

'''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     A__					(					unittest.TestCase	):
    lowercase		    =						MODEL_FOR_MASKED_LM_MAPPING
    lowercase		    =						TF_MODEL_FOR_MASKED_LM_MAPPING



    def     snake_case_	(    self				)  ->					str:





           '''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				)  ->					Tuple:





           '''simple docstring'''
           A_		    =					pipeline(task="""fill-mask"""						, model="""sshleifer/tiny-distilroberta-base"""						, top_k=2						, framework="""tf"""				)
           A_		    =					unmasker("""My name is <mask>"""				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_						, decimals=6				)						, [
                   {"""sequence""": """My name is grouped""", """score""": 2.1e-0_5, """token""": 38015, """token_str""": """ grouped"""},
                   {"""sequence""": """My name is accuser""", """score""": 2.1e-0_5, """token""": 25506, """token_str""": """ accuser"""},
               ]						, )

           A_		    =					unmasker("""The largest city in France is <mask>"""				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_						, decimals=6				)						, [
                   {
                       """sequence""": """The largest city in France is grouped""",
                       """score""": 2.1e-0_5,
                       """token""": 38015,
                       """token_str""": """ grouped""",
                   },
                   {
                       """sequence""": """The largest city in France is accuser""",
                       """score""": 2.1e-0_5,
                       """token""": 25506,
                       """token_str""": """ accuser""",
                   },
               ]						, )

           A_		    =					unmasker("""My name is <mask>"""						, targets=[""" Patrick""", """ Clara""", """ Teven"""]						, top_k=3				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_						, decimals=6				)						, [
                   {"""sequence""": """My name is Clara""", """score""": 2e-0_5, """token""": 13606, """token_str""": """ Clara"""},
                   {"""sequence""": """My name is Patrick""", """score""": 2e-0_5, """token""": 3499, """token_str""": """ Patrick"""},
                   {"""sequence""": """My name is Te""", """score""": 1.9e-0_5, """token""": 2941, """token_str""": """ Te"""},
               ]						, )



    @require_torch
    def     snake_case_	(    self				)  ->					Union[str, Any]:





           '''simple docstring'''
           A_		    =					pipeline(task="""fill-mask"""						, model="""sshleifer/tiny-distilroberta-base"""						, top_k=2						, framework="""pt"""				)

           A_		    =					unmasker("""My name is <mask>"""				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_						, decimals=6				)						, [
                   {"""sequence""": """My name is Maul""", """score""": 2.2e-0_5, """token""": 35676, """token_str""": """ Maul"""},
                   {"""sequence""": """My name isELS""", """score""": 2.2e-0_5, """token""": 16416, """token_str""": """ELS"""},
               ]						, )

           A_		    =					unmasker("""The largest city in France is <mask>"""				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_						, decimals=6				)						, [
                   {
                       """sequence""": """The largest city in France is Maul""",
                       """score""": 2.2e-0_5,
                       """token""": 35676,
                       """token_str""": """ Maul""",
                   },
                   {"""sequence""": """The largest city in France isELS""", """score""": 2.2e-0_5, """token""": 16416, """token_str""": """ELS"""},
               ]						, )

           A_		    =					unmasker("""My name is <mask>"""						, targets=[""" Patrick""", """ Clara""", """ Teven"""]						, top_k=3				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_						, decimals=6				)						, [
                   {"""sequence""": """My name is Patrick""", """score""": 2.1e-0_5, """token""": 3499, """token_str""": """ Patrick"""},
                   {"""sequence""": """My name is Te""", """score""": 2e-0_5, """token""": 2941, """token_str""": """ Te"""},
                   {"""sequence""": """My name is Clara""", """score""": 2e-0_5, """token""": 13606, """token_str""": """ Clara"""},
               ]						, )

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

           self.assertEqual(
               nested_simplify(UpperCAmelCase_						, decimals=6				)						, [
                   [
                       {
                           """score""": 2.2e-0_5,
                           """token""": 35676,
                           """token_str""": """ Maul""",
                           """sequence""": """<s>My name is Maul<mask></s>""",
                       },
                       {"""score""": 2.2e-0_5, """token""": 16416, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
                   ],
                   [
                       {
                           """score""": 2.2e-0_5,
                           """token""": 35676,
                           """token_str""": """ Maul""",
                           """sequence""": """<s>My name is<mask> Maul</s>""",
                       },
                       {"""score""": 2.2e-0_5, """token""": 16416, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
                   ],
               ]						, )



    @require_torch_gpu
    def     snake_case_	(    self				)  ->					str:





           '''simple docstring'''
           A_		    =					pipeline("""fill-mask"""						, model="""hf-internal-testing/tiny-random-distilbert"""						, device=0						, framework="""pt"""				)

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

           A_		    =					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(UpperCAmelCase_						, UpperCAmelCase_				)



    @slow
    @require_torch
    def     snake_case_	(    self				)  ->					Any:





           '''simple docstring'''
           A_		    =					pipeline(task="""fill-mask"""						, model="""distilroberta-base"""						, top_k=2						, framework="""pt"""				)
           self.run_large_test(UpperCAmelCase_				)



    @slow
    @require_tf
    def     snake_case_	(    self				)  ->					Dict:





           '''simple docstring'''
           A_		    =					pipeline(task="""fill-mask"""						, model="""distilroberta-base"""						, top_k=2						, framework="""tf"""				)
           self.run_large_test(UpperCAmelCase_				)



    def     snake_case_	(    self						, UpperCamelCase__				)  ->					List[Any]:





           '''simple docstring'''
           A_		    =					unmasker("""My name is <mask>"""				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_				)						, [
                   {"""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"""},
               ]						, )
           A_		    =					unmasker("""The largest city in France is <mask>"""				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_				)						, [
                   {
                       """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""",
                   },
               ]						, )

           A_		    =					unmasker("""My name is <mask>"""						, targets=[""" Patrick""", """ Clara""", """ Teven"""]						, top_k=3				)
           self.assertEqual(
               nested_simplify(UpperCAmelCase_				)						, [
                   {"""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     snake_case_	(    self				)  ->					Optional[Any]:





           '''simple docstring'''
           A_		    =					pipeline(task="""fill-mask"""						, model="""sshleifer/tiny-distilroberta-base"""						, framework="""pt"""				)
           A_		    =					None
           A_		    =					None
           self.run_pipeline_test(UpperCAmelCase_						, []				)



    @require_tf
    def     snake_case_	(    self				)  ->					str:





           '''simple docstring'''
           A_		    =					pipeline(task="""fill-mask"""						, model="""sshleifer/tiny-distilroberta-base"""						, framework="""tf"""				)
           A_		    =					None
           A_		    =					None
           self.run_pipeline_test(UpperCAmelCase_						, []				)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__						, UpperCamelCase__				)  ->					Optional[int]:





           '''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)"""				)

           A_		    =					FillMaskPipeline(model=UpperCAmelCase_						, tokenizer=UpperCAmelCase_				)
           A_		    =					[
               f'''This is another {tokenizer.mask_token} test''',
           ]
           return fill_masker, examples



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					Optional[int]:





           '''simple docstring'''
           A_		    =					fill_masker.tokenizer
           A_		    =					fill_masker.model

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

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

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

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

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



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					Tuple:





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

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

           # Score equivalence
           A_		    =					fill_masker(f'''This is a {tokenizer.mask_token}'''						, targets=UpperCAmelCase_				)
           A_		    =					[top_mask["token_str"] for top_mask in outputs]
           A_		    =					[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(UpperCAmelCase_				) == set(UpperCAmelCase_				):
                  A_		    =					fill_masker(f'''This is a {tokenizer.mask_token}'''						, targets=UpperCAmelCase_				)
                  A_		    =					[top_mask["score"] for top_mask in unmasked_targets]
                  self.assertEqual(nested_simplify(UpperCAmelCase_				)						, nested_simplify(UpperCAmelCase_				)				)

           # Raises with invalid
           with self.assertRaises(UpperCAmelCase_				):
                  A_		    =					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(UpperCAmelCase_				):
                         A_		    =					fill_masker(f'''This is a {tokenizer.mask_token}'''						, targets=[""""""]				)
                  with self.assertRaises(UpperCAmelCase_				):
                         A_		    =					fill_masker(f'''This is a {tokenizer.mask_token}'''						, targets=""""""				)



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					str:





           '''simple docstring'''
           A_		    =					FillMaskPipeline(model=UpperCAmelCase_						, tokenizer=UpperCAmelCase_						, top_k=2				)
           A_		    =					fill_masker(f'''This is a {tokenizer.mask_token}'''				)
           self.assertEqual(
               UpperCAmelCase_						, [
                   {"""sequence""": ANY(UpperCAmelCase_				), """score""": ANY(UpperCAmelCase_				), """token""": ANY(UpperCAmelCase_				), """token_str""": ANY(UpperCAmelCase_				)},
                   {"""sequence""": ANY(UpperCAmelCase_				), """score""": ANY(UpperCAmelCase_				), """token""": ANY(UpperCAmelCase_				), """token_str""": ANY(UpperCAmelCase_				)},
               ]						, )

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



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					Optional[int]:





           '''simple docstring'''
           A_		    =					tokenizer.get_vocab()
           A_		    =					FillMaskPipeline(model=UpperCAmelCase_						, tokenizer=UpperCAmelCase_				)

           # top_k=2, ntargets=3
           A_		    =					sorted(vocab.keys()				)[:3]
           A_		    =					fill_masker(f'''This is a {tokenizer.mask_token}'''						, top_k=2						, targets=UpperCAmelCase_				)

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



    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					List[str]:





           '''simple docstring'''
           A_		    =					FillMaskPipeline(model=UpperCAmelCase_						, tokenizer=UpperCAmelCase_				)
           A_		    =					tokenizer.get_vocab()
           # String duplicates + id duplicates
           A_		    =					sorted(vocab.keys()				)[:3]
           A_		    =					[targets[0], targets[1], targets[0], targets[2], targets[1]]
           A_		    =					fill_masker(f'''My name is {tokenizer.mask_token}'''						, targets=UpperCAmelCase_						, top_k=10				)

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







    def     snake_case_	(    self						, UpperCamelCase__						, UpperCamelCase__				)  ->					Union[str, Any]:





           '''simple docstring'''
           A_		    =					FillMaskPipeline(model=UpperCAmelCase_						, tokenizer=UpperCAmelCase_				)

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







import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate



@dataclass(frozen=__SCREAMING_SNAKE_CASE    )
class    _SCREAMING_SNAKE_CASE						(	__SCREAMING_SNAKE_CASE    ):







 '''simple docstring'''
 lowercase_		=			field(default="image-classification"    ,							metadata={"include_in_asdict_even_if_is_default": True}    )
 lowercase_		=			Features({"image": Image()}    )
 lowercase_		=			Features({"labels": ClassLabel}    )
 lowercase_		=			"image"
 lowercase_		=			"labels"

 def 							SCREAMING_SNAKE_CASE_					(self				:   Tuple       , UpperCAmelCase_				:   Union[str, Any])       ->Tuple:



   '''simple docstring'''



   if self.label_column not in features:
     raise ValueError(F"""Column {self.label_column} is not present in features.""")
   if not isinstance(features[self.label_column]       , UpperCAmelCase_):
     raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""")
   lowerCamelCase__:      List[Any]			  =copy.deepcopy(self)
   lowerCamelCase__:      Optional[int]			  =self.label_schema.copy()
   lowerCamelCase__:      int			  =features[self.label_column]
   lowerCamelCase__:      int			  =label_schema
   return task_template







 @property
 def 							SCREAMING_SNAKE_CASE_					(self				:   Dict)       ->Dict[str, str]:



   '''simple docstring'''



   return {
       self.image_column: "image",
       self.label_column: "labels",
   }

