
from __future__ import annotations

_A     =  []






def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]					,							__lowerCAmelCase						:						int					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			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						:						list[list[int]]					,							__lowerCAmelCase						:						int       ):




			"""simple docstring"""


			if row >= len(__lowerCAmelCase       ):
						solution.append(__lowerCAmelCase       )
						printboard(__lowerCAmelCase       )
						print()
						return True
			for i in range(len(__lowerCAmelCase       )       ):
						if is_safe(__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase       ):
									lowerCAmelCase_      			=			1
									solve(__lowerCAmelCase					,							row + 1       )
									lowerCAmelCase_      			=			0
			return False






def      lowerCamelCase__       (       __lowerCAmelCase						:						list[list[int]]       ):




			"""simple docstring"""


			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"))
_A     =  8
_A     =  [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_A     =  {
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "Wav2Vec2ForAudioFrameClassification",
			    "Wav2Vec2ForCTC",
			    "Wav2Vec2ForMaskedLM",
			    "Wav2Vec2ForPreTraining",
			    "Wav2Vec2ForSequenceClassification",
			    "Wav2Vec2ForXVector",
			    "Wav2Vec2Model",
			    "Wav2Vec2PreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFWav2Vec2ForCTC",
			    "TFWav2Vec2Model",
			    "TFWav2Vec2PreTrainedModel",
			    "TFWav2Vec2ForSequenceClassification",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A     =  [
			    "FlaxWav2Vec2ForCTC",
			    "FlaxWav2Vec2ForPreTraining",
			    "FlaxWav2Vec2Model",
			    "FlaxWav2Vec2PreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
			from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
			from .processing_wavaveca import WavaVecaProcessor
			from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_wavaveca import (
						    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    WavaVecaForAudioFrameClassification,
						    WavaVecaForCTC,
						    WavaVecaForMaskedLM,
						    WavaVecaForPreTraining,
						    WavaVecaForSequenceClassification,
						    WavaVecaForXVector,
						    WavaVecaModel,
						    WavaVecaPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFWavaVecaForCTC,
						    TFWavaVecaForSequenceClassification,
						    TFWavaVecaModel,
						    TFWavaVecaPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_wavaveca import (
						    FlaxWavaVecaForCTC,
						    FlaxWavaVecaForPreTraining,
						    FlaxWavaVecaModel,
						    FlaxWavaVecaPreTrainedModel,
						)


else:
			import sys

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



