



"""simple docstring"""
import unittest

import numpy as np

from transformers import DistilBertConfig, 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.distilbert.modeling_flax_distilbert import (
						    FlaxDistilBertForMaskedLM,
						    FlaxDistilBertForMultipleChoice,
						    FlaxDistilBertForQuestionAnswering,
						    FlaxDistilBertForSequenceClassification,
						    FlaxDistilBertForTokenClassification,
						    FlaxDistilBertModel,
						)
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					def __init__(       self:							Dict						,     snake_case:							Tuple						,     snake_case:							str=13						,     snake_case:							str=7						,     snake_case:							Any=True						,     snake_case:							Optional[Any]=True						,     snake_case:							Tuple=True						,     snake_case:							Optional[int]=True						,     snake_case:							Optional[int]=99						,     snake_case:							Dict=32						,     snake_case:							Dict=5						,     snake_case:							Union[str, Any]=4						,     snake_case:							Optional[Any]=37						,     snake_case:							str="gelu"						,     snake_case:							int=0.1						,     snake_case:							Any=0.1						,     snake_case:							Any=512						,     snake_case:							Union[str, Any]=16						,     snake_case:							str=2						,     snake_case:							List[Any]=0.0_2						,     snake_case:							str=4						,     )   ->	str:
											snake_case_ :Union[str, Any]						=			parent
											snake_case_ :List[str]						=			batch_size
											snake_case_ :Union[str, Any]						=			seq_length
											snake_case_ :Union[str, Any]						=			is_training
											snake_case_ :str						=			use_attention_mask
											snake_case_ :Tuple						=			use_token_type_ids
											snake_case_ :str						=			use_labels
											snake_case_ :Optional[int]						=			vocab_size
											snake_case_ :int						=			hidden_size
											snake_case_ :Optional[Any]						=			num_hidden_layers
											snake_case_ :Union[str, Any]						=			num_attention_heads
											snake_case_ :Dict						=			intermediate_size
											snake_case_ :List[str]						=			hidden_act
											snake_case_ :Any						=			hidden_dropout_prob
											snake_case_ :Optional[Any]						=			attention_probs_dropout_prob
											snake_case_ :int						=			max_position_embeddings
											snake_case_ :Any						=			type_vocab_size
											snake_case_ :List[str]						=			type_sequence_label_size
											snake_case_ :Optional[int]						=			initializer_range
											snake_case_ :Dict						=			num_choices






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

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

											snake_case_ :Any						=			DistilBertConfig(
											    vocab_size=self.vocab_size						,     dim=self.hidden_size						,     n_layers=self.num_hidden_layers						,     n_heads=self.num_attention_heads						,     hidden_dim=self.intermediate_size						,     hidden_act=self.hidden_act						,     dropout=self.hidden_dropout_prob						,     attention_dropout=self.attention_probs_dropout_prob						,     max_position_embeddings=self.max_position_embeddings						,     initializer_range=self.initializer_range						,     tie_weights_=snake_case						,     )

											return config, input_ids, attention_mask






					def    lowerCAmelCase_				(       self:							Optional[int]						)   ->	Optional[int]:
											snake_case_ :Union[str, Any]						=			self.prepare_config_and_inputs()
											snake_case_ :Union[str, Any]						=			config_and_inputs
											snake_case_ :List[Any]						=			{"""input_ids""": input_ids, """attention_mask""": attention_mask}
											return config, inputs_dict

@require_flax
class 				lowerCamelCase  (			_SCREAMING_SNAKE_CASE    ,						unittest.TestCase				):






					'''simple docstring'''





					_A		:			Dict						=     (
					    (
					        FlaxDistilBertModel,
					        FlaxDistilBertForMaskedLM,
					        FlaxDistilBertForMultipleChoice,
					        FlaxDistilBertForQuestionAnswering,
					        FlaxDistilBertForSequenceClassification,
					        FlaxDistilBertForTokenClassification,
					        FlaxDistilBertForQuestionAnswering,
					    )
					    if is_flax_available()
					    else ()
					)






					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Optional[Any]:
											snake_case_ :Optional[Any]						=			FlaxDistilBertModelTester(self						)






					@slow
					def    lowerCAmelCase_				(       self:							List[Any]						)   ->	Union[str, Any]:
											for model_class_name in self.all_model_classes:
																	snake_case_ :Tuple						=			model_class_name.from_pretrained("""distilbert-base-uncased"""						)
																	snake_case_ :int						=			model(np.ones((1, 1)						)						)
																	self.assertIsNotNone(snake_case						)

@require_flax
class 				lowerCamelCase  (			unittest.TestCase				):






					'''simple docstring'''





					@slow
					def    lowerCAmelCase_				(       self:							Any						)   ->	Optional[int]:
											snake_case_ :int						=			FlaxDistilBertModel.from_pretrained("""distilbert-base-uncased"""						)
											snake_case_ :Optional[Any]						=			np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]]						)
											snake_case_ :Dict						=			np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]						)
											snake_case_ :Any						=			model(snake_case						,     attention_mask=snake_case						)[0]
											snake_case_ :List[Any]						=			(1, 11, 768)
											self.assertEqual(output.shape						,     snake_case						)
											snake_case_ :Tuple						=			np.array([[[-0.1_6_3_9, 0.3_2_9_9, 0.1_6_4_8], [-0.1_7_4_6, 0.3_2_8_9, 0.1_7_1_0], [-0.1_8_8_4, 0.3_3_5_7, 0.1_8_1_0]]]						)

											self.assertTrue(jnp.allclose(output[:, 1:4, 1:4]						,     snake_case						,     atol=1E-4						)						)




'''simple docstring'''






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


__A					:  List[Any]											=      logging.get_logger(__name__)

__A					:  Optional[Any]											=      {
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class    __snake_case       (   _SCREAMING_SNAKE_CASE):







    """simple docstring"""



    lowercase    =					'gpt_neox'

    def __init__(   self    :  Optional[int]    ,						lowerCamelCase    :  Tuple=5_04_32    ,						lowerCamelCase    :  Optional[int]=61_44    ,						lowerCamelCase    :  Tuple=44    ,						lowerCamelCase    :  Any=64    ,						lowerCamelCase    :  List[Any]=2_45_76    ,						lowerCamelCase    :  List[Any]="gelu"    ,						lowerCamelCase    :  Optional[Any]=0.25    ,						lowerCamelCase    :  Any=1_00_00    ,						lowerCamelCase    :  Any=0.0    ,						lowerCamelCase    :  str=0.0    ,						lowerCamelCase    :  Optional[int]=0.1    ,						lowerCamelCase    :  List[Any]=20_48    ,						lowerCamelCase    :  List[Any]=0.02    ,						lowerCamelCase    :  Any=1E-5    ,						lowerCamelCase    :  Dict=True    ,						lowerCamelCase    :  Optional[int]=0    ,						lowerCamelCase    :  List[str]=2    ,						lowerCamelCase    :  Dict=False    ,						lowerCamelCase    :  Tuple=True    ,						lowerCamelCase    :  Optional[int]=None    ,						**lowerCamelCase    :  int    ,						)   ->  Optional[Any]:
      super().__init__(bos_token_id=lowerCamelCase    ,						eos_token_id=lowerCamelCase    ,						**lowerCamelCase		)
      lowerCAmelCase_			:      Tuple			=   vocab_size
      lowerCAmelCase_			:      Union[str, Any]			=   max_position_embeddings
      lowerCAmelCase_			:      Any			=   hidden_size
      lowerCAmelCase_			:      List[Any]			=   num_hidden_layers
      lowerCAmelCase_			:      Optional[int]			=   num_attention_heads
      lowerCAmelCase_			:      str			=   intermediate_size
      lowerCAmelCase_			:      int			=   hidden_act
      lowerCAmelCase_			:      List[Any]			=   rotary_pct
      lowerCAmelCase_			:      Any			=   rotary_emb_base
      lowerCAmelCase_			:      List[str]			=   attention_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   hidden_dropout
      lowerCAmelCase_			:      Tuple			=   classifier_dropout
      lowerCAmelCase_			:      Union[str, Any]			=   initializer_range
      lowerCAmelCase_			:      Any			=   layer_norm_eps
      lowerCAmelCase_			:      str			=   use_cache
      lowerCAmelCase_			:      str			=   tie_word_embeddings
      lowerCAmelCase_			:      str			=   use_parallel_residual
      lowerCAmelCase_			:      Any			=   rope_scaling
      self._rope_scaling_validation()

      if self.hidden_size % self.num_attention_heads != 0:
        raise ValueError(
            """The hidden size is not divisble by the number of attention heads! Make sure to update them!"""		)





    def __lowercase   (   self    :  List[str]		)   ->  List[str]:
      if self.rope_scaling is None:
        return

      if not isinstance(self.rope_scaling    ,						lowerCamelCase		) or len(self.rope_scaling		) != 2:
        raise ValueError(
            """`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
            F'got {self.rope_scaling}'		)
      lowerCAmelCase_			:      Optional[Any]			=   self.rope_scaling.get("""type"""    ,						lowerCamelCase		)
      lowerCAmelCase_			:      int			=   self.rope_scaling.get("""factor"""    ,						lowerCamelCase		)
      if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
        raise ValueError(
            F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'		)
      if rope_scaling_factor is None or not isinstance(lowerCamelCase    ,						lowerCamelCase		) or rope_scaling_factor <= 1.0:
        raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'		)



