




"""simple docstring"""






from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging


UpperCamelCase__						=			logging.get_logger(__name__)

UpperCamelCase__						=			{
    'google/umt5-small': 'https://huggingface.co/google/umt5-small/resolve/main/config.json',
    # See all umt5 models at https://huggingface.co/models?filter=umt5
}




class   a__			(       UpperCamelCase_  ):
						snake_case__             = '''umt5'''
						snake_case__             = ['''past_key_values''']





						def __init__(							self      :	int     ,a__      :	List[Any]=25_0112     ,a__      :	Optional[int]=512     ,a__      :	Tuple=64     ,a__      :	Optional[int]=1024     ,a__      :	Optional[Any]=8     ,a__      :	Tuple=None     ,a__      :	int=6     ,a__      :	List[str]=32     ,a__      :	List[Any]=128     ,a__      :	Union[str, Any]=0.1     ,a__      :	Optional[int]=1E-6     ,a__      :	List[Any]=1.0     ,a__      :	Dict="gated-gelu"     ,a__      :	List[Any]=True     ,a__      :	Dict=True     ,a__      :	List[Any]="T5Tokenizer"     ,a__      :	int=True     ,a__      :	Optional[Any]=0     ,a__      :	Any=1     ,a__      :	Optional[Any]=0     ,**a__      :	Tuple     ,)						->       Any:




											"""simple docstring"""







											super().__init__(
											    is_encoder_decoder=__A     ,tokenizer_class=__A     ,tie_word_embeddings=__A     ,pad_token_id=__A     ,eos_token_id=__A     ,decoder_start_token_id=__A     ,**__A     ,)
											_lowerCAmelCase:List[Any]									=  vocab_size
											_lowerCAmelCase:Tuple									=  d_model
											_lowerCAmelCase:Any									=  d_kv
											_lowerCAmelCase:Dict									=  d_ff
											_lowerCAmelCase:Optional[int]									=  num_layers
											_lowerCAmelCase:Any									=  (
											    num_decoder_layers if num_decoder_layers is not None else self.num_layers
											)  # default = symmetry
											_lowerCAmelCase:List[Any]									=  num_heads
											_lowerCAmelCase:Optional[int]									=  relative_attention_num_buckets
											_lowerCAmelCase:Tuple									=  relative_attention_max_distance
											_lowerCAmelCase:List[str]									=  dropout_rate
											_lowerCAmelCase:Union[str, Any]									=  layer_norm_epsilon
											_lowerCAmelCase:Optional[int]									=  initializer_factor
											_lowerCAmelCase:Optional[int]									=  feed_forward_proj
											_lowerCAmelCase:Optional[int]									=  use_cache

											_lowerCAmelCase:int									=  self.feed_forward_proj.split('''-''')
											_lowerCAmelCase:Optional[int]									=  act_info[-1]
											_lowerCAmelCase:List[Any]									=  act_info[0] == "gated"

											if len(__A) > 1 and act_info[0] != "gated" or len(__A) > 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\'''')

											if feed_forward_proj == "gated-gelu":
																_lowerCAmelCase:str									=  "gelu_new"





						@property
						def       __UpperCamelCase     (							self      :	int)						->       Dict:




											"""simple docstring"""







											return self.d_model





						@property
						def       __UpperCamelCase     (							self      :	int)						->       List[str]:




											"""simple docstring"""







											return self.num_heads







						@property
						def       __UpperCamelCase     (							self      :	Optional[int])						->       Union[str, Any]:




											"""simple docstring"""







											return self.num_layers




class   a__			(       UpperCamelCase_  ):





						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
						def       __UpperCamelCase     (							self      :	List[Any])						->       List[str]:




											"""simple docstring"""







											_lowerCAmelCase:List[str]									=  {
											    "input_ids": {0: "batch", 1: "encoder_sequence"},
											    "attention_mask": {0: "batch", 1: "encoder_sequence"},
											}
											if self.use_past:
																_lowerCAmelCase:Optional[Any]									=  "past_encoder_sequence + sequence"
																_lowerCAmelCase:Union[str, Any]									=  {0: "batch"}
																_lowerCAmelCase:List[Any]									=  {0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
																_lowerCAmelCase:int									=  {0: "batch", 1: "decoder_sequence"}
																_lowerCAmelCase:List[Any]									=  {0: "batch", 1: "decoder_sequence"}

											if self.use_past:
																self.fill_with_past_key_values_(__A     ,direction='''inputs''')

											return common_inputs





						@property
						# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
						def       __UpperCamelCase     (							self      :	str)						->       str:




											"""simple docstring"""







											return 13







						@property
						def       __UpperCamelCase     (							self      :	int)						->       Tuple:




											"""simple docstring"""







											return 5E-4







"""simple docstring"""






import unittest

import numpy as np

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

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.distilbert.modeling_flax_distilbert import (
				    FlaxDistilBertForMaskedLM,
				    FlaxDistilBertForMultipleChoice,
				    FlaxDistilBertForQuestionAnswering,
				    FlaxDistilBertForSequenceClassification,
				    FlaxDistilBertForTokenClassification,
				    FlaxDistilBertModel,
				)




class   a__			(       unittest.TestCase  ):





						def __init__(							self      :	Dict     ,a__      :	List[Any]     ,a__      :	Optional[int]=13     ,a__      :	Dict=7     ,a__      :	Any=True     ,a__      :	Tuple=True     ,a__      :	Union[str, Any]=True     ,a__      :	Dict=True     ,a__      :	Dict=99     ,a__      :	Optional[Any]=32     ,a__      :	Dict=5     ,a__      :	List[Any]=4     ,a__      :	Union[str, Any]=37     ,a__      :	str="gelu"     ,a__      :	Any=0.1     ,a__      :	Optional[int]=0.1     ,a__      :	Tuple=512     ,a__      :	Any=16     ,a__      :	Any=2     ,a__      :	Dict=0.02     ,a__      :	int=4     ,)						->       Union[str, Any]:




											"""simple docstring"""







											_lowerCAmelCase:Optional[int]									=  parent
											_lowerCAmelCase:List[str]									=  batch_size
											_lowerCAmelCase:Tuple									=  seq_length
											_lowerCAmelCase:List[Any]									=  is_training
											_lowerCAmelCase:Dict									=  use_attention_mask
											_lowerCAmelCase:Dict									=  use_token_type_ids
											_lowerCAmelCase:Any									=  use_labels
											_lowerCAmelCase:str									=  vocab_size
											_lowerCAmelCase:int									=  hidden_size
											_lowerCAmelCase:Union[str, Any]									=  num_hidden_layers
											_lowerCAmelCase:List[str]									=  num_attention_heads
											_lowerCAmelCase:Optional[int]									=  intermediate_size
											_lowerCAmelCase:str									=  hidden_act
											_lowerCAmelCase:Optional[int]									=  hidden_dropout_prob
											_lowerCAmelCase:Optional[int]									=  attention_probs_dropout_prob
											_lowerCAmelCase:Any									=  max_position_embeddings
											_lowerCAmelCase:Optional[int]									=  type_vocab_size
											_lowerCAmelCase:Optional[int]									=  type_sequence_label_size
											_lowerCAmelCase:Tuple									=  initializer_range
											_lowerCAmelCase:Dict									=  num_choices





						def       __UpperCamelCase     (							self      :	List[str])						->       int:




											"""simple docstring"""







											_lowerCAmelCase:int									=  ids_tensor([self.batch_size, self.seq_length]     ,self.vocab_size)

											_lowerCAmelCase:List[Any]									=  None
											if self.use_attention_mask:
																_lowerCAmelCase:str									=  random_attention_mask([self.batch_size, self.seq_length])

											_lowerCAmelCase:Dict									=  DistilBertConfig(
											    vocab_size=self.vocab_size     ,dim=self.hidden_size     ,n_layers=self.num_hidden_layers     ,n_heads=self.num_attention_heads     ,hidden_dim=self.intermediate_size     ,hidden_act=self.hidden_act     ,dropout=self.hidden_dropout_prob     ,attention_dropout=self.attention_probs_dropout_prob     ,max_position_embeddings=self.max_position_embeddings     ,initializer_range=self.initializer_range     ,tie_weights_=a__     ,)

											return config, input_ids, attention_mask







						def       __UpperCamelCase     (							self      :	Any)						->       Any:




											"""simple docstring"""







											_lowerCAmelCase:Optional[Any]									=  self.prepare_config_and_inputs()
											_lowerCAmelCase							,   _lowerCAmelCase							,   _lowerCAmelCase:Optional[Any]									=  config_and_inputs
											_lowerCAmelCase:Any									=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
											return config, inputs_dict




@require_flax
class   a__			(       UpperCamelCase_					,	unittest.TestCase  ):
						snake_case__             = (
						    (
						        FlaxDistilBertModel,
						        FlaxDistilBertForMaskedLM,
						        FlaxDistilBertForMultipleChoice,
						        FlaxDistilBertForQuestionAnswering,
						        FlaxDistilBertForSequenceClassification,
						        FlaxDistilBertForTokenClassification,
						        FlaxDistilBertForQuestionAnswering,
						    )
						    if is_flax_available()
						    else ()
						)





						def       __UpperCamelCase     (							self      :	str)						->       Dict:




											"""simple docstring"""







											_lowerCAmelCase:str									=  FlaxDistilBertModelTester(self)







						@slow
						def       __UpperCamelCase     (							self      :	List[str])						->       int:




											"""simple docstring"""







											for model_class_name in self.all_model_classes:
																_lowerCAmelCase:List[str]									=  model_class_name.from_pretrained('''distilbert-base-uncased''')
																_lowerCAmelCase:Tuple									=  model(np.ones((1, 1)))
																self.assertIsNotNone(a__)




@require_flax
class   a__			(       unittest.TestCase  ):





						@slow
						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[Any]:




											"""simple docstring"""







											_lowerCAmelCase:Any									=  FlaxDistilBertModel.from_pretrained('''distilbert-base-uncased''')
											_lowerCAmelCase:Optional[int]									=  np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
											_lowerCAmelCase:Tuple									=  np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
											_lowerCAmelCase:List[str]									=  model(a__     ,attention_mask=a__)[0]
											_lowerCAmelCase:List[str]									=  (1, 11, 768)
											self.assertEqual(output.shape     ,a__)
											_lowerCAmelCase:int									=  np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]])

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]     ,a__     ,atol=1E-4))






