


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.
_snake_case	:						List[Any]         =							{"LayoutLMv2Config", "LayoutLMv3Config"}

@is_pipeline_test
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Tuple	    =    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
							__UpperCAmelCase       :				str	    =    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

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

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

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

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

														__snake_case						:     int									=  text_classifier(["This is great !", "This is bad"]       ,      top_k=2							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [
														        [{"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}],
														    ]       ,      )

														__snake_case						:     int									=  text_classifier("This is great !"       ,      top_k=1							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

														# Legacy behavior
														__snake_case						:     str									=  text_classifier("This is great !"       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

														__snake_case						:     str									=  text_classifier("This is great !"       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [[{"label": "LABEL_0", "score": 0.5_04}, {"label": "LABEL_1", "score": 0.4_96}]]							)

														__snake_case						:     Optional[int]									=  text_classifier(["This is great !", "Something else"]       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [
														        [{"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}],
														    ]       ,      )

														__snake_case						:     str									=  text_classifier(["This is great !", "Something else"]       ,      return_all_scores=lowerCamelCase							)
														self.assertEqual(
														    nested_simplify(lowerCamelCase							)       ,      [
														        {"label": "LABEL_0", "score": 0.5_04},
														        {"label": "LABEL_0", "score": 0.5_04},
														    ]       ,      )

							@require_torch
							def        __snake_case   (				self			:    Optional[Any]							)      ->       str:
														import torch

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

														__snake_case						:     Any									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

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

														__snake_case						:     List[Any]									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "LABEL_0", "score": 0.5_04}]							)

							@slow
							@require_torch
							def        __snake_case   (				self			:    Dict							)      ->       Dict:
														__snake_case						:     Optional[Any]									=  pipeline("text-classification"							)

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

							@slow
							@require_tf
							def        __snake_case   (				self			:    Tuple							)      ->       Dict:
														__snake_case						:     Tuple									=  pipeline("text-classification"       ,      framework="tf"							)

														__snake_case						:     int									=  text_classifier("This is great !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "POSITIVE", "score": 1.0}]							)
														__snake_case						:     Dict									=  text_classifier("This is bad !"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "NEGATIVE", "score": 1.0}]							)
														__snake_case						:     Dict									=  text_classifier("Birds are a type of animal"							)
														self.assertEqual(nested_simplify(lowerCamelCase							)       ,      [{"label": "POSITIVE", "score": 0.9_88}]							)

							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    int							)      ->       Dict:
														__snake_case						:     List[str]									=  TextClassificationPipeline(model=lowerCamelCase       ,      tokenizer=lowerCamelCase							)
														return text_classifier, ["HuggingFace is in", "This is another test"]






							def        __snake_case   (				self			:    Optional[int]       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Dict							)      ->       List[Any]:
														__snake_case						:     List[str]									=  text_classifier.model
														# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
														__snake_case						:     List[str]									=  "HuggingFace is in"
														__snake_case						:     str									=  text_classifier(lowerCamelCase							)

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

														__snake_case						:     Any									=  ["HuggingFace is in ", "Paris is in France"]
														__snake_case						:     Any									=  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
														__snake_case						:     List[str]									=  text_classifier(lowerCamelCase       ,      top_k=lowerCamelCase							)
														__snake_case						:     List[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]       ,      )

														__snake_case						:     Any									=  {"text": "HuggingFace is in ", "text_pair": "Paris is in France"}
														__snake_case						:     List[Any]									=  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.
														__snake_case						:     Dict									=  [["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
														__snake_case						:     Optional[int]									=  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'''







import math





def 		_UpperCAmelCase (				__A				: int   ):
				a_		:	str									=   []
				a_		:	Tuple									=   2
				a_		:	Optional[Any]									=   int(math.sqrt(__A   )   )  # Size of every segment
				a_		:	Optional[Any]									=   [True] * (end + 1)
				a_		:	Union[str, Any]									=   []

				while start <= end:
								if temp[start] is True:
												in_prime.append(__A   )
												for i in range(start * start    ,		end + 1    ,		__A   ):
																a_		:	List[Any]									=   False
								start += 1
				prime += in_prime

				a_		:	Any									=   end + 1
				a_		:	Optional[Any]									=   min(2 * end    ,		__A   )

				while low <= n:
								a_		:	Optional[Any]									=   [True] * (high - low + 1)
								for each in in_prime:
												a_		:	Any									=   math.floor(low / each   ) * each
												if t < low:
																t += each

												for j in range(__A    ,		high + 1    ,		__A   ):
																a_		:	Union[str, Any]									=   False

								for j in range(len(__A   )   ):
												if temp[j] is True:
																prime.append(j + low   )

								a_		:	Tuple									=   high + 1
								a_		:	str									=   min(high + end    ,		__A   )

				return prime




print(sieve(10**6))




