


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(a__)
class  _a							(    a__):




	def __init__( self						:		Optional[int]		, *_SCREAMING_SNAKE_CASE						:		str		, **_SCREAMING_SNAKE_CASE						:		Dict		)->	int:
			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						:		Dict		, _SCREAMING_SNAKE_CASE						:		str=None		)->	str:
			lowerCAmelCase__       :      Union[str, Any]   					= {}
			if top_k is not None:
					lowerCAmelCase__       :      str   					= top_k
			return {}, {}, postprocess_params




	def __call__( self						:		Dict		, _SCREAMING_SNAKE_CASE						:		List[str]		, **_SCREAMING_SNAKE_CASE						:		Optional[int]		)->	Optional[Any]:
			return super().__call__(_lowerCamelCase		, **_lowerCamelCase		)




	def 							UpperCAmelCase__( self						:		Any		, _SCREAMING_SNAKE_CASE						:		Any		)->	Union[str, Any]:
			lowerCAmelCase__       :      Union[str, Any]   					= load_image(_lowerCamelCase		)
			lowerCAmelCase__       :      List[Any]   					= self.image_processor(images=_lowerCamelCase		, return_tensors=self.framework		)
			return model_inputs




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Any		)->	Optional[Any]:
			lowerCAmelCase__       :      int   					= self.model(**_lowerCamelCase		)
			return model_outputs




	def 							UpperCAmelCase__( self						:		Tuple		, _SCREAMING_SNAKE_CASE						:		Optional[Any]		, _SCREAMING_SNAKE_CASE						:		Tuple=5		)->	Optional[int]:
			if top_k > self.model.config.num_labels:
					lowerCAmelCase__       :      Optional[int]   					= self.model.config.num_labels

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

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



# using dfs for finding eulerian path traversal






def 					lowerCamelCase_   (	_a     ,       _a     ,       _a     ,       _a=None	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= (path or []) + [u]
		for v in graph[u]:
				if visited_edge[u][v] is False:
						lowerCAmelCase__					,     lowerCAmelCase__       :      List[Any]   					= True, True
						lowerCAmelCase__       :      Any   					= dfs(_a     ,       _a     ,       _a     ,       _a	)
		return path






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Union[str, Any]   					= 0
		lowerCAmelCase__       :      str   					= -1
		for i in range(_a	):
				if i not in graph.keys():
						continue
				if len(graph[i]	) % 2 == 1:
						odd_degree_nodes += 1
						lowerCAmelCase__       :      Tuple   					= i
		if odd_degree_nodes == 0:
				return 1, odd_node
		if odd_degree_nodes == 2:
				return 2, odd_node
		return 3, odd_node






def 					lowerCamelCase_   (	_a     ,       _a	):




		"""simple docstring"""






		lowerCAmelCase__       :      Optional[Any]   					= [[False for _ in range(max_node + 1	)] for _ in range(max_node + 1	)]
		lowerCAmelCase__					,     lowerCAmelCase__       :      Optional[int]   					= check_circuit_or_path(_a     ,       _a	)
		if check == 3:
				print('''graph is not Eulerian'''	)
				print('''no path'''	)
				return
		lowerCAmelCase__       :      Optional[int]   					= 1
		if check == 2:
				lowerCAmelCase__       :      Any   					= odd_node
				print('''graph has a Euler path'''	)
		if check == 1:
				print('''graph has a Euler cycle'''	)
		lowerCAmelCase__       :      Optional[int]   					= dfs(_a     ,       _a     ,       _a	)
		print(_a	)






def 					lowerCamelCase_   (	):




		"""simple docstring"""






		lowerCAmelCase__       :      List[str]   					= {1: [2, 3, 4], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [4]}
		lowerCAmelCase__       :      Tuple   					= {1: [2, 3, 4, 5], 2: [1, 3], 3: [1, 2], 4: [1, 5], 5: [1, 4]}
		lowerCAmelCase__       :      str   					= {1: [2, 3, 4], 2: [1, 3, 4], 3: [1, 2], 4: [1, 2, 5], 5: [4]}
		lowerCAmelCase__       :      List[str]   					= {1: [2, 3], 2: [1, 3], 3: [1, 2]}
		lowerCAmelCase__       :      List[Any]   					= {
		    1: [],
		    2: []
		    # all degree is zero
		}
		lowerCAmelCase__       :      Optional[Any]   					= 10
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)
		check_euler(_a     ,       _a	)


if __name__ == "__main__":
		main()




