


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      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     def __init__(						self ,						__lowerCamelCase ,						__lowerCamelCase=1_3 ,						__lowerCamelCase=7 ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=True ,						__lowerCamelCase=9_9 ,						__lowerCamelCase=3_2 ,						__lowerCamelCase=5 ,						__lowerCamelCase=4 ,						__lowerCamelCase=3_7 ,						__lowerCamelCase="gelu" ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=0.1 ,						__lowerCamelCase=5_1_2 ,						__lowerCamelCase=1_6 ,						__lowerCamelCase=2 ,						__lowerCamelCase=0.0_2 ,						__lowerCamelCase=4 ,						)			->				Dict:
            _A      :       Dict				    = parent
            _A      :       Optional[int]				    = batch_size
            _A      :       Dict				    = seq_length
            _A      :       Tuple				    = is_training
            _A      :       Optional[Any]				    = use_attention_mask
            _A      :       Tuple				    = use_token_type_ids
            _A      :       Optional[int]				    = use_labels
            _A      :       str				    = vocab_size
            _A      :       int				    = hidden_size
            _A      :       Dict				    = num_hidden_layers
            _A      :       Any				    = num_attention_heads
            _A      :       Optional[int]				    = intermediate_size
            _A      :       int				    = hidden_act
            _A      :       List[Any]				    = hidden_dropout_prob
            _A      :       Optional[int]				    = attention_probs_dropout_prob
            _A      :       Dict				    = max_position_embeddings
            _A      :       Union[str, Any]				    = type_vocab_size
            _A      :       Dict				    = type_sequence_label_size
            _A      :       Dict				    = initializer_range
            _A      :       int				    = num_choices


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       str				    = ids_tensor([self.batch_size, self.seq_length] ,						self.vocab_size)

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

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

            _A      :       int				    = 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  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Dict				    = self.prepare_config_and_inputs()
            _A    ,			_A    ,			_A    ,			_A      :       Tuple				    = 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      lowerCAmelCase__       (				a     ,    unittest.TestCase):




     '''simple docstring'''
     __SCREAMING_SNAKE_CASE  			= True

     __SCREAMING_SNAKE_CASE  			= (
         (
             FlaxRoFormerModel,
             FlaxRoFormerForMaskedLM,
             FlaxRoFormerForSequenceClassification,
             FlaxRoFormerForTokenClassification,
             FlaxRoFormerForMultipleChoice,
             FlaxRoFormerForQuestionAnswering,
         )
         if is_flax_available()
         else ()
     )


     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            _A      :       Dict				    = FlaxRoFormerModelTester(self)


     @slow
     def  _lowerCamelCase (						self)			->				Union[str, Any]:
            for model_class_name in self.all_model_classes:
                   _A      :       int				    = model_class_name.from_pretrained("junnyu/roformer_chinese_small" ,						from_pt=__lowerCamelCase)
                   _A      :       Dict				    = model(np.ones((1, 1)))
                   self.assertIsNotNone(__lowerCamelCase)







@require_flax
class      lowerCAmelCase__       (				unittest.TestCase):




     '''simple docstring'''


     @slow
     def  _lowerCamelCase (						self)			->				Dict:
            _A      :       Any				    = FlaxRoFormerForMaskedLM.from_pretrained("junnyu/roformer_chinese_base")
            _A      :       List[str]				    = jnp.array([[0, 1, 2, 3, 4, 5]])
            _A      :       str				    = model(__lowerCamelCase)[0]

            _A      :       List[str]				    = 5_0_0_0_0

            _A      :       Dict				    = (1, 6, vocab_size)
            self.assertEqual(output.shape ,						__lowerCamelCase)

            _A      :       Optional[Any]				    = jnp.array(
                [[[-0.1_2_0_5, -1.0_2_6_5, 0.2_9_2_2], [-1.5_1_3_4, 0.1_9_7_4, 0.1_5_1_9], [-5.0_1_3_5, -3.9_0_0_3, -0.8_4_0_4]]])

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




import re

from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P


# Sentinels
lowerCAmelCase__      		= object()

# For specifying empty leaf dict `{}`
lowerCAmelCase__      		= object()
def 							_UpperCAmelCase				(UpperCamelCase__     :  str				,							UpperCamelCase__     :  Union[str, Any]						):
       _A      :       str				    = tuple((re.compile(x + "$"						) for x in qs)						)
       for i in range(len(UpperCamelCase__						) - len(UpperCamelCase__						) + 1						):
              _A      :       Tuple				    = [x.match(UpperCamelCase__						) for x, y in zip(UpperCamelCase__				,							ks[i:]						)]
              if matches and all(UpperCamelCase__						):
                     return True
       return False
def 							_UpperCAmelCase				(UpperCamelCase__     :  str						):
       def replace(UpperCamelCase__     :  List[str]				,							UpperCamelCase__     :  Optional[int]						):
              for rule, replacement in rules:
                     if _match(UpperCamelCase__				,							UpperCamelCase__						):
                            return replacement
              return val

       return replace
def 							_UpperCAmelCase				():
       return [
           # embeddings
           (("transformer", "wpe", "embedding"), P("mp"				,							UpperCamelCase__						)),
           (("transformer", "wte", "embedding"), P("mp"				,							UpperCamelCase__						)),
           # atention
           (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("attention", "out_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("attention", "out_proj", "bias"), None),
           # mlp
           (("mlp", "c_fc", "kernel"), P(UpperCamelCase__				,							"mp"						)),
           (("mlp", "c_fc", "bias"), P("mp"						)),
           (("mlp", "c_proj", "kernel"), P("mp"				,							UpperCamelCase__						)),
           (("mlp", "c_proj", "bias"), None),
           # layer norms
           ((r"ln_\d+", "bias"), None),
           ((r"\d+", r"ln_\d+", "scale"), None),
           (("ln_f", "bias"), None),
           (("ln_f", "scale"), None),
       ]






def 							_UpperCAmelCase				(UpperCamelCase__     :  List[str]						):
       _A      :       int				    = _get_partition_rules()
       _A      :       Optional[int]				    = _replacement_rules(UpperCamelCase__						)
       _A      :       Optional[int]				    = {k: _unmatched for k in flatten_dict(UpperCamelCase__						)}
       _A      :       List[str]				    = {k: replace(UpperCamelCase__				,							UpperCamelCase__						) for k, v in initd.items()}
       assert _unmatched not in result.values(), "Incomplete partition spec."
       return freeze(unflatten_dict(UpperCamelCase__						)						)
