



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


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

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



class        lowerCAmelCase__					(  _lowerCamelCase       ):
				A_   :     Dict								=     'nllb-moe'
				A_   :     Optional[int]								=     ['past_key_values']
				A_   :     Any								=     {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}


				def __init__(							self      :  int						, __UpperCamelCase      :  int=128_112						, __UpperCamelCase      :  str=1_024						, __UpperCamelCase      :  Optional[Any]=12						, __UpperCamelCase      :  str=4_096						, __UpperCamelCase      :  Tuple=16						, __UpperCamelCase      :  List[str]=12						, __UpperCamelCase      :  Optional[Any]=4_096						, __UpperCamelCase      :  int=16						, __UpperCamelCase      :  str=0.0_5						, __UpperCamelCase      :  Optional[Any]=0.0_5						, __UpperCamelCase      :  Dict=True						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  Any="relu"						, __UpperCamelCase      :  Any=1_024						, __UpperCamelCase      :  Dict=0.1						, __UpperCamelCase      :  Union[str, Any]=0.1						, __UpperCamelCase      :  int=0.0						, __UpperCamelCase      :  int=0.0_2						, __UpperCamelCase      :  str=2						, __UpperCamelCase      :  str=True						, __UpperCamelCase      :  Dict=False						, __UpperCamelCase      :  List[Any]="float32"						, __UpperCamelCase      :  Tuple=False						, __UpperCamelCase      :  Any=128						, __UpperCamelCase      :  Optional[int]=64						, __UpperCamelCase      :  List[str]=4						, __UpperCamelCase      :  Dict=4						, __UpperCamelCase      :  int=0.0_0_1						, __UpperCamelCase      :  Dict=0.0_0_1						, __UpperCamelCase      :  Optional[Any]="all"						, __UpperCamelCase      :  List[str]=False						, __UpperCamelCase      :  Tuple=False						, __UpperCamelCase      :  Any=1.0						, __UpperCamelCase      :  List[str]=0.2						, __UpperCamelCase      :  str=1						, __UpperCamelCase      :  Optional[int]=0						, __UpperCamelCase      :  Union[str, Any]=2						, __UpperCamelCase      :  Dict=False						, **__UpperCamelCase      :  Optional[Any]						, )		->     Optional[int]:
											A      						=    vocab_size
											A      						=    max_position_embeddings
											A      						=    d_model
											A      						=    encoder_ffn_dim
											A      						=    encoder_layers
											A      						=    encoder_attention_heads
											A      						=    decoder_ffn_dim
											A      						=    decoder_layers
											A      						=    decoder_attention_heads
											A      						=    dropout
											A      						=    attention_dropout
											A      						=    activation_dropout
											A      						=    activation_function
											A      						=    init_std
											A      						=    encoder_layerdrop
											A      						=    decoder_layerdrop
											A      						=    use_cache
											A      						=    encoder_layers
											A      						=    scale_embedding  # scale factor will be sqrt(d_model) if True
											A      						=    router_z_loss_coef
											A      						=    router_aux_loss_coef
											A      						=    decoder_sparse_step
											A      						=    encoder_sparse_step
											A      						=    num_experts
											A      						=    expert_capacity
											A      						=    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}'''       )
											A      						=    router_dtype

											A      						=    router_ignore_padding_tokens
											A      						=    batch_prioritized_routing
											A      						=    second_expert_policy
											A      						=    normalize_router_prob_before_dropping
											A      						=    moe_eval_capacity_token_fraction
											A      						=    moe_token_dropout
											A      						=    output_router_logits
											super().__init__(
											    pad_token_id=__UpperCamelCase						, bos_token_id=__UpperCamelCase						, eos_token_id=__UpperCamelCase						, is_encoder_decoder=__UpperCamelCase						, decoder_start_token_id=__UpperCamelCase						, **__UpperCamelCase						, )







import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
					import jax.numpy as jnp

if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch
class     lowerCamelCase_					(   unittest.TestCase			):







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   {
								    '''task_specific_params''': {
								        '''summarization''': {'''length_penalty''': 1.0, '''max_length''': 1_2_8, '''min_length''': 1_2, '''num_beams''': 4},
								        '''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 1_4_2, '''min_length''': 5_6, '''num_beams''': 4},
								        '''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 6_2, '''min_length''': 1_1, '''num_beams''': 6},
								    }
								}
								__magic_name__		:List[str]   =   {
								    '''task_specific_params.summarization.length_penalty''': 1.0,
								    '''task_specific_params.summarization.max_length''': 1_2_8,
								    '''task_specific_params.summarization.min_length''': 1_2,
								    '''task_specific_params.summarization.num_beams''': 4,
								    '''task_specific_params.summarization_cnn.length_penalty''': 2.0,
								    '''task_specific_params.summarization_cnn.max_length''': 1_4_2,
								    '''task_specific_params.summarization_cnn.min_length''': 5_6,
								    '''task_specific_params.summarization_cnn.num_beams''': 4,
								    '''task_specific_params.summarization_xsum.length_penalty''': 1.0,
								    '''task_specific_params.summarization_xsum.max_length''': 6_2,
								    '''task_specific_params.summarization_xsum.min_length''': 1_1,
								    '''task_specific_params.summarization_xsum.num_beams''': 6,
								}

								self.assertEqual(flatten_dict(__lowerCAmelCase  )							,       __lowerCAmelCase  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       x.transpose()  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       x.transpose((1, 2, 0)  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       transpose(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:int   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Union[str, Any]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(3							,       4  )
								__magic_name__		:Optional[Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       transpose(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:List[str]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Optional[Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(3							,       4  )
								__magic_name__		:Dict   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase  )							,       np.asarray(transpose(__lowerCAmelCase  )  )  )  )

								__magic_name__		:Dict   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Dict   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )							,       np.asarray(transpose(__lowerCAmelCase							,       axes=(1, 2, 0)  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       np.reshape(__lowerCAmelCase							,       (4, 3)  )  )  )

								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4							,       5  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       np.reshape(__lowerCAmelCase							,       (1_2, 5)  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       reshape(__lowerCAmelCase							,       (4, 3)  ).numpy()  )  )

								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:List[str]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       reshape(__lowerCAmelCase							,       (1_2, 5)  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(3							,       4  )
								__magic_name__		:Union[str, Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       reshape(__lowerCAmelCase							,       (4, 3)  ).numpy()  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:Optional[int]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       reshape(__lowerCAmelCase							,       (1_2, 5)  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   np.random.randn(3							,       4  )
								__magic_name__		:Any   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (4, 3)  )							,       np.asarray(reshape(__lowerCAmelCase							,       (4, 3)  )  )  )  )

								__magic_name__		:List[Any]   =   np.random.randn(3							,       4							,       5  )
								__magic_name__		:List[str]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(reshape(__lowerCAmelCase							,       (1_2, 5)  )							,       np.asarray(reshape(__lowerCAmelCase							,       (1_2, 5)  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   np.random.randn(1							,       3							,       4  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       np.squeeze(__lowerCAmelCase  )  )  )

								__magic_name__		:Optional[Any]   =   np.random.randn(1							,       4							,       1							,       5  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       np.squeeze(__lowerCAmelCase							,       axis=2  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:List[Any]   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       squeeze(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:List[str]   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:str   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       squeeze(__lowerCAmelCase							,       axis=2  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:int   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:Tuple   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       squeeze(__lowerCAmelCase  ).numpy()  )  )

								__magic_name__		:Tuple   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:Optional[int]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       squeeze(__lowerCAmelCase							,       axis=2  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   np.random.randn(1							,       3							,       4  )
								__magic_name__		:Optional[Any]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase  )							,       np.asarray(squeeze(__lowerCAmelCase  )  )  )  )

								__magic_name__		:List[Any]   =   np.random.randn(1							,       4							,       1							,       5  )
								__magic_name__		:Optional[Any]   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(squeeze(__lowerCAmelCase							,       axis=2  )							,       np.asarray(squeeze(__lowerCAmelCase							,       axis=2  )  )  )  )







				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Any   =   np.random.randn(3							,       4  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       np.expand_dims(__lowerCAmelCase							,       axis=1  )  )  )







				@require_torch
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Any   =   torch.tensor(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       expand_dims(__lowerCAmelCase							,       axis=1  ).numpy()  )  )







				@require_tf
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:Union[str, Any]   =   np.random.randn(3							,       4  )
								__magic_name__		:Union[str, Any]   =   tf.constant(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       expand_dims(__lowerCAmelCase							,       axis=1  ).numpy()  )  )







				@require_flax
				def       A						(    self  ):
								"""simple docstring"""
								__magic_name__		:List[str]   =   np.random.randn(3							,       4  )
								__magic_name__		:Tuple   =   jnp.array(__lowerCAmelCase  )
								self.assertTrue(np.allclose(expand_dims(__lowerCAmelCase							,       axis=1  )							,       np.asarray(expand_dims(__lowerCAmelCase							,       axis=1  )  )  )  )






