"""simple docstring"""






import gc
import unittest

from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_gpu,
    slow,
)

from .test_pipelines_common import ANY

@is_pipeline_test
class lowerCamelCase		(unittest.TestCase      ):






					'''simple docstring'''


					a   	=		MODEL_FOR_MASKED_LM_MAPPING
					a   	=		TF_MODEL_FOR_MASKED_LM_MAPPING



					def 			lowerCAmelCase_					(						self			:				int		)							->       Optional[Any]:
									super().tearDown()
									# clean-up as much as possible GPU memory occupied by PyTorch
									gc.collect()
									if is_torch_available():
													import torch

													torch.cuda.empty_cache()



					@require_tf
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       List[str]:
									SCREAMING_SNAKE_CASE__		  =				pipeline(task="fill-mask"   , model="sshleifer/tiny-distilroberta-base"   , top_k=2   , framework="tf"		)
									SCREAMING_SNAKE_CASE__		  =				unmasker("My name is <mask>"		)
									self.assertEqual(
									    nested_simplify(_snake_case   , decimals=6		)   , [
									        {"sequence": "My name is grouped", "score": 2.1e-05, "token": 38015, "token_str": " grouped"},
									        {"sequence": "My name is accuser", "score": 2.1e-05, "token": 25506, "token_str": " accuser"},
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				unmasker("The largest city in France is <mask>"		)
									self.assertEqual(
									    nested_simplify(_snake_case   , decimals=6		)   , [
									        {
									            "sequence": "The largest city in France is grouped",
									            "score": 2.1e-05,
									            "token": 38015,
									            "token_str": " grouped",
									        },
									        {
									            "sequence": "The largest city in France is accuser",
									            "score": 2.1e-05,
									            "token": 25506,
									            "token_str": " accuser",
									        },
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				unmasker("My name is <mask>"   , targets=[" Patrick", " Clara", " Teven"]   , top_k=3		)
									self.assertEqual(
									    nested_simplify(_snake_case   , decimals=6		)   , [
									        {"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
									        {"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
									        {"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
									    ]   , )



					@require_torch
					def 			lowerCAmelCase_					(						self			:				Any		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				pipeline(task="fill-mask"   , model="sshleifer/tiny-distilroberta-base"   , top_k=2   , framework="pt"		)

									SCREAMING_SNAKE_CASE__		  =				unmasker("My name is <mask>"		)
									self.assertEqual(
									    nested_simplify(_snake_case   , decimals=6		)   , [
									        {"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
									        {"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				unmasker("The largest city in France is <mask>"		)
									self.assertEqual(
									    nested_simplify(_snake_case   , decimals=6		)   , [
									        {
									            "sequence": "The largest city in France is Maul",
									            "score": 2.2e-05,
									            "token": 35676,
									            "token_str": " Maul",
									        },
									        {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				unmasker("My name is <mask>"   , targets=[" Patrick", " Clara", " Teven"]   , top_k=3		)
									self.assertEqual(
									    nested_simplify(_snake_case   , decimals=6		)   , [
									        {"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
									        {"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
									        {"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				unmasker("My name is <mask> <mask>"   , top_k=2		)

									self.assertEqual(
									    nested_simplify(_snake_case   , decimals=6		)   , [
									        [
									            {
									                "score": 2.2e-05,
									                "token": 35676,
									                "token_str": " Maul",
									                "sequence": "<s>My name is Maul<mask></s>",
									            },
									            {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
									        ],
									        [
									            {
									                "score": 2.2e-05,
									                "token": 35676,
									                "token_str": " Maul",
									                "sequence": "<s>My name is<mask> Maul</s>",
									            },
									            {"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
									        ],
									    ]   , )



					@require_torch_gpu
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       str:
									SCREAMING_SNAKE_CASE__		  =				pipeline("fill-mask"   , model="hf-internal-testing/tiny-random-distilbert"   , device=0   , framework="pt"		)

									# convert model to fp16
									pipe.model.half()

									SCREAMING_SNAKE_CASE__		  =				pipe("Paris is the [MASK] of France."		)
									# We actually don't care about the result, we just want to make sure
									# it works, meaning the float16 tensor got casted back to float32
									# for postprocessing.
									self.assertIsInstance(_snake_case   , _snake_case		)



					@slow
					@require_torch
					def 			lowerCAmelCase_					(						self			:				Union[str, Any]		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				pipeline(task="fill-mask"   , model="distilroberta-base"   , top_k=2   , framework="pt"		)
									self.run_large_test(_snake_case		)



					@slow
					@require_tf
					def 			lowerCAmelCase_					(						self			:				Optional[Any]		)							->       List[Any]:
									SCREAMING_SNAKE_CASE__		  =				pipeline(task="fill-mask"   , model="distilroberta-base"   , top_k=2   , framework="tf"		)
									self.run_large_test(_snake_case		)



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				Optional[Any]		)							->       Union[str, Any]:
									SCREAMING_SNAKE_CASE__		  =				unmasker("My name is <mask>"		)
									self.assertEqual(
									    nested_simplify(_snake_case		)   , [
									        {"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
									        {"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
									    ]   , )
									SCREAMING_SNAKE_CASE__		  =				unmasker("The largest city in France is <mask>"		)
									self.assertEqual(
									    nested_simplify(_snake_case		)   , [
									        {
									            "sequence": "The largest city in France is Paris",
									            "score": 0.251,
									            "token": 2201,
									            "token_str": " Paris",
									        },
									        {
									            "sequence": "The largest city in France is Lyon",
									            "score": 0.214,
									            "token": 12790,
									            "token_str": " Lyon",
									        },
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				unmasker("My name is <mask>"   , targets=[" Patrick", " Clara", " Teven"]   , top_k=3		)
									self.assertEqual(
									    nested_simplify(_snake_case		)   , [
									        {"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
									        {"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
									        {"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
									    ]   , )



					@require_torch
					def 			lowerCAmelCase_					(						self			:				List[Any]		)							->       Dict:
									SCREAMING_SNAKE_CASE__		  =				pipeline(task="fill-mask"   , model="sshleifer/tiny-distilroberta-base"   , framework="pt"		)
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									self.run_pipeline_test(_snake_case   , []		)



					@require_tf
					def 			lowerCAmelCase_					(						self			:				Dict		)							->       Optional[Any]:
									SCREAMING_SNAKE_CASE__		  =				pipeline(task="fill-mask"   , model="sshleifer/tiny-distilroberta-base"   , framework="tf"		)
									SCREAMING_SNAKE_CASE__		  =				None
									SCREAMING_SNAKE_CASE__		  =				None
									self.run_pipeline_test(_snake_case   , []		)



					def 			lowerCAmelCase_					(						self			:				str   , _snake_case			:				Union[str, Any]   , _snake_case			:				Tuple   , _snake_case			:				Any		)							->       Optional[int]:
									if tokenizer is None or tokenizer.mask_token_id is None:
													self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"		)

									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				[
									    F"""This is another {tokenizer.mask_token} test""",
									]
									return fill_masker, examples



					def 			lowerCAmelCase_					(						self			:				Optional[Any]   , _snake_case			:				List[Any]   , _snake_case			:				Any		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				fill_masker.tokenizer
									SCREAMING_SNAKE_CASE__		  =				fill_masker.model

									SCREAMING_SNAKE_CASE__		  =				fill_masker(
									    F"""This is a {tokenizer.mask_token}"""   , )
									self.assertEqual(
									    _snake_case   , [
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				fill_masker([F"""This is a {tokenizer.mask_token}"""]		)
									self.assertEqual(
									    _snake_case   , [
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				fill_masker([F"""This is a {tokenizer.mask_token}""", F"""Another {tokenizer.mask_token} great test."""]		)
									self.assertEqual(
									    _snake_case   , [
									        [
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        ],
									        [
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        ],
									    ]   , )

									with self.assertRaises(_snake_case		):
													fill_masker([None]		)
									# No mask_token is not supported
									with self.assertRaises(_snake_case		):
													fill_masker("This is"		)

									self.run_test_top_k(_snake_case   , _snake_case		)
									self.run_test_targets(_snake_case   , _snake_case		)
									self.run_test_top_k_targets(_snake_case   , _snake_case		)
									self.fill_mask_with_duplicate_targets_and_top_k(_snake_case   , _snake_case		)
									self.fill_mask_with_multiple_masks(_snake_case   , _snake_case		)



					def 			lowerCAmelCase_					(						self			:				Any   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				tokenizer.get_vocab()
									SCREAMING_SNAKE_CASE__		  =				sorted(vocab.keys()		)[:2]
									# Pipeline argument
									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case   , targets=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""		)
									self.assertEqual(
									    _snake_case   , [
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									    ]   , )
									SCREAMING_SNAKE_CASE__		  =				{vocab[el] for el in targets}
									self.assertEqual({el["token"] for el in outputs}   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				[tokenizer.decode([x]		) for x in target_ids]
									self.assertEqual({el["token_str"] for el in outputs}   , set(_snake_case		)		)

									# Call argument
									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , targets=_snake_case		)
									self.assertEqual(
									    _snake_case   , [
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									    ]   , )
									SCREAMING_SNAKE_CASE__		  =				{vocab[el] for el in targets}
									self.assertEqual({el["token"] for el in outputs}   , _snake_case		)
									SCREAMING_SNAKE_CASE__		  =				[tokenizer.decode([x]		) for x in target_ids]
									self.assertEqual({el["token_str"] for el in outputs}   , set(_snake_case		)		)

									# Score equivalence
									SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , targets=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				[top_mask["token_str"] for top_mask in outputs]
									SCREAMING_SNAKE_CASE__		  =				[top_mask["score"] for top_mask in outputs]

									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(_snake_case		) == set(_snake_case		):
													SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , targets=_snake_case		)
													SCREAMING_SNAKE_CASE__		  =				[top_mask["score"] for top_mask in unmasked_targets]
													self.assertEqual(nested_simplify(_snake_case		)   , nested_simplify(_snake_case		)		)

									# Raises with invalid
									with self.assertRaises(_snake_case		):
													SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , targets=[]		)
									# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
									if "" not in tokenizer.get_vocab():
													with self.assertRaises(_snake_case		):
																	SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , targets=[""]		)
													with self.assertRaises(_snake_case		):
																	SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , targets=""		)



					def 			lowerCAmelCase_					(						self			:				Union[str, Any]   , _snake_case			:				List[Any]   , _snake_case			:				Optional[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case   , top_k=2		)
									SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""		)
									self.assertEqual(
									    _snake_case   , [
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									    ]   , )

									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , top_k=2		)
									self.assertEqual(
									    _snake_case   , [
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									    ]   , )
									self.assertEqual(nested_simplify(_snake_case		)   , nested_simplify(_snake_case		)		)



					def 			lowerCAmelCase_					(						self			:				Tuple   , _snake_case			:				int   , _snake_case			:				Dict		)							->       int:
									SCREAMING_SNAKE_CASE__		  =				tokenizer.get_vocab()
									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case		)

									# top_k=2, ntargets=3
									SCREAMING_SNAKE_CASE__		  =				sorted(vocab.keys()		)[:3]
									SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , top_k=2   , targets=_snake_case		)

									# If we use the most probably targets, and filter differently, we should still
									# have the same results
									SCREAMING_SNAKE_CASE__		  =				[el["token_str"] for el in sorted(_snake_case   , key=lambda _snake_case		: x["score"]   , reverse=_snake_case		)]
									# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
									if set(_snake_case		).issubset(_snake_case		):
													SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""This is a {tokenizer.mask_token}"""   , top_k=3   , targets=_snake_case		)
													# They should yield exactly the same result
													self.assertEqual(nested_simplify(_snake_case		)   , nested_simplify(_snake_case		)		)



					def 			lowerCAmelCase_					(						self			:				Optional[int]   , _snake_case			:				Optional[int]   , _snake_case			:				Optional[Any]		)							->       Any:
									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case		)
									SCREAMING_SNAKE_CASE__		  =				tokenizer.get_vocab()
									# String duplicates + id duplicates
									SCREAMING_SNAKE_CASE__		  =				sorted(vocab.keys()		)[:3]
									SCREAMING_SNAKE_CASE__		  =				[targets[0], targets[1], targets[0], targets[2], targets[1]]
									SCREAMING_SNAKE_CASE__		  =				fill_masker(F"""My name is {tokenizer.mask_token}"""   , targets=_snake_case   , top_k=10		)

									# The target list contains duplicates, so we can't output more
									# than them
									self.assertEqual(len(_snake_case		)   , 3		)



					def 			lowerCAmelCase_					(						self			:				Dict   , _snake_case			:				Dict   , _snake_case			:				Optional[Any]		)							->       Optional[int]:
									SCREAMING_SNAKE_CASE__		  =				FillMaskPipeline(model=_snake_case   , tokenizer=_snake_case		)

									SCREAMING_SNAKE_CASE__		  =				fill_masker(
									    F"""This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}"""   , top_k=2		)
									self.assertEqual(
									    _snake_case   , [
									        [
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        ],
									        [
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        ],
									        [
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									            {"sequence": ANY(_snake_case		), "score": ANY(_snake_case		), "token": ANY(_snake_case		), "token_str": ANY(_snake_case		)},
									        ],
									    ]   , )





"""simple docstring"""






from math import pi




def 						SCREAMING_SNAKE_CASE					(  __UpperCAmelCase  ,					__UpperCAmelCase       )							->   float:
				return 2 * pi * radius * (angle / 360)


if __name__ == "__main__":
						print(arc_length(9_0, 1_0))




