
import unittest

import numpy as np

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
						from transformers.models.bert.modeling_flax_bert import (
						    FlaxBertForMaskedLM,
						    FlaxBertForMultipleChoice,
						    FlaxBertForNextSentencePrediction,
						    FlaxBertForPreTraining,
						    FlaxBertForQuestionAnswering,
						    FlaxBertForSequenceClassification,
						    FlaxBertForTokenClassification,
						    FlaxBertModel,
						)





class   UpperCamelCase_      (      unittest.TestCase		):







						'''simple docstring'''



						def __init__(			self							:List[str]		,    lowerCAmelCase__							:List[Any]		,    lowerCAmelCase__							:Union[str, Any]=13		,    lowerCAmelCase__							:Union[str, Any]=7		,    lowerCAmelCase__							:Tuple=True		,    lowerCAmelCase__							:Union[str, Any]=True		,    lowerCAmelCase__							:str=True		,    lowerCAmelCase__							:Tuple=True		,    lowerCAmelCase__							:str=99		,    lowerCAmelCase__							:List[Any]=32		,    lowerCAmelCase__							:List[Any]=5		,    lowerCAmelCase__							:Union[str, Any]=4		,    lowerCAmelCase__							:Any=37		,    lowerCAmelCase__							:int="gelu"		,    lowerCAmelCase__							:Dict=0.1		,    lowerCAmelCase__							:Optional[int]=0.1		,    lowerCAmelCase__							:Tuple=512		,    lowerCAmelCase__							:int=16		,    lowerCAmelCase__							:Optional[int]=2		,    lowerCAmelCase__							:Optional[Any]=0.02		,    lowerCAmelCase__							:str=4		,    )  ->int:
										lowercase         =      parent
										lowercase         =      batch_size
										lowercase         =      seq_length
										lowercase         =      is_training
										lowercase         =      use_attention_mask
										lowercase         =      use_token_type_ids
										lowercase         =      use_labels
										lowercase         =      vocab_size
										lowercase         =      hidden_size
										lowercase         =      num_hidden_layers
										lowercase         =      num_attention_heads
										lowercase         =      intermediate_size
										lowercase         =      hidden_act
										lowercase         =      hidden_dropout_prob
										lowercase         =      attention_probs_dropout_prob
										lowercase         =      max_position_embeddings
										lowercase         =      type_vocab_size
										lowercase         =      type_sequence_label_size
										lowercase         =      initializer_range
										lowercase         =      num_choices





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->List[str]:
										lowercase         =      ids_tensor([self.batch_size, self.seq_length]		,    self.vocab_size     )

										lowercase         =      None
										if self.use_attention_mask:
														lowercase         =      random_attention_mask([self.batch_size, self.seq_length]     )

										lowercase         =      None
										if self.use_token_type_ids:
														lowercase         =      ids_tensor([self.batch_size, self.seq_length]		,    self.type_vocab_size     )

										lowercase         =      BertConfig(
										    vocab_size=self.vocab_size		,    hidden_size=self.hidden_size		,    num_hidden_layers=self.num_hidden_layers		,    num_attention_heads=self.num_attention_heads		,    intermediate_size=self.intermediate_size		,    hidden_act=self.hidden_act		,    hidden_dropout_prob=self.hidden_dropout_prob		,    attention_probs_dropout_prob=self.attention_probs_dropout_prob		,    max_position_embeddings=self.max_position_embeddings		,    type_vocab_size=self.type_vocab_size		,    is_decoder=lowerCAmelCase__		,    initializer_range=self.initializer_range		,    )

										return config, input_ids, token_type_ids, attention_mask





						def        SCREAMING_SNAKE_CASE(			self							:List[Any]     )  ->Any:
										lowercase         =      self.prepare_config_and_inputs()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      config_and_inputs
										lowercase         =      {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
										return config, inputs_dict





						def        SCREAMING_SNAKE_CASE(			self							:List[str]     )  ->Any:
										lowercase         =      self.prepare_config_and_inputs()
										lowercase     ,	lowercase     ,	lowercase     ,	lowercase         =      config_and_inputs

										lowercase         =      True
										lowercase         =      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]     )
										lowercase         =      ids_tensor([self.batch_size, self.seq_length]		,    vocab_size=2     )

										return (
										    config,
										    input_ids,
										    attention_mask,
										    encoder_hidden_states,
										    encoder_attention_mask,
										)





@require_flax
class   UpperCamelCase_      (      __a		,    unittest.TestCase		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					True

						UpperCamelCase      :  Optional[int]				   =					(
						    (
						        FlaxBertModel,
						        FlaxBertForPreTraining,
						        FlaxBertForMaskedLM,
						        FlaxBertForMultipleChoice,
						        FlaxBertForQuestionAnswering,
						        FlaxBertForNextSentencePrediction,
						        FlaxBertForSequenceClassification,
						        FlaxBertForTokenClassification,
						        FlaxBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def        SCREAMING_SNAKE_CASE(			self							:Union[str, Any]     )  ->str:
										lowercase         =      FlaxBertModelTester(self     )





						@slow
						def        SCREAMING_SNAKE_CASE(			self							:int     )  ->str:
										# Only check this for base model, not necessary for all model classes.
										# This will also help speed-up tests.
										lowercase         =      FlaxBertModel.from_pretrained("bert-base-cased"     )
										lowercase         =      model(np.ones((1, 1)     )     )
										self.assertIsNotNone(lowerCAmelCase__     )


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


_snake_case      :							List[str]     	=    logging.get_logger(__name__)

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





class   UpperCamelCase_      (      __a		):







						'''simple docstring'''



						UpperCamelCase      :  List[str]				   =					'''switch_transformers'''
						UpperCamelCase      :  Dict				   =					['''past_key_values''']
						UpperCamelCase      :  Union[str, Any]				   =					{'''hidden_size''': '''d_model''', '''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}





						def __init__(			self							:int		,    lowerCAmelCase__							:str=32128		,    lowerCAmelCase__							:Union[str, Any]=768		,    lowerCAmelCase__							:Tuple=64		,    lowerCAmelCase__							:Any=2048		,    lowerCAmelCase__							:Optional[int]=64		,    lowerCAmelCase__							:Any=12		,    lowerCAmelCase__							:Any=3		,    lowerCAmelCase__							:Union[str, Any]=12		,    lowerCAmelCase__							:Tuple=3		,    lowerCAmelCase__							:str=12		,    lowerCAmelCase__							:Dict=8		,    lowerCAmelCase__							:Union[str, Any]=False		,    lowerCAmelCase__							:List[str]=0.01		,    lowerCAmelCase__							:Union[str, Any]="float32"		,    lowerCAmelCase__							:Optional[int]=False		,    lowerCAmelCase__							:Optional[Any]=32		,    lowerCAmelCase__							:int=128		,    lowerCAmelCase__							:Optional[Any]=0.1		,    lowerCAmelCase__							:Optional[Any]=1E-6		,    lowerCAmelCase__							:Any=0.0_01		,    lowerCAmelCase__							:Union[str, Any]=0.0_01		,    lowerCAmelCase__							:List[Any]=1.0		,    lowerCAmelCase__							:Optional[Any]="relu"		,    lowerCAmelCase__							:Optional[int]=True		,    lowerCAmelCase__							:List[Any]=False		,    lowerCAmelCase__							:int=True		,    lowerCAmelCase__							:int=0		,    lowerCAmelCase__							:Optional[Any]=1		,    **lowerCAmelCase__							:List[Any]		,    )  ->Dict:
										lowercase         =      vocab_size
										lowercase         =      d_model
										lowercase         =      d_kv
										lowercase         =      d_ff

										lowercase         =      num_sparse_encoder_layers

										lowercase         =      num_layers
										lowercase         =      (
										    num_decoder_layers if num_decoder_layers is not None else self.num_layers
										)  # default = symmetry
										lowercase         =      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:
														lowercase         =      self.num_layers // self.num_sparse_encoder_layers
										else:
														lowercase         =      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:
														lowercase         =      self.num_decoder_layers // self.num_sparse_decoder_layers
										else:
														lowercase         =      self.num_decoder_layers  # HACK: this will create 0 sparse layers

										lowercase         =      num_heads
										lowercase         =      num_experts
										lowercase         =      expert_capacity
										lowercase         =      router_bias
										lowercase         =      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}'''     )
										lowercase         =      router_dtype

										lowercase         =      router_ignore_padding_tokens
										lowercase         =      relative_attention_num_buckets
										lowercase         =      relative_attention_max_distance

										lowercase         =      dropout_rate
										lowercase         =      layer_norm_epsilon
										lowercase         =      initializer_factor
										lowercase         =      feed_forward_proj
										lowercase         =      use_cache
										lowercase         =      add_router_probs

										lowercase         =      router_z_loss_coef
										lowercase         =      router_aux_loss_coef

										lowercase         =      self.feed_forward_proj.split("-"     )
										lowercase         =      act_info[-1]
										lowercase         =      act_info[0] == "gated"

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

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