





"""simple docstring"""



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


a_					=     logging.get_logger(__name__)

a_					=     {
    """google/switch-base-8""": """https://huggingface.co/google/switch-base-8/blob/main/config.json""",
}






class 						__snake_case   (						SCREAMING_SNAKE_CASE__	):





 """simple docstring"""


 _lowerCamelCase								=	"""switch_transformers"""
 _lowerCamelCase								=	["""past_key_values"""]
 _lowerCamelCase								=	{"""hidden_size""": """d_model""", """num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}

 def __init__(       self   ,		__lowerCamelCase=3_2128   ,		__lowerCamelCase=768   ,		__lowerCamelCase=64   ,		__lowerCamelCase=2048   ,		__lowerCamelCase=64   ,		__lowerCamelCase=12   ,		__lowerCamelCase=3   ,		__lowerCamelCase=12   ,		__lowerCamelCase=3   ,		__lowerCamelCase=12   ,		__lowerCamelCase=8   ,		__lowerCamelCase=False   ,		__lowerCamelCase=0.0_1   ,		__lowerCamelCase="float32"   ,		__lowerCamelCase=False   ,		__lowerCamelCase=32   ,		__lowerCamelCase=128   ,		__lowerCamelCase=0.1   ,		__lowerCamelCase=1e-6   ,		__lowerCamelCase=0.0_0_1   ,		__lowerCamelCase=0.0_0_1   ,		__lowerCamelCase=1.0   ,		__lowerCamelCase="relu"   ,		__lowerCamelCase=True   ,		__lowerCamelCase=False   ,		__lowerCamelCase=True   ,		__lowerCamelCase=0   ,		__lowerCamelCase=1   ,		**__lowerCamelCase   ,		):



     '''simple docstring'''




     __A       :		Dict							=	vocab_size
     __A       :		Dict							=	d_model
     __A       :		List[Any]							=	d_kv
     __A       :		List[str]							=	d_ff

     __A       :		Optional[int]							=	num_sparse_encoder_layers

     __A       :		Optional[Any]							=	num_layers
     __A       :		int							=	(
         num_decoder_layers if num_decoder_layers is not None else self.num_layers
     )  # default = symmetry
     __A       :		Optional[Any]							=	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       :		str							=	self.num_layers // self.num_sparse_encoder_layers
     else:
         __A       :		Any							=	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       :		List[Any]							=	self.num_decoder_layers // self.num_sparse_decoder_layers
     else:
         __A       :		Tuple							=	self.num_decoder_layers  # HACK: this will create 0 sparse layers

     __A       :		Union[str, Any]							=	num_heads
     __A       :		Optional[int]							=	num_experts
     __A       :		Optional[int]							=	expert_capacity
     __A       :		List[Any]							=	router_bias
     __A       :		Optional[Any]							=	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       :		Union[str, Any]							=	router_dtype

     __A       :		Union[str, Any]							=	router_ignore_padding_tokens
     __A       :		Tuple							=	relative_attention_num_buckets
     __A       :		str							=	relative_attention_max_distance

     __A       :		Dict							=	dropout_rate
     __A       :		Tuple							=	layer_norm_epsilon
     __A       :		Optional[int]							=	initializer_factor
     __A       :		int							=	feed_forward_proj
     __A       :		Optional[Any]							=	use_cache
     __A       :		Tuple							=	add_router_probs

     __A       :		Optional[int]							=	router_z_loss_coef
     __A       :		str							=	router_aux_loss_coef

     __A       :		Dict							=	self.feed_forward_proj.split('''-'''    )
     __A       :		str							=	act_info[-1]
     __A       :		Tuple							=	act_info[0] == "gated"

     if len(snake_case__    ) > 1 and act_info[0] != "gated" or len(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       :		str							=	"gelu_new"

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












"""simple docstring"""



import unittest

from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow


if is_flax_available():
 import optax
 from flax.training.common_utils import onehot

 from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
 from transformers.models.ta.modeling_flax_ta import shift_tokens_right




@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class 			lowerCAmelCase__			(    unittest.TestCase   ):
   @slow
   def       __a							(		self      :	List[str]							):


        '''simple docstring'''
        UpperCAmelCase__						:    Union[str, Any]							=							FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small"							)
        UpperCAmelCase__						:    str							=							AutoTokenizer.from_pretrained("google/mt5-small"							)

        UpperCAmelCase__						:    str							=							tokenizer("Hello there"							,			return_tensors="np"							).input_ids
        UpperCAmelCase__						:    Optional[Any]							=							tokenizer("Hi I am"							,			return_tensors="np"							).input_ids

        UpperCAmelCase__						:    Any							=							shift_tokens_right(snake_case__							,			model.config.pad_token_id							,			model.config.decoder_start_token_id							)

        UpperCAmelCase__						:    Any							=							model(snake_case__							,			decoder_input_ids=snake_case__							).logits
        UpperCAmelCase__						:    Any							=							optax.softmax_cross_entropy(snake_case__							,			onehot(snake_case__							,			logits.shape[-1]							)							).mean()

        UpperCAmelCase__						:    List[str]							=							-(labels.shape[-1] * loss.item())

        UpperCAmelCase__						:    Dict							=							-84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)


