





import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowerCAmelCase       		=      {"""LayoutLMv2Config""", """LayoutLMv3Config"""}
@is_pipeline_test
class 		lowerCamelCase  (      unittest.TestCase       ):
	snake_case_      =   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
	snake_case_      =   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

	if model_mapping is not None:
		snake_case_      =   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
	if tf_model_mapping is not None:
		snake_case_      =   {
		    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
		}



	@require_torch
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    int						=   pipeline(
			    task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="pt"	)

			lowerCAmelCase     :    Tuple						=   text_classifier("This is great !"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "LABEL_0", "score": 0.504}]	)

			lowerCAmelCase     :    Dict						=   text_classifier("This is great !"		,      top_k=2	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]	)

			lowerCAmelCase     :    List[Any]						=   text_classifier(["This is great !", "This is bad"]		,      top_k=2	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [
			        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
			        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
			    ]		,      )

			lowerCAmelCase     :    Tuple						=   text_classifier("This is great !"		,      top_k=1	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "LABEL_0", "score": 0.504}]	)

			# Legacy behavior
			lowerCAmelCase     :    Optional[Any]						=   text_classifier("This is great !"		,      return_all_scores=__lowerCAmelCase	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "LABEL_0", "score": 0.504}]	)

			lowerCAmelCase     :    Union[str, Any]						=   text_classifier("This is great !"		,      return_all_scores=__lowerCAmelCase	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [[{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}]]	)

			lowerCAmelCase     :    Any						=   text_classifier(["This is great !", "Something else"]		,      return_all_scores=__lowerCAmelCase	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [
			        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
			        [{"label": "LABEL_0", "score": 0.504}, {"label": "LABEL_1", "score": 0.496}],
			    ]		,      )

			lowerCAmelCase     :    Any						=   text_classifier(["This is great !", "Something else"]		,      return_all_scores=__lowerCAmelCase	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [
			        {"label": "LABEL_0", "score": 0.504},
			        {"label": "LABEL_0", "score": 0.504},
			    ]		,      )



	@require_torch
	def   _lowerCamelCase    (   self	):
			import torch

			lowerCAmelCase     :    Optional[Any]						=   pipeline(
			    task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="pt"		,      device=torch.device("cpu"	)		,      )

			lowerCAmelCase     :    Any						=   text_classifier("This is great !"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "LABEL_0", "score": 0.504}]	)



	@require_tf
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Any						=   pipeline(
			    task="text-classification"		,      model="hf-internal-testing/tiny-random-distilbert"		,      framework="tf"	)

			lowerCAmelCase     :    Tuple						=   text_classifier("This is great !"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "LABEL_0", "score": 0.504}]	)



	@slow
	@require_torch
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    Dict						=   pipeline("text-classification"	)

			lowerCAmelCase     :    List[Any]						=   text_classifier("This is great !"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "POSITIVE", "score": 1.0}]	)
			lowerCAmelCase     :    List[str]						=   text_classifier("This is bad !"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "NEGATIVE", "score": 1.0}]	)
			lowerCAmelCase     :    Optional[Any]						=   text_classifier("Birds are a type of animal"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "POSITIVE", "score": 0.988}]	)



	@slow
	@require_tf
	def   _lowerCamelCase    (   self	):
			lowerCAmelCase     :    List[str]						=   pipeline("text-classification"		,      framework="tf"	)

			lowerCAmelCase     :    Dict						=   text_classifier("This is great !"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "POSITIVE", "score": 1.0}]	)
			lowerCAmelCase     :    int						=   text_classifier("This is bad !"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "NEGATIVE", "score": 1.0}]	)
			lowerCAmelCase     :    Union[str, Any]						=   text_classifier("Birds are a type of animal"	)
			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": "POSITIVE", "score": 0.988}]	)



	def   _lowerCamelCase    (   self		,      a_		,      a_		,      a_	):
			lowerCAmelCase     :    Any						=   TextClassificationPipeline(model=__lowerCAmelCase		,      tokenizer=__lowerCAmelCase	)
			return text_classifier, ["HuggingFace is in", "This is another test"]





	def   _lowerCamelCase    (   self		,      a_		,      a_	):
			lowerCAmelCase     :    Tuple						=   text_classifier.model
			# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
			lowerCAmelCase     :    Optional[int]						=   "HuggingFace is in"
			lowerCAmelCase     :    Optional[Any]						=   text_classifier(__lowerCAmelCase	)

			self.assertEqual(nested_simplify(__lowerCAmelCase	)		,      [{"label": ANY(__lowerCAmelCase	), "score": ANY(__lowerCAmelCase	)}]	)
			self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()	)

			lowerCAmelCase     :    List[str]						=   ["HuggingFace is in ", "Paris is in France"]
			lowerCAmelCase     :    List[str]						=   text_classifier(__lowerCAmelCase	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [{"label": ANY(__lowerCAmelCase	), "score": ANY(__lowerCAmelCase	)}, {"label": ANY(__lowerCAmelCase	), "score": ANY(__lowerCAmelCase	)}]		,      )
			self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()	)
			self.assertTrue(outputs[1]["label"] in model.config.idalabel.values()	)

			# Forcing to get all results with `top_k=None`
			# This is NOT the legacy format
			lowerCAmelCase     :    Tuple						=   text_classifier(__lowerCAmelCase		,      top_k=__lowerCAmelCase	)
			lowerCAmelCase     :    Optional[Any]						=   len(model.config.idalabel.values()	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [[{"label": ANY(__lowerCAmelCase	), "score": ANY(__lowerCAmelCase	)}] * N, [{"label": ANY(__lowerCAmelCase	), "score": ANY(__lowerCAmelCase	)}] * N]		,      )

			lowerCAmelCase     :    int						=   {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
			lowerCAmelCase     :    List[str]						=   text_classifier(__lowerCAmelCase	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      {"label": ANY(__lowerCAmelCase	), "score": ANY(__lowerCAmelCase	)}		,      )
			self.assertTrue(outputs["label"] in model.config.idalabel.values()	)

			# This might be used a text pair, but tokenizer + pipe interaction
			# makes it hard to understand that it's not using the pair properly
			# https://github.com/huggingface/transformers/issues/17305
			# We disabled this usage instead as it was outputting wrong outputs.
			lowerCAmelCase     :    Optional[int]						=   [["HuggingFace is in ", "Paris is in France"]]
			with self.assertRaises(__lowerCAmelCase	):
					text_classifier(__lowerCAmelCase	)

			# This used to be valid for doing text pairs
			# We're keeping it working because of backward compatibility
			lowerCAmelCase     :    List[str]						=   text_classifier([[["HuggingFace is in ", "Paris is in France"]]]	)
			self.assertEqual(
			    nested_simplify(__lowerCAmelCase	)		,      [{"label": ANY(__lowerCAmelCase	), "score": ANY(__lowerCAmelCase	)}]		,      )
			self.assertTrue(outputs[0]["label"] in model.config.idalabel.values()	)










'''simple docstring'''






from __future__ import annotations

lowerCAmelCase       		=      []






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






def      __A						(							a_						:  list[list[int]]      ,a_						:  int	):
		if row >= len(a_	):
				solution.append(a_	)
				printboard(a_	)
				print()
				return True
		for i in range(len(a_	)	):
				if is_safe(a_      ,a_      ,a_	):
						lowerCAmelCase     :    Any						=   1
						solve(a_      ,row + 1	)
						lowerCAmelCase     :    List[Any]						=   0
		return False






def      __A						(							a_						:  list[list[int]]	):
		for i in range(len(a_	)	):
				for j in range(len(a_	)	):
						if board[i][j] == 1:
								print("Q"      ,end=" "	)
						else:
								print("."      ,end=" "	)
				print()


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

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



