



"""simple docstring"""

from typing import List, Union

import numpy as np

from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline


lowercase__					:						List[str]												=			logging.get_logger(__name__)




class  _UpperCAmelCase (    lowerCAmelCase__):

 def 		_snake_case      (  self	: Any						,  lowercase_	: Tuple       ):
     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  int       =			[label.strip() for label in labels.split(''','''       ) if label.strip()]
     return labels






 def __call__(  self	: List[str]						,  lowercase_	: int						,  lowercase_	: Optional[int]						,  lowercase_	: str       ):
     if len(lowercase_       ) == 0 or len(lowercase_       ) == 0:
         raise ValueError('''You must include at least one label and at least one sequence.'''       )
     if hypothesis_template.format(labels[0]       ) == hypothesis_template:
         raise ValueError(
             (
                 '''The provided hypothesis_template "{}" was not able to be formatted with the target labels. '''
                 '''Make sure the passed template includes formatting syntax such as {{}} where the label should go.'''
             ).format(lowercase_       )       )

     if isinstance(lowercase_						,  lowercase_       ):
         snake_case_				:  List[Any]       =			[sequences]

     snake_case_				:  Optional[int]       =			[]
     for sequence in sequences:
         sequence_pairs.extend([[sequence, hypothesis_template.format(lowercase_       )] for label in labels]       )

     return sequence_pairs, sequences





@add_end_docstrings(lowerCAmelCase__)
class  _UpperCAmelCase (    lowerCAmelCase__):

 def __init__(  self	: List[str]						,  lowercase_	: Optional[Any]=ZeroShotClassificationArgumentHandler()						,  *lowercase_	: str						,  **lowercase_	: Union[str, Any]       ):
     snake_case_				:  str       =			args_parser
     super().__init__(*lowercase_						,  **lowercase_       )
     if self.entailment_id == -1:
         logger.warning(
             '''Failed to determine \'entailment\' label id from the label2id mapping in the model config. Setting to '''
             '''-1. Define a descriptive label2id mapping in the model config to ensure correct outputs.'''       )

 @property
 def 		_snake_case      (  self	: Optional[int]       ):
     for label, ind in self.model.config.labelaid.items():
         if label.lower().startswith('''entail'''       ):
             return ind
     return -1

 def 		_snake_case      (  self	: List[str]						,  lowercase_	: str						,  lowercase_	: Tuple=True						,  lowercase_	: List[str]=True						,  lowercase_	: List[str]=TruncationStrategy.ONLY_FIRST						,  **lowercase_	: Union[str, Any]       ):
     snake_case_				:  Optional[int]       =			self.framework
     if self.tokenizer.pad_token is None:
         # Override for tokenizers not supporting padding
         logger.error(
             '''Tokenizer was not supporting padding necessary for zero-shot, attempting to use '''
             ''' `pad_token=eos_token`'''       )
         snake_case_				:  Any       =			self.tokenizer.eos_token
     try:
         snake_case_				:  List[Any]       =			self.tokenizer(
             lowercase_						,  add_special_tokens=lowercase_						,  return_tensors=lowercase_						,  padding=lowercase_						,  truncation=lowercase_						,  )
     except Exception as e:
         if "too short" in str(lowercase_       ):
             # tokenizers might yell that we want to truncate
             # to a value that is not even reached by the input.
             # In that case we don't want to truncate.
             # It seems there's not a really better way to catch that
             # exception.

             snake_case_				:  str       =			self.tokenizer(
                 lowercase_						,  add_special_tokens=lowercase_						,  return_tensors=lowercase_						,  padding=lowercase_						,  truncation=TruncationStrategy.DO_NOT_TRUNCATE						,  )
         else:
             raise e

     return inputs

 def 		_snake_case      (  self	: int						,  **lowercase_	: Union[str, Any]       ):
     if kwargs.get('''multi_class'''						,  lowercase_       ) is not None:
         snake_case_				:  List[str]       =			kwargs['''multi_class''']
         logger.warning(
             '''The `multi_class` argument has been deprecated and renamed to `multi_label`. '''
             '''`multi_class` will be removed in a future version of Transformers.'''       )
     snake_case_				:  str       =			{}
     if "candidate_labels" in kwargs:
         snake_case_				:  str       =			self._args_parser._parse_labels(kwargs['''candidate_labels''']       )
     if "hypothesis_template" in kwargs:
         snake_case_				:  int       =			kwargs['''hypothesis_template''']

     snake_case_				:  List[str]       =			{}
     if "multi_label" in kwargs:
         snake_case_				:  str       =			kwargs['''multi_label''']
     return preprocess_params, {}, postprocess_params

 def __call__(  self	: List[Any]						,  lowercase_	: Union[str, List[str]]						,  *lowercase_	: List[str]						,  **lowercase_	: Optional[Any]						,  ):
     if len(lowercase_       ) == 0:
         pass
     elif len(lowercase_       ) == 1 and "candidate_labels" not in kwargs:
         snake_case_				:  Tuple       =			args[0]
     else:
         raise ValueError(f"Unable to understand extra arguments {args}"       )

     return super().__call__(lowercase_						,  **lowercase_       )

 def 		_snake_case      (  self	: int						,  lowercase_	: List[Any]						,  lowercase_	: str=None						,  lowercase_	: Tuple="This example is {}."       ):
     snake_case_,			snake_case_				:  Optional[Any]       =			self._args_parser(lowercase_						,  lowercase_						,  lowercase_       )

     for i, (candidate_label, sequence_pair) in enumerate(zip(lowercase_						,  lowercase_       )       ):
         snake_case_				:  int       =			self._parse_and_tokenize([sequence_pair]       )

         yield {
             "candidate_label": candidate_label,
             "sequence": sequences[0],
             "is_last": i == len(lowercase_       ) - 1,
             **model_input,
         }

 def 		_snake_case      (  self	: Optional[int]						,  lowercase_	: Optional[Any]       ):
     snake_case_				:  List[Any]       =			inputs['''candidate_label''']
     snake_case_				:  Tuple       =			inputs['''sequence''']
     snake_case_				:  Tuple       =			{k: inputs[k] for k in self.tokenizer.model_input_names}
     snake_case_				:  List[Any]       =			self.model(**lowercase_       )

     snake_case_				:  Any       =			{
         '''candidate_label''': candidate_label,
         '''sequence''': sequence,
         '''is_last''': inputs['''is_last'''],
         **outputs,
     }
     return model_outputs






 def 		_snake_case      (  self	: Union[str, Any]						,  lowercase_	: Optional[int]						,  lowercase_	: Any=False       ):
     snake_case_				:  List[str]       =			[outputs['''candidate_label'''] for outputs in model_outputs]
     snake_case_				:  Optional[Any]       =			[outputs['''sequence'''] for outputs in model_outputs]
     snake_case_				:  List[str]       =			np.concatenate([output['''logits'''].numpy() for output in model_outputs]       )
     snake_case_				:  str       =			logits.shape[0]
     snake_case_				:  Any       =			len(lowercase_       )
     snake_case_				:  int       =			N // n
     snake_case_				:  str       =			logits.reshape((num_sequences, n, -1)       )

     if multi_label or len(lowercase_       ) == 1:
         # softmax over the entailment vs. contradiction dim for each label independently
         snake_case_				:  Optional[Any]       =			self.entailment_id
         snake_case_				:  Optional[Any]       =			-1 if entailment_id == 0 else 0
         snake_case_				:  Optional[int]       =			reshaped_outputs[..., [contradiction_id, entailment_id]]
         snake_case_				:  Any       =			np.exp(lowercase_       ) / np.exp(lowercase_       ).sum(-1						,  keepdims=lowercase_       )
         snake_case_				:  Tuple       =			scores[..., 1]
     else:
         # softmax the "entailment" logits over all candidate labels
         snake_case_				:  Any       =			reshaped_outputs[..., self.entailment_id]
         snake_case_				:  List[Any]       =			np.exp(lowercase_       ) / np.exp(lowercase_       ).sum(-1						,  keepdims=lowercase_       )

     snake_case_				:  List[str]       =			list(reversed(scores[0].argsort()       )       )
     return {
         "sequence": sequences[0],
         "labels": [candidate_labels[i] for i in top_inds],
         "scores": scores[0, top_inds].tolist(),
     }










"""simple docstring"""

import math
import sys

import cva
import numpy as np




def __lowercase    (							_a	,    _a					):
    # For applying gaussian function for each element in matrix.
    snake_case_				:  Dict       =			math.sqrt(_a					)
    snake_case_				:  Tuple       =			1 / (sigma * math.sqrt(2 * math.pi					))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5					)




def __lowercase    (							_a	,    _a	,    _a	,    _a					):
    snake_case_				:  Optional[int]       =			kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]




def __lowercase    (							_a	,    _a					):
    # Creates a gaussian kernel of given dimension.
    snake_case_				:  Union[str, Any]       =			np.zeros((kernel_size, kernel_size)					)
    for i in range(0	,    _a					):
        for j in range(0	,    _a					):
            snake_case_				:  Union[str, Any]       =			math.sqrt(
                abs(i - kernel_size // 2					) ** 2 + abs(j - kernel_size // 2					) ** 2					)
    return vec_gaussian(_a	,    _a					)




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    ):
    snake_case_				:  int       =			np.zeros(img.shape					)
    snake_case_				:  str       =			get_gauss_kernel(_a	,    _a					)
    snake_case_,			snake_case_				:  Any       =			img.shape
    for i in range(kernel_size // 2	,    size_x - kernel_size // 2					):
        for j in range(kernel_size // 2	,    size_y - kernel_size // 2					):
            snake_case_				:  List[Any]       =			get_slice(_a	,    _a	,    _a	,    _a					)
            snake_case_				:  Union[str, Any]       =			img_s - img_s[kernel_size // 2, kernel_size // 2]
            snake_case_				:  Optional[Any]       =			vec_gaussian(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  Dict       =			np.multiply(_a	,    _a					)
            snake_case_				:  List[Any]       =			np.sum(_a					) / np.sum(_a					)
            snake_case_				:  Dict       =			val
    return imga




def __lowercase    (							_a					):
    snake_case_				:  Optional[int]       =			args[1] if args[1:] else '''../image_data/lena.jpg'''
    snake_case_				:  int       =			float(args[2]					) if args[2:] else 1.0
    snake_case_				:  Dict       =			float(args[3]					) if args[3:] else 1.0
    if args[4:]:
        snake_case_				:  Optional[Any]       =			int(args[4]					)
        snake_case_				:  Union[str, Any]       =			kernel_size + abs(kernel_size % 2 - 1					)
    else:
        snake_case_				:  Union[str, Any]       =			5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
     lowercase__					,lowercase__					,lowercase__					,lowercase__					:						Tuple												=			parse_args(sys.argv)
     lowercase__					:						str												=			cva.imread(filename, 0)
     cva.imshow('''input image''', img)

     lowercase__					:						Tuple												=			img / 2_55
     lowercase__					:						Optional[int]												=			out.astype('''float32''')
     lowercase__					:						Union[str, Any]												=			bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
     lowercase__					:						List[str]												=			out * 2_55
     lowercase__					:						List[str]												=			np.uinta(out)
     cva.imshow('''output image''', out)
     cva.waitKey(0)
     cva.destroyAllWindows()





