


import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any      ):
      """simple docstring"""




      if model.config.model_type == "gpt2":
            return model.transformer.h[0].mlp.c_fc
      return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
    import torch
    import torch.nn as nn



    class 							_UpperCamelCase (						nn.Module						):

       '''simple docstring'''







       def __init__(  self  : List[str]       ,  _lowerCamelCase  : nn.Module       ,  _lowerCamelCase  : int							):






             '''simple docstring'''



             super().__init__()
             __lowerCamelCase     :      Optional[Any]       						=						module
             __lowerCamelCase     :      Union[str, Any]       						=						nn.Sequential(
                 nn.Linear(module.in_features       ,  _lowerCamelCase       ,  bias=_lowerCamelCase							)       ,  nn.Linear(_lowerCamelCase       ,  module.out_features       ,  bias=_lowerCamelCase							)       ,  )
             __lowerCamelCase     :      List[str]       						=						(2.0 / (5 * min(module.in_features       ,  module.out_features							))) ** 0.5
             nn.init.normal_(self.adapter[0].weight       ,  std=_lowerCamelCase							)
             nn.init.zeros_(self.adapter[1].weight							)
             self.adapter.to(module.weight.device							)


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






             '''simple docstring'''



             return self.module(_lowerCamelCase       ,  *_lowerCamelCase       ,  **_lowerCamelCase							) + self.adapter(_lowerCamelCase							)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   a_				:							Tuple    = "bigscience/bloom-1b7"

   # Constant values
   a_				:							str    = 2.1_09_65_95_52_69_25_74

   a_				:							Optional[int]    = "Hello my name is"
   a_				:							int    = set()
   EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"						)
   EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"						)
   EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"						)
   a_				:							str    = 10

   def   _snake_case    (  self  : Tuple							):






         '''simple docstring'''



         __lowerCamelCase     :      Tuple       						=						AutoTokenizer.from_pretrained(self.model_name							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         super().setUp()

         # Models and tokenizer
         __lowerCamelCase     :      Dict       						=						AutoModelForCausalLM.from_pretrained(
             self.model_name       ,  torch_dtype=torch.floataa       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Any       						=						AutoModelForCausalLM.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         del self.model_fpaa
         del self.model_abit

         gc.collect()
         torch.cuda.empty_cache()

   def   _snake_case    (  self  : List[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Any       						=						self.model_abit.config

         self.assertTrue(hasattr(_lowerCamelCase       ,  """quantization_config"""							)							)

         __lowerCamelCase     :      int       						=						config.to_dict()
         __lowerCamelCase     :      List[str]       						=						config.to_diff_dict()

         __lowerCamelCase     :      Optional[int]       						=						config.to_json_string()

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         from bitsandbytes.nn import Paramsabit

         __lowerCamelCase     :      Dict       						=						self.model_fpaa.get_memory_footprint()
         __lowerCamelCase     :      Union[str, Any]       						=						self.model_abit.get_memory_footprint()

         self.assertAlmostEqual(mem_fpaa / mem_abit       ,  self.EXPECTED_RELATIVE_DIFFERENCE							)
         __lowerCamelCase     :      Optional[Any]       						=						get_some_linear_layer(self.model_abit							)
         self.assertTrue(linear.weight.__class__ == Paramsabit							)

   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         from transformers import TaPreTrainedModel

         self.model_fpaa.get_memory_footprint()
         self.model_abit.get_memory_footprint()

         for name, module in self.model_abit.named_modules():
               if isinstance(_lowerCamelCase       ,  torch.nn.Linear							):
                     if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                           # 4-bit parameters are packed in uint8 variables
                           self.assertTrue(module.weight.dtype == torch.uinta							)

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






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)
         __lowerCamelCase     :      Any       						=						self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)

         self.assertIn(self.tokenizer.decode(output_sequences[0]       ,  skip_special_tokens=_lowerCamelCase							)       ,  self.EXPECTED_OUTPUTS							)

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






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						BitsAndBytesConfig()
         __lowerCamelCase     :      Union[str, Any]       						=						True

         __lowerCamelCase     :      Dict       						=						AutoModelForCausalLM.from_pretrained(
             self.model_name       ,  quantization_config=_lowerCamelCase       ,  device_map="""auto"""							)

         __lowerCamelCase     :      str       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)
         __lowerCamelCase     :      List[Any]       						=						model_abit_from_config.generate(
             input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)

         self.assertIn(self.tokenizer.decode(output_sequences[0]       ,  skip_special_tokens=_lowerCamelCase							)       ,  self.EXPECTED_OUTPUTS							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         with self.assertRaises(_lowerCamelCase							), tempfile.TemporaryDirectory() as tmpdirname:
               self.model_abit.save_pretrained(_lowerCamelCase							)

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      Union[str, Any]       						=						BitsAndBytesConfig()

         with self.assertRaises(_lowerCamelCase							):
               __lowerCamelCase     :      List[Any]       						=						AutoModelForCausalLM.from_pretrained(
                   self.model_name       ,  quantization_config=_lowerCamelCase       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""       ,  bnb_abit_quant_type="""nf4"""       ,  )

   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         with self.assertRaises(_lowerCamelCase							):
               # Tries with `str`
               self.model_abit.to("""cpu"""							)

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `dtype``
               self.model_abit.to(torch.floataa							)

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `device`
               self.model_abit.to(torch.device("""cuda:0"""							)							)

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `device`
               self.model_abit.float()

         with self.assertRaises(_lowerCamelCase							):
               # Tries with a `device`
               self.model_abit.half()

         # Test if we did not break anything
         __lowerCamelCase     :      Union[str, Any]       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)

         __lowerCamelCase     :      str       						=						self.model_fpaa.to(torch.floataa							)
         __lowerCamelCase     :      str       						=						self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)

         # Check this does not throw an error
         __lowerCamelCase     :      List[Any]       						=						self.model_fpaa.to("""cpu"""							)

         # Check this does not throw an error
         __lowerCamelCase     :      str       						=						self.model_fpaa.half()

         # Check this does not throw an error
         __lowerCamelCase     :      Dict       						=						self.model_fpaa.float()


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






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa							)



@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 							_UpperCamelCase (						unittest.TestCase						):

   '''simple docstring'''







   @classmethod
   def   _snake_case    (  cls  : Union[str, Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[int]       						=						"""t5-small"""
         __lowerCamelCase     :      List[str]       						=						"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
         __lowerCamelCase     :      List[str]       						=						AutoTokenizer.from_pretrained(cls.model_name							)
         __lowerCamelCase     :      Optional[Any]       						=						"""Translate in German: Hello, my dog is cute"""

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         gc.collect()
         torch.cuda.empty_cache()

   def   _snake_case    (  self  : List[str]							):






         '''simple docstring'''



         from transformers import TaForConditionalGeneration

         __lowerCamelCase     :      List[str]       						=						TaForConditionalGeneration._keep_in_fpaa_modules
         __lowerCamelCase     :      int       						=						None

         # test with `t5-small`
         __lowerCamelCase     :      Optional[int]       						=						TaForConditionalGeneration.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Any       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Union[str, Any]       						=						model.generate(**_lowerCamelCase							)

         # test with `flan-t5-small`
         __lowerCamelCase     :      List[str]       						=						TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Optional[int]       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Union[str, Any]       						=						model.generate(**_lowerCamelCase							)
         __lowerCamelCase     :      int       						=						modules


   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         import bitsandbytes as bnb

         from transformers import TaForConditionalGeneration

         # test with `t5-small`
         __lowerCamelCase     :      Union[str, Any]       						=						TaForConditionalGeneration.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)

         # there was a bug with decoders - this test checks that it is fixed
         self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q       ,  bnb.nn.Linearabit							)							)

         __lowerCamelCase     :      Dict       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Optional[int]       						=						model.generate(**_lowerCamelCase							)

         # test with `flan-t5-small`
         __lowerCamelCase     :      Optional[Any]       						=						TaForConditionalGeneration.from_pretrained(
             self.dense_act_model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         __lowerCamelCase     :      Dict       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							).to(0							)
         __lowerCamelCase     :      Any       						=						model.generate(**_lowerCamelCase							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         super().setUp()
         # model_name
         __lowerCamelCase     :      int       						=						"""bigscience/bloom-560m"""
         __lowerCamelCase     :      Union[str, Any]       						=						"""t5-small"""

         # Different types of model

         __lowerCamelCase     :      Any       						=						AutoModel.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         # Sequence classification model
         __lowerCamelCase     :      Dict       						=						AutoModelForSequenceClassification.from_pretrained(
             self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         # CausalLM model
         __lowerCamelCase     :      Any       						=						AutoModelForCausalLM.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)
         # Seq2seq model
         __lowerCamelCase     :      Dict       						=						AutoModelForSeqaSeqLM.from_pretrained(
             self.seq_to_seq_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""auto"""							)

   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         del self.base_model
         del self.sequence_model
         del self.model_abit
         del self.seq_to_seq_model

         gc.collect()
         torch.cuda.empty_cache()


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         from bitsandbytes.nn import Paramsabit

         self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit							)

         # Other heads should be nn.Parameter
         self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter							)
         self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter							)
         self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         super().setUp()

   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         del self.pipe

         gc.collect()
         torch.cuda.empty_cache()


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						pipeline(
             """text-generation"""       ,  model=self.model_name       ,  model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}       ,  max_new_tokens=self.MAX_NEW_TOKENS       ,  )

         # Real second forward pass
         __lowerCamelCase     :      Union[str, Any]       						=						self.pipe(self.input_text							)
         self.assertIn(pipeline_output[0]["""generated_text"""]       ,  self.EXPECTED_OUTPUTS							)



@require_torch_multi_gpu
class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         super().setUp()


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      int       						=						AutoModelForCausalLM.from_pretrained(
             self.model_name       ,  load_in_abit=_lowerCamelCase       ,  device_map="""balanced"""							)

         # Check correct device map
         self.assertEqual(set(model_parallel.hf_device_map.values()							)       ,  {0, 1}							)

         # Check that inference pass works on the model
         __lowerCamelCase     :      Tuple       						=						self.tokenizer(self.input_text       ,  return_tensors="""pt"""							)

         # Second real batch
         __lowerCamelCase     :      Tuple       						=						model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0							)       ,  max_new_tokens=1_0							)
         self.assertIn(self.tokenizer.decode(output_parallel[0]       ,  skip_special_tokens=_lowerCamelCase							)       ,  self.EXPECTED_OUTPUTS							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   def   _snake_case    (  self  : int							):






         '''simple docstring'''



         __lowerCamelCase     :      str       						=						"""facebook/opt-350m"""
         super().setUp()


   def   _snake_case    (  self  : str							):






         '''simple docstring'''



         if version.parse(importlib.metadata.version("""bitsandbytes"""							)							) < version.parse("""0.37.0"""							):
               return

         # Step 1: freeze all parameters
         __lowerCamelCase     :      Optional[int]       						=						AutoModelForCausalLM.from_pretrained(self.model_name       ,  load_in_abit=_lowerCamelCase							)

         self.assertEqual(set(model.hf_device_map.values()							)       ,  {torch.cuda.current_device()}							)

         for param in model.parameters():
               __lowerCamelCase     :      int       						=						False  # freeze the model - train adapters later
               if param.ndim == 1:
                     # cast the small parameters (e.g. layernorm) to fp32 for stability
                     __lowerCamelCase     :      str       						=						param.data.to(torch.floataa							)

        # Step 2: add adapters
         for _, module in model.named_modules():
               if "OPTAttention" in repr(type(_lowerCamelCase							)							):
                     __lowerCamelCase     :      List[Any]       						=						LoRALayer(module.q_proj       ,  rank=1_6							)
                     __lowerCamelCase     :      Dict       						=						LoRALayer(module.k_proj       ,  rank=1_6							)
                     __lowerCamelCase     :      List[Any]       						=						LoRALayer(module.v_proj       ,  rank=1_6							)

        # Step 3: dummy batch
         __lowerCamelCase     :      Tuple       						=						self.tokenizer("""Test batch """       ,  return_tensors="""pt"""							).to(0							)

         # Step 4: Check if the gradient is not None
         with torch.cuda.amp.autocast():
               __lowerCamelCase     :      List[Any]       						=						model.forward(**_lowerCamelCase							)
               out.logits.norm().backward()

         for module in model.modules():
               if isinstance(_lowerCamelCase       ,  _lowerCamelCase							):
                     self.assertTrue(module.adapter[1].weight.grad is not None							)
                     self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0							)
               elif isinstance(_lowerCamelCase       ,  nn.Embedding							):
                     self.assertTrue(module.weight.grad is None							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							List[str]    = "gpt2-xl"
   a_				:							Dict    = 3.31_91_85_48_54_15_21_87







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


__UpperCamelCase    :						Tuple     =							logging.get_logger(__name__)

__UpperCamelCase    :						Any     =							{
    'facebook/nllb-moe-54B': 'https://huggingface.co/facebook/nllb-moe-54b/resolve/main/config.json',
}



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							int    = "nllb-moe"
   a_				:							Optional[int]    = ["past_key_values"]
   a_				:							Optional[Any]    = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}

   def __init__(  self  : int       ,  _lowerCamelCase  : Union[str, Any]=1_2_8_1_1_2       ,  _lowerCamelCase  : List[str]=1_0_2_4       ,  _lowerCamelCase  : Optional[int]=1_2       ,  _lowerCamelCase  : int=4_0_9_6       ,  _lowerCamelCase  : Union[str, Any]=1_6       ,  _lowerCamelCase  : int=1_2       ,  _lowerCamelCase  : Optional[int]=4_0_9_6       ,  _lowerCamelCase  : Any=1_6       ,  _lowerCamelCase  : Any=0.05       ,  _lowerCamelCase  : List[Any]=0.05       ,  _lowerCamelCase  : Tuple=True       ,  _lowerCamelCase  : Any=True       ,  _lowerCamelCase  : Optional[int]="relu"       ,  _lowerCamelCase  : Dict=1_0_2_4       ,  _lowerCamelCase  : Optional[int]=0.1       ,  _lowerCamelCase  : Optional[int]=0.1       ,  _lowerCamelCase  : Optional[int]=0.0       ,  _lowerCamelCase  : List[Any]=0.02       ,  _lowerCamelCase  : List[str]=2       ,  _lowerCamelCase  : List[str]=True       ,  _lowerCamelCase  : Union[str, Any]=False       ,  _lowerCamelCase  : Optional[Any]="float32"       ,  _lowerCamelCase  : Union[str, Any]=False       ,  _lowerCamelCase  : Tuple=1_2_8       ,  _lowerCamelCase  : Dict=6_4       ,  _lowerCamelCase  : Any=4       ,  _lowerCamelCase  : List[Any]=4       ,  _lowerCamelCase  : str=0.001       ,  _lowerCamelCase  : Dict=0.001       ,  _lowerCamelCase  : Any="all"       ,  _lowerCamelCase  : Optional[Any]=False       ,  _lowerCamelCase  : List[Any]=False       ,  _lowerCamelCase  : Optional[Any]=1.0       ,  _lowerCamelCase  : List[str]=0.2       ,  _lowerCamelCase  : List[Any]=1       ,  _lowerCamelCase  : str=0       ,  _lowerCamelCase  : int=2       ,  _lowerCamelCase  : int=False       ,  **_lowerCamelCase  : List[str]       ,  ):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						vocab_size
         __lowerCamelCase     :      str       						=						max_position_embeddings
         __lowerCamelCase     :      int       						=						d_model
         __lowerCamelCase     :      Optional[Any]       						=						encoder_ffn_dim
         __lowerCamelCase     :      str       						=						encoder_layers
         __lowerCamelCase     :      Tuple       						=						encoder_attention_heads
         __lowerCamelCase     :      Union[str, Any]       						=						decoder_ffn_dim
         __lowerCamelCase     :      Dict       						=						decoder_layers
         __lowerCamelCase     :      Any       						=						decoder_attention_heads
         __lowerCamelCase     :      Union[str, Any]       						=						dropout
         __lowerCamelCase     :      Dict       						=						attention_dropout
         __lowerCamelCase     :      int       						=						activation_dropout
         __lowerCamelCase     :      Union[str, Any]       						=						activation_function
         __lowerCamelCase     :      Optional[int]       						=						init_std
         __lowerCamelCase     :      Union[str, Any]       						=						encoder_layerdrop
         __lowerCamelCase     :      Dict       						=						decoder_layerdrop
         __lowerCamelCase     :      List[str]       						=						use_cache
         __lowerCamelCase     :      Any       						=						encoder_layers
         __lowerCamelCase     :      List[Any]       						=						scale_embedding  # scale factor will be sqrt(d_model) if True
         __lowerCamelCase     :      Optional[Any]       						=						router_z_loss_coef
         __lowerCamelCase     :      Union[str, Any]       						=						router_aux_loss_coef
         __lowerCamelCase     :      Dict       						=						decoder_sparse_step
         __lowerCamelCase     :      Optional[int]       						=						encoder_sparse_step
         __lowerCamelCase     :      Any       						=						num_experts
         __lowerCamelCase     :      int       						=						expert_capacity
         __lowerCamelCase     :      Dict       						=						router_bias
         if router_dtype not in ["float32", "float16", "bfloat16"]:
               raise ValueError(F"""`router_dtype` must be one of 'float32', 'float16' or 'bfloat16', got {router_dtype}"""							)
         __lowerCamelCase     :      Tuple       						=						router_dtype

         __lowerCamelCase     :      Dict       						=						router_ignore_padding_tokens
         __lowerCamelCase     :      str       						=						batch_prioritized_routing
         __lowerCamelCase     :      List[str]       						=						second_expert_policy
         __lowerCamelCase     :      Tuple       						=						normalize_router_prob_before_dropping
         __lowerCamelCase     :      Dict       						=						moe_eval_capacity_token_fraction
         __lowerCamelCase     :      Optional[int]       						=						moe_token_dropout
         __lowerCamelCase     :      Dict       						=						output_router_logits
         super().__init__(
             pad_token_id=_lowerCamelCase       ,  bos_token_id=_lowerCamelCase       ,  eos_token_id=_lowerCamelCase       ,  is_encoder_decoder=_lowerCamelCase       ,  decoder_start_token_id=_lowerCamelCase       ,  **_lowerCamelCase       ,  )



