"""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

__UpperCamelCase									=			logging.get_logger(__name__)

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




class  UpperCamelCase					(				__a   ):
       SCREAMING_SNAKE_CASE_			=      """imagegpt"""
       SCREAMING_SNAKE_CASE_			=      ["""past_key_values"""]
       SCREAMING_SNAKE_CASE_			=      {
           """hidden_size""": """n_embd""",
           """max_position_embeddings""": """n_positions""",
           """num_attention_heads""": """n_head""",
           """num_hidden_layers""": """n_layer""",
       }







       def __init__( self,							lowerCAmelCase__=512 + 1,							lowerCAmelCase__=32 * 32,							lowerCAmelCase__=512,							lowerCAmelCase__=24,							lowerCAmelCase__=8,							lowerCAmelCase__=None,							lowerCAmelCase__="quick_gelu",							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=0.1,							lowerCAmelCase__=1e-5,							lowerCAmelCase__=0.02,							lowerCAmelCase__=True,							lowerCAmelCase__=True,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							lowerCAmelCase__=False,							**lowerCAmelCase__,							)      ->      Optional[int]:
            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_					       =					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_					       =					scale_attn_weights
            snake_case_					       =					use_cache
            snake_case_					       =					scale_attn_by_inverse_layer_idx
            snake_case_					       =					reorder_and_upcast_attn
            snake_case_					       =					tie_word_embeddings

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




class  UpperCamelCase					(				__a   ):







       @property
       def 		a_						( self)      ->      List[str]:
            return OrderedDict(
                [
                    ('input_ids', {0: 'batch', 1: 'sequence'}),
                ])







       def 		a_						( self,							lowerCAmelCase__,							lowerCAmelCase__ = 1,							lowerCAmelCase__ = -1,							lowerCAmelCase__ = False,							lowerCAmelCase__ = None,							lowerCAmelCase__ = 3,							lowerCAmelCase__ = 32,							lowerCAmelCase__ = 32,							)      ->      Optional[Any]:
            snake_case_					       =					self._generate_dummy_images(lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__,							lowerCAmelCase__)
            snake_case_					       =					dict(preprocessor(images=lowerCAmelCase__,							return_tensors=lowerCAmelCase__))

            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()




