
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


_lowerCamelCase       :				Optional[int]       =			{
    '''configuration_encodec''': [
        '''ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''EncodecConfig''',
    ],
    '''feature_extraction_encodec''': ['''EncodecFeatureExtractor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase       :				Optional[int]       =			[
			    '''ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''EncodecModel''',
			    '''EncodecPreTrainedModel''',
			]

if TYPE_CHECKING:
			from .configuration_encodec import (
			    ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    EncodecConfig,
			)
			from .feature_extraction_encodec import EncodecFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_encodec import (
						    ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
						    EncodecModel,
						    EncodecPreTrainedModel,
						)

else:
			import sys

			_lowerCamelCase       :				List[str]       =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

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    SCREAMING_SNAKE_CASE__     (	unittest.TestCase					):





	'''simple docstring'''



	@slow
	def  A							(  self	:    int	):


				'''simple docstring'''


				_snake_case		=			FlaxXLMRobertaModel.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			AutoTokenizer.from_pretrained('xlm-roberta-base'	)
				_snake_case		=			'The dog is cute and lives in the garden house'
				_snake_case		=			jnp.array([tokenizer.encode(lowercase	)]	)

				_snake_case		=			(1, 12, 768)  # batch_size, sequence_length, embedding_vector_dim
				_snake_case		=			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]]	)

				_snake_case		=			model(lowercase	)['last_hidden_state']
				self.assertEqual(output.shape			,  lowercase	)
				# compare the actual values for a slice of last dim
				self.assertTrue(jnp.allclose(output[:, :, -1]			,  lowercase			,  atol=1E-3	)	)