


'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase   : Dict             =				{
    'configuration_squeezebert': [
        'SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SqueezeBertConfig',
        'SqueezeBertOnnxConfig',
    ],
    'tokenization_squeezebert': ['SqueezeBertTokenizer'],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : Optional[Any]             =				['SqueezeBertTokenizerFast']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase   : int             =				[
					    'SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'SqueezeBertForMaskedLM',
					    'SqueezeBertForMultipleChoice',
					    'SqueezeBertForQuestionAnswering',
					    'SqueezeBertForSequenceClassification',
					    'SqueezeBertForTokenClassification',
					    'SqueezeBertModel',
					    'SqueezeBertModule',
					    'SqueezeBertPreTrainedModel',
					]


if TYPE_CHECKING:
					from .configuration_squeezebert import (
					    SQUEEZEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SqueezeBertConfig,
					    SqueezeBertOnnxConfig,
					)
					from .tokenization_squeezebert import SqueezeBertTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_squeezebert_fast import SqueezeBertTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_squeezebert import (
										    SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SqueezeBertForMaskedLM,
										    SqueezeBertForMultipleChoice,
										    SqueezeBertForQuestionAnswering,
										    SqueezeBertForSequenceClassification,
										    SqueezeBertForTokenClassification,
										    SqueezeBertModel,
										    SqueezeBertModule,
										    SqueezeBertPreTrainedModel,
										)

else:
					import sys

					lowerCAmelCase   : str             =				_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






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       lowercase_					(unittest.TestCase   ):





				@slow
				def 						__UpperCamelCase    (				self)  -> Optional[int]:
											a__      =AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'       ,  return_dict=lowercase_).to(lowercase_)
											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(lowercase_)       ,  labels=labels.to(lowercase_)).loss
											a__      =-(labels.shape[-1] * loss.item())

											a__      =-84.91_27
											self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)

