






from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase__				   =		logging.get_logger(__name__)

UpperCAmelCase__				   =		{
    "EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      __snake_case       =  '''gpt_neo'''
      __snake_case       =  ['''past_key_values''']
      __snake_case       =  {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}







      def __init__(      self      :						Dict ,					__UpperCAmelCase      :						str=50_257 ,					__UpperCAmelCase      :						Optional[int]=2_048 ,					__UpperCAmelCase      :						Optional[Any]=2_048 ,					__UpperCAmelCase      :						Optional[int]=24 ,					__UpperCAmelCase      :						Optional[int]=[[["global", "local"], 12]] ,					__UpperCAmelCase      :						str=16 ,					__UpperCAmelCase      :						Any=None ,					__UpperCAmelCase      :						List[str]=256 ,					__UpperCAmelCase      :						Optional[Any]="gelu_new" ,					__UpperCAmelCase      :						Tuple=0.0 ,					__UpperCAmelCase      :						Optional[int]=0.0 ,					__UpperCAmelCase      :						Union[str, Any]=0.0 ,					__UpperCAmelCase      :						Optional[Any]=0.1 ,					__UpperCAmelCase      :						Any=1e-5 ,					__UpperCAmelCase      :						Dict=0.02 ,					__UpperCAmelCase      :						Optional[int]=True ,					__UpperCAmelCase      :						Tuple=50_256 ,					__UpperCAmelCase      :						Optional[int]=50_256 ,					**__UpperCAmelCase      :						Optional[int] ,					)    ->Union[str, Any]:




       """simple docstring"""


       a		  =      vocab_size
       a		  =      max_position_embeddings
       a		  =      hidden_size
       a		  =      num_layers
       a		  =      num_heads
       a		  =      intermediate_size
       a		  =      window_size
       a		  =      activation_function
       a		  =      resid_dropout
       a		  =      embed_dropout
       a		  =      attention_dropout
       a		  =      classifier_dropout
       a		  =      layer_norm_epsilon
       a		  =      initializer_range
       a		  =      use_cache

       a		  =      bos_token_id
       a		  =      eos_token_id

       a		  =      attention_types
       a		  =      self.expand_attention_types_params(__UpperCAmelCase  )

       if len(self.attention_layers  ) != self.num_layers:
        raise ValueError(
            '''Configuration for convolutional module is incorrect. '''
            '''It is required that `len(config.attention_layers)` == `config.num_layers` '''
            F"""but is `len(config.attention_layers) = {len(self.attention_layers  )}`, """
            F"""`config.num_layers = {self.num_layers}`. """
            '''`config.attention_layers` is prepared using `config.attention_types`. '''
            '''Please verify the value of `config.attention_types` argument.'''  )

       super().__init__(bos_token_id=__UpperCAmelCase ,					eos_token_id=__UpperCAmelCase ,					**__UpperCAmelCase  )







      @staticmethod
      def       __lowerCAmelCase					(      __UpperCAmelCase      :						List[str]  )    ->Optional[int]:




       """simple docstring"""


       a		  =      []
       for item in attention_types:
        for _ in range(item[1]  ):
         attentions.extend(item[0]  )
       return attentions
def 						_a    (      a :str	,							a :Any	,							a :Union[str, Any]	,							a :Optional[int] )							->			List[Any]:
 import torch

 a		  =      input.size()
 a		  =      len(a )
 a		  =      shape[dimension]

 a		  =      torch.arange(0	,							a	,							a )
 a		  =      torch.div(sizedim - size	,							a	,							rounding_mode='''floor''' ) + 1
 a		  =      torch.arange(a ) + low_indices[:min_length][:, None]

 a		  =      [slice(a )] * rank
 a		  =      indices
 a		  =      input[s]

 a		  =      list(range(0	,							rank + 1 ) )
 perm.append(perm.pop(dimension + 1 ) )

 return sliced.permute(a )
def 						_a    (      a :List[str]	,							a :List[str] )							->			Any:
 import torch

 a		  =      torch.arange(1	,							a )
 a		  =      torch.remainder(a	,							a )
 a		  =      remainders == 0
 a		  =      candidates[divisor_indices]
 a		  =      torch.max(a )
 return largest_divisor, torch.div(a	,							a	,							rounding_mode='''floor''' )






class 		lowercase_					(	lowercase   ):






      '''simple docstring'''




      @property
      def       __lowerCAmelCase					(      self      :						Optional[int]  )    ->Mapping[str, Mapping[int, str]]:




       """simple docstring"""


       a		  =      OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}  )
       if self.use_past:
        self.fill_with_past_key_values_(__UpperCAmelCase ,					direction='''inputs'''  )
        a		  =      {0: '''batch''', 1: '''past_sequence + sequence'''}
       else:
        a		  =      {0: '''batch''', 1: '''sequence'''}

       return common_inputs







      @property
      def       __lowerCAmelCase					(      self      :						int  )    ->int:




       """simple docstring"""


       return self._config.num_heads







      def       __lowerCAmelCase					(      self      :						Optional[int] ,					__UpperCAmelCase      :						PreTrainedTokenizer ,					__UpperCAmelCase      :						int = -1 ,					__UpperCAmelCase      :						int = -1 ,					__UpperCAmelCase      :						bool = False ,					__UpperCAmelCase      :						Optional[TensorType] = None ,					)    ->Mapping[str, Any]:




       """simple docstring"""


       a		  =      super(__UpperCAmelCase ,					self  ).generate_dummy_inputs(
           __UpperCAmelCase ,					batch_size=__UpperCAmelCase ,					seq_length=__UpperCAmelCase ,					is_pair=__UpperCAmelCase ,					framework=__UpperCAmelCase  )

       # We need to order the input in the way they appears in the forward()
       a		  =      OrderedDict({'''input_ids''': common_inputs['''input_ids''']}  )

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''  )
        else:
         import torch

         a  ,     a		  =      common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         a		  =      seqlen + 2
         a		  =      (
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         a		  =      [
             (torch.zeros(__UpperCAmelCase  ), torch.zeros(__UpperCAmelCase  )) for _ in range(self.num_layers  )
         ]

       a		  =      common_inputs['''attention_mask''']
       if self.use_past:
        a		  =      ordered_inputs['''attention_mask'''].dtype
        a		  =      torch.cat(
            [ordered_inputs['''attention_mask'''], torch.ones(__UpperCAmelCase ,					__UpperCAmelCase ,					dtype=__UpperCAmelCase  )] ,					dim=1  )

       return ordered_inputs







      @property
      def       __lowerCAmelCase					(      self      :						Dict  )    ->int:




       """simple docstring"""


       return 13



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   snake_case_       (		unittest.TestCase       ):






   def __init__(      self				:     Tuple	,						lowercase_				:     List[Any]	,						lowercase_				:     Union[str, Any]=2	,						lowercase_				:     Union[str, Any]=56	,						lowercase_				:     Tuple=True	,						lowercase_				:     Optional[Any]=True	,						lowercase_				:     Optional[Any]=True	,						lowercase_				:     int=True	,						lowercase_				:     Any=99	,						lowercase_				:     int=32	,						lowercase_				:     str=2	,						lowercase_				:     Union[str, Any]=2	,						lowercase_				:     Dict=7	,						lowercase_				:     Dict="gelu_new"	,						lowercase_				:     Tuple=0.1	,						lowercase_				:     List[Any]=0.1	,						lowercase_				:     Tuple=5_12	,						lowercase_				:     Optional[Any]=16	,						lowercase_				:     List[Any]=2	,						lowercase_				:     Dict=0.02	,						lowercase_				:     int=4	,						lowercase_				:     Tuple="block_sparse"	,						lowercase_				:     Dict=True	,						lowercase_				:     Optional[int]=False	,						lowercase_				:     Dict=2	,						lowercase_				:     int=3	,						)					->						Union[str, Any]:
       lowercase__ :   Dict    					=				parent
       lowercase__ :   Dict    					=				batch_size
       lowercase__ :   Tuple    					=				seq_length
       lowercase__ :   Dict    					=				is_training
       lowercase__ :   Dict    					=				use_attention_mask
       lowercase__ :   Tuple    					=				use_token_type_ids
       lowercase__ :   Optional[int]    					=				use_labels
       lowercase__ :   List[Any]    					=				vocab_size
       lowercase__ :   Any    					=				hidden_size
       lowercase__ :   List[Any]    					=				num_hidden_layers
       lowercase__ :   Union[str, Any]    					=				num_attention_heads
       lowercase__ :   str    					=				intermediate_size
       lowercase__ :   int    					=				hidden_act
       lowercase__ :   str    					=				hidden_dropout_prob
       lowercase__ :   List[str]    					=				attention_probs_dropout_prob
       lowercase__ :   Optional[Any]    					=				max_position_embeddings
       lowercase__ :   Union[str, Any]    					=				type_vocab_size
       lowercase__ :   Dict    					=				type_sequence_label_size
       lowercase__ :   Any    					=				initializer_range
       lowercase__ :   List[str]    					=				num_choices

       lowercase__ :   str    					=				rescale_embeddings
       lowercase__ :   Optional[Any]    					=				attention_type
       lowercase__ :   Optional[int]    					=				use_bias
       lowercase__ :   Optional[int]    					=				block_size
       lowercase__ :   str    					=				num_random_blocks






   def       __UpperCamelCase     (      self				:     str  )					->						Optional[Any]:
       lowercase__ :   List[Any]    					=				ids_tensor([self.batch_size, self.seq_length]	,						self.vocab_size  )

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

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

       lowercase__ :   int    					=				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=lowercase_	,						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]  )					->						int:
       lowercase__ :   int    					=				self.prepare_config_and_inputs()
       lowercase__					,   lowercase__					,   lowercase__					,   lowercase__ :   Dict    					=				config_and_inputs
       lowercase__ :   Union[str, Any]    					=				{
           "input_ids": input_ids,
           "token_type_ids": token_type_ids,
           "attention_mask": attention_mask,
       }
       return config, inputs_dict



@require_flax
class   snake_case_       (		__A  ,unittest.TestCase       ):
   __A	:		Optional[int]         =				(
       (
           FlaxBigBirdForCausalLM,
           FlaxBigBirdModel,
           FlaxBigBirdForPreTraining,
           FlaxBigBirdForMaskedLM,
           FlaxBigBirdForMultipleChoice,
           FlaxBigBirdForQuestionAnswering,
           FlaxBigBirdForSequenceClassification,
           FlaxBigBirdForTokenClassification,
       )
       if is_flax_available()
       else ()
   )

   __A	:		List[str]         =				False
   __A	:		Any         =				False






   def       __UpperCamelCase     (      self				:     List[str]  )					->						List[Any]:
       lowercase__ :   Union[str, Any]    					=				FlaxBigBirdModelTester(self  )






   @slow
   # copied from `test_modeling_flax_common` because it takes much longer than other models
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Dict:
       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[str]  )					->						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				:     Tuple  )					->						str:
       super().test_no_automatic_init()






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






   @slow
   def       __UpperCamelCase     (      self				:     Optional[int]  )					->						Tuple:
       for model_class_name in self.all_model_classes:
           lowercase__ :   Optional[Any]    					=				model_class_name.from_pretrained("google/bigbird-roberta-base"  )
           self.assertIsNotNone(lowercase_  )






   def       __UpperCamelCase     (      self				:     int  )					->						Optional[int]:
       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				:     str  )					->						Any:
       lowercase__					,   lowercase__ :   Dict    					=				self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
           with self.subTest(model_class.__name__  ):
               lowercase__ :   Union[str, Any]    					=				self._prepare_for_class(lowercase_	,						lowercase_  )
               lowercase__ :   Optional[Any]    					=				model_class(lowercase_  )

               @jax.jit
               def model_jitted(lowercase_				:     Tuple	,						lowercase_				:     int=None	,						**lowercase_				:     Dict  ):
                   return model(input_ids=lowercase_	,						attention_mask=lowercase_	,						**lowercase_  )

               with self.subTest("JIT Enabled"  ):
                   lowercase__ :   int    					=				model_jitted(**lowercase_  ).to_tuple()

               with self.subTest("JIT Disabled"  ):
                   with jax.disable_jit():
                       lowercase__ :   Any    					=				model_jitted(**lowercase_  ).to_tuple()

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







   def       __UpperCamelCase     (      self				:     List[Any]	,						lowercase_				:     str	,						lowercase_				:     Union[str, Any]	,						lowercase_				:     Optional[int]	,						lowercase_				:     List[Any]=1E-5	,						lowercase_				:     Any="outputs"	,						lowercase_				:     List[str]=None  )					->						List[Any]:
       # `bigbird_block_sparse_attention` in `FlaxBigBird` returns `attention_probs = None`, while in PyTorch version,
       # an effort was done to return `attention_probs` (yet to be verified).
       if name.startswith("outputs.attentions"  ):
           return
       else:
           super().check_pt_flax_outputs(lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_	,						lowercase_  )



