





"""simple docstring"""





lowerCAmelCase__     			=			'''
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
'''

lowerCAmelCase__     			=			[{'''type''': '''code''', '''content''': INSTALL_CONTENT}]
lowerCAmelCase__     			=			{
    '''{processor_class}''': '''FakeProcessorClass''',
    '''{model_class}''': '''FakeModelClass''',
    '''{object_class}''': '''FakeObjectClass''',
}













"""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        _lowerCamelCase      (       unittest.TestCase ):


						@slow
						def     snake_case_   (self		) ->   List[str]:
								UpperCamelCase   =	AutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"   ,							return_dict=__a		).to(__a		)
								UpperCamelCase   =	AutoTokenizer.from_pretrained("google/mt5-small"		)

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

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

								UpperCamelCase   =	-84.9127
								self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 1e-4		)






