






from .imports import is_rich_available


if is_rich_available():
		from rich.traceback import install

		install(show_locals=False)

else:
		raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')




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 							lowerCAmelCase__  (	unittest.TestCase						):







					@slow
					def  _lowercase			(       self      :     List[Any]):
											A__	:							Optional[int]						    = AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"			,		return_dict=_A).to(_A)
											A__	:							Any						    = AutoTokenizer.from_pretrained("google/mt5-small")

											A__	:							int						    = tokenizer("Hello there"			,		return_tensors="pt").input_ids
											A__	:							List[str]						    = tokenizer("Hi I am"			,		return_tensors="pt").input_ids

											A__	:							int						    = model(input_ids.to(_A)			,		labels=labels.to(_A)).loss
											A__	:							Optional[int]						    = -(labels.shape[-1] * loss.item())

											A__	:							List[str]						    = -84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)