'''simple docstring'''




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


__SCREAMING_SNAKE_CASE			:	List[str]	=    logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE			:	List[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       lowerCamelCase_      (lowercase_       ):
      '''simple docstring'''

      __UpperCamelCase:    Dict         =		"gpt_neox"




      def __init__(			self		:							int		,   A		:							Dict=50432		,   A		:							Tuple=6144		,   A		:							Any=44		,   A		:							Optional[int]=64		,   A		:							List[str]=24576		,   A		:							Tuple="gelu"		,   A		:							Tuple=0.25		,   A		:							Optional[int]=10000		,   A		:							Dict=0.0		,   A		:							int=0.0		,   A		:							Union[str, Any]=0.1		,   A		:							str=2048		,   A		:							List[str]=0.02		,   A		:							str=1E-5		,   A		:							List[Any]=True		,   A		:							str=0		,   A		:							Dict=2		,   A		:							Any=False		,   A		:							Optional[int]=True		,   A		:							Dict=None		,   **A		:							Tuple		,   ):
             super().__init__(bos_token_id=lowerCamelCase_		,   eos_token_id=lowerCamelCase_		,   **lowerCamelCase_			)
             _UpperCAmelCase	:    Union[str, Any]				 =       vocab_size
             _UpperCAmelCase	:    Dict				 =       max_position_embeddings
             _UpperCAmelCase	:    List[str]				 =       hidden_size
             _UpperCAmelCase	:    Tuple				 =       num_hidden_layers
             _UpperCAmelCase	:    int				 =       num_attention_heads
             _UpperCAmelCase	:    int				 =       intermediate_size
             _UpperCAmelCase	:    Dict				 =       hidden_act
             _UpperCAmelCase	:    Optional[Any]				 =       rotary_pct
             _UpperCAmelCase	:    List[str]				 =       rotary_emb_base
             _UpperCAmelCase	:    str				 =       attention_dropout
             _UpperCAmelCase	:    Dict				 =       hidden_dropout
             _UpperCAmelCase	:    Any				 =       classifier_dropout
             _UpperCAmelCase	:    List[Any]				 =       initializer_range
             _UpperCAmelCase	:    Any				 =       layer_norm_eps
             _UpperCAmelCase	:    Tuple				 =       use_cache
             _UpperCAmelCase	:    Union[str, Any]				 =       tie_word_embeddings
             _UpperCAmelCase	:    Tuple				 =       use_parallel_residual
             _UpperCAmelCase	:    str				 =       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 	_A      (			self		:							List[Any]			):



             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}"""			)
             _UpperCAmelCase	:    str				 =       self.rope_scaling.get("type"		,   lowerCamelCase_			)
             _UpperCAmelCase	:    List[Any]				 =       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}"""			)








'''simple docstring'''




import unittest

from transformers import DonutProcessor


__UpperCAmelCase            =       """naver-clova-ix/donut-base"""




class      UpperCamelCase__					(					unittest.TestCase			):




					"""simple docstring"""


					def lowerCamelCase_			(						self				:			List[str]						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Any		=			DonutProcessor.from_pretrained(lowerCamelCase_						)



					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							SCREAMING_SNAKE_CASE :				Optional[int]		=			{
							    """name""": """John Doe""",
							    """age""": """99""",
							    """city""": """Atlanta""",
							    """state""": """GA""",
							    """zip""": """30301""",
							    """phone""": """123-4567""",
							    """nicknames""": [{"""nickname""": """Johnny"""}, {"""nickname""": """JD"""}],
							}

							SCREAMING_SNAKE_CASE :				List[Any]		=			(
							    """<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"""
							    """<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"""
							    """<s_nicknames><s_nickname>Johnny</s_nickname>"""
							    """<sep/><s_nickname>JD</s_nickname></s_nicknames>"""
							)
							SCREAMING_SNAKE_CASE :				List[Any]		=			self.processor.tokenajson(lowerCamelCase_						)

							self.assertDictEqual(lowerCamelCase_      ,						lowerCamelCase_						)



