import unittest

import numpy as np
import timeout_decorator  # noqa

from transformers import MBartConfig, is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor


if is_flax_available():
				import os

				# The slow tests are often failing with OOM error on GPU
				# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
				# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
				lowerCAmelCase__	:  int						=  '''platform'''

				import jax
				import jax.numpy as jnp

				from transformers import AutoTokenizer
				from transformers.models.mbart.modeling_flax_mbart import (
				    FlaxMBartForConditionalGeneration,
				    FlaxMBartForQuestionAnswering,
				    FlaxMBartForSequenceClassification,
				    FlaxMBartModel,
				    shift_tokens_right,
				)
def 					UpperCamelCase__		(							A__     ,					A__     ,					A__=None     ,					A__=None     ,					A__=None     ,					A__=None     ,					A__=None     ,					A__=None     ,					)  ->   Union[str, Any]:
				if attention_mask is None:
								snake_case__   :      Optional[int]   				= np.where(input_ids != config.pad_token_id     ,					1     ,					0       )
				if decoder_attention_mask is None:
								snake_case__   :      str   				= np.where(decoder_input_ids != config.pad_token_id     ,					1     ,					0       )
				if head_mask is None:
								snake_case__   :      Tuple   				= np.ones((config.encoder_layers, config.encoder_attention_heads)       )
				if decoder_head_mask is None:
								snake_case__   :      Optional[int]   				= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
				if cross_attn_head_mask is None:
								snake_case__   :      Optional[int]   				= np.ones((config.decoder_layers, config.decoder_attention_heads)       )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				}





class 					__snake_case    (     unittest.TestCase      ):




					def __init__(  self       ,			__UpperCamelCase       ,			__UpperCamelCase=13       ,			__UpperCamelCase=7       ,			__UpperCamelCase=True       ,			__UpperCamelCase=False       ,			__UpperCamelCase=99       ,			__UpperCamelCase=16       ,			__UpperCamelCase=2       ,			__UpperCamelCase=4       ,			__UpperCamelCase=4       ,			__UpperCamelCase="gelu"       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=0.1       ,			__UpperCamelCase=32       ,			__UpperCamelCase=2       ,			__UpperCamelCase=1       ,			__UpperCamelCase=0       ,			__UpperCamelCase=2       ,			__UpperCamelCase=0.0_2       ,			)	->    Union[str, Any]:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= parent
									snake_case__   :      Optional[Any]   				= batch_size
									snake_case__   :      Union[str, Any]   				= seq_length
									snake_case__   :      Tuple   				= is_training
									snake_case__   :      Optional[int]   				= use_labels
									snake_case__   :      Tuple   				= vocab_size
									snake_case__   :      Any   				= hidden_size
									snake_case__   :      Tuple   				= num_hidden_layers
									snake_case__   :      Union[str, Any]   				= num_attention_heads
									snake_case__   :      Union[str, Any]   				= intermediate_size
									snake_case__   :      int   				= hidden_act
									snake_case__   :      List[str]   				= hidden_dropout_prob
									snake_case__   :      str   				= attention_probs_dropout_prob
									snake_case__   :      Union[str, Any]   				= max_position_embeddings
									snake_case__   :      List[str]   				= eos_token_id
									snake_case__   :      Tuple   				= pad_token_id
									snake_case__   :      Optional[int]   				= bos_token_id
									snake_case__   :      Union[str, Any]   				= decoder_start_token_id
									snake_case__   :      Optional[Any]   				= initializer_range




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= np.clip(ids_tensor([self.batch_size, self.seq_length - 1]       ,			self.vocab_size						)       ,			3       ,			self.vocab_size						)
									snake_case__   :      List[Any]   				= np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1)       ,			dtype=np.intaa						))       ,			-1						)

									snake_case__   :      List[str]   				= shift_tokens_right(__UpperCamelCase       ,			1						)

									snake_case__   :      Optional[int]   				= MBartConfig(
									    vocab_size=self.vocab_size       ,			d_model=self.hidden_size       ,			encoder_layers=self.num_hidden_layers       ,			decoder_layers=self.num_hidden_layers       ,			encoder_attention_heads=self.num_attention_heads       ,			decoder_attention_heads=self.num_attention_heads       ,			encoder_ffn_dim=self.intermediate_size       ,			decoder_ffn_dim=self.intermediate_size       ,			dropout=self.hidden_dropout_prob       ,			attention_dropout=self.attention_probs_dropout_prob       ,			max_position_embeddings=self.max_position_embeddings       ,			eos_token_id=self.eos_token_id       ,			bos_token_id=self.bos_token_id       ,			pad_token_id=self.pad_token_id       ,			decoder_start_token_id=self.decoder_start_token_id       ,			initializer_range=self.initializer_range       ,			use_cache=__UpperCamelCase       ,			)
									snake_case__   :      str   				= prepare_mbart_inputs_dict(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									return config, inputs_dict




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Optional[int]   				= self.prepare_config_and_inputs()
									return config, inputs_dict




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    List[Any]:



									'''simple docstring'''







									snake_case__   :      int   				= 20
									snake_case__   :      Optional[int]   				= model_class_name(__UpperCamelCase						)

									snake_case__   :      str   				= model.encode(inputs_dict['input_ids']						)

									snake_case__				,    snake_case__   :      str   				= (
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									snake_case__   :      List[str]   				= model.init_cache(decoder_input_ids.shape[0]       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      Union[str, Any]   				= jnp.ones((decoder_input_ids.shape[0], max_decoder_length)       ,			dtype='i4'						)

									snake_case__   :      Optional[Any]   				= jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]       ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,			)
									snake_case__   :      Dict   				= model.decode(
									    decoder_input_ids[:, :-1]       ,			__UpperCamelCase       ,			decoder_attention_mask=__UpperCamelCase       ,			past_key_values=__UpperCamelCase       ,			decoder_position_ids=__UpperCamelCase       ,			)

									snake_case__   :      Optional[Any]   				= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,			dtype='i4'						)
									snake_case__   :      Any   				= model.decode(
									    decoder_input_ids[:, -1:]       ,			__UpperCamelCase       ,			decoder_attention_mask=__UpperCamelCase       ,			past_key_values=outputs_cache.past_key_values       ,			decoder_position_ids=__UpperCamelCase       ,			)

									snake_case__   :      Optional[Any]   				= model.decode(__UpperCamelCase       ,			__UpperCamelCase						)

									snake_case__   :      Union[str, Any]   				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
									self.parent.assertTrue(diff < 1E-3       ,			msg=F"""Max diff is {diff}"""						)




					def      __a     (  self       ,			__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)	->    str:



									'''simple docstring'''







									snake_case__   :      str   				= 20
									snake_case__   :      Optional[Any]   				= model_class_name(__UpperCamelCase						)

									snake_case__   :      Tuple   				= model.encode(inputs_dict['input_ids']						)

									snake_case__				,    snake_case__   :      Tuple   				= (
									    inputs_dict['decoder_input_ids'],
									    inputs_dict['decoder_attention_mask'],
									)

									snake_case__   :      Tuple   				= jnp.concatenate(
									    [
									        decoder_attention_mask,
									        jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1])						),
									    ]       ,			axis=-1       ,			)

									snake_case__   :      Optional[int]   				= model.init_cache(decoder_input_ids.shape[0]       ,			__UpperCamelCase       ,			__UpperCamelCase						)
									snake_case__   :      List[str]   				= jnp.broadcast_to(
									    jnp.arange(decoder_input_ids.shape[-1] - 1						)[None, :]       ,			(decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1)       ,			)

									snake_case__   :      int   				= model.decode(
									    decoder_input_ids[:, :-1]       ,			__UpperCamelCase       ,			decoder_attention_mask=__UpperCamelCase       ,			past_key_values=__UpperCamelCase       ,			decoder_position_ids=__UpperCamelCase       ,			)
									snake_case__   :      List[Any]   				= jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]]       ,			dtype='i4'						)
									snake_case__   :      Union[str, Any]   				= model.decode(
									    decoder_input_ids[:, -1:]       ,			__UpperCamelCase       ,			past_key_values=outputs_cache.past_key_values       ,			decoder_attention_mask=__UpperCamelCase       ,			decoder_position_ids=__UpperCamelCase       ,			)

									snake_case__   :      Union[str, Any]   				= model.decode(__UpperCamelCase       ,			__UpperCamelCase       ,			decoder_attention_mask=__UpperCamelCase						)

									snake_case__   :      List[str]   				= np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])						)						)
									self.parent.assertTrue(diff < 1E-3       ,			msg=F"""Max diff is {diff}"""						)





@require_flax
class 					__snake_case    (     unittest.TestCase      ):
					__lowerCamelCase			   =  99




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Union[str, Any]   				= np.array(
									    [
									        [71, 82, 18, 33, 46, 91, 2],
									        [68, 34, 26, 58, 30, 82, 2],
									        [5, 97, 17, 39, 94, 40, 2],
									        [76, 83, 94, 25, 70, 78, 2],
									        [87, 59, 41, 35, 48, 66, 2],
									        [55, 13, 16, 58, 5, 2, 1],  # note padding
									        [64, 27, 31, 51, 12, 75, 2],
									        [52, 64, 86, 17, 83, 39, 2],
									        [48, 61, 9, 24, 71, 82, 2],
									        [26, 1, 60, 48, 22, 13, 2],
									        [21, 5, 62, 28, 14, 76, 2],
									        [45, 98, 37, 86, 59, 48, 2],
									        [70, 70, 50, 9, 28, 0, 2],
									    ]       ,			dtype=np.intaa       ,			)

									snake_case__   :      int   				= input_ids.shape[0]
									snake_case__   :      List[Any]   				= MBartConfig(
									    vocab_size=self.vocab_size       ,			d_model=24       ,			encoder_layers=2       ,			decoder_layers=2       ,			encoder_attention_heads=2       ,			decoder_attention_heads=2       ,			encoder_ffn_dim=32       ,			decoder_ffn_dim=32       ,			max_position_embeddings=48       ,			eos_token_id=2       ,			pad_token_id=1       ,			bos_token_id=0       ,			)
									return config, input_ids, batch_size




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__				,    snake_case__				,    snake_case__   :      Optional[int]   				= self._get_config_and_data()
									snake_case__   :      int   				= FlaxMBartForSequenceClassification(__UpperCamelCase						)
									snake_case__   :      Tuple   				= model(input_ids=__UpperCamelCase       ,			decoder_input_ids=__UpperCamelCase						)
									snake_case__   :      List[str]   				= (batch_size, config.num_labels)
									self.assertEqual(outputs['logits'].shape       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__				,    snake_case__				,    snake_case__   :      Optional[Any]   				= self._get_config_and_data()
									snake_case__   :      Dict   				= FlaxMBartForQuestionAnswering(__UpperCamelCase						)
									snake_case__   :      Dict   				= model(input_ids=__UpperCamelCase						)

									self.assertEqual(outputs['start_logits'].shape       ,			input_ids.shape						)
									self.assertEqual(outputs['end_logits'].shape       ,			input_ids.shape						)




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__				,    snake_case__				,    snake_case__   :      Any   				= self._get_config_and_data()
									snake_case__   :      Any   				= FlaxMBartForConditionalGeneration(__UpperCamelCase						)
									snake_case__   :      Optional[int]   				= lm_model(input_ids=__UpperCamelCase						)
									snake_case__   :      Dict   				= (batch_size, input_ids.shape[1], config.vocab_size)
									self.assertEqual(outputs['logits'].shape       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      str   				= MBartConfig(
									    vocab_size=self.vocab_size       ,			d_model=14       ,			encoder_layers=2       ,			decoder_layers=2       ,			encoder_attention_heads=2       ,			decoder_attention_heads=2       ,			encoder_ffn_dim=8       ,			decoder_ffn_dim=8       ,			max_position_embeddings=48       ,			)
									snake_case__   :      Optional[Any]   				= FlaxMBartForConditionalGeneration(__UpperCamelCase						)
									snake_case__   :      Optional[Any]   				= np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]]       ,			dtype=np.intaa						)
									snake_case__   :      Any   				= np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]]       ,			dtype=np.intaa						)
									snake_case__   :      List[Any]   				= lm_model(input_ids=__UpperCamelCase       ,			decoder_input_ids=__UpperCamelCase						)
									snake_case__   :      int   				= (*summary.shape, config.vocab_size)
									self.assertEqual(outputs['logits'].shape       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									snake_case__   :      int   				= np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]]       ,			dtype=np.intaa						)
									snake_case__   :      str   				= shift_tokens_right(__UpperCamelCase       ,			1						)
									snake_case__   :      int   				= np.equal(__UpperCamelCase       ,			1						).astype(np.floataa						).sum()
									snake_case__   :      List[Any]   				= np.equal(__UpperCamelCase       ,			1						).astype(np.floataa						).sum()
									self.assertEqual(shifted.shape       ,			input_ids.shape						)
									self.assertEqual(__UpperCamelCase       ,			n_pad_before - 1						)
									self.assertTrue(np.equal(shifted[:, 0]       ,			2						).all()						)





@require_flax
class 					__snake_case    (     _lowerCamelCase       ,unittest.TestCase       ,_lowerCamelCase      ):
					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  (
					    (
					        FlaxMBartModel,
					        FlaxMBartForConditionalGeneration,
					        FlaxMBartForSequenceClassification,
					        FlaxMBartForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)
					__lowerCamelCase			   =  (FlaxMBartForConditionalGeneration,) if is_flax_available() else ()




					def      __a     (  self						)	->    Tuple:



									'''simple docstring'''







									snake_case__   :      str   				= FlaxMBartModelTester(self						)




					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Optional[int]   				= self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Any   				= self.model_tester.prepare_config_and_inputs()
									for model_class in self.all_model_classes:
													self.model_tester.check_use_cache_forward_with_attn_mask(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						)




					def      __a     (  self						)	->    Any:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Optional[int]   				= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__						):
																	snake_case__   :      List[str]   				= self._prepare_for_class(__UpperCamelCase       ,			__UpperCamelCase						)
																	snake_case__   :      Any   				= model_class(__UpperCamelCase						)

																	@jax.jit
																	def encode_jitted(__UpperCamelCase       ,			__UpperCamelCase=None       ,			**__UpperCamelCase						):
																					return model.encode(input_ids=__UpperCamelCase       ,			attention_mask=__UpperCamelCase						)

																	with self.subTest('JIT Enabled'						):
																					snake_case__   :      List[str]   				= encode_jitted(**__UpperCamelCase						).to_tuple()

																	with self.subTest('JIT Disabled'						):
																					with jax.disable_jit():
																									snake_case__   :      str   				= encode_jitted(**__UpperCamelCase						).to_tuple()

																	self.assertEqual(len(__UpperCamelCase						)       ,			len(__UpperCamelCase						)						)
																	for jitted_output, output in zip(__UpperCamelCase       ,			__UpperCamelCase						):
																					self.assertEqual(jitted_output.shape       ,			output.shape						)




					def      __a     (  self						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__				,    snake_case__   :      Tuple   				= self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													with self.subTest(model_class.__name__						):
																	snake_case__   :      Any   				= model_class(__UpperCamelCase						)
																	snake_case__   :      Any   				= model.encode(inputs_dict['input_ids']       ,			inputs_dict['attention_mask']						)

																	snake_case__   :      Optional[int]   				= {
																	    'decoder_input_ids': inputs_dict['decoder_input_ids'],
																	    'decoder_attention_mask': inputs_dict['decoder_attention_mask'],
																	    'encoder_outputs': encoder_outputs,
																	}

																	@jax.jit
																	def decode_jitted(__UpperCamelCase       ,			__UpperCamelCase       ,			__UpperCamelCase						):
																					return model.decode(
																					    decoder_input_ids=__UpperCamelCase       ,			decoder_attention_mask=__UpperCamelCase       ,			encoder_outputs=__UpperCamelCase       ,			)

																	with self.subTest('JIT Enabled'						):
																					snake_case__   :      Optional[Any]   				= decode_jitted(**__UpperCamelCase						).to_tuple()

																	with self.subTest('JIT Disabled'						):
																					with jax.disable_jit():
																									snake_case__   :      Tuple   				= decode_jitted(**__UpperCamelCase						).to_tuple()

																	self.assertEqual(len(__UpperCamelCase						)       ,			len(__UpperCamelCase						)						)
																	for jitted_output, output in zip(__UpperCamelCase       ,			__UpperCamelCase						):
																					self.assertEqual(jitted_output.shape       ,			output.shape						)




					@slow
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									for model_class_name in self.all_model_classes:
													snake_case__   :      Union[str, Any]   				= model_class_name.from_pretrained('facebook/mbart-large-cc25'       ,			from_pt=__UpperCamelCase						)
													# FlaxMBartForSequenceClassification expects eos token in input_ids
													snake_case__   :      str   				= np.ones((1, 1)						) * model.config.eos_token_id
													snake_case__   :      Optional[Any]   				= model(__UpperCamelCase						)
													self.assertIsNotNone(__UpperCamelCase						)





@require_flax
@require_sentencepiece
@require_tokenizers
class 					__snake_case    (     unittest.TestCase      ):
					__lowerCamelCase			   =  [
					    """ UN Chief Says There Is No Military Solution in Syria""",
					]
					__lowerCamelCase			   =  [
					    """Şeful ONU declară că nu există o soluţie militară în Siria""",
					]
					__lowerCamelCase			   =  """facebook/mbart-large-en-ro"""




					@cached_property
					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									return AutoTokenizer.from_pretrained(self.model_name						)




					@cached_property
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







									snake_case__   :      Dict   				= FlaxMBartForConditionalGeneration.from_pretrained(self.model_name       ,			from_pt=__UpperCamelCase						)
									return model




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Any:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.translate_src_text(**__UpperCamelCase						)
									self.assertListEqual(self.expected_text       ,			__UpperCamelCase						)




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







									snake_case__   :      Dict   				= self.tokenizer(self.src_text       ,			**__UpperCamelCase       ,			return_tensors='np'						)
									snake_case__   :      Dict   				= self.model.generate(
									    model_inputs.input_ids       ,			attention_mask=model_inputs.attention_mask       ,			decoder_start_token_id=self.tokenizer.lang_code_to_id['ro_RO']       ,			early_stopping=__UpperCamelCase       ,			num_beams=2       ,			).sequences
									snake_case__   :      Tuple   				= self.tokenizer.batch_decode(__UpperCamelCase       ,			skip_special_tokens=__UpperCamelCase						)
									return generated_words




					@slow
					def      __a     (  self						)	->    Optional[int]:



									'''simple docstring'''







									self._assert_generated_batch_equal_expected()



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,
)


lowerCAmelCase__	:  int						=  {
    '''configuration_switch_transformers''': [
        '''SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''SwitchTransformersConfig''',
        '''SwitchTransformersOnnxConfig''',
    ]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowerCAmelCase__	:  List[Any]						=  [
				    '''SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST''',
				    '''SwitchTransformersEncoderModel''',
				    '''SwitchTransformersForConditionalGeneration''',
				    '''SwitchTransformersModel''',
				    '''SwitchTransformersPreTrainedModel''',
				    '''SwitchTransformersTop1Router''',
				    '''SwitchTransformersSparseMLP''',
				]


if TYPE_CHECKING:
				from .configuration_switch_transformers import (
				    SWITCH_TRANSFORMERS_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SwitchTransformersConfig,
				    SwitchTransformersOnnxConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_switch_transformers import (
								    SWITCH_TRANSFORMERS_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SwitchTransformersEncoderModel,
								    SwitchTransformersForConditionalGeneration,
								    SwitchTransformersModel,
								    SwitchTransformersPreTrainedModel,
								    SwitchTransformersSparseMLP,
								    SwitchTransformersTopaRouter,
								)


else:
				import sys

				lowerCAmelCase__	:  Union[str, Any]						=  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


