



"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__snake_case							:   int      =    {
    'configuration_xlm_roberta_xl': [
        'XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'XLMRobertaXLConfig',
        'XLMRobertaXLOnnxConfig',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__snake_case							:   Optional[int]      =    [
	    'XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMRobertaXLForCausalLM',
	    'XLMRobertaXLForMaskedLM',
	    'XLMRobertaXLForMultipleChoice',
	    'XLMRobertaXLForQuestionAnswering',
	    'XLMRobertaXLForSequenceClassification',
	    'XLMRobertaXLForTokenClassification',
	    'XLMRobertaXLModel',
	    'XLMRobertaXLPreTrainedModel',
	]

if TYPE_CHECKING:
	from .configuration_xlm_roberta_xl import (
	    XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XLMRobertaXLConfig,
	    XLMRobertaXLOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm_roberta_xl import (
		    XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMRobertaXLForCausalLM,
		    XLMRobertaXLForMaskedLM,
		    XLMRobertaXLForMultipleChoice,
		    XLMRobertaXLForQuestionAnswering,
		    XLMRobertaXLForSequenceClassification,
		    XLMRobertaXLForTokenClassification,
		    XLMRobertaXLModel,
		    XLMRobertaXLPreTrainedModel,
		)

else:
	import sys

	__snake_case							:   Optional[int]      =    _LazyModule(__name__, globals()['__file__'], _import_structure)




"""simple docstring"""






import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
	import optax
	from flax.training.common_utils import onehot

	from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
	from transformers.models.ta.modeling_flax_ta import shift_tokens_right



@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			A__   (  unittest.TestCase							):




	'''simple docstring'''





	@slow
	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       str:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[Any]												=       FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
				__lowerCAmelCase      :  Optional[Any]												=       AutoTokenizer.from_pretrained("google/mt5-small")

				__lowerCAmelCase      :  Tuple												=       tokenizer("Hello there"       ,			return_tensors="np").input_ids
				__lowerCAmelCase      :  Dict												=       tokenizer("Hi I am"       ,			return_tensors="np").input_ids

				__lowerCAmelCase      :  str												=       shift_tokens_right(_SCREAMING_SNAKE_CASE       ,			model.config.pad_token_id       ,			model.config.decoder_start_token_id)

				__lowerCAmelCase      :  Optional[int]												=       model(_SCREAMING_SNAKE_CASE       ,			decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
				__lowerCAmelCase      :  int												=       optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE       ,			onehot(_SCREAMING_SNAKE_CASE       ,			logits.shape[-1])).mean()

				__lowerCAmelCase      :  List[str]												=       -(labels.shape[-1] * loss.item())

				__lowerCAmelCase      :  str												=       -84.9127
				self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)