

from typing import TYPE_CHECKING

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


A_	:			Dict			=      {
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			List[str]			=      [
	    'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TapasForMaskedLM',
	    'TapasForQuestionAnswering',
	    'TapasForSequenceClassification',
	    'TapasModel',
	    'TapasPreTrainedModel',
	    'load_tf_weights_in_tapas',
	]
try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_	:			str			=      [
	    'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFTapasForMaskedLM',
	    'TFTapasForQuestionAnswering',
	    'TFTapasForSequenceClassification',
	    'TFTapasModel',
	    'TFTapasPreTrainedModel',
	]


if TYPE_CHECKING:
	from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
	from .tokenization_tapas import TapasTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tapas import (
		    TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TapasForMaskedLM,
		    TapasForQuestionAnswering,
		    TapasForSequenceClassification,
		    TapasModel,
		    TapasPreTrainedModel,
		    load_tf_weights_in_tapas,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_tapas import (
		    TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFTapasForMaskedLM,
		    TFTapasForQuestionAnswering,
		    TFTapasForSequenceClassification,
		    TFTapasModel,
		    TFTapasPreTrainedModel,
		)


else:
	import sys

	A_	:			Optional[Any]			=      _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    _a						(unittest.TestCase    ):


					'''simple docstring'''






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

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

						A__							:   Union[str, Any]					       =		model(input_ids.to(A__      )   ,	labels=labels.to(A__      )      ).loss
						A__							:   Union[str, Any]					       =		-(labels.shape[-1] * loss.item())

						A__							:   Any					       =		-8_4.9_1_2_7
						self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )



