import unittest

import numpy as np

from transformers import RobertaConfig, 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.roberta.modeling_flax_roberta import (
      FlaxRobertaForCausalLM,
      FlaxRobertaForMaskedLM,
      FlaxRobertaForMultipleChoice,
      FlaxRobertaForQuestionAnswering,
      FlaxRobertaForSequenceClassification,
      FlaxRobertaForTokenClassification,
      FlaxRobertaModel,
  )
class  A_			(							unittest.TestCase			):

  def __init__(		self			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[Any]  ,				__SCREAMING_SNAKE_CASE			:   List[str]=13  ,				__SCREAMING_SNAKE_CASE			:   Dict=7  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=True  ,				__SCREAMING_SNAKE_CASE			:   int=True  ,				__SCREAMING_SNAKE_CASE			:   int=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=99  ,				__SCREAMING_SNAKE_CASE			:   int=32  ,				__SCREAMING_SNAKE_CASE			:   int=5  ,				__SCREAMING_SNAKE_CASE			:   Any=4  ,				__SCREAMING_SNAKE_CASE			:   Dict=37  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.1  ,				__SCREAMING_SNAKE_CASE			:   str=5_12  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=16  ,				__SCREAMING_SNAKE_CASE			:   Tuple=2  ,				__SCREAMING_SNAKE_CASE			:   int=0.02  ,				__SCREAMING_SNAKE_CASE			:   int=4  ,				):
   __a							=	parent
   __a							=	batch_size
   __a							=	seq_length
   __a							=	is_training
   __a							=	use_attention_mask
   __a							=	use_token_type_ids
   __a							=	use_labels
   __a							=	vocab_size
   __a							=	hidden_size
   __a							=	num_hidden_layers
   __a							=	num_attention_heads
   __a							=	intermediate_size
   __a							=	hidden_act
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	max_position_embeddings
   __a							=	type_vocab_size
   __a							=	type_sequence_label_size
   __a							=	initializer_range
   __a							=	num_choices

  def 		_UpperCAmelCase (		self			:   str							):
   __a							=	ids_tensor([self.batch_size, self.seq_length]  ,				self.vocab_size							)

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

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

   __a							=	RobertaConfig(
       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=__SCREAMING_SNAKE_CASE  ,				initializer_range=self.initializer_range  ,				)

   return config, input_ids, token_type_ids, attention_mask

  def 		_UpperCAmelCase (		self			:   Optional[Any]							):
   __a							=	self.prepare_config_and_inputs()
   __a							,  __a							,  __a							,  __a							=	config_and_inputs
   __a							=	{"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
   return config, inputs_dict

  def 		_UpperCAmelCase (		self			:   List[str]							):
   __a							=	self.prepare_config_and_inputs()
   __a							,  __a							,  __a							,  __a							=	config_and_inputs

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

   return (
       config,
       input_ids,
       token_type_ids,
       encoder_hidden_states,
       encoder_attention_mask,
   )
@require_flax
class  A_			(							a_      ,							unittest.TestCase			):
  _SCREAMING_SNAKE_CASE     					=      True

  _SCREAMING_SNAKE_CASE     					=      (
      (
          FlaxRobertaModel,
          FlaxRobertaForCausalLM,
          FlaxRobertaForMaskedLM,
          FlaxRobertaForSequenceClassification,
          FlaxRobertaForTokenClassification,
          FlaxRobertaForMultipleChoice,
          FlaxRobertaForQuestionAnswering,
      )
      if is_flax_available()
      else ()
  )

  def 		_UpperCAmelCase (		self			:   int							):
   __a							=	FlaxRobertaModelTester(self							)

  @slow
  def 		_UpperCAmelCase (		self			:   Union[str, Any]							):
   for model_class_name in self.all_model_classes:
    __a							=	model_class_name.from_pretrained("roberta-base"  ,				from_pt=__SCREAMING_SNAKE_CASE							)
    __a							=	model(np.ones((1, 1)							)							)
    self.assertIsNotNone(__SCREAMING_SNAKE_CASE							)



from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE			:						Optional[Any]				  =		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			:						List[Any]				  =		{
    """shi-labs/nat-mini-in1k-224""": """https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json""",
    # See all Nat models at https://huggingface.co/models?filter=nat
}
class  A_			(							a_      ,							a_			):
  _SCREAMING_SNAKE_CASE     					=      """nat"""

  _SCREAMING_SNAKE_CASE     					=      {
      """num_attention_heads""": """num_heads""",
      """num_hidden_layers""": """num_layers""",
  }

  def __init__(		self			:   str  ,				__SCREAMING_SNAKE_CASE			:   Tuple=4  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=3  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=64  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=[3, 4, 6, 5]  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]=[2, 4, 8, 16]  ,				__SCREAMING_SNAKE_CASE			:   int=7  ,				__SCREAMING_SNAKE_CASE			:   List[str]=3.0  ,				__SCREAMING_SNAKE_CASE			:   List[Any]=True  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=0.0  ,				__SCREAMING_SNAKE_CASE			:   int=0.1  ,				__SCREAMING_SNAKE_CASE			:   Union[str, Any]="gelu"  ,				__SCREAMING_SNAKE_CASE			:   Optional[Any]=0.02  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=1E-5  ,				__SCREAMING_SNAKE_CASE			:   Optional[int]=0.0  ,				__SCREAMING_SNAKE_CASE			:   str=None  ,				__SCREAMING_SNAKE_CASE			:   int=None  ,				**__SCREAMING_SNAKE_CASE			:   List[str]  ,				):
   super().__init__(**__SCREAMING_SNAKE_CASE							)

   __a							=	patch_size
   __a							=	num_channels
   __a							=	embed_dim
   __a							=	depths
   __a							=	len(__SCREAMING_SNAKE_CASE							)
   __a							=	num_heads
   __a							=	kernel_size
   __a							=	mlp_ratio
   __a							=	qkv_bias
   __a							=	hidden_dropout_prob
   __a							=	attention_probs_dropout_prob
   __a							=	drop_path_rate
   __a							=	hidden_act
   __a							=	layer_norm_eps
   __a							=	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   __a							=	int(embed_dim * 2 ** (len(__SCREAMING_SNAKE_CASE							) - 1)							)
   __a							=	layer_scale_init_value
   __a							=	["stem"] + [f"""stage{idx}""" for idx in range(1  ,				len(__SCREAMING_SNAKE_CASE							) + 1							)]
   __a							,  __a							=	get_aligned_output_features_output_indices(
       out_features=__SCREAMING_SNAKE_CASE  ,				out_indices=__SCREAMING_SNAKE_CASE  ,				stage_names=self.stage_names							)


