





"""simple docstring"""






from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
		import tensorflow as tf

		from ..tf_utils import stable_softmax


if is_torch_available():
		import torch


A 			=  logging.get_logger(__name__)




@add_end_docstrings(
    _UpperCamelCase   ,      R'''
        top_k (`int`, defaults to 5):
            The number of predictions to return.
        targets (`str` or `List[str]`, *optional*):
            When passed, the model will limit the scores to the passed targets instead of looking up in the whole
            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting
            token will be used (with a warning, and that might be slower).

    '''   ,      )
class 				__lowercase			(			_UpperCamelCase							):







					'''simple docstring'''




					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if self.framework == "tf":
													__a :						Any			       =  tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()
									elif self.framework == "pt":
													__a :						Optional[int]			       =  torch.nonzero(input_ids == self.tokenizer.mask_token_id	,			as_tuple=_UpperCAmelCase     )
									else:
													raise ValueError('''Unsupported framework'''     )
									return masked_index







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Union[str, Any]			       =  self.get_masked_index(_UpperCAmelCase     )
									__a :						List[str]			       =  np.prod(masked_index.shape     )
									if numel < 1:
													raise PipelineException(
													    '''fill-mask'''	,			self.model.base_model_prefix	,			f"""No mask_token ({self.tokenizer.mask_token}) found on the input"""	,			)







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
													for model_input in model_inputs:
																	self._ensure_exactly_one_mask_token(model_input['''input_ids'''][0]     )
									else:
													for input_ids in model_inputs["input_ids"]:
																	self._ensure_exactly_one_mask_token(_UpperCAmelCase     )







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=None	,			**_UpperCAmelCase     ):
									if return_tensors is None:
													__a :						str			       =  self.framework
									__a :						List[str]			       =  self.tokenizer(_UpperCAmelCase	,			return_tensors=_UpperCAmelCase     )
									self.ensure_exactly_one_mask_token(_UpperCAmelCase     )
									return model_inputs







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase     ):
									__a :						Optional[int]			       =  self.model(**_UpperCAmelCase     )
									__a :						Optional[int]			       =  model_inputs['''input_ids''']
									return model_outputs







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=5	,			_UpperCAmelCase=None     ):
									# Cap top_k if there are targets
									if target_ids is not None and target_ids.shape[0] < top_k:
													__a :						int			       =  target_ids.shape[0]
									__a :						Optional[int]			       =  model_outputs['''input_ids'''][0]
									__a :						int			       =  model_outputs['''logits''']

									if self.framework == "tf":
													__a :						Optional[Any]			       =  tf.where(input_ids == self.tokenizer.mask_token_id     ).numpy()[:, 0]

													__a :						Tuple			       =  outputs.numpy()

													__a :						Dict			       =  outputs[0, masked_index, :]
													__a :						List[str]			       =  stable_softmax(_UpperCAmelCase	,			axis=-1     )
													if target_ids is not None:
																	__a :						Any			       =  tf.gather_nd(tf.squeeze(_UpperCAmelCase	,			0     )	,			target_ids.reshape(-1	,			1     )     )
																	__a :						List[str]			       =  tf.expand_dims(_UpperCAmelCase	,			0     )

													__a :						int			       =  tf.math.top_k(_UpperCAmelCase	,			k=_UpperCAmelCase     )
													__a       ,						__a :						str			       =  topk.values.numpy(), topk.indices.numpy()
									else:
													__a :						List[str]			       =  torch.nonzero(input_ids == self.tokenizer.mask_token_id	,			as_tuple=_UpperCAmelCase     ).squeeze(-1     )
													# Fill mask pipeline supports only one ${mask_token} per sample

													__a :						Any			       =  outputs[0, masked_index, :]
													__a :						int			       =  logits.softmax(dim=-1     )
													if target_ids is not None:
																	__a :						str			       =  probs[..., target_ids]

													__a       ,						__a :						Optional[int]			       =  probs.topk(_UpperCAmelCase     )

									__a :						Union[str, Any]			       =  []
									__a :						Dict			       =  values.shape[0] == 1
									for i, (_values, _predictions) in enumerate(zip(values.tolist()	,			predictions.tolist()     )     ):
													__a :						str			       =  []
													for v, p in zip(_values	,			_predictions     ):
																	# Copy is important since we're going to modify this array in place
																	__a :						List[Any]			       =  input_ids.numpy().copy()
																	if target_ids is not None:
																					__a :						str			       =  target_ids[p].tolist()

																	__a :						Tuple			       =  p
																	# Filter padding out:
																	__a :						Dict			       =  tokens[np.where(tokens != self.tokenizer.pad_token_id     )]
																	# Originally we skip special tokens to give readable output.
																	# For multi masks though, the other [MASK] would be removed otherwise
																	# making the output look odd, so we add them back
																	__a :						str			       =  self.tokenizer.decode(_UpperCAmelCase	,			skip_special_tokens=_UpperCAmelCase     )
																	__a :						Dict			       =  {'''score''': v, '''token''': p, '''token_str''': self.tokenizer.decode([p]     ), '''sequence''': sequence}
																	row.append(_UpperCAmelCase     )
													result.append(_UpperCAmelCase     )
									if single_mask:
													return result[0]
									return result







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase	,			_UpperCAmelCase=None     ):
									if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ):
													__a :						Any			       =  [targets]
									try:
													__a :						Dict			       =  self.tokenizer.get_vocab()
									except Exception:
													__a :						int			       =  {}
									__a :						List[Any]			       =  []
									for target in targets:
													__a :						Optional[Any]			       =  vocab.get(_UpperCAmelCase	,			_UpperCAmelCase     )
													if id_ is None:
																	__a :						Any			       =  self.tokenizer(
																	    _UpperCAmelCase	,			add_special_tokens=_UpperCAmelCase	,			return_attention_mask=_UpperCAmelCase	,			return_token_type_ids=_UpperCAmelCase	,			max_length=1	,			truncation=_UpperCAmelCase	,			)['''input_ids''']
																	if len(_UpperCAmelCase     ) == 0:
																					logger.warning(
																					    f"""The specified target token `{target}` does not exist in the model vocabulary. """
																					    '''We cannot replace it with anything meaningful, ignoring it'''     )
																					continue
																	__a :						Optional[Any]			       =  input_ids[0]
																	# XXX: If users encounter this pass
																	# it becomes pretty slow, so let's make sure
																	# The warning enables them to fix the input to
																	# get faster performance.
																	logger.warning(
																	    f"""The specified target token `{target}` does not exist in the model vocabulary. """
																	    f"""Replacing with `{self.tokenizer.convert_ids_to_tokens(id_     )}`."""     )
													target_ids.append(id_     )
									__a :						Tuple			       =  list(set(_UpperCAmelCase     )     )
									if len(_UpperCAmelCase     ) == 0:
													raise ValueError('''At least one target must be provided when passed.'''     )
									__a :						str			       =  np.array(_UpperCAmelCase     )
									return target_ids







					def  _lowerCamelCase		(						self	,			_UpperCAmelCase=None	,			_UpperCAmelCase=None     ):
									__a :						Union[str, Any]			       =  {}

									if targets is not None:
													__a :						Any			       =  self.get_target_ids(_UpperCAmelCase	,			_UpperCAmelCase     )
													__a :						int			       =  target_ids

									if top_k is not None:
													__a :						Tuple			       =  top_k

									if self.tokenizer.mask_token_id is None:
													raise PipelineException(
													    '''fill-mask'''	,			self.model.base_model_prefix	,			'''The tokenizer does not define a `mask_token`.'''     )
									return {}, {}, postprocess_params







					def __call__(						self	,			_UpperCAmelCase	,			*_UpperCAmelCase	,			**_UpperCAmelCase     ):
									__a :						List[Any]			       =  super().__call__(_UpperCAmelCase	,			**_UpperCAmelCase     )
									if isinstance(_UpperCAmelCase	,			_UpperCAmelCase     ) and len(_UpperCAmelCase     ) == 1:
													return outputs[0]
									return outputs






"""simple docstring"""






from math import log

from scipy.constants import Boltzmann, physical_constants

A 			=  300  # TEMPERATURE (unit = K)

def     __A      (			a_						:float   ,       a_						:float   ,       a_						:float   ,       )		->		float:

				if donor_conc <= 0:
								raise ValueError('''Donor concentration should be positive''')
				elif acceptor_conc <= 0:
								raise ValueError('''Acceptor concentration should be positive''')
				elif intrinsic_conc <= 0:
								raise ValueError('''Intrinsic concentration should be positive''')
				elif donor_conc <= intrinsic_conc:
								raise ValueError(
								    '''Donor concentration should be greater than intrinsic concentration''')
				elif acceptor_conc <= intrinsic_conc:
								raise ValueError(
								    '''Acceptor concentration should be greater than intrinsic concentration''')
				else:
								return (
								    Boltzmann
								    * T
								    * log((donor_conc * acceptor_conc) / intrinsic_conc**2)
								    / physical_constants["electron volt"][0]
								)


if __name__ == "__main__":
		import doctest

		doctest.testmod()