


from collections import UserDict
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_torch_available():
			from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING

if is_tf_available():
			from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
			from ..tf_utils import stable_softmax

_snake_case					:		Union[str, Any]      =    logging.get_logger(__name__)

@add_end_docstrings(_lowerCAmelCase				)
class     a		(_lowerCAmelCase				):




							"""simple docstring"""


							def __init__(							self	:  List[Any]   ,     **lowerCamelCase	:  Tuple    )     ->   Optional[Any]:
														super().__init__(**lowerCamelCase    )

														requires_backends(self   ,     "vision"    )
														self.check_model_type(
														    TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
														    if self.framework == "tf"
														    else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING    )

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

							def    __snake_case (							self	:  str   ,     **lowerCamelCase	:  Dict    )     ->   Dict:
														__snake_case		:      Any										=       {}
														if "candidate_labels" in kwargs:
																					__snake_case		:      Dict										=       kwargs["candidate_labels"]
														if "hypothesis_template" in kwargs:
																					__snake_case		:      Any										=       kwargs["hypothesis_template"]

														return preprocess_params, {}, {}

							def    __snake_case (							self	:  Tuple   ,     lowerCamelCase	:  Any   ,     lowerCamelCase	:  Any=None   ,     lowerCamelCase	:  Optional[int]="This is a photo of {}."    )     ->   Union[str, Any]:
														__snake_case		:      Union[str, Any]										=       load_image(lowerCamelCase    )
														__snake_case		:      Optional[int]										=       self.image_processor(images=[image]   ,     return_tensors=self.framework    )
														__snake_case		:      Any										=       candidate_labels
														__snake_case		:      Optional[Any]										=       [hypothesis_template.format(lowerCamelCase    ) for x in candidate_labels]
														__snake_case		:      Optional[Any]										=       self.tokenizer(lowerCamelCase   ,     return_tensors=self.framework   ,     padding=lowerCamelCase    )
														__snake_case		:      int										=       [text_inputs]
														return inputs

							def    __snake_case (							self	:  int   ,     lowerCamelCase	:  Union[str, Any]    )     ->   str:
														__snake_case		:      Tuple										=       model_inputs.pop("candidate_labels"    )
														__snake_case		:      Union[str, Any]										=       model_inputs.pop("text_inputs"    )
														if isinstance(text_inputs[0]   ,     lowerCamelCase    ):
																					__snake_case		:      Dict										=       text_inputs[0]
														else:
																					# Batching case.
																					__snake_case		:      List[Any]										=       text_inputs[0][0]

														__snake_case		:      Dict										=       self.model(**lowerCamelCase   ,     **lowerCamelCase    )

														__snake_case		:      Optional[int]										=       {
														    "candidate_labels": candidate_labels,
														    "logits": outputs.logits_per_image,
														}
														return model_outputs






							def    __snake_case (							self	:  Dict   ,     lowerCamelCase	:  Tuple    )     ->   List[Any]:
														__snake_case		:      Optional[int]										=       model_outputs.pop("candidate_labels"    )
														__snake_case		:      Optional[int]										=       model_outputs["logits"][0]
														if self.framework == "pt":
																					__snake_case		:      Union[str, Any]										=       logits.softmax(dim=-1    ).squeeze(-1    )
																					__snake_case		:      str										=       probs.tolist()
																					if not isinstance(lowerCamelCase   ,     lowerCamelCase    ):
																												__snake_case		:      int										=       [scores]
														elif self.framework == "tf":
																					__snake_case		:      List[Any]										=       stable_softmax(lowerCamelCase   ,     axis=-1    )
																					__snake_case		:      str										=       probs.numpy().tolist()
														else:
																					raise ValueError(F'Unsupported framework: {self.framework}'    )

														__snake_case		:      Optional[Any]										=       [
														    {"score": score, "label": candidate_label}
														    for score, candidate_label in sorted(zip(lowerCamelCase   ,     lowerCamelCase    )   ,     key=lambda lowerCamelCase    : -x[0]    )
														]
														return result





from __future__ import annotations

_snake_case					:		Union[str, Any]      =    []







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														if board[row][i] == 1:
																					return False
							for i in range(len(__lowerCamelCase     )     ):
														if board[i][column] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     -1							,     -1     )     ):
														if board[i][j] == 1:
																					return False
							for i, j in zip(range(__lowerCamelCase							,     -1							,     -1     )							,     range(__lowerCamelCase							,     len(__lowerCamelCase     )     )     ):
														if board[i][j] == 1:
																					return False
							return True







def     lowerCAmelCase_					( __lowerCamelCase							,     __lowerCamelCase     ):
							if row >= len(__lowerCamelCase     ):
														solution.append(__lowerCamelCase     )
														printboard(__lowerCamelCase     )
														print()
														return True
							for i in range(len(__lowerCamelCase     )     ):
														if is_safe(__lowerCamelCase							,     __lowerCamelCase							,     __lowerCamelCase     ):
																					__snake_case		:      Optional[Any]										=       1
																					solve(__lowerCamelCase							,     row + 1     )
																					__snake_case		:      Union[str, Any]										=       0
							return False







def     lowerCAmelCase_					( __lowerCamelCase     ):
							for i in range(len(__lowerCamelCase     )     ):
														for j in range(len(__lowerCamelCase     )     ):
																					if board[i][j] == 1:
																												print("Q"							,     end=" "     )
																					else:
																												print("."							,     end=" "     )
														print()


# n=int(input("The no. of queens"))
_snake_case					:		List[str]      =    8
_snake_case					:		Optional[int]      =    [[0 for i in range(n)] for j in range(n)]
solve(board, 0)

print("The total no. of solutions are :", len(solution))

