




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

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


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

lowercase__							:	List[Any]											=		{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  '''gptj'''
						lowerCAmelCase_							=  {
						    '''max_position_embeddings''': '''n_positions''',
						    '''hidden_size''': '''n_embd''',
						    '''num_attention_heads''': '''n_head''',
						    '''num_hidden_layers''': '''n_layer''',
						}






						def __init__(		self				:	str			,      __lowercase				:	List[Any]=5_04_00			,      __lowercase				:	str=20_48			,      __lowercase				:	int=40_96			,      __lowercase				:	Union[str, Any]=28			,      __lowercase				:	str=16			,      __lowercase				:	Dict=64			,      __lowercase				:	List[Any]=None			,      __lowercase				:	Tuple="gelu_new"			,      __lowercase				:	Dict=0.0			,      __lowercase				:	str=0.0			,      __lowercase				:	Optional[Any]=0.0			,      __lowercase				:	Optional[Any]=1E-5			,      __lowercase				:	Dict=0.02			,      __lowercase				:	Any=True			,      __lowercase				:	Optional[Any]=5_02_56			,      __lowercase				:	Tuple=5_02_56			,      __lowercase				:	List[Any]=False			,      **__lowercase				:	List[str]			,      ):






							"""simple docstring"""






							snake_case_						       =	vocab_size
							snake_case_						       =	n_positions
							snake_case_						       =	n_embd
							snake_case_						       =	n_layer
							snake_case_						       =	n_head
							snake_case_						       =	n_inner
							snake_case_						       =	rotary_dim
							snake_case_						       =	activation_function
							snake_case_						       =	resid_pdrop
							snake_case_						       =	embd_pdrop
							snake_case_						       =	attn_pdrop
							snake_case_						       =	layer_norm_epsilon
							snake_case_						       =	initializer_range
							snake_case_						       =	use_cache

							snake_case_						       =	bos_token_id
							snake_case_						       =	eos_token_id

							super().__init__(
							    bos_token_id=__lowercase			,      eos_token_id=__lowercase			,      tie_word_embeddings=__lowercase			,      **__lowercase		)







class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						def __init__(		self				:	Tuple			,      __lowercase				:	PretrainedConfig			,      __lowercase				:	str = "default"			,      __lowercase				:	List[PatchingSpec] = None			,      __lowercase				:	bool = False			,      ):






							"""simple docstring"""






							super().__init__(__lowercase			,      task=__lowercase			,      patching_specs=__lowercase			,      use_past=__lowercase		)
							if not getattr(self._config			,      "pad_token_id"			,      __lowercase		):
								# TODO: how to do that better?
								snake_case_						       =	0






						@property
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
							if self.use_past:
								self.fill_with_past_key_values_(__lowercase			,      direction="inputs"		)
								snake_case_						       =	{0: "batch", 1: "past_sequence + sequence"}
							else:
								snake_case_						       =	{0: "batch", 1: "sequence"}

							return common_inputs






						@property
						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							return self._config.n_layer






						@property
						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							return self._config.n_head






						def 			snake_case__		(		self				:	Any			,      __lowercase				:	PreTrainedTokenizer			,      __lowercase				:	int = -1			,      __lowercase				:	int = -1			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[TensorType] = None			,      ):






							"""simple docstring"""






							snake_case_						       =	super(__lowercase			,      self		).generate_dummy_inputs(
							    __lowercase			,      batch_size=__lowercase			,      seq_length=__lowercase			,      is_pair=__lowercase			,      framework=__lowercase		)

							# We need to order the input in the way they appears in the forward()
							snake_case_						       =	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

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

							snake_case_						       =	common_inputs["attention_mask"]
							if self.use_past:
								snake_case_						       =	ordered_inputs["attention_mask"].dtype
								snake_case_						       =	torch.cat(
								    [ordered_inputs["attention_mask"], torch.ones(__lowercase			,      __lowercase			,      dtype=__lowercase		)]			,      dim=1		)

							return ordered_inputs






						@property
						def 			snake_case__		(		self				:	Optional[int]		):






							"""simple docstring"""






							return 13






from __future__ import annotations

from dataclasses import dataclass
@dataclass
class 						UpperCAmelCase  :







						'''simple docstring'''





						lowerCAmelCase_							=  42
						lowerCAmelCase_							=  None
						lowerCAmelCase_							=  None







def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	def is_valid_tree(_A				) -> bool:
		if node is None:
			return True

		if not isinstance(_A	,      _A				):
			return False

		try:
			float(node.data				)
		except (TypeError, ValueError):
			return False

		return is_valid_tree(node.left				) and is_valid_tree(node.right				)

	if not is_valid_tree(_A				):
		raise ValueError(
		    "Each node should be type of TreeNode and data should be float."				)

	def is_binary_search_tree_recursive_check(
	    _A	,      _A	,      _A				) -> bool:

		if node is None:
			return True

		return (
		    left_bound < node.data < right_bound
		    and is_binary_search_tree_recursive_check(node.left	,      _A	,      node.data				)
		    and is_binary_search_tree_recursive_check(
		        node.right	,      node.data	,      _A				)
		)

	return is_binary_search_tree_recursive_check(_A	,      -float("inf"				)	,      float("inf"				)				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()
