
import unittest

import numpy as np

from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
       FlaxRoFormerForMaskedLM,
       FlaxRoFormerForMultipleChoice,
       FlaxRoFormerForQuestionAnswering,
       FlaxRoFormerForSequenceClassification,
       FlaxRoFormerForTokenClassification,
       FlaxRoFormerModel,
   )






class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   def __init__(					self     :							int			,			lowerCAmelCase_     :							Optional[int]			,			lowerCAmelCase_     :							Any=1_3			,			lowerCAmelCase_     :							Any=7			,			lowerCAmelCase_     :							List[str]=True			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							List[Any]=True			,			lowerCAmelCase_     :							Union[str, Any]=True			,			lowerCAmelCase_     :							int=9_9			,			lowerCAmelCase_     :							Optional[Any]=3_2			,			lowerCAmelCase_     :							Union[str, Any]=5			,			lowerCAmelCase_     :							int=4			,			lowerCAmelCase_     :							int=3_7			,			lowerCAmelCase_     :							str="gelu"			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]=5_1_2			,			lowerCAmelCase_     :							int=1_6			,			lowerCAmelCase_     :							Optional[Any]=2			,			lowerCAmelCase_     :							List[Any]=0.02			,			lowerCAmelCase_     :							Union[str, Any]=4			,			):

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


   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      _A:  Any					=       ids_tensor([self.batch_size, self.seq_length]			,			self.vocab_size     )

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

      _A:  Tuple					=       None
      if self.use_token_type_ids:
         _A:  List[str]					=       ids_tensor([self.batch_size, self.seq_length]			,			self.type_vocab_size     )

      _A:  Optional[Any]					=       RoFormerConfig(
          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  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  Dict					=       self.prepare_config_and_inputs()
      _A			,  _A			,  _A			,  _A:  str					=       config_and_inputs
      _A:  str					=       {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
      return config, inputs_dict






@require_flax
class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							unittest.TestCase   ):





   '''simple docstring'''
   __UpperCamelCase		:    Optional[int] 				=    True

   __UpperCamelCase		:    Union[str, Any] 				=    (
       (
           FlaxRoFormerModel,
           FlaxRoFormerForMaskedLM,
           FlaxRoFormerForSequenceClassification,
           FlaxRoFormerForTokenClassification,
           FlaxRoFormerForMultipleChoice,
           FlaxRoFormerForQuestionAnswering,
       )
       if is_flax_available()
       else ()
   )


   def  __magic_name__				(					self     :							Optional[Any]     ):

      """simple docstring"""
      _A:  str					=       FlaxRoFormerModelTester(self     )


   @slow
   def  __magic_name__				(					self     :							Optional[int]     ):

      """simple docstring"""
      for model_class_name in self.all_model_classes:
         _A:  str					=       model_class_name.from_pretrained('''junnyu/roformer_chinese_small'''			,			from_pt=lowerCAmelCase_     )
         _A:  List[Any]					=       model(np.ones((1, 1)     )     )
         self.assertIsNotNone(lowerCAmelCase_     )






@require_flax
class UpperCAmelCase   (						unittest.TestCase   ):





   '''simple docstring'''


   @slow
   def  __magic_name__				(					self     :							Any     ):

      """simple docstring"""
      _A:  List[str]					=       FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base'''     )
      _A:  Any					=       jnp.array([[0, 1, 2, 3, 4, 5]]     )
      _A:  int					=       model(lowerCAmelCase_     )[0]

      _A:  int					=       5_0_0_0_0

      _A:  Any					=       (1, 6, vocab_size)
      self.assertEqual(output.shape			,			lowerCAmelCase_     )

      _A:  int					=       jnp.array(
          [[[-0.1205, -1.0265, 0.2922], [-1.5134, 0.1974, 0.1519], [-5.0135, -3.9003, -0.8404]]]     )

      self.assertTrue(jnp.allclose(output[:, :3, :3]			,			lowerCAmelCase_			,			atol=1e-4     )     )





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


UpperCAmelCase__		:     Any     			=      logging.get_logger(__name__)






class UpperCAmelCase   (						SCREAMING_SNAKE_CASE__							,							SCREAMING_SNAKE_CASE__   ):





   '''simple docstring'''
   __UpperCamelCase		:    Dict 				=    '''maskformer-swin'''

   __UpperCamelCase		:    Any 				=    {
       '''num_attention_heads''': '''num_heads''',
       '''num_hidden_layers''': '''num_layers''',
   }


   def __init__(					self     :							Optional[Any]			,			lowerCAmelCase_     :							int=2_2_4			,			lowerCAmelCase_     :							Tuple=4			,			lowerCAmelCase_     :							Any=3			,			lowerCAmelCase_     :							Dict=9_6			,			lowerCAmelCase_     :							Union[str, Any]=[2, 2, 6, 2]			,			lowerCAmelCase_     :							Optional[Any]=[3, 6, 1_2, 2_4]			,			lowerCAmelCase_     :							Optional[Any]=7			,			lowerCAmelCase_     :							Optional[Any]=4.0			,			lowerCAmelCase_     :							int=True			,			lowerCAmelCase_     :							Optional[Any]=0.0			,			lowerCAmelCase_     :							Union[str, Any]=0.0			,			lowerCAmelCase_     :							Dict=0.1			,			lowerCAmelCase_     :							Optional[Any]="gelu"			,			lowerCAmelCase_     :							Optional[int]=False			,			lowerCAmelCase_     :							Dict=0.02			,			lowerCAmelCase_     :							str=1e-5			,			lowerCAmelCase_     :							Union[str, Any]=None			,			lowerCAmelCase_     :							Any=None			,			**lowerCAmelCase_     :							int			,			):

      """simple docstring"""
      super().__init__(**lowerCAmelCase_     )

      _A:  List[Any]					=       image_size
      _A:  Optional[int]					=       patch_size
      _A:  Optional[Any]					=       num_channels
      _A:  str					=       embed_dim
      _A:  Any					=       depths
      _A:  str					=       len(lowerCAmelCase_     )
      _A:  Any					=       num_heads
      _A:  int					=       window_size
      _A:  Dict					=       mlp_ratio
      _A:  str					=       qkv_bias
      _A:  List[str]					=       hidden_dropout_prob
      _A:  List[Any]					=       attention_probs_dropout_prob
      _A:  Dict					=       drop_path_rate
      _A:  List[Any]					=       hidden_act
      _A:  Optional[int]					=       use_absolute_embeddings
      _A:  Tuple					=       layer_norm_eps
      _A:  Union[str, Any]					=       initializer_range
      # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
      # this indicates the channel dimension after the last stage of the model
      _A:  Any					=       int(embed_dim * 2 ** (len(lowerCAmelCase_     ) - 1)     )
      _A:  Tuple					=       ['''stem'''] + [F"""stage{idx}""" for idx in range(1			,			len(lowerCAmelCase_     ) + 1     )]
      _A			,  _A:  str					=       get_aligned_output_features_output_indices(
          out_features=lowerCAmelCase_			,			out_indices=lowerCAmelCase_			,			stage_names=self.stage_names     )



