






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.
__UpperCAmelCase     							=						{'LayoutLMv2Config', 'LayoutLMv3Config'}



@is_pipeline_test
class   lowerCamelCase				(unittest.TestCase      ):







		'''simple docstring'''



		_snake_case   :   Union[str, Any]							=  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
		_snake_case   :   Any							=  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

		if model_mapping is not None:
				_snake_case   :   Any							=  {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   :   List[str]							=  {
				    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
				}



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							List[str]:
							UpperCAmelCase_   :				Tuple						=						pipeline(
							    task='text-classification'    ,		model='hf-internal-testing/tiny-random-distilbert'    ,		framework='pt'       )

							UpperCAmelCase_   :				List[str]						=						text_classifier('This is great !'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'LABEL_0', 'score': 0.5_04}]       )

							UpperCAmelCase_   :				List[Any]						=						text_classifier('This is great !'    ,		top_k=2       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]       )

							UpperCAmelCase_   :				int						=						text_classifier(['This is great !', 'This is bad']    ,		top_k=2       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		[
							        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
							        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
							    ]    ,		)

							UpperCAmelCase_   :				Dict						=						text_classifier('This is great !'    ,		top_k=1       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'LABEL_0', 'score': 0.5_04}]       )

							# Legacy behavior
							UpperCAmelCase_   :				Any						=						text_classifier('This is great !'    ,		return_all_scores=_UpperCamelCase       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'LABEL_0', 'score': 0.5_04}]       )

							UpperCAmelCase_   :				Union[str, Any]						=						text_classifier('This is great !'    ,		return_all_scores=_UpperCamelCase       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		[[{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}]]       )

							UpperCAmelCase_   :				str						=						text_classifier(['This is great !', 'Something else']    ,		return_all_scores=_UpperCamelCase       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		[
							        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
							        [{'label': 'LABEL_0', 'score': 0.5_04}, {'label': 'LABEL_1', 'score': 0.4_96}],
							    ]    ,		)

							UpperCAmelCase_   :				Tuple						=						text_classifier(['This is great !', 'Something else']    ,		return_all_scores=_UpperCamelCase       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		[
							        {'label': 'LABEL_0', 'score': 0.5_04},
							        {'label': 'LABEL_0', 'score': 0.5_04},
							    ]    ,		)



		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							Any:
							import torch

							UpperCAmelCase_   :				Union[str, Any]						=						pipeline(
							    task='text-classification'    ,		model='hf-internal-testing/tiny-random-distilbert'    ,		framework='pt'    ,		device=torch.device('cpu'       )    ,		)

							UpperCAmelCase_   :				int						=						text_classifier('This is great !'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'LABEL_0', 'score': 0.5_04}]       )



		@require_tf
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Union[str, Any]						=						pipeline(
							    task='text-classification'    ,		model='hf-internal-testing/tiny-random-distilbert'    ,		framework='tf'       )

							UpperCAmelCase_   :				int						=						text_classifier('This is great !'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'LABEL_0', 'score': 0.5_04}]       )



		@slow
		@require_torch
		def  __UpperCAmelCase      (							self       )     ->							Optional[int]:
							UpperCAmelCase_   :				List[Any]						=						pipeline('text-classification'       )

							UpperCAmelCase_   :				Tuple						=						text_classifier('This is great !'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'POSITIVE', 'score': 1.0}]       )
							UpperCAmelCase_   :				List[Any]						=						text_classifier('This is bad !'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'NEGATIVE', 'score': 1.0}]       )
							UpperCAmelCase_   :				str						=						text_classifier('Birds are a type of animal'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'POSITIVE', 'score': 0.9_88}]       )



		@slow
		@require_tf
		def  __UpperCAmelCase      (							self       )     ->							Any:
							UpperCAmelCase_   :				Dict						=						pipeline('text-classification'    ,		framework='tf'       )

							UpperCAmelCase_   :				Optional[Any]						=						text_classifier('This is great !'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'POSITIVE', 'score': 1.0}]       )
							UpperCAmelCase_   :				Optional[int]						=						text_classifier('This is bad !'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'NEGATIVE', 'score': 1.0}]       )
							UpperCAmelCase_   :				Union[str, Any]						=						text_classifier('Birds are a type of animal'       )
							self.assertEqual(nested_simplify(_UpperCamelCase       )    ,		[{'label': 'POSITIVE', 'score': 0.9_88}]       )



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Optional[int]						=						TextClassificationPipeline(model=_UpperCamelCase    ,		tokenizer=_UpperCamelCase       )
							return text_classifier, ["HuggingFace is in", "This is another test"]



		def  __UpperCAmelCase      (							self    ,		_UpperCamelCase    ,		_UpperCamelCase       )     ->							List[str]:
							UpperCAmelCase_   :				str						=						text_classifier.model
							# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
							UpperCAmelCase_   :				Tuple						=						'HuggingFace is in'
							UpperCAmelCase_   :				str						=						text_classifier(_UpperCamelCase       )

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

							UpperCAmelCase_   :				Optional[Any]						=						['HuggingFace is in ', 'Paris is in France']
							UpperCAmelCase_   :				str						=						text_classifier(_UpperCamelCase       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		[{'label': ANY(_UpperCamelCase       ), 'score': ANY(_UpperCamelCase       )}, {'label': ANY(_UpperCamelCase       ), 'score': ANY(_UpperCamelCase       )}]    ,		)
							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
							UpperCAmelCase_   :				Optional[Any]						=						text_classifier(_UpperCamelCase    ,		top_k=_UpperCamelCase       )
							UpperCAmelCase_   :				List[Any]						=						len(model.config.idalabel.values()       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		[[{'label': ANY(_UpperCamelCase       ), 'score': ANY(_UpperCamelCase       )}] * N, [{'label': ANY(_UpperCamelCase       ), 'score': ANY(_UpperCamelCase       )}] * N]    ,		)

							UpperCAmelCase_   :				Optional[Any]						=						{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
							UpperCAmelCase_   :				int						=						text_classifier(_UpperCamelCase       )
							self.assertEqual(
							    nested_simplify(_UpperCamelCase       )    ,		{'label': ANY(_UpperCamelCase       ), 'score': ANY(_UpperCamelCase       )}    ,		)
							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.
							UpperCAmelCase_   :				List[Any]						=						[['HuggingFace is in ', 'Paris is in France']]
							with self.assertRaises(_UpperCamelCase       ):
												text_classifier(_UpperCamelCase       )

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














import random
import sys

import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap

__UpperCAmelCase     							=						'Usage of script: script_name <size_of_canvas:int>'

__UpperCAmelCase     							=						[0] * 100 + [1] * 10
random.shuffle(choice)




def 					lowercase__				(					__snake_case   :							int  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Any						=						[[False for i in range(__snake_case  )] for j in range(__snake_case  )]
					return canvas




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					for i, row in enumerate(__snake_case  ):
										for j, _ in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Tuple						=						bool(random.getrandbits(1  )  )




def 					lowercase__				(					__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				List[Any]						=						np.array(__snake_case  )
					UpperCAmelCase_   :				Any						=						np.array(create_canvas(current_canvas.shape[0]  )  )
					for r, row in enumerate(__snake_case  ):
										for c, pt in enumerate(__snake_case  ):
															UpperCAmelCase_   :				Optional[int]						=						__judge_point(
															    __snake_case					,	current_canvas[r - 1 : r + 2, c - 1 : c + 2]  )

					UpperCAmelCase_   :				List[Any]						=						next_gen_canvas
					del next_gen_canvas  # cleaning memory as we move on.
					UpperCAmelCase_   :				list[list[bool]]						=						current_canvas.tolist()
					return return_canvas




def 					lowercase__				(					__snake_case   :							bool					,	__snake_case   :							list[list[bool]]  ):


					'''simple docstring'''


					UpperCAmelCase_   :				Optional[int]						=						0
					UpperCAmelCase_   :				List[Any]						=						0
					# finding dead or alive neighbours count.
					for i in neighbours:
										for status in i:
															if status:
																				alive += 1
															else:
																				dead += 1

    # handling duplicate entry for focus pt.
					if pt:
										alive -= 1
					else:
										dead -= 1

					# running the rules of game here.
					UpperCAmelCase_   :				List[Any]						=						pt
					if pt:
										if alive < 2:
															UpperCAmelCase_   :				str						=						False
										elif alive == 2 or alive == 3:
															UpperCAmelCase_   :				int						=						True
										elif alive > 3:
															UpperCAmelCase_   :				List[Any]						=						False
					else:
										if alive == 3:
															UpperCAmelCase_   :				int						=						True

					return state


if __name__ == "__main__":
		if len(sys.argv) != 2:
				raise Exception(usage_doc)

		__UpperCAmelCase     							=						int(sys.argv[1])
		# main working structure of this module.
		__UpperCAmelCase     							=						create_canvas(canvas_size)
		seed(c)
		__UpperCAmelCase							,       __UpperCAmelCase     							=						plt.subplots()
		fig.show()
		__UpperCAmelCase     							=						ListedColormap(['w', 'k'])
		try:
				while True:
						__UpperCAmelCase     							=						run(c)
						ax.matshow(c, cmap=cmap)
						fig.canvas.draw()
						ax.cla()
		except KeyboardInterrupt:
				# do nothing.
				pass






