




'''simple docstring'''







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

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
   from ... import FeatureExtractionMixin, TensorType

A     =logging.get_logger(__name__)

A     ={
    'openai/imagegpt-small': '',
    'openai/imagegpt-medium': '',
    'openai/imagegpt-large': '',
}


class 			_a  (	__a ):
       __a				:     Any   				=					"""imagegpt"""
       __a				:     List[str]   				=					["""past_key_values"""]
       __a				:     str   				=					{
           """hidden_size""": """n_embd""",
           """max_position_embeddings""": """n_positions""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }
       def __init__(	self						:	Optional[int]   ,				lowercase						:	List[str]=512 + 1   ,				lowercase						:	Optional[int]=32 * 32   ,				lowercase						:	Dict=512   ,				lowercase						:	List[str]=24   ,				lowercase						:	Optional[int]=8   ,				lowercase						:	Tuple=None   ,				lowercase						:	List[str]="quick_gelu"   ,				lowercase						:	Any=0.1   ,				lowercase						:	Any=0.1   ,				lowercase						:	str=0.1   ,				lowercase						:	str=1E-5   ,				lowercase						:	Dict=0.02   ,				lowercase						:	Union[str, Any]=True   ,				lowercase						:	int=True   ,				lowercase						:	Optional[Any]=False   ,				lowercase						:	List[Any]=False   ,				lowercase						:	Optional[int]=False   ,				**lowercase						:	List[Any]   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		vocab_size
           UpperCAmelCase			       =		n_positions
           UpperCAmelCase			       =		n_embd
           UpperCAmelCase			       =		n_layer
           UpperCAmelCase			       =		n_head
           UpperCAmelCase			       =		n_inner
           UpperCAmelCase			       =		activation_function
           UpperCAmelCase			       =		resid_pdrop
           UpperCAmelCase			       =		embd_pdrop
           UpperCAmelCase			       =		attn_pdrop
           UpperCAmelCase			       =		layer_norm_epsilon
           UpperCAmelCase			       =		initializer_range
           UpperCAmelCase			       =		scale_attn_weights
           UpperCAmelCase			       =		use_cache
           UpperCAmelCase			       =		scale_attn_by_inverse_layer_idx
           UpperCAmelCase			       =		reorder_and_upcast_attn
           UpperCAmelCase			       =		tie_word_embeddings

           super().__init__(tie_word_embeddings=lowercase   ,				**lowercase				)






class 			_a  (	__a ):
       @property
       def     A      (	self						:	List[Any]				):



           '''simple docstring'''

           return OrderedDict(
               [
                   ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
               ]				)







       def     A      (	self						:	List[Any]   ,				lowercase						:	"FeatureExtractionMixin"   ,				lowercase						:	int = 1   ,				lowercase						:	int = -1   ,				lowercase						:	bool = False   ,				lowercase						:	Optional["TensorType"] = None   ,				lowercase						:	int = 3   ,				lowercase						:	int = 32   ,				lowercase						:	int = 32   ,				):



           '''simple docstring'''

           UpperCAmelCase			       =		self._generate_dummy_images(lowercase   ,				lowercase   ,				lowercase   ,				lowercase				)
           UpperCAmelCase			       =		dict(preprocessor(images=lowercase   ,				return_tensors=lowercase				)				)

           return inputs










'''simple docstring'''







from queue import PriorityQueue
from typing import Any

import numpy as np





def 						snake_case_							(_a	:							dict    ,   _a	:							str    ,   _a	:							set    ,   _a	:							set    ,   _a	:							dict    ,   _a	:							dict    ,   _a	:							PriorityQueue    ,   _a	:							dict    ,   _a	:							float | int    ,   ):
    for nxt, d in graph[v]:
        if nxt in visited_forward:
            continue
        UpperCAmelCase			       =		cst_fwd.get(_a    ,   np.inf     )
        UpperCAmelCase			       =		cst_fwd[v] + d
        if new_cost_f < old_cost_f:
            queue.put((new_cost_f, nxt)     )
            UpperCAmelCase			       =		new_cost_f
            UpperCAmelCase			       =		v
        if nxt in visited_backward:
            if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
                UpperCAmelCase			       =		cst_fwd[v] + d + cst_bwd[nxt]
    return shortest_distance





def 						snake_case_							(_a	:							str    ,   _a	:							str    ,   _a	:							dict    ,   _a	:							dict     ):
    UpperCAmelCase			       =		-1

    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		set()
    UpperCAmelCase			       =		{source: 0}
    UpperCAmelCase			       =		{destination: 0}
    UpperCAmelCase			       =		{source: None}
    UpperCAmelCase			       =		{destination: None}
    UpperCAmelCase			       =		PriorityQueue()
    UpperCAmelCase			       =		PriorityQueue()

    UpperCAmelCase			       =		np.inf

    queue_forward.put((0, source)     )
    queue_backward.put((0, destination)     )

    if source == destination:
        return 0

    while not queue_forward.empty() and not queue_backward.empty():
        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_forward.get()
        visited_forward.add(_a     )

        UpperCAmelCase       ,     UpperCAmelCase			       =		queue_backward.get()
        visited_backward.add(_a     )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        UpperCAmelCase			       =		pass_and_relaxation(
            _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   _a    ,   )

        if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
            break

    if shortest_distance != np.inf:
        UpperCAmelCase			       =		shortest_distance
    return shortest_path_distance


A     ={
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
A     ={
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
   import doctest

   doctest.testmod()




