
"""simple docstring"""


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.
A_    							=					{"""LayoutLMv2Config""", """LayoutLMv3Config"""}




@is_pipeline_test
class  __lowerCamelCase  ( unittest.TestCase			):
				a__:      Optional[Any]          =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
				a__:      Dict          =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  pipeline(
							    task='''text-classification'''							,						model='''hf-internal-testing/tiny-random-distilbert'''							,						framework='''pt'''   )

							lowerCamelCase_					=  text_classifier('''This is great !'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]   )

							lowerCamelCase_					=  text_classifier('''This is great !'''							,						top_k=2   )
							self.assertEqual(
							    nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}]   )

							lowerCamelCase_					=  text_classifier(['''This is great !''', '''This is bad''']							,						top_k=2   )
							self.assertEqual(
							    nested_simplify(UpperCAmelCase   )							,						[
							        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
							        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
							    ]							,						)

							lowerCamelCase_					=  text_classifier('''This is great !'''							,						top_k=1   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]   )

							# Legacy behavior
							lowerCamelCase_					=  text_classifier('''This is great !'''							,						return_all_scores=UpperCAmelCase   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]   )

							lowerCamelCase_					=  text_classifier('''This is great !'''							,						return_all_scores=UpperCAmelCase   )
							self.assertEqual(
							    nested_simplify(UpperCAmelCase   )							,						[[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}]]   )

							lowerCamelCase_					=  text_classifier(['''This is great !''', '''Something else''']							,						return_all_scores=UpperCAmelCase   )
							self.assertEqual(
							    nested_simplify(UpperCAmelCase   )							,						[
							        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
							        [{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}, {'''label''': '''LABEL_1''', '''score''': 0.4_9_6}],
							    ]							,						)

							lowerCamelCase_					=  text_classifier(['''This is great !''', '''Something else''']							,						return_all_scores=UpperCAmelCase   )
							self.assertEqual(
							    nested_simplify(UpperCAmelCase   )							,						[
							        {'''label''': '''LABEL_0''', '''score''': 0.5_0_4},
							        {'''label''': '''LABEL_0''', '''score''': 0.5_0_4},
							    ]							,						)




				@require_torch
				def        UpperCAmelCase__						(				self   ):
							import torch

							lowerCamelCase_					=  pipeline(
							    task='''text-classification'''							,						model='''hf-internal-testing/tiny-random-distilbert'''							,						framework='''pt'''							,						device=torch.device('''cpu'''   )							,						)

							lowerCamelCase_					=  text_classifier('''This is great !'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]   )




				@require_tf
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  pipeline(
							    task='''text-classification'''							,						model='''hf-internal-testing/tiny-random-distilbert'''							,						framework='''tf'''   )

							lowerCamelCase_					=  text_classifier('''This is great !'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''LABEL_0''', '''score''': 0.5_0_4}]   )




				@slow
				@require_torch
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  pipeline('''text-classification'''   )

							lowerCamelCase_					=  text_classifier('''This is great !'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
							lowerCamelCase_					=  text_classifier('''This is bad !'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
							lowerCamelCase_					=  text_classifier('''Birds are a type of animal'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''POSITIVE''', '''score''': 0.9_8_8}]   )




				@slow
				@require_tf
				def        UpperCAmelCase__						(				self   ):
							lowerCamelCase_					=  pipeline('''text-classification'''							,						framework='''tf'''   )

							lowerCamelCase_					=  text_classifier('''This is great !'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''POSITIVE''', '''score''': 1.0}]   )
							lowerCamelCase_					=  text_classifier('''This is bad !'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''NEGATIVE''', '''score''': 1.0}]   )
							lowerCamelCase_					=  text_classifier('''Birds are a type of animal'''   )
							self.assertEqual(nested_simplify(UpperCAmelCase   )							,						[{'''label''': '''POSITIVE''', '''score''': 0.9_8_8}]   )




				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  TextClassificationPipeline(model=UpperCAmelCase							,						tokenizer=UpperCAmelCase   )
							return text_classifier, ["HuggingFace is in", "This is another test"]





				def        UpperCAmelCase__						(				self							,						UpperCAmelCase							,						UpperCAmelCase   ):
							lowerCamelCase_					=  text_classifier.model
							# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
							lowerCamelCase_					=  '''HuggingFace is in'''
							lowerCamelCase_					=  text_classifier(UpperCAmelCase   )

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

							lowerCamelCase_					=  ['''HuggingFace is in ''', '''Paris is in France''']
							lowerCamelCase_					=  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
							lowerCamelCase_					=  text_classifier(UpperCAmelCase							,						top_k=UpperCAmelCase   )
							lowerCamelCase_					=  len(model.config.idalabel.values()   )
							self.assertEqual(
							    nested_simplify(UpperCAmelCase   )							,						[[{'''label''': ANY(UpperCAmelCase   ), '''score''': ANY(UpperCAmelCase   )}] * N, [{'''label''': ANY(UpperCAmelCase   ), '''score''': ANY(UpperCAmelCase   )}] * N]							,						)

							lowerCamelCase_					=  {'''text''': '''HuggingFace is in ''', '''text_pair''': '''Paris is in France'''}
							lowerCamelCase_					=  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.
							lowerCamelCase_					=  [['''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
							lowerCamelCase_					=  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()   )





"""simple docstring"""


import math






def  lowercase						(	lowerCAmelCase__						):
			lowerCamelCase_					=  [True] * n
			lowerCamelCase_					=  False
			lowerCamelCase_					=  False
			lowerCamelCase_					=  True

			for i in range(3	,int(n**0.5 + 1						)	,2						):
						lowerCamelCase_					=  i * 2
						while index < n:
									lowerCamelCase_					=  False
									lowerCamelCase_					=  index + i

			lowerCamelCase_					=  [2]

			for i in range(3	,lowerCAmelCase__	,2						):
						if is_prime[i]:
									primes.append(lowerCAmelCase__						)

			return primes






def  lowercase						(	lowerCAmelCase__ = 999_966_663_333						):
			lowerCamelCase_					=  math.floor(math.sqrt(lowerCAmelCase__						)						) + 100
			lowerCamelCase_					=  prime_sieve(lowerCAmelCase__						)

			lowerCamelCase_					=  0
			lowerCamelCase_					=  0
			lowerCamelCase_					=  primes[prime_index]

			while (last_prime**2) <= limit:
						lowerCamelCase_					=  primes[prime_index + 1]

						lowerCamelCase_					=  last_prime**2
						lowerCamelCase_					=  next_prime**2

						# Get numbers divisible by lps(current)
						lowerCamelCase_					=  lower_bound + last_prime
						while upper_bound > current <= limit:
									matches_sum += current
									current += last_prime

						# Reset the upper_bound
						while (upper_bound - next_prime) > limit:
									upper_bound -= next_prime

						# Add the numbers divisible by ups(current)
						lowerCamelCase_					=  upper_bound - next_prime
						while current > lower_bound:
									matches_sum += current
									current -= next_prime

						# Remove the numbers divisible by both ups and lps
						lowerCamelCase_					=  0
						while upper_bound > current <= limit:
									if current <= lower_bound:
												# Increment the current number
												current += last_prime * next_prime
												continue

									if current > limit:
												break

									# Remove twice since it was added by both ups and lps
									matches_sum -= current * 2

									# Increment the current number
									current += last_prime * next_prime

						# Setup for next pair
						lowerCamelCase_					=  next_prime
						prime_index += 1

			return matches_sum


if __name__ == "__main__":
						print(solution())



