





import warnings
from typing import Dict

import numpy as np

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


if is_tf_available():
     from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

if is_torch_available():
     from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

def     a_       (					lowerCAmelCase_ :     int      ):
   return 1.0 / (1.0 + np.exp(-_outputs      ))

def     a_       (					lowerCAmelCase_ :     List[str]      ):
   __lowerCAmelCase				      =      np.max(_outputs,      axis=-1,      keepdims=lowerCAmelCase_      )
   __lowerCAmelCase				      =      np.exp(_outputs - maxes      )
   return shifted_exp / shifted_exp.sum(axis=-1,      keepdims=lowerCAmelCase_      )



class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					"""sigmoid"""
  a_              =					"""softmax"""
  a_              =					"""none"""





@add_end_docstrings(
    _UpperCamelCase						,       R"""
        return_all_scores (`bool`, *optional*, defaults to `False`):
            Whether to return all prediction scores or just the one of the predicted class.
        function_to_apply (`str`, *optional*, defaults to `\"default\"`):
            The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:

            - `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
              has several labels, will apply the softmax function on the output.
            - `\"sigmoid\"`: Applies the sigmoid function on the output.
            - `\"softmax\"`: Applies the softmax function on the output.
            - `\"none\"`: Does not apply any function on the output.
    """						,       )
class 							_UpperCAmelCase       (       _UpperCamelCase       ):

  """simple docstring"""


  a_              =					False
  a_              =					ClassificationFunction.NONE






  def __init__(							self					:						str   ,    **lowerCAmelCase_					:						Any	)				->		Any:
     super().__init__(**lowerCAmelCase_	)

     self.check_model_type(
         TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
         if self.framework == 'tf'
         else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING	)






  def 							lowercase						(							self					:						Any   ,    lowerCAmelCase_					:						str=None   ,    lowerCAmelCase_					:						Union[str, Any]=None   ,    lowerCAmelCase_					:						Any=""   ,    **lowerCAmelCase_					:						List[Any]	)				->		Union[str, Any]:
     # Using "" as default argument because we're going to use `top_k=None` in user code to declare
     # "No top_k"
     __lowerCAmelCase				      =      tokenizer_kwargs

     __lowerCAmelCase				      =      {}
     if hasattr(self.model.config   ,    'return_all_scores'	) and return_all_scores is None:
        __lowerCAmelCase				      =      self.model.config.return_all_scores

     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) or top_k is None:
        __lowerCAmelCase				      =      top_k
        __lowerCAmelCase				      =      False
     elif return_all_scores is not None:
        warnings.warn(
            '`return_all_scores` is now deprecated,  if want a similar functionality use `top_k=None` instead of'
            ' `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.'   ,    lowerCAmelCase_   ,    )
        if return_all_scores:
           __lowerCAmelCase				      =      None
        else:
           __lowerCAmelCase				      =      1

     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        __lowerCAmelCase				      =      ClassificationFunction[function_to_apply.upper()]

     if function_to_apply is not None:
        __lowerCAmelCase				      =      function_to_apply
     return preprocess_params, {}, postprocess_params






  def __call__(							self					:						Union[str, Any]   ,    *lowerCAmelCase_					:						Tuple   ,    **lowerCAmelCase_					:						Any	)				->		List[Any]:
     __lowerCAmelCase				      =      super().__call__(*lowerCAmelCase_   ,    **lowerCAmelCase_	)
     # TODO try and retrieve it in a nicer way from _sanitize_parameters.
     __lowerCAmelCase				      =      'top_k' not in kwargs
     if isinstance(args[0]   ,    lowerCAmelCase_	) and _legacy:
        # This pipeline is odd, and return a list when single item is run
        return [result]
     else:
        return result






  def 							lowercase						(							self					:						List[str]   ,    lowerCAmelCase_					:						Any   ,    **lowerCAmelCase_					:						Optional[Any]	)				->		Dict[str, GenericTensor]:
     __lowerCAmelCase				      =      self.framework
     if isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        return self.tokenizer(**lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_	)
     elif isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	) and len(lowerCAmelCase_	) == 1 and isinstance(inputs[0]   ,    lowerCAmelCase_	) and len(inputs[0]	) == 2:
        # It used to be valid to use a list of list of list for text pairs, keeping this path for BC
        return self.tokenizer(
            text=inputs[0][0]   ,    text_pair=inputs[0][1]   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_	)
     elif isinstance(lowerCAmelCase_   ,    lowerCAmelCase_	):
        # This is likely an invalid usage of the pipeline attempting to pass text pairs.
        raise ValueError(
            'The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a'
            ' dictionary `{"text": "My text", "text_pair": "My pair"}` in order to send a text pair.'	)
     return self.tokenizer(lowerCAmelCase_   ,    return_tensors=lowerCAmelCase_   ,    **lowerCAmelCase_	)






  def 							lowercase						(							self					:						Union[str, Any]   ,    lowerCAmelCase_					:						Any	)				->		Union[str, Any]:
     return self.model(**lowerCAmelCase_	)






  def 							lowercase						(							self					:						List[Any]   ,    lowerCAmelCase_					:						Optional[int]   ,    lowerCAmelCase_					:						Tuple=None   ,    lowerCAmelCase_					:						Optional[Any]=1   ,    lowerCAmelCase_					:						List[str]=True	)				->		Optional[Any]:
     # `_legacy` is used to determine if we're running the naked pipeline and in backward
     # compatibility mode, or if running the pipeline with `pipeline(..., top_k=1)` we're running
     # the more natural result containing the list.
     # Default value before `set_parameters`
     if function_to_apply is None:
        if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
           __lowerCAmelCase				      =      ClassificationFunction.SIGMOID
        elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
           __lowerCAmelCase				      =      ClassificationFunction.SOFTMAX
        elif hasattr(self.model.config   ,    'function_to_apply'	) and function_to_apply is None:
           __lowerCAmelCase				      =      self.model.config.function_to_apply
        else:
           __lowerCAmelCase				      =      ClassificationFunction.NONE

     __lowerCAmelCase				      =      model_outputs['logits'][0]
     __lowerCAmelCase				      =      outputs.numpy()

     if function_to_apply == ClassificationFunction.SIGMOID:
        __lowerCAmelCase				      =      sigmoid(lowerCAmelCase_	)
     elif function_to_apply == ClassificationFunction.SOFTMAX:
        __lowerCAmelCase				      =      softmax(lowerCAmelCase_	)
     elif function_to_apply == ClassificationFunction.NONE:
        __lowerCAmelCase				      =      outputs
     else:
        raise ValueError(f"""Unrecognized `function_to_apply` argument: {function_to_apply}"""	)

     if top_k == 1 and _legacy:
        return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}

     __lowerCAmelCase				      =      [
         {'label': self.model.config.idalabel[i], 'score': score.item()} for i, score in enumerate(lowerCAmelCase_	)
     ]
     if not _legacy:
        dict_scores.sort(key=lambda lowerCAmelCase_	: x["score"]   ,    reverse=lowerCAmelCase_	)
        if top_k is not None:
           __lowerCAmelCase				      =      dict_scores[:top_k]
     return dict_scores






'''simple docstring'''





import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm

def 			a_	(				_lowerCAmelCase			,_lowerCAmelCase			,_lowerCAmelCase			) -> int:
  __lowerCamelCase			:			Optional[Any]       			=					1.5
  __lowerCamelCase			:			Tuple       			=					int(factor * num_class_images			)
  __lowerCamelCase			:			Tuple       			=					ClipClient(
      url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			)

  os.makedirs(F'{class_data_dir}/images'			,exist_ok=_lowerCAmelCase			)
  if len(list(Path(F'{class_data_dir}/images'			).iterdir()			)			) >= num_class_images:
    return

  while True:
    __lowerCamelCase			:			Dict       			=					client.query(text=_lowerCAmelCase			)
    if len(_lowerCAmelCase			) >= factor * num_class_images or num_images > 1E4:
      break
    else:
      __lowerCamelCase			:			Union[str, Any]       			=					int(factor * num_images			)
      __lowerCamelCase			:			List[str]       			=					ClipClient(
          url='https://knn.laion.ai/knn-service'			,indice_name='laion_400m'			,num_images=_lowerCAmelCase			,aesthetic_weight=0.1			,)

  __lowerCamelCase			:			List[Any]       			=					0
  __lowerCamelCase			:			Tuple       			=					0
  __lowerCamelCase			:			int       			=					tqdm(desc='downloading real regularization images'			,total=_lowerCAmelCase			)

  with open(F'{class_data_dir}/caption.txt'			,'w'			) as fa, open(F'{class_data_dir}/urls.txt'			,'w'			) as fa, open(
      F'{class_data_dir}/images.txt'			,'w'			) as fa:
    while total < num_class_images:
      __lowerCamelCase			:			List[str]       			=					class_images[count]
      count += 1
      try:
        __lowerCamelCase			:			int       			=					requests.get(images['url']			)
        if img.status_code == 200:
          __lowerCamelCase			:			str       			=					Image.open(BytesIO(img.content			)			)
          with open(F'{class_data_dir}/images/{total}.jpg'			,'wb'			) as f:
            f.write(img.content			)
          fa.write(images['caption'] + '\n'			)
          fa.write(images['url'] + '\n'			)
          fa.write(F'{class_data_dir}/images/{total}.jpg' + '\n'			)
          total += 1
          pbar.update(1			)
        else:
          continue
      except Exception:
        continue
  return

def 			a_	(				) -> Dict:
  __lowerCamelCase			:			Any       			=					argparse.ArgumentParser(''			,add_help=_lowerCAmelCase			)
  parser.add_argument('--class_prompt'			,help='text prompt to retrieve images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--class_data_dir'			,help='path to save images'			,required=_lowerCAmelCase			,type=_lowerCAmelCase			)
  parser.add_argument('--num_class_images'			,help='number of images to download'			,default=200			,type=_lowerCAmelCase			)
  return parser.parse_args()


if __name__ == "__main__":
   _UpperCamelCase											=		parse_args()
   retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)






