
"""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 	_lowercase       (    unittest.TestCase		):




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




	def       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      Optional[int]          =   ids_tensor([self.batch_size, self.seq_length]		,			self.vocab_size )

			lowerCamelCase					:      str          =   None
			if self.use_attention_mask:
					lowerCamelCase					:      int          =   random_attention_mask([self.batch_size, self.seq_length] )

			lowerCamelCase					:      Tuple          =   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_=UpperCAmelCase_		,			)

			return config, input_ids, attention_mask




	def       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Dict          =   self.prepare_config_and_inputs()
			lowerCamelCase     ,   lowerCamelCase     ,   lowerCamelCase					:      int          =   config_and_inputs
			lowerCamelCase					:      List[str]          =   {'input_ids': input_ids, 'attention_mask': attention_mask}
			return config, inputs_dict






@require_flax
class 	_lowercase       (    __UpperCAmelCase    ,	unittest.TestCase		):
	lowercase_				=     (
	    (
	        FlaxDistilBertModel,
	        FlaxDistilBertForMaskedLM,
	        FlaxDistilBertForMultipleChoice,
	        FlaxDistilBertForQuestionAnswering,
	        FlaxDistilBertForSequenceClassification,
	        FlaxDistilBertForTokenClassification,
	        FlaxDistilBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)




	def       _UpperCamelCase   (			self )		->							Optional[int]:
			lowerCamelCase					:      Union[str, Any]          =   FlaxDistilBertModelTester(self )




	@slow
	def       _UpperCamelCase   (			self )		->							List[str]:
			for model_class_name in self.all_model_classes:
					lowerCamelCase					:      int          =   model_class_name.from_pretrained('distilbert-base-uncased' )
					lowerCamelCase					:      List[str]          =   model(np.ones((1, 1) ) )
					self.assertIsNotNone(UpperCAmelCase_ )






@require_flax
class 	_lowercase       (    unittest.TestCase		):




	@slow
	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
			lowerCamelCase					:      Any          =   np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
			lowerCamelCase					:      int          =   np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
			lowerCamelCase					:      int          =   model(UpperCAmelCase_		,			attention_mask=UpperCAmelCase_ )[0]
			lowerCamelCase					:      Optional[int]          =   (1, 11, 768)
			self.assertEqual(output.shape		,			UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   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]		,			UpperCAmelCase_		,			atol=1E-4 ) )








"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

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


_A						=			logging.get_logger(__name__)

_A						=			{
    'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
    'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
    'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
    # See all BigBird models at https://huggingface.co/models?filter=big_bird
}






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'big_bird'




	def __init__(			self		,			UpperCAmelCase_=50358		,			UpperCAmelCase_=768		,			UpperCAmelCase_=12		,			UpperCAmelCase_=12		,			UpperCAmelCase_=3072		,			UpperCAmelCase_="gelu_new"		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=0.1		,			UpperCAmelCase_=4096		,			UpperCAmelCase_=2		,			UpperCAmelCase_=0.02		,			UpperCAmelCase_=1E-1_2		,			UpperCAmelCase_=True		,			UpperCAmelCase_=0		,			UpperCAmelCase_=1		,			UpperCAmelCase_=2		,			UpperCAmelCase_=66		,			UpperCAmelCase_="block_sparse"		,			UpperCAmelCase_=True		,			UpperCAmelCase_=False		,			UpperCAmelCase_=64		,			UpperCAmelCase_=3		,			UpperCAmelCase_=None		,			**UpperCAmelCase_		,			)		->							Union[str, Any]:
			super().__init__(
			    pad_token_id=UpperCAmelCase_		,			bos_token_id=UpperCAmelCase_		,			eos_token_id=UpperCAmelCase_		,			sep_token_id=UpperCAmelCase_		,			**UpperCAmelCase_		,			)

			lowerCamelCase					:      Tuple          =   vocab_size
			lowerCamelCase					:      str          =   max_position_embeddings
			lowerCamelCase					:      str          =   hidden_size
			lowerCamelCase					:      Optional[int]          =   num_hidden_layers
			lowerCamelCase					:      List[str]          =   num_attention_heads
			lowerCamelCase					:      List[str]          =   intermediate_size
			lowerCamelCase					:      Optional[int]          =   hidden_act
			lowerCamelCase					:      Optional[int]          =   hidden_dropout_prob
			lowerCamelCase					:      Dict          =   attention_probs_dropout_prob
			lowerCamelCase					:      int          =   initializer_range
			lowerCamelCase					:      Union[str, Any]          =   type_vocab_size
			lowerCamelCase					:      Union[str, Any]          =   layer_norm_eps
			lowerCamelCase					:      int          =   use_cache

			lowerCamelCase					:      Tuple          =   rescale_embeddings
			lowerCamelCase					:      Dict          =   attention_type
			lowerCamelCase					:      Optional[int]          =   use_bias
			lowerCamelCase					:      Optional[int]          =   block_size
			lowerCamelCase					:      Tuple          =   num_random_blocks
			lowerCamelCase					:      List[Any]          =   classifier_dropout






class 	_lowercase       (    __UpperCAmelCase		):




	@property
	def       _UpperCamelCase   (			self )		->							Mapping[str, Mapping[int, str]]:
			if self.task == "multiple-choice":
					lowerCamelCase					:      Tuple          =   {0: 'batch', 1: 'choice', 2: 'sequence'}
			else:
					lowerCamelCase					:      int          =   {0: 'batch', 1: 'sequence'}
			return OrderedDict(
			    [
			        ('input_ids', dynamic_axis),
			        ('attention_mask', dynamic_axis),
			    ] )






