



from __future__ import annotations

import unittest

import numpy as np

from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
 import tensorflow as tf

 from transformers import GPTaTokenizer, TFOPTForCausalLM, TFOPTModel





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase=None   , _UpperCAmelCase=None				):
    if attention_mask is None:
        __a						       =				tf.cast(tf.math.not_equal(_UpperCAmelCase   , config.pad_token_id				)   , tf.inta				)
    return {"input_ids": input_ids, "attention_mask": attention_mask}

@require_tf
class     _A       :
       UpperCamelCase__  :    List[str]											=						OPTConfig
       UpperCamelCase__  :    Any											=						{}
       UpperCamelCase__  :    Optional[int]											=						'''gelu'''



       def __init__(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=13     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=7     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=True     ,		__SCREAMING_SNAKE_CASE							:     int=False     ,		__SCREAMING_SNAKE_CASE							:     List[str]=99     ,		__SCREAMING_SNAKE_CASE							:     str=16     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=2     ,		__SCREAMING_SNAKE_CASE							:     List[str]=4     ,		__SCREAMING_SNAKE_CASE							:     str=4     ,		__SCREAMING_SNAKE_CASE							:     List[Any]="gelu"     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.1     ,		__SCREAMING_SNAKE_CASE							:     str=0.1     ,		__SCREAMING_SNAKE_CASE							:     int=20     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=2     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=1     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=0     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=16     ,		__SCREAMING_SNAKE_CASE							:     str=16     ,		):


           '''simple docstring'''

           __a						       =				parent
           __a						       =				batch_size
           __a						       =				seq_length
           __a						       =				is_training
           __a						       =				use_labels
           __a						       =				vocab_size
           __a						       =				hidden_size
           __a						       =				num_hidden_layers
           __a						       =				num_attention_heads
           __a						       =				intermediate_size
           __a						       =				hidden_act
           __a						       =				hidden_dropout_prob
           __a						       =				attention_probs_dropout_prob
           __a						       =				max_position_embeddings
           __a						       =				eos_token_id
           __a						       =				pad_token_id
           __a						       =				bos_token_id
           __a						       =				embed_dim
           __a						       =				word_embed_proj_dim
           __a						       =				False



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				ids_tensor([self.batch_size, self.seq_length - 1]     ,		self.vocab_size)
           __a						       =				tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size)     ,		1)
           __a						       =				tf.concat([input_ids, eos_tensor]     ,		axis=1)

           __a						       =				self.config_cls(
               vocab_size=self.vocab_size     ,		hidden_size=self.hidden_size     ,		num_hidden_layers=self.num_hidden_layers     ,		num_attention_heads=self.num_attention_heads     ,		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     ,		embed_dim=self.embed_dim     ,		word_embed_proj_dim=self.word_embed_proj_dim     ,		is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,		**self.config_updates     ,		)
           __a						       =				prepare_opt_inputs_dict(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           return config, inputs_dict





       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				TFOPTModel(config=__SCREAMING_SNAKE_CASE)
           __a						       =				inputs_dict['''input_ids''']

           __a						       =				input_ids[:1, :]
           __a						       =				inputs_dict['''attention_mask'''][:1, :]
           __a						       =				1

           # first forward pass
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		use_cache=__SCREAMING_SNAKE_CASE)

           __a       ,   __a						       =				outputs.to_tuple()

           # create hypothetical next token and extent to next_input_ids
           __a						       =				ids_tensor((self.batch_size, 3)     ,		config.vocab_size)
           __a						       =				tf.cast(ids_tensor((self.batch_size, 3)     ,		2)     ,		tf.inta)

           # append to next input_ids and
           __a						       =				tf.concat([input_ids, next_tokens]     ,		axis=-1)
           __a						       =				tf.concat([attention_mask, next_attn_mask]     ,		axis=-1)

           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE)[0]
           __a						       =				model(__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE     ,		past_key_values=__SCREAMING_SNAKE_CASE)[0]

           self.parent.assertEqual(next_tokens.shape[1]     ,		output_from_past.shape[1])

           # select random slice
           __a						       =				int(ids_tensor((1,)     ,		output_from_past.shape[-1]))
           __a						       =				output_from_no_past[:, -3:, random_slice_idx]
           __a						       =				output_from_past[:, :, random_slice_idx]

           # test that outputs are equal for slice
           tf.debugging.assert_near(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		rtol=1E-3)


@require_tf
class     _A       (	__UpperCAmelCase      ,__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Optional[Any]											=						(TFOPTModel, TFOPTForCausalLM) if is_tf_available() else ()
       UpperCamelCase__  :    int											=						(TFOPTForCausalLM,) if is_tf_available() else ()
       UpperCamelCase__  :    str											=						(
           {'''feature-extraction''': TFOPTModel, '''text-generation''': TFOPTForCausalLM} if is_tf_available() else {}
       )
       UpperCamelCase__  :    Optional[Any]											=						False
       UpperCamelCase__  :    Optional[int]											=						False
       UpperCamelCase__  :    Union[str, Any]											=						False
       UpperCamelCase__  :    int											=						10



       def 		_lowerCamelCase			(		self							:     Union[str, Any]):


           '''simple docstring'''

           __a						       =				TFOPTModelTester(self)
           __a						       =				ConfigTester(self     ,		config_class=__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     List[Any]):


           '''simple docstring'''

           self.config_tester.run_common_tests()



       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				self.model_tester.prepare_config_and_inputs_for_common()
           self.model_tester.check_decoder_model_past_large_inputs(*__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a       ,   __a						       =				self.model_tester.prepare_config_and_inputs_for_common()

           def _get_word_embedding_weight(__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     int):
               if hasattr(__SCREAMING_SNAKE_CASE     ,		'''weight'''):
                   return embedding_layer.weight
               else:
                   # Here we build the word embeddings weights if not exists.
                   # And then we retry to get the attribute once built.
                   model.build()
                   if hasattr(__SCREAMING_SNAKE_CASE     ,		'''weight'''):
                       return embedding_layer.weight
                   else:
                       return None

           for model_class in self.all_model_classes:
               for size in [config.vocab_size - 10, config.vocab_size + 10]:
                   # build the embeddings
                   __a						       =				model_class(config=__SCREAMING_SNAKE_CASE)
                   __a						       =				_get_word_embedding_weight(__SCREAMING_SNAKE_CASE     ,		model.get_input_embeddings())
                   __a						       =				_get_word_embedding_weight(__SCREAMING_SNAKE_CASE     ,		model.get_output_embeddings())

                   # reshape the embeddings
                   model.resize_token_embeddings(__SCREAMING_SNAKE_CASE)
                   __a						       =				_get_word_embedding_weight(__SCREAMING_SNAKE_CASE     ,		model.get_input_embeddings())
                   __a						       =				_get_word_embedding_weight(__SCREAMING_SNAKE_CASE     ,		model.get_output_embeddings())

                   # check that the resized embeddings size matches the desired size.
                   __a						       =				size if size is not None else config.vocab_size

                   self.assertEqual(new_input_embeddings.shape[0]     ,		__SCREAMING_SNAKE_CASE)

                   # check that weights remain the same after resizing
                   __a						       =				True
                   for pa, pa in zip(old_input_embeddings.value()     ,		new_input_embeddings.value()):
                       if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
                           __a						       =				False
                   self.assertTrue(__SCREAMING_SNAKE_CASE)

                   if old_output_embeddings is not None and new_output_embeddings is not None:
                       self.assertEqual(new_output_embeddings.shape[0]     ,		__SCREAMING_SNAKE_CASE)

                       __a						       =				True
                       for pa, pa in zip(old_output_embeddings.value()     ,		new_output_embeddings.value()):
                           if tf.math.reduce_sum(tf.math.abs(pa - pa)) > 0:
                               __a						       =				False
                       self.assertTrue(__SCREAMING_SNAKE_CASE)





def      __snake_case	(			_UpperCAmelCase				):
    return tf.constant(_UpperCAmelCase   , dtype=tf.intaa				)

@require_tf
class     _A       (	unittest.TestCase    ):
       UpperCamelCase__  :    Optional[int]											=						99



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				tf.ones((4, 1)     ,		dtype=tf.intaa) * 2
           __a						       =				tf.concat([ids_tensor((4, 6)     ,		self.vocab_size - 3) + 3, eos_column_vector]     ,		axis=1)
           __a						       =				input_ids.shape[0]
           __a						       =				OPTConfig(
               vocab_size=self.vocab_size     ,		hidden_size=24     ,		num_hidden_layers=2     ,		num_attention_heads=2     ,		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


@require_sentencepiece
@require_tf
class     _A       (	unittest.TestCase    ):



       @slow
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           __a						       =				TFOPTModel.from_pretrained('''facebook/opt-350m''')
           __a						       =				_long_tensor([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]])
           __a						       =				tf.not_equal(__SCREAMING_SNAKE_CASE     ,		model.config.pad_token_id)
           with tf.GradientTape():
               __a						       =				model(input_ids=__SCREAMING_SNAKE_CASE     ,		attention_mask=__SCREAMING_SNAKE_CASE).last_hidden_state
           __a						       =				(1, 11, 512)
           self.assertEqual(output.shape     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				tf.constant(
               [[-0.28_73, -1.92_18, -0.30_33], [-1.27_10, -0.13_38, -0.19_02], [0.40_95, 0.12_14, -1.31_21]])
           self.assertTrue(np.allclose(output[:, :3, :3]     ,		__SCREAMING_SNAKE_CASE     ,		atol=4E-3))

           __a						       =				tf.function(__SCREAMING_SNAKE_CASE     ,		jit_compile=__SCREAMING_SNAKE_CASE)
           __a						       =				xla_generate(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)[0]
           self.assertTrue(np.allclose(output[:, :3, :3]     ,		__SCREAMING_SNAKE_CASE     ,		atol=4E-2))


@require_tf
@slow
class     _A       (	unittest.TestCase    ):



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           super().setUp()
           __a						       =				'''facebook/opt-350m'''





       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				TFOPTForCausalLM.from_pretrained(self.path_model)
           __a						       =				GPTaTokenizer.from_pretrained(self.path_model)

           __a						       =				[
               '''Today is a beautiful day and I want to''',
               '''In the city of''',
               '''Paris is the capital of France and''',
               '''Computers and mobile phones have taken''',
           ]
           # verify that prompt without BOS token is identical to Metaseq -> add_special_tokens=False
           __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors='''tf'''     ,		padding=__SCREAMING_SNAKE_CASE     ,		add_special_tokens=__SCREAMING_SNAKE_CASE)
           __a						       =				tf.math.reduce_mean(model(inputs.input_ids     ,		attention_mask=inputs.attention_mask)[0]     ,		axis=-1)
           __a						       =				tf.constant(
               [
                   [1.38_51, -13.89_23, -10.52_29, -10.75_33, -0.23_09, -10.23_84, -0.53_65, -9.09_47, -5.16_70],
                   [-4.70_73, -10.62_76, -3.94_15, -21.52_42, -0.28_22, -0.28_22, -0.28_22, -0.28_22, -0.28_22],
                   [0.62_47, -3.42_29, -8.91_79, -1.42_97, -14.16_50, 1.41_46, -9.02_18, -0.27_03, -0.27_03],
                   [6.47_83, -1.99_13, -10.79_26, -2.33_36, 1.50_92, -0.99_74, -6.82_13, 1.34_77, 1.34_77],
               ])
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-4))

           __a						       =				tf.function(__SCREAMING_SNAKE_CASE     ,		jit_compile=__SCREAMING_SNAKE_CASE)
           __a						       =				tf.math.reduce_mean(xla_generate(inputs.input_ids     ,		attention_mask=inputs.attention_mask)[0]     ,		axis=-1)
           self.assertTrue(np.allclose(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		atol=1E-4))


@require_tf
@slow
class     _A       (	unittest.TestCase    ):



       @property
       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           return [
               "Today is a beautiful day and I want",
               "In the city of",
               "Paris is the capital of France and",
               "Computers and mobile phones have taken",
           ]



       def 		_lowerCamelCase			(		self							:     List[str]):


           '''simple docstring'''

           __a						       =				'''facebook/opt-125m'''

           __a						       =				[
               '''Today is a beautiful day and I want to''',
               '''In the city of New York, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           __a						       =				[]
           __a						       =				GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
           __a						       =				TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)

           for prompt in self.prompts:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors='''tf''').input_ids

               __a						       =				model.generate(__SCREAMING_SNAKE_CASE     ,		max_length=10)

               __a						       =				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,		skip_special_tokens=__SCREAMING_SNAKE_CASE)
               predicted_outputs += generated_string

           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				'''facebook/opt-350m'''

           __a						       =				GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
           __a						       =				TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)

           __a						       =				'''left'''

           # use different length sentences to test batching
           __a						       =				[
               '''Hello, my dog is a little''',
               '''Today, I''',
           ]

           __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors='''tf'''     ,		padding=__SCREAMING_SNAKE_CASE)
           __a						       =				inputs['''input_ids''']

           __a						       =				model.generate(input_ids=__SCREAMING_SNAKE_CASE     ,		attention_mask=inputs['''attention_mask'''])

           __a						       =				tokenizer(sentences[0]     ,		return_tensors='''tf''').input_ids
           __a						       =				model.generate(input_ids=__SCREAMING_SNAKE_CASE)

           __a						       =				inputs_non_padded.shape[-1] - tf.math.reduce_sum(
               tf.cast(inputs['''attention_mask'''][-1]     ,		tf.intaa))
           __a						       =				tokenizer(sentences[1]     ,		return_tensors='''tf''').input_ids
           __a						       =				model.generate(input_ids=__SCREAMING_SNAKE_CASE     ,		max_length=model.config.max_length - num_paddings)

           __a						       =				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,		skip_special_tokens=__SCREAMING_SNAKE_CASE)
           __a						       =				tokenizer.decode(output_non_padded[0]     ,		skip_special_tokens=__SCREAMING_SNAKE_CASE)
           __a						       =				tokenizer.decode(output_padded[0]     ,		skip_special_tokens=__SCREAMING_SNAKE_CASE)

           __a						       =				[
               '''Hello, my dog is a little bit of a dork.\nI\'m a little bit''',
               '''Today, I was in the middle of a conversation with a friend about the''',
           ]
           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		[non_padded_sentence, padded_sentence])





       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				'''facebook/opt-350m'''

           __a						       =				[
               '''Today is a beautiful day and I want to''',
               '''In the city of San Francisco, the city''',
               '''Paris is the capital of France and the capital''',
               '''Computers and mobile phones have taken over the''',
           ]

           __a						       =				[]
           __a						       =				GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE)
           __a						       =				TFOPTForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE)

           for prompt in self.prompts:
               __a						       =				tokenizer(__SCREAMING_SNAKE_CASE     ,		return_tensors='''tf''').input_ids

               __a						       =				model.generate(__SCREAMING_SNAKE_CASE     ,		max_length=10)

               __a						       =				tokenizer.batch_decode(__SCREAMING_SNAKE_CASE     ,		skip_special_tokens=__SCREAMING_SNAKE_CASE)
               predicted_outputs += generated_string

           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)

__snake_case						:Any      		=     {
    '''google/switch-base-8''': '''https://huggingface.co/google/switch-base-8/blob/main/config.json''',
}

class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    Optional[int]											=						'''switch_transformers'''
       UpperCamelCase__  :    Optional[Any]											=						['''past_key_values''']
       UpperCamelCase__  :    Optional[Any]											=						{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}



       def __init__(		self							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     str=32_128     ,		__SCREAMING_SNAKE_CASE							:     int=768     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=2_048     ,		__SCREAMING_SNAKE_CASE							:     List[str]=64     ,		__SCREAMING_SNAKE_CASE							:     int=12     ,		__SCREAMING_SNAKE_CASE							:     Any=3     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=12     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=3     ,		__SCREAMING_SNAKE_CASE							:     Any=12     ,		__SCREAMING_SNAKE_CASE							:     Tuple=8     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]=0.01     ,		__SCREAMING_SNAKE_CASE							:     Dict="float32"     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=False     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]=32     ,		__SCREAMING_SNAKE_CASE							:     int=128     ,		__SCREAMING_SNAKE_CASE							:     Any=0.1     ,		__SCREAMING_SNAKE_CASE							:     int=1E-6     ,		__SCREAMING_SNAKE_CASE							:     Dict=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[str]=0.0_01     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1.0     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]="relu"     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=True     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]=False     ,		__SCREAMING_SNAKE_CASE							:     str=True     ,		__SCREAMING_SNAKE_CASE							:     int=0     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=1     ,		**__SCREAMING_SNAKE_CASE							:     Dict     ,		):


           '''simple docstring'''

           __a						       =				vocab_size
           __a						       =				d_model
           __a						       =				d_kv
           __a						       =				d_ff

           __a						       =				num_sparse_encoder_layers

           __a						       =				num_layers
           __a						       =				(
               num_decoder_layers if num_decoder_layers is not None else self.num_layers
           )  # default = symmetry
           __a						       =				num_sparse_decoder_layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_encoder_layers > 0:
               __a						       =				self.num_layers // self.num_sparse_encoder_layers
           else:
               __a						       =				self.num_layers  # HACK: this will create 0 sparse layers

           # This tells us, each how many encoder layer we'll have to set a sparse layer.
           if self.num_sparse_decoder_layers > 0:
               __a						       =				self.num_decoder_layers // self.num_sparse_decoder_layers
           else:
               __a						       =				self.num_decoder_layers  # HACK: this will create 0 sparse layers

           __a						       =				num_heads
           __a						       =				num_experts
           __a						       =				expert_capacity
           __a						       =				router_bias
           __a						       =				router_jitter_noise
           if router_dtype not in ["float32", "float16", "bfloat16"]:
               raise ValueError(F'`router_dtype` must be one of \'float32\', \'float16\' or \'bfloat16\', got {router_dtype}')
           __a						       =				router_dtype

           __a						       =				router_ignore_padding_tokens
           __a						       =				relative_attention_num_buckets
           __a						       =				relative_attention_max_distance

           __a						       =				dropout_rate
           __a						       =				layer_norm_epsilon
           __a						       =				initializer_factor
           __a						       =				feed_forward_proj
           __a						       =				use_cache
           __a						       =				add_router_probs

           __a						       =				router_z_loss_coef
           __a						       =				router_aux_loss_coef

           __a						       =				self.feed_forward_proj.split('''-''')
           __a						       =				act_info[-1]
           __a						       =				act_info[0] == '''gated'''

           if len(__SCREAMING_SNAKE_CASE) > 1 and act_info[0] != "gated" or len(__SCREAMING_SNAKE_CASE) > 2:
               raise ValueError(
                   F'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
                   '''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
                   '''\'gated-gelu\' or \'relu\'''')

           # for backwards compatibility
           if feed_forward_proj == "gated-gelu":
               __a						       =				'''gelu_new'''

           super().__init__(
               pad_token_id=__SCREAMING_SNAKE_CASE     ,		eos_token_id=__SCREAMING_SNAKE_CASE     ,		is_encoder_decoder=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE     ,		)





