


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 A  (   unittest.TestCase     ):






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




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






      def        lowerCamelCase       (					self						:     Any )					->						str:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=ids_tensor([self.batch_size, self.seq_length]			,   self.vocab_size )

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

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

             _lowerCamelCase       :		Union[str, 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=lowercase_			,   initializer_range=self.initializer_range			,   )

             return config, input_ids, token_type_ids, attention_mask






      def        lowerCamelCase       (					self						:     str )					->						Optional[Any]:
             """simple docstring"""




             _lowerCamelCase       :		Tuple		=self.prepare_config_and_inputs()
             _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase	,     _lowerCamelCase       :		Optional[int]		=config_and_inputs
             _lowerCamelCase       :		Tuple		={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
             return config, inputs_dict




@require_flax
class A  (   UpperCamelCase_    ,							unittest.TestCase     ):
      UpperCamelCase__							:  Union[str, Any]      =True

      UpperCamelCase__							:  Tuple      =(
          (
              FlaxRoFormerModel,
              FlaxRoFormerForMaskedLM,
              FlaxRoFormerForSequenceClassification,
              FlaxRoFormerForTokenClassification,
              FlaxRoFormerForMultipleChoice,
              FlaxRoFormerForQuestionAnswering,
          )
          if is_flax_available()
          else ()
      )






      def        lowerCamelCase       (					self						:     List[str] )					->						int:
             """simple docstring"""




             _lowerCamelCase       :		str		=FlaxRoFormerModelTester(self )






      @slow
      def        lowerCamelCase       (					self						:     List[str] )					->						Optional[int]:
             """simple docstring"""




             for model_class_name in self.all_model_classes:
                    _lowerCamelCase       :		int		=model_class_name.from_pretrained('junnyu/roformer_chinese_small'			,   from_pt=lowercase_ )
                    _lowerCamelCase       :		Union[str, Any]		=model(np.ones((1, 1) ) )
                    self.assertIsNotNone(lowercase_ )




@require_flax
class A  (   unittest.TestCase     ):






      @slow
      def        lowerCamelCase       (					self						:     int )					->						Union[str, Any]:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=FlaxRoFormerForMaskedLM.from_pretrained('junnyu/roformer_chinese_base' )
             _lowerCamelCase       :		Union[str, Any]		=jnp.array([[0, 1, 2, 3, 4, 5]] )
             _lowerCamelCase       :		int		=model(lowercase_ )[0]

             _lowerCamelCase       :		List[str]		=5_0000

             _lowerCamelCase       :		str		=(1, 6, vocab_size)
             self.assertEqual(output.shape			,   lowercase_ )

             _lowerCamelCase       :		Tuple		=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]			,   lowercase_			,   atol=1E-4 ) )






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase											=       logging.get_logger(__name__)

lowerCamelCase											=       {
    'transfo-xl-wt103': 'https://huggingface.co/transfo-xl-wt103/resolve/main/config.json',
}




class A  (   UpperCamelCase_     ):
      UpperCamelCase__							:  Dict      ='transfo-xl'
      UpperCamelCase__							:  int      =['mems']
      UpperCamelCase__							:  List[str]      ={
          'n_token': 'vocab_size',
          'hidden_size': 'd_model',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }






      def __init__(					self						:     Tuple			,   lowercase_						:     List[Any]=26_7735			,   lowercase_						:     List[Any]=[2_0000, 4_0000, 20_0000]			,   lowercase_						:     Tuple=1024			,   lowercase_						:     Optional[int]=1024			,   lowercase_						:     str=16			,   lowercase_						:     int=64			,   lowercase_						:     Any=4096			,   lowercase_						:     List[Any]=4			,   lowercase_						:     List[Any]=False			,   lowercase_						:     List[str]=18			,   lowercase_						:     Tuple=1600			,   lowercase_						:     List[str]=1000			,   lowercase_						:     Any=True			,   lowercase_						:     str=True			,   lowercase_						:     Optional[int]=0			,   lowercase_						:     List[Any]=-1			,   lowercase_						:     Optional[int]=True			,   lowercase_						:     Any=0.1			,   lowercase_						:     Tuple=0.0			,   lowercase_						:     str=True			,   lowercase_						:     Union[str, Any]="normal"			,   lowercase_						:     Dict=0.01			,   lowercase_						:     Optional[Any]=0.01			,   lowercase_						:     List[Any]=0.02			,   lowercase_						:     List[Any]=1E-5			,   lowercase_						:     Tuple=0			,   **lowercase_						:     Tuple			,   )					->						Dict:
             """simple docstring"""




             _lowerCamelCase       :		Optional[Any]		=vocab_size
             _lowerCamelCase       :		Optional[Any]		=[]
             self.cutoffs.extend(lowercase_ )
             if proj_share_all_but_first:
                    _lowerCamelCase       :		List[str]		=[False] + [True] * len(self.cutoffs )
             else:
                    _lowerCamelCase       :		str		=[False] + [False] * len(self.cutoffs )
             _lowerCamelCase       :		str		=d_model
             _lowerCamelCase       :		str		=d_embed
             _lowerCamelCase       :		str		=d_head
             _lowerCamelCase       :		Dict		=d_inner
             _lowerCamelCase       :		Union[str, Any]		=div_val
             _lowerCamelCase       :		Dict		=pre_lnorm
             _lowerCamelCase       :		Tuple		=n_layer
             _lowerCamelCase       :		Optional[Any]		=n_head
             _lowerCamelCase       :		List[Any]		=mem_len
             _lowerCamelCase       :		List[Any]		=same_length
             _lowerCamelCase       :		Optional[int]		=attn_type
             _lowerCamelCase       :		List[str]		=clamp_len
             _lowerCamelCase       :		int		=sample_softmax
             _lowerCamelCase       :		List[Any]		=adaptive
             _lowerCamelCase       :		List[str]		=dropout
             _lowerCamelCase       :		Any		=dropatt
             _lowerCamelCase       :		Dict		=untie_r
             _lowerCamelCase       :		List[Any]		=init
             _lowerCamelCase       :		List[str]		=init_range
             _lowerCamelCase       :		int		=proj_init_std
             _lowerCamelCase       :		Optional[int]		=init_std
             _lowerCamelCase       :		Tuple		=layer_norm_epsilon
             super().__init__(eos_token_id=lowercase_			,   **lowercase_ )






      @property
      def        lowerCamelCase       (					self						:     List[str] )					->						Dict:
             """simple docstring"""




             logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )
             return -1






      @max_position_embeddings.setter
      def        lowerCamelCase       (					self						:     int			,   lowercase_						:     int )					->						List[str]:
             """simple docstring"""




             raise NotImplementedError(
                 F'''The model {self.model_type} is one of the few models that has no sequence length limit.''' )


