offload"""
      _SCREAMING_SNAKE_CASE       	=			True

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			model_cls.from_pretrained(args.model_name      , device_map="""auto"""      , **SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model loading"""  )

   _SCREAMING_SNAKE_CASE       	=			compute_module_sizes(SCREAMING_SNAKE_CASE_  )
   _SCREAMING_SNAKE_CASE       	=			{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]
   _SCREAMING_SNAKE_CASE       	=			"""\n""".join([F"- {device}: {size // 2**20}MiB" for device, size in device_size.items()]  )
   print(F"\nTheoretical use:\n{message}"  )

   _SCREAMING_SNAKE_CASE       	=			AutoTokenizer.from_pretrained(args.tokenizer_name  )

   _SCREAMING_SNAKE_CASE       	=			start_measure()
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   _SCREAMING_SNAKE_CASE       	=			[]
   for prompt in PROMPTS:
      _SCREAMING_SNAKE_CASE       	=			tokenizer(SCREAMING_SNAKE_CASE_      , return_tensors="""pt"""  ).to(0  )
      _SCREAMING_SNAKE_CASE       	=			inputs["""input_ids"""][0].tolist()
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			model.generate(inputs["""input_ids"""]  )
      _SCREAMING_SNAKE_CASE       	=			time.time()
      _SCREAMING_SNAKE_CASE       	=			outputs[0].tolist()
      _SCREAMING_SNAKE_CASE       	=			len(SCREAMING_SNAKE_CASE_  ) if outputs[: len(SCREAMING_SNAKE_CASE_  )] != tokens else len(SCREAMING_SNAKE_CASE_  ) - len(SCREAMING_SNAKE_CASE_  )
      _SCREAMING_SNAKE_CASE       	=			after_generate - before_generate

      _SCREAMING_SNAKE_CASE       	=			tokenizer.decode(SCREAMING_SNAKE_CASE_      , skip_special_tokens=SCREAMING_SNAKE_CASE_  )
      texts_outs.append(SCREAMING_SNAKE_CASE_  )
      generation_times.append(SCREAMING_SNAKE_CASE_  )
      gen_tokens.append(SCREAMING_SNAKE_CASE_  )
      print(F"Prompt: {prompt}\nGeneration {text_out}\nIn {generation_time:.2f}s for {num_gen_tokens} tokens\n"  )

   _SCREAMING_SNAKE_CASE       	=			end_measure(SCREAMING_SNAKE_CASE_  )
   log_measures(SCREAMING_SNAKE_CASE_      , """Model generation"""  )

   _SCREAMING_SNAKE_CASE       	=			[gen / tok for gen, tok in zip(SCREAMING_SNAKE_CASE_      , SCREAMING_SNAKE_CASE_  )]
   _SCREAMING_SNAKE_CASE       	=			sum(SCREAMING_SNAKE_CASE_  ) / len(SCREAMING_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"  )
   _SCREAMING_SNAKE_CASE       	=			sum(generation_times_per_token[1:]  ) / (len(SCREAMING_SNAKE_CASE_  ) - 1)
   print(F"Average time of generation per token (excluding the first): {avg_gen:.2f}s"  )


if __name__ == "__main__":
     main()






