

"""simple docstring"""



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():
       from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 					a						(       unittest.TestCase				):



    """simple docstring"""



    @slow
    def 				UpperCamelCase	(     self: int       ):






         """simple docstring"""


         A__										=  AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""    ,						return_dict=UpperCamelCase       ).to(UpperCamelCase       )
         A__										=  AutoTokenizer.from_pretrained("""google/mt5-small"""       )

         A__										=  tokenizer("""Hello there"""    ,						return_tensors="""pt"""       ).input_ids
         A__										=  tokenizer("""Hi I am"""    ,						return_tensors="""pt"""       ).input_ids

         A__										=  model(input_ids.to(UpperCamelCase       )    ,						labels=labels.to(UpperCamelCase       )       ).loss
         A__										=  -(labels.shape[-1] * loss.item())

         A__										=  -84.9_127
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1e-4       )









"""simple docstring"""



SCREAMING_SNAKE_CASE_					:	str							  =						'0.21.0'

from .accelerator import Accelerator
from .big_modeling import (
    cpu_offload,
    cpu_offload_with_hook,
    disk_offload,
    dispatch_model,
    init_empty_weights,
    init_on_device,
    load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
    DeepSpeedPlugin,
    DistributedDataParallelKwargs,
    DistributedType,
    FullyShardedDataParallelPlugin,
    GradScalerKwargs,
    InitProcessGroupKwargs,
    find_executable_batch_size,
    infer_auto_device_map,
    is_rich_available,
    load_checkpoint_in_model,
    synchronize_rng_states,
)


if is_rich_available():
 from .utils import rich






