

import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter






class     __magic_name__     (							TensorFormatter[Mapping, np.ndarray, Mapping]     ):


 def __init__(    self		:			Optional[int]      ,    lowerCAmelCase__		:			str=None      ,    **lowerCAmelCase__		:			int     )     ->   Optional[Any]:
   super().__init__(features=lowerCAmelCase__     )
   UpperCAmelCase          =	np_array_kwargs


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			Dict     )     ->   Union[str, Any]:
   if isinstance(lowerCAmelCase__      ,    lowerCAmelCase__     ):
     if column and all(
         isinstance(lowerCAmelCase__      ,    np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
       return np.stack(lowerCAmelCase__     )
     else:
       # don't use np.array(column, dtype=object)
       # since it fails in certain cases
       # see https://stackoverflow.com/q/51005699
       UpperCAmelCase          =	np.empty(len(lowerCAmelCase__     )      ,    dtype=lowerCAmelCase__     )
       UpperCAmelCase          =	column
       return out
   return column


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			Union[str, Any]     )     ->   List[Any]:
   if isinstance(lowerCAmelCase__      ,    (str, bytes, type(lowerCAmelCase__     ))     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    (np.character, np.ndarray)     ) and np.issubdtype(value.dtype      ,    np.character     ):
     return value
   elif isinstance(lowerCAmelCase__      ,    np.number     ):
     return value

   UpperCAmelCase          =	{}

   if isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.integer     ):
     UpperCAmelCase          =	{"dtype": np.intaa}
   elif isinstance(lowerCAmelCase__      ,    np.ndarray     ) and np.issubdtype(value.dtype      ,    np.floating     ):
     UpperCAmelCase          =	{"dtype": np.floataa}
   elif config.PIL_AVAILABLE and "PIL" in sys.modules:
     import PIL.Image

     if isinstance(lowerCAmelCase__      ,    PIL.Image.Image     ):
       return np.asarray(lowerCAmelCase__      ,    **self.np_array_kwargs     )

   return np.asarray(lowerCAmelCase__      ,    **{**default_dtype, **self.np_array_kwargs}     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			str     )     ->   Optional[int]:
   # support for torch, tf, jax etc.
   if config.TORCH_AVAILABLE and "torch" in sys.modules:
     import torch

     if isinstance(lowerCAmelCase__      ,    torch.Tensor     ):
       return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
   if hasattr(lowerCAmelCase__      ,    "__array__"     ) and not isinstance(lowerCAmelCase__      ,    (np.ndarray, np.character, np.number)     ):
     UpperCAmelCase          =	data_struct.__array__()
   # support for nested types like struct of list of struct
   if isinstance(lowerCAmelCase__      ,    np.ndarray     ):
     if data_struct.dtype == object:
       return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   if isinstance(lowerCAmelCase__      ,    (list, tuple)     ):
     return self._consolidate([self.recursive_tensorize(lowerCAmelCase__     ) for substruct in data_struct]     )
   return self._tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			dict     )     ->   Union[str, Any]:
   return map_nested(self._recursive_tensorize      ,    lowerCAmelCase__      ,    map_list=lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			int      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_row(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_row(lowerCAmelCase__     )
   return self.recursive_tensorize(lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			pa.Table     )     ->   np.ndarray:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_column(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_column(lowerCAmelCase__      ,    pa_table.column_names[0]     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   UpperCAmelCase          =	self._consolidate(lowerCAmelCase__     )
   return column


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			pa.Table     )     ->   Mapping:
   UpperCAmelCase          =	self.numpy_arrow_extractor().extract_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.python_features_decoder.decode_batch(lowerCAmelCase__     )
   UpperCAmelCase          =	self.recursive_tensorize(lowerCAmelCase__     )
   for column_name in batch:
     UpperCAmelCase          =	self._consolidate(batch[column_name]     )
   return batch






from typing import Dict, Optional, Tuple

import torch
def   _lowerCAmelCase(						__A      ):
  UpperCAmelCase          =	boundaries[1] - boundaries[0]
  UpperCAmelCase          =	boundaries + step / 2
  UpperCAmelCase          =	torch.cat([bin_centers, (bin_centers[-1] + step).unsqueeze(-1      )] ,							dim=0      )
  return bin_centers
def   _lowerCAmelCase(						__A ,							__A ,							):
  UpperCAmelCase          =	_calculate_bin_centers(__A      )
  return (
      torch.sum(aligned_distance_error_probs * bin_centers ,							dim=-1      ),
      bin_centers[-1],
  )
def   _lowerCAmelCase(						__A ,							__A = 31 ,							__A = 64 ,							**__A ,							):
  UpperCAmelCase          =	torch.linspace(0 ,							__A ,							steps=(no_bins - 1) ,							device=logits.device      )

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

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





def   _lowerCAmelCase(						__A ,							__A = None ,							__A = 31 ,							__A = 64 ,							__A = 1E-8 ,							**__A ,							):
  if residue_weights is None:
    UpperCAmelCase          =	logits.new_ones(logits.shape[-2]      )

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

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

  UpperCAmelCase          =	1.24 * (clipped_n - 15) ** (1.0 / 3) - 1.8

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

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

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

  UpperCAmelCase          =	per_alignment * residue_weights

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



