


"""simple docstring"""


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

__lowerCamelCase      :Optional[Any]	    =		logging.get_logger(__name__)


@add_end_docstrings(__lowerCAmelCase)
class 			A__	(						__lowerCAmelCase):


     """simple docstring"""

     def __init__(	self:						str     ,		*__a:						int     ,		**__a:						List[str]	)->    Tuple:
           super().__init__(*_UpperCamelCase     ,		**_UpperCamelCase	)
           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 		a__						(	self:						Dict     ,		__a:						List[str]=None	)->    Optional[int]:
           lowerCamelCase					:					Optional[int]        =	{}
           if top_k is not None:
                 lowerCamelCase					:					str        =	top_k
           return {}, {}, postprocess_params

     def __call__(	self:						Optional[int]     ,		__a:						Union[str, List[str], "Image.Image", List["Image.Image"]]     ,		**__a:						Optional[int]	)->    Dict:
           return super().__call__(_UpperCamelCase     ,		**_UpperCamelCase	)

     def 		a__						(	self:						Optional[int]     ,		__a:						Optional[int]	)->    str:
           lowerCamelCase					:					Tuple        =	load_image(_UpperCamelCase	)
           lowerCamelCase					:					Any        =	self.image_processor(images=_UpperCamelCase     ,		return_tensors=self.framework	)
           return model_inputs

     def 		a__						(	self:						str     ,		__a:						Union[str, Any]	)->    List[str]:
           lowerCamelCase					:					Any        =	self.model(**_UpperCamelCase	)
           return model_outputs







     def 		a__						(	self:						Optional[Any]     ,		__a:						List[str]     ,		__a:						List[str]=5	)->    str:


           if top_k > self.model.config.num_labels:
                 lowerCamelCase					:					Union[str, Any]        =	self.model.config.num_labels

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

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








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCamelCase      :Tuple	    =		logging.get_logger(__name__)

__lowerCamelCase      :Any	    =		{
    'vinvino02/glpn-kitti': 'https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json',
    # See all GLPN models at https://huggingface.co/models?filter=glpn
}


class 			A__	(						__lowercase):


     """simple docstring"""
     snake_case__	:					Tuple													='''glpn'''

     def __init__(	self:						Dict     ,		__a:						List[str]=3     ,		__a:						Optional[int]=4     ,		__a:						Dict=[2, 2, 2, 2]     ,		__a:						str=[8, 4, 2, 1]     ,		__a:						Optional[int]=[32, 64, 160, 256]     ,		__a:						Dict=[7, 3, 3, 3]     ,		__a:						Dict=[4, 2, 2, 2]     ,		__a:						Optional[Any]=[1, 2, 5, 8]     ,		__a:						Tuple=[4, 4, 4, 4]     ,		__a:						int="gelu"     ,		__a:						Union[str, Any]=0.0     ,		__a:						str=0.0     ,		__a:						Union[str, Any]=0.02     ,		__a:						str=0.1     ,		__a:						Union[str, Any]=1e-6     ,		__a:						Any=64     ,		__a:						Dict=10     ,		__a:						Union[str, Any]=-1     ,		**__a:						Optional[Any]     ,		)->    Dict:
           super().__init__(**__a	)

           lowerCamelCase					:					Dict        =	num_channels
           lowerCamelCase					:					Any        =	num_encoder_blocks
           lowerCamelCase					:					Dict        =	depths
           lowerCamelCase					:					List[str]        =	sr_ratios
           lowerCamelCase					:					Dict        =	hidden_sizes
           lowerCamelCase					:					Tuple        =	patch_sizes
           lowerCamelCase					:					Optional[int]        =	strides
           lowerCamelCase					:					Optional[Any]        =	mlp_ratios
           lowerCamelCase					:					Union[str, Any]        =	num_attention_heads
           lowerCamelCase					:					List[str]        =	hidden_act
           lowerCamelCase					:					Any        =	hidden_dropout_prob
           lowerCamelCase					:					Optional[int]        =	attention_probs_dropout_prob
           lowerCamelCase					:					List[Any]        =	initializer_range
           lowerCamelCase					:					Dict        =	drop_path_rate
           lowerCamelCase					:					Any        =	layer_norm_eps
           lowerCamelCase					:					Optional[Any]        =	decoder_hidden_size
           lowerCamelCase					:					Tuple        =	max_depth
           lowerCamelCase					:					Optional[Any]        =	head_in_index




