


'''simple docstring'''



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


if is_sentencepiece_available():
		from ..ta.tokenization_ta import TaTokenizer
else:
		from ...utils.dummy_sentencepiece_objects import TaTokenizer

__A	=TaTokenizer

if is_tokenizers_available():
		from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
		from ...utils.dummy_tokenizers_objects import TaTokenizerFast

__A	=TaTokenizerFast

__A	={'configuration_mt5': ['MT5Config', 'MT5OnnxConfig']}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=[
		    'MT5EncoderModel',
		    'MT5ForConditionalGeneration',
		    'MT5ForQuestionAnswering',
		    'MT5Model',
		    'MT5PreTrainedModel',
		    'MT5Stack',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['TFMT5EncoderModel', 'TFMT5ForConditionalGeneration', 'TFMT5Model']

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__A	=['FlaxMT5EncoderModel', 'FlaxMT5ForConditionalGeneration', 'FlaxMT5Model']


if TYPE_CHECKING:
		from .configuration_mta import MTaConfig, MTaOnnxConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_mta import (
				    MTaEncoderModel,
				    MTaForConditionalGeneration,
				    MTaForQuestionAnswering,
				    MTaModel,
				    MTaPreTrainedModel,
				    MTaStack,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
		import sys

		__A	=_LazyModule(
		    __name__,
		    globals()['__file__'],
		    _import_structure,
		    extra_objects={'MT5Tokenizer': MTaTokenizer, 'MT5TokenizerFast': MTaTokenizerFast},
		    module_spec=__spec__,
		)



'''simple docstring'''



import unittest

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


if is_flax_available():
		import jax.numpy as jnp

		from transformers import FlaxXLMRobertaModel


@require_sentencepiece
@require_tokenizers
@require_flax
class 		_snake_case					(     unittest.TestCase	):




					@slow
					def 				snake_case__						(      self):
								UpperCAmelCase__  :					Union[str, Any]        =     FlaxXLMRobertaModel.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					List[str]        =     AutoTokenizer.from_pretrained("""xlm-roberta-base""")
								UpperCAmelCase__  :					Dict        =     """The dog is cute and lives in the garden house"""
								UpperCAmelCase__  :					Dict        =     jnp.array([tokenizer.encode(_lowerCamelCase)])

								UpperCAmelCase__  :					int        =     (1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
								UpperCAmelCase__  :					Any        =     jnp.array(
								    [[-0.0101, 0.1218, -0.0803, 0.0801, 0.1327, 0.0776, -0.1215, 0.2383, 0.3338, 0.3106, 0.0300, 0.0252]])

								UpperCAmelCase__  :					Any        =     model(_lowerCamelCase)["""last_hidden_state"""]
								self.assertEqual(output.shape							,							_lowerCamelCase)
								# compare the actual values for a slice of last dim
								self.assertTrue(jnp.allclose(output[:, :, -1]							,							_lowerCamelCase							,							atol=1e-3))