




'''simple docstring'''
from typing import Dict, Optional, Tuple

import torch
def     snake_case_  (       a__		:     torch.Tensor      ):



				"""simple docstring"""




				__lowercase          =						boundaries[1] - boundaries[0]
				__lowercase          =						boundaries + step / 2
				__lowercase          =						torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )]   ,dim=0      )
				return bin_centers
def     snake_case_  (       a__		:     torch.Tensor   ,a__		:     torch.Tensor   ,):



				"""simple docstring"""




				__lowercase          =						_calculate_bin_centers(__lowercase      )
				return (
				    torch.sum(aligned_distance_error_probs * bin_centers   ,dim=-1      ),
				    bin_centers[-1],
				)
def     snake_case_  (       a__		:     torch.Tensor   ,a__		:     int = 31   ,a__		:     int = 64   ,**a__		:     Tuple   ,):



				"""simple docstring"""




				__lowercase          =						torch.linspace(0   ,__lowercase   ,steps=(no_bins - 1)   ,device=logits.device      )

				__lowercase          =						torch.nn.functional.softmax(__lowercase   ,dim=-1      )
				__lowercase    ,__lowercase          =						_calculate_expected_aligned_error(
				    alignment_confidence_breaks=__lowercase   ,aligned_distance_error_probs=__lowercase   ,)

				return {
				    "aligned_confidence_probs": aligned_confidence_probs,
				    "predicted_aligned_error": predicted_aligned_error,
				    "max_predicted_aligned_error": max_predicted_aligned_error,
				}



def     snake_case_  (       a__		:     torch.Tensor   ,a__		:     Optional[torch.Tensor] = None   ,a__		:     int = 31   ,a__		:     int = 64   ,a__		:     float = 1e-8   ,**a__		:     List[Any]   ,):



				"""simple docstring"""




				if residue_weights is None:
								__lowercase          =						logits.new_ones(logits.shape[-2]      )

				__lowercase          =						torch.linspace(0   ,__lowercase   ,steps=(no_bins - 1)   ,device=logits.device      )

				__lowercase          =						_calculate_bin_centers(__lowercase      )
				torch.sum(__lowercase      )
				__lowercase          =						logits.shape[-2]
				__lowercase          =						max(__lowercase   ,19      )

				__lowercase          =						1.2_4 * (clipped_n - 15) ** (1.0 / 3) - 1.8

				__lowercase          =						torch.nn.functional.softmax(__lowercase   ,dim=-1      )

				__lowercase          =						1.0 / (1 + (bin_centers**2) / (da**2))
				__lowercase          =						torch.sum(probs * tm_per_bin   ,dim=-1      )

				__lowercase          =						residue_weights / (eps + residue_weights.sum())
				__lowercase          =						torch.sum(predicted_tm_term * normed_residue_mask   ,dim=-1      )

				__lowercase          =						per_alignment * residue_weights

				__lowercase          =						(weighted == torch.max(__lowercase      )).nonzero()[0]
				return per_alignment[tuple(__lowercase      )]










'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
  import torch_xla.core.xla_model as xm
def     snake_case_  (       a__		:     int   ,a__		:     bool = False      ):



    """simple docstring"""




    if device_specific:
        seed += AcceleratorState().process_index
    random.seed(a__      )
    np.random.seed(a__      )
    torch.manual_seed(a__      )
    if is_xpu_available():
        torch.xpu.manual_seed_all(a__      )
    elif is_npu_available():
        torch.npu.manual_seed_all(a__      )
    else:
        torch.cuda.manual_seed_all(a__      )
    # ^^ safe to call this function even if cuda is not available
    if is_tpu_available():
        xm.set_rng_state(a__      )
def     snake_case_  (       a__		:     Optional[RNGType] = None   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    if rng_type == RNGType.TORCH:
        __lowercase          =						torch.get_rng_state()
    elif rng_type == RNGType.CUDA:
        __lowercase          =						torch.cuda.get_rng_state()
    elif rng_type == RNGType.XLA:
        assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
        __lowercase          =						torch.tensor(xm.get_rng_state()      )
    elif rng_type == RNGType.NPU:
        assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
        __lowercase          =						torch.npu.get_rng_state()
    elif rng_type == RNGType.XPU:
        assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
        __lowercase          =						torch.xpu.get_rng_state()
    elif rng_type == RNGType.GENERATOR:
        assert generator is not None, "Need a generator to synchronize its seed."
        __lowercase          =						generator.get_state()

    # Broadcast the rng state from device 0 to other devices
    __lowercase          =						AcceleratorState()
    if state.distributed_type == DistributedType.TPU:
        __lowercase          =						rng_state.to(xm.xla_device()      )
        xm.collective_broadcast([rng_state]      )
        xm.mark_step()
        __lowercase          =						rng_state.cpu()
    elif (
        state.distributed_type in CUDA_DISTRIBUTED_TYPES
        or state.distributed_type == DistributedType.MULTI_NPU
        or state.distributed_type == DistributedType.MULTI_XPU
    ):
        __lowercase          =						rng_state.to(state.device      )
        torch.distributed.broadcast(a__   ,0      )
        __lowercase          =						rng_state.cpu()
    elif state.distributed_type == DistributedType.MULTI_CPU:
        torch.distributed.broadcast(a__   ,0      )

    # Set the broadcast rng state
    if rng_type == RNGType.TORCH:
        torch.set_rng_state(a__      )
    elif rng_type == RNGType.CUDA:
        torch.cuda.set_rng_state(a__      )
    elif rng_type == RNGType.NPU:
        torch.npu.set_rng_state(a__      )
    elif rng_type == RNGType.XPU:
        torch.xpu.set_rng_state(a__      )
    elif rng_type == RNGType.XLA:
        xm.set_rng_state(rng_state.item()      )
    elif rng_type == RNGType.GENERATOR:
        generator.set_state(a__      )



def     snake_case_  (       a__		:     List[Union[str, RNGType]]   ,a__		:     Optional[torch.Generator] = None      ):



    """simple docstring"""




    for rng_type in rng_types:
        synchronize_rng_state(RNGType(a__      )   ,generator=a__      )




