


'''simple docstring'''




import unittest

from transformers import BigBirdConfig, 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

       from transformers.models.big_bird.modeling_flax_big_bird import (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
           FlaxBigBirdModel,
       )

class 						UpperCAmelCase_ (    unittest.TestCase						):





     def __init__(					self			: List[Any]       , UpperCAmelCase__			: Any       , UpperCAmelCase__			: str=2       , UpperCAmelCase__			: int=5_6       , UpperCAmelCase__			: Tuple=True       , UpperCAmelCase__			: int=True       , UpperCAmelCase__			: str=True       , UpperCAmelCase__			: Dict=True       , UpperCAmelCase__			: List[Any]=9_9       , UpperCAmelCase__			: int=3_2       , UpperCAmelCase__			: List[str]=2       , UpperCAmelCase__			: str=2       , UpperCAmelCase__			: Any=7       , UpperCAmelCase__			: Any="gelu_new"       , UpperCAmelCase__			: List[str]=0.1       , UpperCAmelCase__			: Any=0.1       , UpperCAmelCase__			: Any=5_1_2       , UpperCAmelCase__			: List[Any]=1_6       , UpperCAmelCase__			: List[Any]=2       , UpperCAmelCase__			: Union[str, Any]=0.02       , UpperCAmelCase__			: Any=4       , UpperCAmelCase__			: Optional[int]="block_sparse"       , UpperCAmelCase__			: Dict=True       , UpperCAmelCase__			: Tuple=False       , UpperCAmelCase__			: Optional[Any]=2       , UpperCAmelCase__			: Optional[Any]=3       , )							->    List[str]:
        lowerCAmelCase	=     parent
        lowerCAmelCase	=     batch_size
        lowerCAmelCase	=     seq_length
        lowerCAmelCase	=     is_training
        lowerCAmelCase	=     use_attention_mask
        lowerCAmelCase	=     use_token_type_ids
        lowerCAmelCase	=     use_labels
        lowerCAmelCase	=     vocab_size
        lowerCAmelCase	=     hidden_size
        lowerCAmelCase	=     num_hidden_layers
        lowerCAmelCase	=     num_attention_heads
        lowerCAmelCase	=     intermediate_size
        lowerCAmelCase	=     hidden_act
        lowerCAmelCase	=     hidden_dropout_prob
        lowerCAmelCase	=     attention_probs_dropout_prob
        lowerCAmelCase	=     max_position_embeddings
        lowerCAmelCase	=     type_vocab_size
        lowerCAmelCase	=     type_sequence_label_size
        lowerCAmelCase	=     initializer_range
        lowerCAmelCase	=     num_choices

        lowerCAmelCase	=     rescale_embeddings
        lowerCAmelCase	=     attention_type
        lowerCAmelCase	=     use_bias
        lowerCAmelCase	=     block_size
        lowerCAmelCase	=     num_random_blocks





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

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

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

        lowerCAmelCase	=     BigBirdConfig(
            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       , attention_type=self.attention_type       , block_size=self.block_size       , num_random_blocks=self.num_random_blocks       , use_bias=self.use_bias       , rescale_embeddings=self.rescale_embeddings       , )

        return config, input_ids, token_type_ids, attention_mask





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




@require_flax
class 						UpperCAmelCase_ (    __snake_case			,      unittest.TestCase						):
     lowerCamelCase							:      int            =	(
         (
             FlaxBigBirdForCausalLM,
             FlaxBigBirdModel,
             FlaxBigBirdForPreTraining,
             FlaxBigBirdForMaskedLM,
             FlaxBigBirdForMultipleChoice,
             FlaxBigBirdForQuestionAnswering,
             FlaxBigBirdForSequenceClassification,
             FlaxBigBirdForTokenClassification,
         )
         if is_flax_available()
         else ()
     )

     lowerCamelCase							:      Optional[Any]            =	False
     lowerCamelCase							:      Tuple            =	False





     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    Optional[Any]:
        lowerCAmelCase	=     FlaxBigBirdModelTester(self       )





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 				__UpperCAmelCase (					self			: Optional[Any]       )							->    Optional[int]:
        super().test_from_pretrained_save_pretrained()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 				__UpperCAmelCase (					self			: List[Any]       )							->    Optional[Any]:
        super().test_from_pretrained_with_no_automatic_init()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 				__UpperCAmelCase (					self			: Any       )							->    List[Any]:
        super().test_no_automatic_init()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 				__UpperCAmelCase (					self			: List[str]       )							->    Any:
        super().test_hidden_states_output()





     @slow
     def 				__UpperCAmelCase (					self			: Tuple       )							->    Optional[int]:




        for model_class_name in self.all_model_classes:
           lowerCAmelCase	=     model_class_name.from_pretrained('google/bigbird-roberta-base'       )
           self.assertIsNotNone(_SCREAMING_SNAKE_CASE       )





     def 				__UpperCAmelCase (					self			: Union[str, Any]       )							->    Union[str, Any]:




        if self.test_attn_probs:
           super().test_attention_outputs()





     @slow
     # copied from `test_modeling_flax_common` because it takes much longer than other models
     def 				__UpperCAmelCase (					self			: Tuple       )							->    Optional[int]:
        lowerCAmelCase	,    lowerCAmelCase	=     self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__       ):
              lowerCAmelCase	=     self._prepare_for_class(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       )
              lowerCAmelCase	=     model_class(_SCREAMING_SNAKE_CASE       )

              @jax.jit
              def model_jitted(UpperCAmelCase__			: int       , UpperCAmelCase__			: str=None       , **UpperCAmelCase__			: str       ):
                 return model(input_ids=_SCREAMING_SNAKE_CASE       , attention_mask=_SCREAMING_SNAKE_CASE       , **_SCREAMING_SNAKE_CASE       )

              with self.subTest('JIT Enabled'       ):
                 lowerCAmelCase	=     model_jitted(**_SCREAMING_SNAKE_CASE       ).to_tuple()

              with self.subTest('JIT Disabled'       ):
                 with jax.disable_jit():
                    lowerCAmelCase	=     model_jitted(**_SCREAMING_SNAKE_CASE       ).to_tuple()

              self.assertEqual(len(_SCREAMING_SNAKE_CASE       )       , len(_SCREAMING_SNAKE_CASE       )       )
              for jitted_output, output in zip(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       ):
                 self.assertEqual(jitted_output.shape       , output.shape       )





     def 				__UpperCAmelCase (					self			: str       , UpperCAmelCase__			: Optional[Any]       , UpperCAmelCase__			: Tuple       , UpperCAmelCase__			: Tuple       , UpperCAmelCase__			: Optional[int]=1E-5       , UpperCAmelCase__			: Tuple="outputs"       , UpperCAmelCase__			: Dict=None       )							->    Tuple:




        if name.startswith('outputs.attentions'       ):
           return
        else:
           super().check_pt_flax_outputs(_SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       , _SCREAMING_SNAKE_CASE       )













import re

import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey

from ..utils import logging


__magic_name__   :  Optional[int]        =							logging.get_logger(__name__)
def 		lowercase__  (				_UpperCamelCase) ->			Dict:
      """simple docstring"""

      UpperCamelCase					       =  r'\w+[.]\d+'
      UpperCamelCase					       =  re.findall(_UpperCamelCase       ,							_UpperCamelCase)
      for pat in pats:
            UpperCamelCase					       =  key.replace(_UpperCamelCase       ,							'_'.join(pat.split('.')))
      return key
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase) ->			Tuple:
      """simple docstring"""

      UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
      if (
          any('norm' in str_ for str_ in pt_tuple_key)
          and (pt_tuple_key[-1] == "bias")
          and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
          and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
      ):
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor
      elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('scale',)
            return renamed_pt_tuple_key, pt_tensor

      # embedding
      if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
            UpperCamelCase					       =  pt_tuple_key[:-1] + ('embedding',)
            return renamed_pt_tuple_key, pt_tensor

      # conv layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
            UpperCamelCase					       =  pt_tensor.transpose(2       ,							3       ,							1       ,							0)
            return renamed_pt_tuple_key, pt_tensor

      # linear layer
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('kernel',)
      if pt_tuple_key[-1] == "weight":
            UpperCamelCase					       =  pt_tensor.T
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm weight
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('weight',)
      if pt_tuple_key[-1] == "gamma":
            return renamed_pt_tuple_key, pt_tensor

      # old PyTorch layer norm bias
      UpperCamelCase					       =  pt_tuple_key[:-1] + ('bias',)
      if pt_tuple_key[-1] == "beta":
            return renamed_pt_tuple_key, pt_tensor

      return pt_tuple_key, pt_tensor
def 		lowercase__  (				_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase=42) ->			Optional[Any]:
      """simple docstring"""

      UpperCamelCase					       =  {k: v.numpy() for k, v in pt_state_dict.items()}

      # Step 2: Since the model is stateless, get random Flax params
      UpperCamelCase					       =  flax_model.init_weights(PRNGKey(_UpperCamelCase))

      UpperCamelCase					       =  flatten_dict(_UpperCamelCase)
      UpperCamelCase					       =  {}

      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
            UpperCamelCase					       =  rename_key(_UpperCamelCase)
            UpperCamelCase					       =  tuple(renamed_pt_key.split('.'))

            # Correctly rename weight parameters
            UpperCamelCase       ,  UpperCamelCase					       =  rename_key_and_reshape_tensor(_UpperCamelCase       ,							_UpperCamelCase       ,							_UpperCamelCase)

            if flax_key in random_flax_state_dict:
                  if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
                        raise ValueError(
                            F'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                            F'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.')

        # also add unexpected weight so that warning is thrown
            UpperCamelCase					       =  jnp.asarray(_UpperCamelCase)

      return unflatten_dict(_UpperCamelCase)



