


'''simple docstring'''




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  lowerCAmelCase				(				unittest.TestCase							):



	def __init__(					self ,					snake_case__ ,					snake_case__=13 ,					snake_case__=7 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=True ,					snake_case__=99 ,					snake_case__=32 ,					snake_case__=5 ,					snake_case__=4 ,					snake_case__=37 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=16 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=4 ,					):
		lowerCAmelCase					:    Any      							= parent
		lowerCAmelCase					:    Dict      							= batch_size
		lowerCAmelCase					:    int      							= seq_length
		lowerCAmelCase					:    List[str]      							= is_training
		lowerCAmelCase					:    Tuple      							= use_attention_mask
		lowerCAmelCase					:    Optional[Any]      							= use_token_type_ids
		lowerCAmelCase					:    str      							= use_labels
		lowerCAmelCase					:    List[str]      							= vocab_size
		lowerCAmelCase					:    Union[str, Any]      							= hidden_size
		lowerCAmelCase					:    Optional[Any]      							= num_hidden_layers
		lowerCAmelCase					:    Optional[Any]      							= num_attention_heads
		lowerCAmelCase					:    Optional[Any]      							= intermediate_size
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    List[Any]      							= hidden_dropout_prob
		lowerCAmelCase					:    Union[str, Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    List[Any]      							= max_position_embeddings
		lowerCAmelCase					:    Union[str, Any]      							= type_vocab_size
		lowerCAmelCase					:    Optional[Any]      							= type_sequence_label_size
		lowerCAmelCase					:    Optional[Any]      							= initializer_range
		lowerCAmelCase					:    List[str]      							= num_choices



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Tuple      							= ids_tensor([self.batch_size, self.seq_length] ,					self.vocab_size    )

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

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

		lowerCAmelCase					:    Any      							= 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=snake_case__ ,					initializer_range=self.initializer_range ,					)

		return config, input_ids, token_type_ids, attention_mask



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    Union[str, Any]      							= self.prepare_config_and_inputs()
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    Dict      							= config_and_inputs
		lowerCAmelCase					:    Optional[int]      							= {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
		return config, inputs_dict



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    List[Any]      							= self.prepare_config_and_inputs()
		lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase					:    List[Any]      							= config_and_inputs

		lowerCAmelCase					:    Optional[int]      							= True
		lowerCAmelCase					:    List[str]      							= floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
		lowerCAmelCase					:    str      							= 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  lowerCAmelCase				(				a      ,				unittest.TestCase							):
	_lowerCamelCase   :					List[Any]			      = True

	_lowerCamelCase   :					Any			      = (
	    (
	        FlaxBertModel,
	        FlaxBertForPreTraining,
	        FlaxBertForMaskedLM,
	        FlaxBertForMultipleChoice,
	        FlaxBertForQuestionAnswering,
	        FlaxBertForNextSentencePrediction,
	        FlaxBertForSequenceClassification,
	        FlaxBertForTokenClassification,
	        FlaxBertForQuestionAnswering,
	    )
	    if is_flax_available()
	    else ()
	)



	def 				lowercase     (					self    ):
		lowerCAmelCase					:    str      							= FlaxBertModelTester(self    )



	@slow
	def 				lowercase     (					self    ):
		# Only check this for base model, not necessary for all model classes.
		# This will also help speed-up tests.
		lowerCAmelCase					:    List[Any]      							= FlaxBertModel.from_pretrained('bert-base-cased'    )
		lowerCAmelCase					:    Dict      							= model(np.ones((1, 1)    )    )
		self.assertIsNotNone(snake_case__    )




'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

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


_lowerCAmelCase  :		Tuple		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		List[str]		     =	{
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					int			      = """xmod"""



	def __init__(					self ,					snake_case__=3_0522 ,					snake_case__=768 ,					snake_case__=12 ,					snake_case__=12 ,					snake_case__=3072 ,					snake_case__="gelu" ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=512 ,					snake_case__=2 ,					snake_case__=0.0_2 ,					snake_case__=1e-1_2 ,					snake_case__=1 ,					snake_case__=0 ,					snake_case__=2 ,					snake_case__="absolute" ,					snake_case__=True ,					snake_case__=None ,					snake_case__=False ,					snake_case__=2 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=True ,					snake_case__=("en_XX",) ,					snake_case__=None ,					**snake_case__ ,					):
		super().__init__(pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					**snake_case__    )

		lowerCAmelCase					:    Dict      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= hidden_size
		lowerCAmelCase					:    int      							= num_hidden_layers
		lowerCAmelCase					:    List[Any]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Optional[int]      							= intermediate_size
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= attention_probs_dropout_prob
		lowerCAmelCase					:    str      							= max_position_embeddings
		lowerCAmelCase					:    int      							= type_vocab_size
		lowerCAmelCase					:    List[Any]      							= initializer_range
		lowerCAmelCase					:    Any      							= layer_norm_eps
		lowerCAmelCase					:    Dict      							= position_embedding_type
		lowerCAmelCase					:    Optional[Any]      							= use_cache
		lowerCAmelCase					:    Union[str, Any]      							= classifier_dropout
		lowerCAmelCase					:    int      							= pre_norm
		lowerCAmelCase					:    Optional[Any]      							= adapter_reduction_factor
		lowerCAmelCase					:    Any      							= adapter_layer_norm
		lowerCAmelCase					:    Dict      							= adapter_reuse_layer_norm
		lowerCAmelCase					:    Any      							= ln_before_adapter
		lowerCAmelCase					:    Optional[Any]      							= list(snake_case__    )
		lowerCAmelCase					:    List[Any]      							= default_language



class  lowerCAmelCase				(				a							):



	@property
	def 				lowercase     (					self    ):
		if self.task == "multiple-choice":
			lowerCAmelCase					:    List[Any]      							= {0: 'batch', 1: 'choice', 2: 'sequence'}
		else:
			lowerCAmelCase					:    Optional[int]      							= {0: 'batch', 1: 'sequence'}
		return OrderedDict(
		    [
		        ('input_ids', dynamic_axis),
		        ('attention_mask', dynamic_axis),
		    ]    )
