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

import torch


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			torch.Tensor:


					'''simple docstring'''
					__a:						List[str]		    =					boundaries[1] - boundaries[0]
					__a:						Dict		    =					boundaries + step / 2
					__a:						Tuple		    =					torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1 )]	,				dim=0 )
					return bin_centers


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_	,				)     ->			Tuple[torch.Tensor, torch.Tensor]:


					'''simple docstring'''
					__a:						Dict		    =					_calculate_bin_centers(lowerCamelCase_ )
					return (
					    torch.sum(aligned_distance_error_probs * bin_centers	,				dim=-1 ),
					    bin_centers[-1],
					)


def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ = 31	,				lowerCamelCase_ = 64	,				**lowerCamelCase_	,				)     ->			Dict[str, torch.Tensor]:


					'''simple docstring'''
					__a:						str		    =					torch.linspace(0	,				lowerCamelCase_	,				steps=(no_bins - 1)	,				device=logits.device )

					__a:						Union[str, Any]		    =					torch.nn.functional.softmax(lowerCamelCase_	,				dim=-1 )
					__a:						str		    =					_calculate_expected_aligned_error(
					    alignment_confidence_breaks=lowerCamelCase_	,				aligned_distance_error_probs=lowerCamelCase_	,				)

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




def     lowerCamelCase_    (	lowerCamelCase_	,				lowerCamelCase_ = None	,				lowerCamelCase_ = 31	,				lowerCamelCase_ = 64	,				lowerCamelCase_ = 1e-8	,				**lowerCamelCase_	,				)     ->			torch.Tensor:


					'''simple docstring'''
					if residue_weights is None:
										__a:						str		    =					logits.new_ones(logits.shape[-2] )

					__a:						str		    =					torch.linspace(0	,				lowerCamelCase_	,				steps=(no_bins - 1)	,				device=logits.device )

					__a:						int		    =					_calculate_bin_centers(lowerCamelCase_ )
					torch.sum(lowerCamelCase_ )
					__a:						Any		    =					logits.shape[-2]
					__a:						Any		    =					max(lowerCamelCase_	,				19 )

					__a:						Tuple		    =					1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

					__a:						str		    =					torch.nn.functional.softmax(lowerCamelCase_	,				dim=-1 )

					__a:						Any		    =					1.0 / (1 + (bin_centers**2) / (da**2))
					__a:						Optional[int]		    =					torch.sum(probs * tm_per_bin	,				dim=-1 )

					__a:						Dict		    =					residue_weights / (eps + residue_weights.sum())
					__a:						Optional[Any]		    =					torch.sum(predicted_tm_term * normed_residue_mask	,				dim=-1 )

					__a:						Optional[int]		    =					per_alignment * residue_weights

					__a:						Tuple		    =					(weighted == torch.max(lowerCamelCase_ )).nonzero()[0]
					return per_alignment[tuple(lowerCamelCase_ )]


"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


lowercase		:						List[str] =					{
    'configuration_open_llama': ['OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'OpenLlamaConfig'],
}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Tuple =					['LlamaTokenizer']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						Dict =					['LlamaTokenizerFast']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:						List[Any] =					[
			    'OpenLlamaForCausalLM',
			    'OpenLlamaModel',
			    'OpenLlamaPreTrainedModel',
			    'OpenLlamaForSequenceClassification',
			]


if TYPE_CHECKING:
			from .configuration_open_llama import OPEN_LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenLlamaConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from transformers import LlamaTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_open_llama import (
						    OpenLlamaForCausalLM,
						    OpenLlamaForSequenceClassification,
						    OpenLlamaModel,
						    OpenLlamaPreTrainedModel,
						)


else:
			import sys

			lowercase		:						Dict =					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
