

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


_lowerCAmelCase  :      Optional[int]			 =logging.get_logger(__name__)

_lowerCAmelCase  :      Optional[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     (				_a		):






					'''simple docstring'''



					__magic_name__        =   "gptj"
					__magic_name__        =   {
					    "max_position_embeddings": "n_positions",
					    "hidden_size": "n_embd",
					    "num_attention_heads": "n_head",
					    "num_hidden_layers": "n_layer",
					}





					def __init__(				self				,      lowerCamelCase__=5_0_4_0_0				,      lowerCamelCase__=2_0_4_8				,      lowerCamelCase__=4_0_9_6				,      lowerCamelCase__=2_8				,      lowerCamelCase__=1_6				,      lowerCamelCase__=6_4				,      lowerCamelCase__=None				,      lowerCamelCase__="gelu_new"				,      lowerCamelCase__=0.0				,      lowerCamelCase__=0.0				,      lowerCamelCase__=0.0				,      lowerCamelCase__=1e-5				,      lowerCamelCase__=0.02				,      lowerCamelCase__=True				,      lowerCamelCase__=5_0_2_5_6				,      lowerCamelCase__=5_0_2_5_6				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__:	Optional[Any]									= vocab_size
						UpperCAmelCase__:	Union[str, Any]									= n_positions
						UpperCAmelCase__:	Optional[Any]									= n_embd
						UpperCAmelCase__:	Optional[Any]									= n_layer
						UpperCAmelCase__:	List[Any]									= n_head
						UpperCAmelCase__:	Tuple									= n_inner
						UpperCAmelCase__:	Optional[int]									= rotary_dim
						UpperCAmelCase__:	Optional[int]									= activation_function
						UpperCAmelCase__:	Tuple									= resid_pdrop
						UpperCAmelCase__:	Union[str, Any]									= embd_pdrop
						UpperCAmelCase__:	Dict									= attn_pdrop
						UpperCAmelCase__:	str									= layer_norm_epsilon
						UpperCAmelCase__:	Union[str, Any]									= initializer_range
						UpperCAmelCase__:	Union[str, Any]									= use_cache

						UpperCAmelCase__:	Dict									= bos_token_id
						UpperCAmelCase__:	Dict									= eos_token_id

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

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__				,      lowerCamelCase__ = "default"				,      lowerCamelCase__ = None				,      lowerCamelCase__ = False				,      ):
						super().__init__(lowerCamelCase__				,      task=lowerCamelCase__				,      patching_specs=lowerCamelCase__				,      use_past=lowerCamelCase__						)
						if not getattr(self._config				,      "pad_token_id"				,      lowerCamelCase__						):
							# TODO: how to do that better?
							UpperCAmelCase__:	Optional[int]									= 0





					@property
					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Tuple									= OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}						)
						if self.use_past:
							self.fill_with_past_key_values_(lowerCamelCase__				,      direction="inputs"						)
							UpperCAmelCase__:	int									= {0: "batch", 1: "past_sequence + sequence"}
						else:
							UpperCAmelCase__:	int									= {0: "batch", 1: "sequence"}

						return common_inputs





					@property
					def    _UpperCAmelCase    (				self						):
						return self._config.n_layer





					@property
					def    _UpperCAmelCase    (				self						):
						return self._config.n_head





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = -1				,      lowerCamelCase__ = -1				,      lowerCamelCase__ = False				,      lowerCamelCase__ = None				,      ):
						UpperCAmelCase__:	Any									= super(lowerCamelCase__				,      self						).generate_dummy_inputs(
						    lowerCamelCase__				,      batch_size=lowerCamelCase__				,      seq_length=lowerCamelCase__				,      is_pair=lowerCamelCase__				,      framework=lowerCamelCase__						)

						# We need to order the input in the way they appears in the forward()
						UpperCAmelCase__:	str									= 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

								UpperCAmelCase__							,						UpperCAmelCase__:	Dict									= common_inputs["input_ids"].shape
								# Not using the same length for past_key_values
								UpperCAmelCase__:	Tuple									= seqlen + 2
								UpperCAmelCase__:	Union[str, Any]									= (
								    batch,
								    self.num_attention_heads,
								    past_key_values_length,
								    self._config.hidden_size // self.num_attention_heads,
								)
								UpperCAmelCase__:	Any									= [
								    (torch.zeros(lowerCamelCase__						), torch.zeros(lowerCamelCase__						)) for _ in range(self.num_layers						)
								]

						UpperCAmelCase__:	Optional[Any]									= common_inputs["attention_mask"]
						if self.use_past:
							UpperCAmelCase__:	Any									= ordered_inputs["attention_mask"].dtype
							UpperCAmelCase__:	Optional[int]									= torch.cat(
							    [ordered_inputs["attention_mask"], torch.ones(lowerCamelCase__				,      lowerCamelCase__				,      dtype=lowerCamelCase__						)]				,      dim=1						)

						return ordered_inputs





					@property
					def    _UpperCAmelCase    (				self						):
						return 1_3


from __future__ import annotations

from typing import Any

class __UpperCamelCase     :






					'''simple docstring'''



					def __init__(				self				,      lowerCamelCase__						):
						UpperCAmelCase__:	Optional[int]									= num_of_nodes
						UpperCAmelCase__:	list[list[int]]									= []
						UpperCAmelCase__:	dict[int, int]									= {}





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):
						self.m_edges.append([u_node, v_node, weight]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] == u_node:
							return u_node
						return self.find_component(self.m_component[u_node]						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__						):

						if self.m_component[u_node] != u_node:
							for k in self.m_component:
								UpperCAmelCase__:	Optional[Any]									= self.find_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						):

						if component_size[u_node] <= component_size[v_node]:
							UpperCAmelCase__:	Tuple									= v_node
							component_size[v_node] += component_size[u_node]
							self.set_component(lowerCamelCase__						)

						elif component_size[u_node] >= component_size[v_node]:
							UpperCAmelCase__:	Dict									= self.find_component(lowerCamelCase__						)
							component_size[u_node] += component_size[v_node]
							self.set_component(lowerCamelCase__						)





					def    _UpperCAmelCase    (				self						):
						UpperCAmelCase__:	Dict									= []
						UpperCAmelCase__:	int									= 0

						UpperCAmelCase__:	list[Any]									= [-1] * self.m_num_of_nodes

						# A list of components (initialized to all of the nodes)
						for node in range(self.m_num_of_nodes						):
							self.m_component.update({node: node}						)
							component_size.append(1						)

						UpperCAmelCase__:	Union[str, Any]									= self.m_num_of_nodes

						while num_of_components > 1:
							for edge in self.m_edges:
								UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

								UpperCAmelCase__:	str									= self.m_component[u]
								UpperCAmelCase__:	Optional[Any]									= self.m_component[v]

								if u_component != v_component:

									for component in (u_component, v_component):
										if (
										    minimum_weight_edge[component] == -1
										    or minimum_weight_edge[component][2] > w
										):
											UpperCAmelCase__:	List[Any]									= [u, v, w]

							for edge in minimum_weight_edge:
								if isinstance(lowerCamelCase__				,      lowerCamelCase__						):
									UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	str									= edge

									UpperCAmelCase__:	str									= self.m_component[u]
									UpperCAmelCase__:	int									= self.m_component[v]

									if u_component != v_component:
										mst_weight += w
										self.union(lowerCamelCase__				,      lowerCamelCase__				,      lowerCamelCase__						)
										print(F"Added edge [{u} - {v}]\nAdded weight: {w}\n"						)
										num_of_components -= 1

							UpperCAmelCase__:	Any									= [-1] * self.m_num_of_nodes
						print(F"The total weight of the minimal spanning tree is: {mst_weight}"						)



def 			_A	(      ):
	pass


if __name__ == "__main__":
					import doctest

					doctest.testmod()