'''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 		SCREAMING_SNAKE_CASE     (					unittest.TestCase	):







				'''simple docstring'''
				UpperCAmelCase__							=							MODEL_FOR_MASKED_LM_MAPPING
				UpperCAmelCase__							=							TF_MODEL_FOR_MASKED_LM_MAPPING





				def  snake_case__				(						self					:						str					)							->Dict:


										'''simple docstring'''




										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  snake_case__				(						self					:						Any					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Tuple   			=						pipeline(task="fill-mask"       ,		model="sshleifer/tiny-distilroberta-base"       ,		top_k=2       ,		framework="tf"					)
										_UpperCamelCase			:			List[Any]   			=						unmasker("My name is <mask>"					)
										self.assertEqual(
										    nested_simplify(lowercase__       ,		decimals=6					)       ,		[
										        {"sequence": "My name is grouped", "score": 2.1e-05, "token": 38_015, "token_str": " grouped"},
										        {"sequence": "My name is accuser", "score": 2.1e-05, "token": 25_506, "token_str": " accuser"},
										    ]       ,		)

										_UpperCamelCase			:			Dict   			=						unmasker("The largest city in France is <mask>"					)
										self.assertEqual(
										    nested_simplify(lowercase__       ,		decimals=6					)       ,		[
										        {
										            "sequence": "The largest city in France is grouped",
										            "score": 2.1e-05,
										            "token": 38_015,
										            "token_str": " grouped",
										        },
										        {
										            "sequence": "The largest city in France is accuser",
										            "score": 2.1e-05,
										            "token": 25_506,
										            "token_str": " accuser",
										        },
										    ]       ,		)

										_UpperCamelCase			:			Union[str, Any]   			=						unmasker("My name is <mask>"       ,		targets=[" Patrick", " Clara", " Teven"]       ,		top_k=3					)
										self.assertEqual(
										    nested_simplify(lowercase__       ,		decimals=6					)       ,		[
										        {"sequence": "My name is Clara", "score": 2e-05, "token": 13_606, "token_str": " Clara"},
										        {"sequence": "My name is Patrick", "score": 2e-05, "token": 3_499, "token_str": " Patrick"},
										        {"sequence": "My name is Te", "score": 1.9e-05, "token": 2_941, "token_str": " Te"},
										    ]       ,		)





				@require_torch
				def  snake_case__				(						self					:						int					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						pipeline(task="fill-mask"       ,		model="sshleifer/tiny-distilroberta-base"       ,		top_k=2       ,		framework="pt"					)

										_UpperCamelCase			:			List[str]   			=						unmasker("My name is <mask>"					)
										self.assertEqual(
										    nested_simplify(lowercase__       ,		decimals=6					)       ,		[
										        {"sequence": "My name is Maul", "score": 2.2e-05, "token": 35_676, "token_str": " Maul"},
										        {"sequence": "My name isELS", "score": 2.2e-05, "token": 16_416, "token_str": "ELS"},
										    ]       ,		)

										_UpperCamelCase			:			Dict   			=						unmasker("The largest city in France is <mask>"					)
										self.assertEqual(
										    nested_simplify(lowercase__       ,		decimals=6					)       ,		[
										        {
										            "sequence": "The largest city in France is Maul",
										            "score": 2.2e-05,
										            "token": 35_676,
										            "token_str": " Maul",
										        },
										        {"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16_416, "token_str": "ELS"},
										    ]       ,		)

										_UpperCamelCase			:			Optional[Any]   			=						unmasker("My name is <mask>"       ,		targets=[" Patrick", " Clara", " Teven"]       ,		top_k=3					)
										self.assertEqual(
										    nested_simplify(lowercase__       ,		decimals=6					)       ,		[
										        {"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3_499, "token_str": " Patrick"},
										        {"sequence": "My name is Te", "score": 2e-05, "token": 2_941, "token_str": " Te"},
										        {"sequence": "My name is Clara", "score": 2e-05, "token": 13_606, "token_str": " Clara"},
										    ]       ,		)

										_UpperCamelCase			:			Optional[int]   			=						unmasker("My name is <mask> <mask>"       ,		top_k=2					)

										self.assertEqual(
										    nested_simplify(lowercase__       ,		decimals=6					)       ,		[
										        [
										            {
										                "score": 2.2e-05,
										                "token": 35_676,
										                "token_str": " Maul",
										                "sequence": "<s>My name is Maul<mask></s>",
										            },
										            {"score": 2.2e-05, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
										        ],
										        [
										            {
										                "score": 2.2e-05,
										                "token": 35_676,
										                "token_str": " Maul",
										                "sequence": "<s>My name is<mask> Maul</s>",
										            },
										            {"score": 2.2e-05, "token": 16_416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
										        ],
										    ]       ,		)





				@require_torch_gpu
				def  snake_case__				(						self					:						List[Any]					)							->Tuple:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						pipeline("fill-mask"       ,		model="hf-internal-testing/tiny-random-distilbert"       ,		device=0       ,		framework="pt"					)

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

										_UpperCamelCase			:			int   			=						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(lowercase__       ,		lowercase__					)





				@slow
				@require_torch
				def  snake_case__				(						self					:						str					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						pipeline(task="fill-mask"       ,		model="distilroberta-base"       ,		top_k=2       ,		framework="pt"					)
										self.run_large_test(lowercase__					)





				@slow
				@require_tf
				def  snake_case__				(						self					:						List[str]					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						pipeline(task="fill-mask"       ,		model="distilroberta-base"       ,		top_k=2       ,		framework="tf"					)
										self.run_large_test(lowercase__					)





				def  snake_case__				(						self					:						int       ,		lowercase__					:						str					)							->List[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			Union[str, Any]   			=						unmasker("My name is <mask>"					)
										self.assertEqual(
										    nested_simplify(lowercase__					)       ,		[
										        {"sequence": "My name is John", "score": 0.0_0_8, "token": 610, "token_str": " John"},
										        {"sequence": "My name is Chris", "score": 0.0_0_7, "token": 1_573, "token_str": " Chris"},
										    ]       ,		)
										_UpperCamelCase			:			int   			=						unmasker("The largest city in France is <mask>"					)
										self.assertEqual(
										    nested_simplify(lowercase__					)       ,		[
										        {
										            "sequence": "The largest city in France is Paris",
										            "score": 0.2_5_1,
										            "token": 2_201,
										            "token_str": " Paris",
										        },
										        {
										            "sequence": "The largest city in France is Lyon",
										            "score": 0.2_1_4,
										            "token": 12_790,
										            "token_str": " Lyon",
										        },
										    ]       ,		)

										_UpperCamelCase			:			List[str]   			=						unmasker("My name is <mask>"       ,		targets=[" Patrick", " Clara", " Teven"]       ,		top_k=3					)
										self.assertEqual(
										    nested_simplify(lowercase__					)       ,		[
										        {"sequence": "My name is Patrick", "score": 0.0_0_5, "token": 3_499, "token_str": " Patrick"},
										        {"sequence": "My name is Clara", "score": 0.0_0_0, "token": 13_606, "token_str": " Clara"},
										        {"sequence": "My name is Te", "score": 0.0_0_0, "token": 2_941, "token_str": " Te"},
										    ]       ,		)





				@require_torch
				def  snake_case__				(						self					:						Any					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			List[Any]   			=						pipeline(task="fill-mask"       ,		model="sshleifer/tiny-distilroberta-base"       ,		framework="pt"					)
										_UpperCamelCase			:			Optional[int]   			=						None
										_UpperCamelCase			:			List[str]   			=						None
										self.run_pipeline_test(lowercase__       ,		[]					)





				@require_tf
				def  snake_case__				(						self					:						List[str]					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						pipeline(task="fill-mask"       ,		model="sshleifer/tiny-distilroberta-base"       ,		framework="tf"					)
										_UpperCamelCase			:			str   			=						None
										_UpperCamelCase			:			Union[str, Any]   			=						None
										self.run_pipeline_test(lowercase__       ,		[]					)





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						Dict       ,		lowercase__					:						str       ,		lowercase__					:						Dict					)							->List[str]:


										'''simple docstring'''




										if tokenizer is None or tokenizer.mask_token_id is None:
																self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)"					)

										_UpperCamelCase			:			Dict   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__					)
										_UpperCamelCase			:			str   			=						[
										    f'''This is another {tokenizer.mask_token} test''',
										]
										return fill_masker, examples





				def  snake_case__				(						self					:						List[str]       ,		lowercase__					:						str       ,		lowercase__					:						Tuple					)							->Optional[Any]:


										'''simple docstring'''




										_UpperCamelCase			:			int   			=						fill_masker.tokenizer
										_UpperCamelCase			:			Any   			=						fill_masker.model

										_UpperCamelCase			:			Any   			=						fill_masker(
										    f'''This is a {tokenizer.mask_token}'''       ,		)
										self.assertEqual(
										    lowercase__       ,		[
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										    ]       ,		)

										_UpperCamelCase			:			Any   			=						fill_masker([f'''This is a {tokenizer.mask_token}''']					)
										self.assertEqual(
										    lowercase__       ,		[
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										    ]       ,		)

										_UpperCamelCase			:			Optional[int]   			=						fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.''']					)
										self.assertEqual(
										    lowercase__       ,		[
										        [
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        ],
										        [
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        ],
										    ]       ,		)

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

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





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						Optional[int]       ,		lowercase__					:						Optional[Any]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Any   			=						tokenizer.get_vocab()
										_UpperCamelCase			:			List[Any]   			=						sorted(vocab.keys()					)[:2]
										# Pipeline argument
										_UpperCamelCase			:			Dict   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__       ,		targets=lowercase__					)
										_UpperCamelCase			:			Dict   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''					)
										self.assertEqual(
										    lowercase__       ,		[
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										    ]       ,		)
										_UpperCamelCase			:			Optional[int]   			=						{vocab[el] for el in targets}
										self.assertEqual({el["token"] for el in outputs}       ,		lowercase__					)
										_UpperCamelCase			:			List[Any]   			=						[tokenizer.decode([x]					) for x in target_ids]
										self.assertEqual({el["token_str"] for el in outputs}       ,		set(lowercase__					)					)

										# Call argument
										_UpperCamelCase			:			Optional[int]   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__					)
										_UpperCamelCase			:			int   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''       ,		targets=lowercase__					)
										self.assertEqual(
										    lowercase__       ,		[
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										    ]       ,		)
										_UpperCamelCase			:			Union[str, Any]   			=						{vocab[el] for el in targets}
										self.assertEqual({el["token"] for el in outputs}       ,		lowercase__					)
										_UpperCamelCase			:			Optional[Any]   			=						[tokenizer.decode([x]					) for x in target_ids]
										self.assertEqual({el["token_str"] for el in outputs}       ,		set(lowercase__					)					)

										# Score equivalence
										_UpperCamelCase			:			Optional[Any]   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''       ,		targets=lowercase__					)
										_UpperCamelCase			:			List[str]   			=						[top_mask["token_str"] for top_mask in outputs]
										_UpperCamelCase			:			Optional[int]   			=						[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(lowercase__					) == set(lowercase__					):
																_UpperCamelCase			:			List[Any]   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''       ,		targets=lowercase__					)
																_UpperCamelCase			:			Union[str, Any]   			=						[top_mask["score"] for top_mask in unmasked_targets]
																self.assertEqual(nested_simplify(lowercase__					)       ,		nested_simplify(lowercase__					)					)

										# Raises with invalid
										with self.assertRaises(lowercase__					):
																_UpperCamelCase			:			List[str]   			=						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(lowercase__					):
																						_UpperCamelCase			:			List[Any]   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''       ,		targets=[""]					)
																with self.assertRaises(lowercase__					):
																						_UpperCamelCase			:			str   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''       ,		targets=""					)





				def  snake_case__				(						self					:						Union[str, Any]       ,		lowercase__					:						Union[str, Any]       ,		lowercase__					:						Union[str, Any]					)							->Any:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__       ,		top_k=2					)
										_UpperCamelCase			:			int   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''					)
										self.assertEqual(
										    lowercase__       ,		[
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										    ]       ,		)

										_UpperCamelCase			:			Optional[int]   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__					)
										_UpperCamelCase			:			Optional[int]   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''       ,		top_k=2					)
										self.assertEqual(
										    lowercase__       ,		[
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										    ]       ,		)
										self.assertEqual(nested_simplify(lowercase__					)       ,		nested_simplify(lowercase__					)					)





				def  snake_case__				(						self					:						int       ,		lowercase__					:						Tuple       ,		lowercase__					:						Tuple					)							->Dict:


										'''simple docstring'''




										_UpperCamelCase			:			List[str]   			=						tokenizer.get_vocab()
										_UpperCamelCase			:			Optional[int]   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__					)

										# top_k=2, ntargets=3
										_UpperCamelCase			:			List[str]   			=						sorted(vocab.keys()					)[:3]
										_UpperCamelCase			:			int   			=						fill_masker(f'''This is a {tokenizer.mask_token}'''       ,		top_k=2       ,		targets=lowercase__					)

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





				def  snake_case__				(						self					:						Any       ,		lowercase__					:						Dict       ,		lowercase__					:						Union[str, Any]					)							->Optional[int]:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__					)
										_UpperCamelCase			:			Union[str, Any]   			=						tokenizer.get_vocab()
										# String duplicates + id duplicates
										_UpperCamelCase			:			str   			=						sorted(vocab.keys()					)[:3]
										_UpperCamelCase			:			Optional[int]   			=						[targets[0], targets[1], targets[0], targets[2], targets[1]]
										_UpperCamelCase			:			Union[str, Any]   			=						fill_masker(f'''My name is {tokenizer.mask_token}'''       ,		targets=lowercase__       ,		top_k=10					)

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







				def  snake_case__				(						self					:						str       ,		lowercase__					:						Optional[Any]       ,		lowercase__					:						Optional[Any]					)							->str:


										'''simple docstring'''




										_UpperCamelCase			:			Optional[int]   			=						FillMaskPipeline(model=lowercase__       ,		tokenizer=lowercase__					)

										_UpperCamelCase			:			List[str]   			=						fill_masker(
										    f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}'''       ,		top_k=2					)
										self.assertEqual(
										    lowercase__       ,		[
										        [
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        ],
										        [
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        ],
										        [
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										            {"sequence": ANY(lowercase__					), "score": ANY(lowercase__					), "token": ANY(lowercase__					), "token_str": ANY(lowercase__					)},
										        ],
										    ]       ,		)



'''simple docstring'''





def 						__A							(     UpperCAmelCase					)     ->			List[Any]:




						'''simple docstring'''
						_UpperCamelCase			:			str   			=						[]
						_UpperCamelCase			:			Optional[Any]   			=						set({"(", "[", "{"}					)
						_UpperCamelCase			:			int   			=						set({")", "]", "}"}					)
						_UpperCamelCase			:			Dict   			=						{"{": "}", "[": "]", "(": ")"}

						for i in range(len(UpperCAmelCase					)					):
												if s[i] in open_brackets:
																		stack.append(s[i]					)

												elif s[i] in closed_brackets and (
												    len(UpperCAmelCase					) == 0 or (len(UpperCAmelCase					) > 0 and open_to_closed[stack.pop()] != s[i])
												):
																		return False

						return len(UpperCAmelCase					) == 0
def 						__A							(     )     ->			str:




						'''simple docstring'''
						_UpperCamelCase			:			Any   			=						input("Enter sequence of brackets: "					)
						if is_balanced(UpperCAmelCase					):
												print(UpperCAmelCase     ,"is balanced"					)
						else:
												print(UpperCAmelCase     ,"is not balanced"					)


if __name__ == "__main__":
			main()


