
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase__          =		{"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__          =		[
					    """FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """FocalNetForImageClassification""",
					    """FocalNetForMaskedImageModeling""",
					    """FocalNetBackbone""",
					    """FocalNetModel""",
					    """FocalNetPreTrainedModel""",
					]

if TYPE_CHECKING:
					from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_focalnet import (
										    FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FocalNetBackbone,
										    FocalNetForImageClassification,
										    FocalNetForMaskedImageModeling,
										    FocalNetModel,
										    FocalNetPreTrainedModel,
										)

else:
					import sys

					lowerCamelCase__          =		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

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__          =		logging.get_logger(__name__)







@add_end_docstrings(__magic_name__	)
class      A__							(     __magic_name__	):






				def __init__(						self					:      int    ,     *a					:      Dict    ,     **a					:      Union[str, Any]   ):






							'''simple docstring'''







							super().__init__(*a    ,     **a   )
							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 							_lowerCamelCase		(						self					:      Dict    ,     a					:      List[str]=None   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Any         =		{}
							if top_k is not None:
										lowerCAmelCase__      :      Tuple         =		top_k
							return {}, {}, postprocess_params






				def __call__(						self					:      Any    ,     a					:      Union[str, List[str], "Image.Image", List["Image.Image"]]    ,     **a					:      List[Any]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any    ,     a					:      Optional[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[Any]         =		load_image(a   )
							lowerCAmelCase__      :      Optional[int]         =		self.image_processor(images=a    ,     return_tensors=self.framework   )
							return model_inputs






				def 							_lowerCamelCase		(						self					:      Optional[int]    ,     a					:      List[str]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		self.model(**a   )
							return model_outputs






				def 							_lowerCamelCase		(						self					:      Optional[Any]    ,     a					:      List[Any]    ,     a					:      List[Any]=5   ):






							'''simple docstring'''







							if top_k > self.model.config.num_labels:
										lowerCAmelCase__      :      Optional[int]         =		self.model.config.num_labels

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

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