


'''simple docstring'''

# Lint as: python3
import itertools
import os
import re


lowerCamelCase							:   Any  						=	re.compile(R'([A-Z]+)([A-Z][a-z])')
lowerCamelCase							:   str  						=	re.compile(R'([a-z\d])([A-Z])')

lowerCamelCase							:   Optional[int]  						=	re.compile(R'(?<!_)_(?!_)')
lowerCamelCase							:   List[Any]  						=	re.compile(R'(_{2,})')

lowerCamelCase							:   str  						=	R'^\w+(\.\w+)*$'

lowerCamelCase							:   Dict  						=	R'<>:/\|?*'

def _SCREAMING_SNAKE_CASE      (A       )       ->       Any:






       """simple docstring"""







       lowercase__      			= _uppercase_uppercase_re.sub(R'''\1_\2'''  , A       )
       lowercase__      			= _lowercase_uppercase_re.sub(R'''\1_\2'''  , A       )
       return name.lower()

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       lowercase__      			= _single_underscore_re.split(A       )
       lowercase__      			= [_multiple_underscores_re.split(A       ) for n in name]
       return "".join(n.capitalize() for n in itertools.chain.from_iterable(A       ) if n != ''''''       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       Tuple:






       """simple docstring"""







       if os.path.basename(A       ) != name:
              raise ValueError(f"Should be a dataset name, not a path: {name}"       )
       return camelcase_to_snakecase(A       )

def _SCREAMING_SNAKE_CASE      (A  , A       )       ->       Optional[Any]:






       """simple docstring"""







       if os.path.basename(A       ) != name:
              raise ValueError(f"Should be a dataset name, not a path: {name}"       )
       if not re.match(_split_re  , A       ):
              raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."       )
       return f"{filename_prefix_for_name(A       )}-{split}"

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None       )       ->       List[str]:






       """simple docstring"""







       lowercase__      			= filename_prefix_for_split(A  , A       )
       if filetype_suffix:
              prefix += f".{filetype_suffix}"
       lowercase__      			= os.path.join(A  , A       )
       return f"{filepath}*"

def _SCREAMING_SNAKE_CASE      (A  , A  , A  , A=None  , A=None       )       ->       Optional[Any]:






       """simple docstring"""







       lowercase__      			= filename_prefix_for_split(A  , A       )
       lowercase__      			= os.path.join(A  , A       )

       if shard_lengths:
              lowercase__      			= len(A       )
              lowercase__      			= [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(A       )]
              if filetype_suffix:
                     lowercase__      			= [filename + f".{filetype_suffix}" for filename in filenames]
              return filenames
       else:
              lowercase__      			= prefix
              if filetype_suffix:
                     filename += f".{filetype_suffix}"
              return [filename]








from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from PIL import Image

 from ..image_utils import load_image

if is_tf_available():
 import tensorflow as tf

 from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
 from ..tf_utils import stable_softmax

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

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






@add_end_docstrings(_a   )
class _lowerCamelCase(						_a   ):
       def __init__(					self,      *lowerCamelCase,      **lowerCamelCase)       ->   int:




           """simple docstring"""

           super().__init__(*lowerCamelCase,      **lowerCamelCase)
           requires_backends(self,      'vision')
           self.check_model_type(
               TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
               if self.framework == 'tf'
               else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING)
       def 			UpperCamelCase     (					self,      lowerCamelCase=None)       ->   int:




           """simple docstring"""

           _lowercase       :							Dict      		=		{}
           if top_k is not None:
               _lowercase       :							List[str]      		=		top_k
           return {}, {}, postprocess_params
       def __call__(					self,      lowerCamelCase,      **lowerCamelCase)       ->   Tuple:




           """simple docstring"""

           return super().__call__(lowerCamelCase,      **lowerCamelCase)
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   str:




           """simple docstring"""

           _lowercase       :							Optional[Any]      		=		load_image(lowerCamelCase)
           _lowercase       :							List[str]      		=		self.image_processor(images=lowerCamelCase,      return_tensors=self.framework)
           return model_inputs
       def 			UpperCamelCase     (					self,      lowerCamelCase)       ->   List[str]:




           """simple docstring"""

           _lowercase       :							Optional[int]      		=		self.model(**lowerCamelCase)
           return model_outputs




       def 			UpperCamelCase     (					self,      lowerCamelCase,      lowerCamelCase=5)       ->   Dict:




           """simple docstring"""

           if top_k > self.model.config.num_labels:
               _lowercase       :							List[Any]      		=		self.model.config.num_labels

           if self.framework == "pt":
               _lowercase       :							int      		=		model_outputs.logits.softmax(-1)[0]
               _lowercase		,				_lowercase       :							Union[str, Any]      		=		probs.topk(lowerCamelCase)
           elif self.framework == "tf":
               _lowercase       :							int      		=		stable_softmax(model_outputs.logits,      axis=-1)[0]
               _lowercase       :							List[Any]      		=		tf.math.top_k(lowerCamelCase,      k=lowerCamelCase)
               _lowercase		,				_lowercase       :							Any      		=		topk.values.numpy(), topk.indices.numpy()
           else:
               raise ValueError(F'''Unsupported framework: {self.framework}''')

           _lowercase       :							str      		=		scores.tolist()
           _lowercase       :							str      		=		ids.tolist()
           return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase,      lowerCamelCase)]


