
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        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			__UpperCamelCase      :						List[Any]					      = MODEL_FOR_MASKED_LM_MAPPING
			__UpperCamelCase      :						List[Any]					      = TF_MODEL_FOR_MASKED_LM_MAPPING

			def       lowerCAmelCase__	(       self :       List[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 :       int      ):
							UpperCamelCase_:							Tuple	=						pipeline(task="""fill-mask"""						,	model="""sshleifer/tiny-distilroberta-base"""						,	top_k=2						,	framework="""tf"""      )
							UpperCamelCase_:							Optional[int]	=						unmasker("""My name is <mask>"""      )
							self.assertEqual(
							    nested_simplify(snake_case_						,	decimals=6      )						,	[
							        {"""sequence""": """My name is grouped""", """score""": 2.1e-05, """token""": 3_8015, """token_str""": """ grouped"""},
							        {"""sequence""": """My name is accuser""", """score""": 2.1e-05, """token""": 2_5506, """token_str""": """ accuser"""},
							    ]						,	)

							UpperCamelCase_:							List[Any]	=						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""": 3_8015,
							            """token_str""": """ grouped""",
							        },
							        {
							            """sequence""": """The largest city in France is accuser""",
							            """score""": 2.1e-05,
							            """token""": 2_5506,
							            """token_str""": """ accuser""",
							        },
							    ]						,	)

							UpperCamelCase_:							Dict	=						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""": 1_3606, """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 :       Tuple      ):
							UpperCamelCase_:							Tuple	=						pipeline(task="""fill-mask"""						,	model="""sshleifer/tiny-distilroberta-base"""						,	top_k=2						,	framework="""pt"""      )

							UpperCamelCase_:							Tuple	=						unmasker("""My name is <mask>"""      )
							self.assertEqual(
							    nested_simplify(snake_case_						,	decimals=6      )						,	[
							        {"""sequence""": """My name is Maul""", """score""": 2.2e-05, """token""": 3_5676, """token_str""": """ Maul"""},
							        {"""sequence""": """My name isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
							    ]						,	)

							UpperCamelCase_:							Dict	=						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""": 3_5676,
							            """token_str""": """ Maul""",
							        },
							        {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS"""},
							    ]						,	)

							UpperCamelCase_:							int	=						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""": 1_3606, """token_str""": """ Clara"""},
							    ]						,	)

							UpperCamelCase_:							Tuple	=						unmasker("""My name is <mask> <mask>"""						,	top_k=2      )

							self.assertEqual(
							    nested_simplify(snake_case_						,	decimals=6      )						,	[
							        [
							            {
							                """score""": 2.2e-05,
							                """token""": 3_5676,
							                """token_str""": """ Maul""",
							                """sequence""": """<s>My name is Maul<mask></s>""",
							            },
							            {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
							        ],
							        [
							            {
							                """score""": 2.2e-05,
							                """token""": 3_5676,
							                """token_str""": """ Maul""",
							                """sequence""": """<s>My name is<mask> Maul</s>""",
							            },
							            {"""score""": 2.2e-05, """token""": 1_6416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
							        ],
							    ]						,	)

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

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

							UpperCamelCase_:							List[Any]	=						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 :       Optional[int]      ):
							UpperCamelCase_:							Union[str, Any]	=						pipeline(task="""fill-mask"""						,	model="""distilroberta-base"""						,	top_k=2						,	framework="""pt"""      )
							self.run_large_test(snake_case_      )

			@slow
			@require_tf
			def       lowerCAmelCase__	(       self :       str      ):
							UpperCamelCase_:							List[Any]	=						pipeline(task="""fill-mask"""						,	model="""distilroberta-base"""						,	top_k=2						,	framework="""tf"""      )
							self.run_large_test(snake_case_      )

			def       lowerCAmelCase__	(       self :       Optional[int]						,	snake_case_ :       Dict      ):
							UpperCamelCase_:							Any	=						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"""},
							    ]						,	)
							UpperCamelCase_:							int	=						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""": 1_2790,
							            """token_str""": """ Lyon""",
							        },
							    ]						,	)

							UpperCamelCase_:							Optional[Any]	=						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""": 1_3606, """token_str""": """ Clara"""},
							        {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 2941, """token_str""": """ Te"""},
							    ]						,	)

			@require_torch
			def       lowerCAmelCase__	(       self :       Union[str, Any]      ):
							UpperCamelCase_:							Optional[int]	=						pipeline(task="""fill-mask"""						,	model="""sshleifer/tiny-distilroberta-base"""						,	framework="""pt"""      )
							UpperCamelCase_:							Any	=						None
							UpperCamelCase_:							List[str]	=						None
							self.run_pipeline_test(snake_case_						,	[]      )

			@require_tf
			def       lowerCAmelCase__	(       self :       Tuple      ):
							UpperCamelCase_:							int	=						pipeline(task="""fill-mask"""						,	model="""sshleifer/tiny-distilroberta-base"""						,	framework="""tf"""      )
							UpperCamelCase_:							Dict	=						None
							UpperCamelCase_:							Optional[int]	=						None
							self.run_pipeline_test(snake_case_						,	[]      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       int						,	snake_case_ :       str						,	snake_case_ :       Tuple      ):
							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_:							Optional[Any]	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_      )
							UpperCamelCase_:							List[Any]	=						[
							    f'''This is another {tokenizer.mask_token} test''',
							]
							return fill_masker, examples

			def       lowerCAmelCase__	(       self :       Union[str, Any]						,	snake_case_ :       Any						,	snake_case_ :       List[str]      ):
							UpperCamelCase_:							str	=						fill_masker.tokenizer
							UpperCamelCase_:							str	=						fill_masker.model

							UpperCamelCase_:							Optional[int]	=						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_      )},
							    ]						,	)

							UpperCamelCase_:							int	=						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_      )},
							    ]						,	)

							UpperCamelCase_:							List[Any]	=						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 :       List[str]						,	snake_case_ :       Optional[int]						,	snake_case_ :       Optional[int]      ):
							UpperCamelCase_:							Optional[int]	=						tokenizer.get_vocab()
							UpperCamelCase_:							Optional[Any]	=						sorted(vocab.keys()      )[:2]
							# Pipeline argument
							UpperCamelCase_:							Dict	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_						,	targets=snake_case_      )
							UpperCamelCase_:							str	=						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_      )},
							    ]						,	)
							UpperCamelCase_:							Union[str, Any]	=						{vocab[el] for el in targets}
							self.assertEqual({el["""token"""] for el in outputs}						,	snake_case_      )
							UpperCamelCase_:							List[str]	=						[tokenizer.decode([x]      ) for x in target_ids]
							self.assertEqual({el["""token_str"""] for el in outputs}						,	set(snake_case_      )      )

							# Call argument
							UpperCamelCase_:							Union[str, Any]	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_      )
							UpperCamelCase_:							Any	=						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_      )},
							    ]						,	)
							UpperCamelCase_:							List[Any]	=						{vocab[el] for el in targets}
							self.assertEqual({el["""token"""] for el in outputs}						,	snake_case_      )
							UpperCamelCase_:							Optional[Any]	=						[tokenizer.decode([x]      ) for x in target_ids]
							self.assertEqual({el["""token_str"""] for el in outputs}						,	set(snake_case_      )      )

							# Score equivalence
							UpperCamelCase_:							Optional[Any]	=						fill_masker(f'''This is a {tokenizer.mask_token}'''						,	targets=snake_case_      )
							UpperCamelCase_:							str	=						[top_mask["""token_str"""] for top_mask in outputs]
							UpperCamelCase_:							Tuple	=						[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_      ):
											UpperCamelCase_:							Optional[int]	=						fill_masker(f'''This is a {tokenizer.mask_token}'''						,	targets=snake_case_      )
											UpperCamelCase_:							Optional[int]	=						[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_      ):
											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(snake_case_      ):
															UpperCamelCase_:							Union[str, Any]	=						fill_masker(f'''This is a {tokenizer.mask_token}'''						,	targets=[""""""]      )
											with self.assertRaises(snake_case_      ):
															UpperCamelCase_:							List[Any]	=						fill_masker(f'''This is a {tokenizer.mask_token}'''						,	targets=""""""      )

			def       lowerCAmelCase__	(       self :       Dict						,	snake_case_ :       Tuple						,	snake_case_ :       List[str]      ):
							UpperCamelCase_:							str	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_						,	top_k=2      )
							UpperCamelCase_:							List[Any]	=						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_      )},
							    ]						,	)

							UpperCamelCase_:							int	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_      )
							UpperCamelCase_:							Dict	=						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 :       Union[str, Any]						,	snake_case_ :       int						,	snake_case_ :       Union[str, Any]      ):
							UpperCamelCase_:							Optional[Any]	=						tokenizer.get_vocab()
							UpperCamelCase_:							Optional[int]	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_      )

							# top_k=2, ntargets=3
							UpperCamelCase_:							Tuple	=						sorted(vocab.keys()      )[:3]
							UpperCamelCase_:							List[str]	=						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
							UpperCamelCase_:							int	=						[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_      ):
											UpperCamelCase_:							Tuple	=						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 :       Union[str, Any]						,	snake_case_ :       Tuple						,	snake_case_ :       int      ):
							UpperCamelCase_:							Any	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_      )
							UpperCamelCase_:							Dict	=						tokenizer.get_vocab()
							# String duplicates + id duplicates
							UpperCamelCase_:							Optional[Any]	=						sorted(vocab.keys()      )[:3]
							UpperCamelCase_:							Tuple	=						[targets[0], targets[1], targets[0], targets[2], targets[1]]
							UpperCamelCase_:							Optional[int]	=						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 :       Any						,	snake_case_ :       List[Any]						,	snake_case_ :       Dict      ):
							UpperCamelCase_:							List[str]	=						FillMaskPipeline(model=snake_case_						,	tokenizer=snake_case_      )

							UpperCamelCase_:							Union[str, Any]	=						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_      )},
							        ],
							    ]						,	)








import inspect
import os
import sys
import unittest

import accelerate
from accelerate.test_utils import execute_subprocess_async, require_tpu

class        _UpperCamelCase				(					unittest.TestCase  ):






			'''simple docstring'''

			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							List[Any]	=						inspect.getfile(accelerate.test_utils      )
							UpperCamelCase_:							List[str]	=						os.path.sep.join(mod_file.split(os.path.sep      )[:-1] + ["""scripts""", """test_script.py"""]      )
							UpperCamelCase_:							str	=						os.path.sep.join(inspect.getfile(self.__class__      ).split(os.path.sep      )[:-1]      )




			@require_tpu
			def       lowerCAmelCase__	(       self :       Optional[int]      ):
							UpperCamelCase_:							Any	=						f'''
            {self.test_dir}/xla_spawn.py
            --num_cores 8
            {self.test_file_path}
        '''.split()
							UpperCamelCase_:							Dict	=						[sys.executable] + distributed_args
							execute_subprocess_async(snake_case_						,	env=os.environ.copy()      )






