


"""simple docstring"""





import argparse
import logging
import os
import sys
import tempfile
from pathlib import Path

import lightning_base
import pytest
import pytorch_lightning as pl
import torch
from convert_pl_checkpoint_to_hf import convert_pl_to_hf
from distillation import distill_main
from finetune import SummarizationModule, main
from huggingface_hub import list_models
from parameterized import parameterized
from run_eval import generate_summaries_or_translations
from torch import nn

from transformers import AutoConfig, AutoModelForSeqaSeqLM
from transformers.testing_utils import CaptureStderr, CaptureStdout, TestCasePlus, require_torch_gpu, slow
from utils import label_smoothed_nll_loss, lmap, load_json


logging.basicConfig(level=logging.DEBUG)

snake_case__		:  Optional[int]     					=					logging.getLogger()
snake_case__		:  Any     					=					torch.cuda.is_available()
snake_case__		:  Any     					=					{
    '''max_tokens_per_batch''': None,
    '''supervise_forward''': True,
    '''normalize_hidden''': True,
    '''label_smoothing''': 0.2,
    '''eval_max_gen_length''': None,
    '''eval_beams''': 1,
    '''val_metric''': '''loss''',
    '''save_top_k''': 1,
    '''adafactor''': True,
    '''early_stopping_patience''': 2,
    '''logger_name''': '''default''',
    '''length_penalty''': 0.5,
    '''cache_dir''': '''''',
    '''task''': '''summarization''',
    '''num_workers''': 2,
    '''alpha_hid''': 0,
    '''freeze_embeds''': True,
    '''enc_only''': False,
    '''tgt_suffix''': '''''',
    '''resume_from_checkpoint''': None,
    '''sortish_sampler''': True,
    '''student_decoder_layers''': 1,
    '''val_check_interval''': 1.0,
    '''output_dir''': '''''',
    '''fp16''': False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    '''no_teacher''': False,
    '''fp16_opt_level''': '''O1''',
    '''gpus''': 1 if CUDA_AVAILABLE else 0,
    '''n_tpu_cores''': 0,
    '''max_grad_norm''': 1.0,
    '''do_train''': True,
    '''do_predict''': True,
    '''accumulate_grad_batches''': 1,
    '''server_ip''': '''''',
    '''server_port''': '''''',
    '''seed''': 42,
    '''model_name_or_path''': '''sshleifer/bart-tiny-random''',
    '''config_name''': '''''',
    '''tokenizer_name''': '''facebook/bart-large''',
    '''do_lower_case''': False,
    '''learning_rate''': 0.3,
    '''lr_scheduler''': '''linear''',
    '''weight_decay''': 0.0,
    '''adam_epsilon''': 1e-08,
    '''warmup_steps''': 0,
    '''max_epochs''': 1,
    '''train_batch_size''': 2,
    '''eval_batch_size''': 2,
    '''max_source_length''': 12,
    '''max_target_length''': 12,
    '''val_max_target_length''': 12,
    '''test_max_target_length''': 12,
    '''fast_dev_run''': False,
    '''no_cache''': False,
    '''n_train''': -1,
    '''n_val''': -1,
    '''n_test''': -1,
    '''student_encoder_layers''': 1,
    '''freeze_encoder''': False,
    '''auto_scale_batch_size''': False,
    '''overwrite_output_dir''': False,
    '''student''': None,
}


def   _snake_case    (    _snake_case					:      Path			, _snake_case					:      list      ):
      lowerCAmelCase       :						Dict							= '''\n'''.join(_snake_case      )
      Path(_snake_case      ).open('''w'''      ).writelines(_snake_case      )


snake_case__		:  Dict     					=					[''' Sam ate lunch today.''', '''Sams lunch ingredients.''']
snake_case__		:  List[str]     					=					['''A very interesting story about what I ate for lunch.''', '''Avocado, celery, turkey, coffee''']
snake_case__		:  List[Any]     					=					'''patrickvonplaten/t5-tiny-random'''
snake_case__		:  Optional[Any]     					=					'''sshleifer/t5-tinier-random'''
snake_case__		:  Union[str, Any]     					=					'''sshleifer/bart-tiny-random'''
snake_case__		:  List[Any]     					=					'''sshleifer/tiny-mbart'''
snake_case__		:  Optional[int]     					=					'''sshleifer/tiny-marian-en-de'''
snake_case__		:  Tuple     					=					'''stas/tiny-wmt19-en-de'''


snake_case__		:  Optional[Any]     					=					logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks


def   _snake_case    (    _snake_case					:      str      ):
      for split in ["train", "val", "test"]:
            _dump_articles(os.path.join(_snake_case			, f'''{split}.source'''      )			, _snake_case      )
            _dump_articles(os.path.join(_snake_case			, f'''{split}.target'''      )			, _snake_case      )
      return tmp_dir



class 		snake_case_(							a__      ):






     @classmethod
     def   lowerCamelCase__  (						cls	:       Optional[int]					):
           logging.disable(logging.CRITICAL					)  # remove noisy download output from tracebacks
           return cls






     @slow
     @require_torch_gpu
     def   lowerCamelCase__  (						self	:       Optional[int]					):
           lowerCAmelCase       :						Optional[int]							= list_models()
           lowerCAmelCase       :						Tuple							= '''sshleifer'''
           lowerCAmelCase       :						Union[str, Any]							= [x.modelId for x in model_list if x.modelId.startswith(UpperCamelCase_					)]
           lowerCAmelCase       :						List[Any]							= ['''sshleifer/blenderbot-3B''', '''sshleifer/blenderbot-90M''']
           lowerCAmelCase       :						int							= []
           for m in model_ids:
                 if m in allowed_to_be_broken:
                       continue
                 try:
                       AutoConfig.from_pretrained(UpperCamelCase_					)
                 except Exception:
                       failures.append(UpperCamelCase_					)
           assert not failures, F'''The following models could not be loaded through AutoConfig: {failures}'''






     def   lowerCamelCase__  (						self	:       List[Any]					):
           lowerCAmelCase       :						Dict							= {'''student_encoder_layers''': 2, '''student_decoder_layers''': 1, '''no_teacher''': True}
           self._test_distiller_cli(UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						Union[str, Any]							= {
               '''student_encoder_layers''': 2,
               '''student_decoder_layers''': 1,
               '''max_epochs''': 4,
               '''val_check_interval''': 0.25,
               '''alpha_hid''': 2.0,
               '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
           }
           lowerCAmelCase       :						Any							= self._test_distiller_cli(UpperCamelCase_       ,					check_contents=UpperCamelCase_					)

           lowerCAmelCase       :						Any							= list(Path(model.output_dir					).glob('''*.ckpt'''					)					)
           self.assertEqual(1       ,					len(UpperCamelCase_					)					)
           lowerCAmelCase       :						Tuple							= list(Path(model.output_dir					).glob('''**/*.bin'''					)					)
           self.assertEqual(len(UpperCamelCase_					)       ,					2					)
           lowerCAmelCase       :						Dict							= lmap(str.strip       ,					Path(model.hparams.data_dir					).joinpath('''test.source'''					).open().readlines()					)
           lowerCAmelCase       :						Union[str, Any]							= tempfile.mktemp()  # XXX: not being cleaned up
           generate_summaries_or_translations(UpperCamelCase_       ,					UpperCamelCase_       ,					str(model.output_dir / '''best_tfmr'''					)					)
           self.assertTrue(Path(UpperCamelCase_					).exists()					)

           lowerCAmelCase       :						int							= self.get_auto_remove_tmp_dir()
           convert_pl_to_hf(ckpts[0]       ,					transformer_ckpts[0].parent       ,					UpperCamelCase_					)
           assert os.path.exists(os.path.join(UpperCamelCase_       ,					'''pytorch_model.bin'''					)					)






     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						str							= AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase_					)
           lowerCAmelCase,   lowerCAmelCase       :						Optional[Any]							= model.dummy_inputs['''input_ids'''], model.dummy_inputs['''attention_mask''']
           lowerCAmelCase       :						Union[str, Any]							= torch.tensor([[0, 4, 8, 2], [0, 8, 2, 1]]       ,					dtype=torch.long       ,					device=model.device					)
           lowerCAmelCase       :						str							= target_ids[:, :-1].contiguous()  # Why this line?
           lowerCAmelCase       :						Optional[Any]							= target_ids[:, 1:].clone()  # why clone?
           lowerCAmelCase       :						Union[str, Any]							= model(
               UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					decoder_input_ids=UpperCamelCase_       ,					labels=UpperCamelCase_       ,					use_cache=UpperCamelCase_					).loss

           lowerCAmelCase       :						Optional[Any]							= model(UpperCamelCase_       ,					attention_mask=UpperCamelCase_       ,					decoder_input_ids=UpperCamelCase_       ,					use_cache=UpperCamelCase_					).logits

           lowerCAmelCase       :						Dict							= nn.functional.log_softmax(UpperCamelCase_       ,					dim=-1					)
           lowerCAmelCase,   lowerCAmelCase       :						List[Any]							= label_smoothed_nll_loss(
               UpperCamelCase_       ,					UpperCamelCase_       ,					0.1       ,					ignore_index=model.config.pad_token_id					)
           with self.assertRaises(UpperCamelCase_					):
                 # TODO: understand why this breaks
                 self.assertEqual(UpperCamelCase_       ,					UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       List[str]					):
           lowerCAmelCase       :						List[str]							= {
               '''student_encoder_layers''': 2,
               '''student_decoder_layers''': 1,
               '''num_train_epochs''': 4,
               '''val_check_interval''': 0.25,
               '''alpha_hid''': 2.0,
               '''task''': '''translation''',
               '''model_name_or_path''': '''IGNORE_THIS_IT_DOESNT_GET_USED''',
               '''tokenizer_name''': MBART_TINY,
               '''teacher''': MBART_TINY,
               '''src_lang''': '''en_XX''',
               '''tgt_lang''': '''ro_RO''',
           }
           lowerCAmelCase       :						List[Any]							= self._test_distiller_cli(UpperCamelCase_       ,					check_contents=UpperCamelCase_					)
           assert model.model.config.model_type == "mbart"

           lowerCAmelCase       :						Union[str, Any]							= list(Path(model.output_dir					).glob('''*.ckpt'''					)					)
           self.assertEqual(1       ,					len(UpperCamelCase_					)					)
           lowerCAmelCase       :						Tuple							= list(Path(model.output_dir					).glob('''**/*.bin'''					)					)
           lowerCAmelCase       :						List[Any]							= list(Path(model.output_dir					).glob('''best_tfmr/*'''					)					)
           assert len(UpperCamelCase_					) > 2
           self.assertEqual(len(UpperCamelCase_					)       ,					2					)






     def   lowerCamelCase__  (						self	:       Dict					):
           lowerCAmelCase       :						Optional[Any]							= {
               '''student_encoder_layers''': 1,
               '''student_decoder_layers''': 1,
               '''alpha_hid''': 2.0,
               '''teacher''': T5_TINY,
               '''model_name_or_path''': T5_TINY,
               '''tokenizer_name''': T5_TINY,
           }
           self._test_distiller_cli(UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       int					):
           lowerCAmelCase       :						int							= {
               '''teacher''': T5_TINY,
               '''student''': T5_TINIER,
               '''model_name_or_path''': T5_TINIER,
               '''tokenizer_name''': T5_TINIER,
           }
           self._test_distiller_cli(UpperCamelCase_					)






     def   lowerCamelCase__  (						self	:       Tuple       ,					UpperCamelCase_	:       Tuple       ,					UpperCamelCase_	:       Any=True					):
           lowerCAmelCase       :						List[str]							= {
               '''label_smoothing''': 0.0,
               '''early_stopping_patience''': -1,
               '''train_batch_size''': 1,
               '''eval_batch_size''': 2,
               '''max_epochs''': 2,
               '''alpha_mlm''': 0.2,
               '''alpha_ce''': 0.8,
               '''do_predict''': True,
               '''model_name_or_path''': '''sshleifer/tinier_bart''',
               '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
               '''val_check_interval''': 0.5,
           }
           default_updates.update(UpperCamelCase_					)
           lowerCAmelCase       :						dict							= CHEAP_ARGS.copy()
           lowerCAmelCase       :						Optional[Any]							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
           lowerCAmelCase       :						Tuple							= self.get_auto_remove_tmp_dir()

           args_d.update(data_dir=UpperCamelCase_       ,					output_dir=UpperCamelCase_       ,					**UpperCamelCase_					)
           lowerCAmelCase       :						List[Any]							= distill_main(argparse.Namespace(**UpperCamelCase_					)					)
           if not check_contents:
                 return model
           lowerCAmelCase       :						Any							= os.listdir(UpperCamelCase_					)
           lowerCAmelCase       :						Dict							= {os.path.basename(UpperCamelCase_					) for p in contents}
           lowerCAmelCase       :						List[str]							= [p for p in contents if p.endswith('''ckpt'''					)]
           assert len(UpperCamelCase_					) > 0

           self.assertIn('''test_generations.txt'''       ,					UpperCamelCase_					)
           self.assertIn('''test_results.txt'''       ,					UpperCamelCase_					)

           lowerCAmelCase       :						List[str]							= load_json(model.metrics_save_path					)
           lowerCAmelCase       :						Optional[int]							= metrics['''val'''][-1]
           self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']       ,					0.01					)
           self.assertGreaterEqual(1.0       ,					last_step_stats['''val_avg_gen_time''']					)
           self.assertIsInstance(last_step_stats[F'''val_avg_{model.val_metric}''']       ,					UpperCamelCase_					)
           lowerCAmelCase       :						Any							= int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) + 1					)
           self.assertEqual(len(metrics['''val''']					)       ,					UpperCamelCase_					)
           self.assertEqual(len(metrics['''test''']					)       ,					1					)
           return model



class 		snake_case_(							a__      ):






     @parameterized.expand(
         [T5_TINY, BART_TINY, MBART_TINY, MARIAN_TINY, FSMT_TINY]       ,					)
     def   lowerCamelCase__  (						self	:       Tuple       ,					UpperCamelCase_	:       str					):
           lowerCAmelCase       :						dict							= CHEAP_ARGS.copy()
           lowerCAmelCase       :						Union[str, Any]							= '''translation''' if model in [MBART_TINY, MARIAN_TINY, FSMT_TINY] else '''summarization'''
           lowerCAmelCase       :						Tuple							= 0.1 if task == '''translation''' else 0

           lowerCAmelCase       :						Dict							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
           lowerCAmelCase       :						Optional[Any]							= self.get_auto_remove_tmp_dir()
           args_d.update(
               data_dir=UpperCamelCase_       ,					model_name_or_path=UpperCamelCase_       ,					tokenizer_name=UpperCamelCase_       ,					train_batch_size=2       ,					eval_batch_size=2       ,					output_dir=UpperCamelCase_       ,					do_predict=UpperCamelCase_       ,					task=UpperCamelCase_       ,					src_lang='''en_XX'''       ,					tgt_lang='''ro_RO'''       ,					freeze_encoder=UpperCamelCase_       ,					freeze_embeds=UpperCamelCase_       ,					)
           assert "n_train" in args_d
           lowerCAmelCase       :						Any							= argparse.Namespace(**UpperCamelCase_					)
           lowerCAmelCase       :						Any							= main(UpperCamelCase_					)

           lowerCAmelCase       :						Optional[Any]							= module.model.get_input_embeddings()
           assert not input_embeds.weight.requires_grad
           if model == T5_TINY:
                 lowerCAmelCase       :						Union[str, Any]							= module.model.lm_head
                 assert not lm_head.weight.requires_grad
                 assert (lm_head.weight == input_embeds.weight).all().item()
           elif model == FSMT_TINY:
                 lowerCAmelCase       :						Dict							= module.model.model
                 lowerCAmelCase       :						Optional[Any]							= fsmt.decoder.embed_positions
                 assert not embed_pos.weight.requires_grad
                 assert not fsmt.decoder.embed_tokens.weight.requires_grad
                 # check that embeds are not the same
                 assert fsmt.decoder.embed_tokens != fsmt.encoder.embed_tokens
           else:
                 lowerCAmelCase       :						Optional[int]							= module.model.model
                 lowerCAmelCase       :						List[str]							= bart.decoder.embed_positions
                 assert not embed_pos.weight.requires_grad
                 assert not bart.shared.weight.requires_grad
                 # check that embeds are the same
                 assert bart.decoder.embed_tokens == bart.encoder.embed_tokens
                 assert bart.decoder.embed_tokens == bart.shared

           lowerCAmelCase       :						str							= load_json(module.output_dir / '''text_batch.json'''					)
           assert isinstance(UpperCamelCase_       ,					UpperCamelCase_					)
           assert len(UpperCamelCase_					) >= 4






     def   lowerCamelCase__  (						self	:       Tuple					):
           lowerCAmelCase       :						dict							= CHEAP_ARGS.copy()

           lowerCAmelCase       :						List[str]							= '''summarization'''
           lowerCAmelCase       :						List[Any]							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

           args_d.update(
               data_dir=UpperCamelCase_       ,					tokenizer_name=UpperCamelCase_       ,					train_batch_size=2       ,					eval_batch_size=2       ,					do_predict=UpperCamelCase_       ,					task=UpperCamelCase_       ,					src_lang='''en_XX'''       ,					tgt_lang='''ro_RO'''       ,					freeze_encoder=UpperCamelCase_       ,					freeze_embeds=UpperCamelCase_       ,					)

           # test models whose config includes the extra_model_args
           lowerCAmelCase       :						Union[str, Any]							= BART_TINY
           lowerCAmelCase       :						List[Any]							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase       :						Any							= args_d.copy()
           args_da.update(
               model_name_or_path=UpperCamelCase_       ,					output_dir=UpperCamelCase_       ,					)
           lowerCAmelCase       :						Optional[Any]							= ('''encoder_layerdrop''', '''decoder_layerdrop''', '''dropout''', '''attention_dropout''')
           for p in extra_model_params:
                 lowerCAmelCase       :						Union[str, Any]							= 0.5
           lowerCAmelCase       :						Optional[int]							= argparse.Namespace(**UpperCamelCase_					)
           lowerCAmelCase       :						Optional[Any]							= main(UpperCamelCase_					)
           for p in extra_model_params:
                 assert getattr(model.config       ,					UpperCamelCase_					) == 0.5, F'''failed to override the model config for param {p}'''

           # test models whose config doesn't include the extra_model_args
           lowerCAmelCase       :						Optional[Any]							= T5_TINY
           lowerCAmelCase       :						Optional[Any]							= self.get_auto_remove_tmp_dir()
           lowerCAmelCase       :						Union[str, Any]							= args_d.copy()
           args_da.update(
               model_name_or_path=UpperCamelCase_       ,					output_dir=UpperCamelCase_       ,					)
           lowerCAmelCase       :						List[str]							= '''encoder_layerdrop'''
           lowerCAmelCase       :						Tuple							= 0.5
           lowerCAmelCase       :						Any							= argparse.Namespace(**UpperCamelCase_					)
           with pytest.raises(UpperCamelCase_					) as excinfo:
                 lowerCAmelCase       :						str							= main(UpperCamelCase_					)
           assert str(excinfo.value					) == F'''model config doesn\'t have a `{unsupported_param}` attribute'''






     def   lowerCamelCase__  (						self	:       Optional[Any]					):
           lowerCAmelCase       :						dict							= CHEAP_ARGS.copy()

           lowerCAmelCase       :						Any							= '''summarization'''
           lowerCAmelCase       :						Optional[Any]							= make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)

           lowerCAmelCase       :						Optional[Any]							= BART_TINY
           lowerCAmelCase       :						Any							= self.get_auto_remove_tmp_dir()

           args_d.update(
               data_dir=UpperCamelCase_       ,					model_name_or_path=UpperCamelCase_       ,					output_dir=UpperCamelCase_       ,					tokenizer_name=UpperCamelCase_       ,					train_batch_size=2       ,					eval_batch_size=2       ,					do_predict=UpperCamelCase_       ,					task=UpperCamelCase_       ,					src_lang='''en_XX'''       ,					tgt_lang='''ro_RO'''       ,					freeze_encoder=UpperCamelCase_       ,					freeze_embeds=UpperCamelCase_       ,					)

           # emulate finetune.py
           lowerCAmelCase       :						Union[str, Any]							= argparse.ArgumentParser()
           lowerCAmelCase       :						str							= pl.Trainer.add_argparse_args(UpperCamelCase_					)
           lowerCAmelCase       :						List[Any]							= SummarizationModule.add_model_specific_args(UpperCamelCase_       ,					os.getcwd()					)
           lowerCAmelCase       :						str							= {'''--help''': True}

           # --help test
           with pytest.raises(UpperCamelCase_					) as excinfo:
                 with CaptureStdout() as cs:
                       lowerCAmelCase       :						Tuple							= parser.parse_args(UpperCamelCase_					)
                 assert False, "--help is expected to sys.exit"
           assert excinfo.type == SystemExit
           lowerCAmelCase       :						Tuple							= lightning_base.arg_to_scheduler_metavar
           assert expected in cs.out, "--help is expected to list the supported schedulers"

           # --lr_scheduler=non_existing_scheduler test
           lowerCAmelCase       :						Any							= '''non_existing_scheduler'''
           lowerCAmelCase       :						Optional[Any]							= {F'''--lr_scheduler={unsupported_param}'''}
           with pytest.raises(UpperCamelCase_					) as excinfo:
                 with CaptureStderr() as cs:
                       lowerCAmelCase       :						str							= parser.parse_args(UpperCamelCase_					)
                 assert False, "invalid argument is expected to sys.exit"
           assert excinfo.type == SystemExit
           lowerCAmelCase       :						List[str]							= F'''invalid choice: \'{unsupported_param}\''''
           assert expected in cs.err, F'''should have bailed on invalid choice of scheduler {unsupported_param}'''

           # --lr_scheduler=existing_scheduler test
           lowerCAmelCase       :						List[str]							= '''cosine'''
           lowerCAmelCase       :						str							= args_d.copy()
           lowerCAmelCase       :						List[Any]							= supported_param
           lowerCAmelCase       :						List[str]							= argparse.Namespace(**UpperCamelCase_					)
           lowerCAmelCase       :						Tuple							= main(UpperCamelCase_					)
           assert (
               getattr(model.hparams       ,					'''lr_scheduler'''					) == supported_param
           ), F'''lr_scheduler={supported_param} shouldn\'t fail'''






"""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()


