


import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TextaTextGenerationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available

from .test_pipelines_common import ANY


if is_torch_available():
			import torch

@is_pipeline_test
class     a		(unittest.TestCase				):




							"""simple docstring"""


							__UpperCAmelCase   :							List[Any]					  =  MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							__UpperCAmelCase   :							List[str]					  =  TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Dict   ,     lowerCamelCase	:  Optional[int]    )     ->   Dict:
														__snake_case		:      Dict										=       TextaTextGenerationPipeline(model=__a   ,     tokenizer=__a    )
														return generator, ["Something to write", "Something else"]

							def    __snake_case (							self	:  List[Any]   ,     lowerCamelCase	:  Union[str, Any]   ,     lowerCamelCase	:  Any    )     ->   Optional[Any]:
														__snake_case		:      str										=       generator("Something there"    )
														self.assertEqual(__a   ,     [{"generated_text": ANY(__a    )}]    )
														# These are encoder decoder, they don't just append to incoming string
														self.assertFalse(outputs[0]["generated_text"].startswith("Something there"    )    )

														__snake_case		:      Dict										=       generator(["This is great !", "Something else"]   ,     num_return_sequences=2   ,     do_sample=__a    )
														self.assertEqual(
														    __a   ,     [
														        [{"generated_text": ANY(__a    )}, {"generated_text": ANY(__a    )}],
														        [{"generated_text": ANY(__a    )}, {"generated_text": ANY(__a    )}],
														    ]   ,     )

														__snake_case		:      Dict										=       generator(
														    ["This is great !", "Something else"]   ,     num_return_sequences=2   ,     batch_size=2   ,     do_sample=__a    )
														self.assertEqual(
														    __a   ,     [
														        [{"generated_text": ANY(__a    )}, {"generated_text": ANY(__a    )}],
														        [{"generated_text": ANY(__a    )}, {"generated_text": ANY(__a    )}],
														    ]   ,     )

														with self.assertRaises(__a    ):
																					generator(4    )

							@require_torch
							def    __snake_case (							self	:  Tuple    )     ->   Union[str, Any]:
														__snake_case		:      Tuple										=       pipeline("text2text-generation"   ,     model="patrickvonplaten/t5-tiny-random"   ,     framework="pt"    )
														# do_sample=False necessary for reproducibility
														__snake_case		:      Union[str, Any]										=       generator("Something there"   ,     do_sample=__a    )
														self.assertEqual(__a   ,     [{"generated_text": ""}]    )

														__snake_case		:      Optional[Any]										=       3
														__snake_case		:      Optional[int]										=       generator(
														    "Something there"   ,     num_return_sequences=__a   ,     num_beams=__a   ,     )
														__snake_case		:      Optional[Any]										=       [
														    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
														    {"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
														    {"generated_text": ""},
														]
														self.assertEqual(__a   ,     __a    )

														__snake_case		:      str										=       generator("This is a test"   ,     do_sample=__a   ,     num_return_sequences=2   ,     return_tensors=__a    )
														self.assertEqual(
														    __a   ,     [
														        {"generated_token_ids": ANY(torch.Tensor    )},
														        {"generated_token_ids": ANY(torch.Tensor    )},
														    ]   ,     )
														__snake_case		:      int										=       generator.model.config.eos_token_id
														__snake_case		:      int										=       "<pad>"
														__snake_case		:      str										=       generator(
														    ["This is a test", "This is a second test"]   ,     do_sample=__a   ,     num_return_sequences=2   ,     batch_size=2   ,     return_tensors=__a   ,     )
														self.assertEqual(
														    __a   ,     [
														        [
														            {"generated_token_ids": ANY(torch.Tensor    )},
														            {"generated_token_ids": ANY(torch.Tensor    )},
														        ],
														        [
														            {"generated_token_ids": ANY(torch.Tensor    )},
														            {"generated_token_ids": ANY(torch.Tensor    )},
														        ],
														    ]   ,     )






							@require_tf
							def    __snake_case (							self	:  Optional[Any]    )     ->   int:
														__snake_case		:      List[str]										=       pipeline("text2text-generation"   ,     model="patrickvonplaten/t5-tiny-random"   ,     framework="tf"    )
														# do_sample=False necessary for reproducibility
														__snake_case		:      Dict										=       generator("Something there"   ,     do_sample=__a    )
														self.assertEqual(__a   ,     [{"generated_text": ""}]    )





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))

