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       ):







 """simple docstring"""






 lowerCamelCase	:Optional[int]    			=		MODEL_FOR_MASKED_LM_MAPPING
 lowerCamelCase	:Optional[int]    			=		TF_MODEL_FOR_MASKED_LM_MAPPING



 def      UpperCAmelCase							(					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      UpperCAmelCase							(					self							)  ->					Any:
        _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(lowerCAmelCase_		,				decimals=6							)		,				[
                {"""sequence""": """My name is grouped""", """score""": 2.1E-05, """token""": 3_80_15, """token_str""": """ grouped"""},
                {"""sequence""": """My name is accuser""", """score""": 2.1E-05, """token""": 2_55_06, """token_str""": """ accuser"""},
            ]		,				)

        _A        =  unmasker("""The largest city in France is <mask>"""							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase_		,				decimals=6							)		,				[
                {
                    """sequence""": """The largest city in France is grouped""",
                    """score""": 2.1E-05,
                    """token""": 3_80_15,
                    """token_str""": """ grouped""",
                },
                {
                    """sequence""": """The largest city in France is accuser""",
                    """score""": 2.1E-05,
                    """token""": 2_55_06,
                    """token_str""": """ accuser""",
                },
            ]		,				)

        _A        =  unmasker("""My name is <mask>"""		,				targets=[""" Patrick""", """ Clara""", """ Teven"""]		,				top_k=3							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase_		,				decimals=6							)		,				[
                {"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 1_36_06, """token_str""": """ Clara"""},
                {"""sequence""": """My name is Patrick""", """score""": 2E-05, """token""": 34_99, """token_str""": """ Patrick"""},
                {"""sequence""": """My name is Te""", """score""": 1.9E-05, """token""": 29_41, """token_str""": """ Te"""},
            ]		,				)



 @require_torch
 def      UpperCAmelCase							(					self							)  ->					Optional[int]:
        _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(lowerCAmelCase_		,				decimals=6							)		,				[
                {"""sequence""": """My name is Maul""", """score""": 2.2E-05, """token""": 3_56_76, """token_str""": """ Maul"""},
                {"""sequence""": """My name isELS""", """score""": 2.2E-05, """token""": 1_64_16, """token_str""": """ELS"""},
            ]		,				)

        _A        =  unmasker("""The largest city in France is <mask>"""							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase_		,				decimals=6							)		,				[
                {
                    """sequence""": """The largest city in France is Maul""",
                    """score""": 2.2E-05,
                    """token""": 3_56_76,
                    """token_str""": """ Maul""",
                },
                {"""sequence""": """The largest city in France isELS""", """score""": 2.2E-05, """token""": 1_64_16, """token_str""": """ELS"""},
            ]		,				)

        _A        =  unmasker("""My name is <mask>"""		,				targets=[""" Patrick""", """ Clara""", """ Teven"""]		,				top_k=3							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase_		,				decimals=6							)		,				[
                {"""sequence""": """My name is Patrick""", """score""": 2.1E-05, """token""": 34_99, """token_str""": """ Patrick"""},
                {"""sequence""": """My name is Te""", """score""": 2E-05, """token""": 29_41, """token_str""": """ Te"""},
                {"""sequence""": """My name is Clara""", """score""": 2E-05, """token""": 1_36_06, """token_str""": """ Clara"""},
            ]		,				)

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

        self.assertEqual(
            nested_simplify(lowerCAmelCase_		,				decimals=6							)		,				[
                [
                    {
                        """score""": 2.2E-05,
                        """token""": 3_56_76,
                        """token_str""": """ Maul""",
                        """sequence""": """<s>My name is Maul<mask></s>""",
                    },
                    {"""score""": 2.2E-05, """token""": 1_64_16, """token_str""": """ELS""", """sequence""": """<s>My name isELS<mask></s>"""},
                ],
                [
                    {
                        """score""": 2.2E-05,
                        """token""": 3_56_76,
                        """token_str""": """ Maul""",
                        """sequence""": """<s>My name is<mask> Maul</s>""",
                    },
                    {"""score""": 2.2E-05, """token""": 1_64_16, """token_str""": """ELS""", """sequence""": """<s>My name is<mask>ELS</s>"""},
                ],
            ]		,				)



 @require_torch_gpu
 def      UpperCAmelCase							(					self							)  ->					Dict:
        _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(lowerCAmelCase_		,				lowerCAmelCase_							)



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



 @slow
 @require_tf
 def      UpperCAmelCase							(					self							)  ->					str:
        _A        =  pipeline(task="""fill-mask"""		,				model="""distilroberta-base"""		,				top_k=2		,				framework="""tf"""							)
        self.run_large_test(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					Any:
        _A        =  unmasker("""My name is <mask>"""							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase_							)		,				[
                {"""sequence""": """My name is John""", """score""": 0.008, """token""": 6_10, """token_str""": """ John"""},
                {"""sequence""": """My name is Chris""", """score""": 0.007, """token""": 15_73, """token_str""": """ Chris"""},
            ]		,				)
        _A        =  unmasker("""The largest city in France is <mask>"""							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase_							)		,				[
                {
                    """sequence""": """The largest city in France is Paris""",
                    """score""": 0.251,
                    """token""": 22_01,
                    """token_str""": """ Paris""",
                },
                {
                    """sequence""": """The largest city in France is Lyon""",
                    """score""": 0.214,
                    """token""": 1_27_90,
                    """token_str""": """ Lyon""",
                },
            ]		,				)

        _A        =  unmasker("""My name is <mask>"""		,				targets=[""" Patrick""", """ Clara""", """ Teven"""]		,				top_k=3							)
        self.assertEqual(
            nested_simplify(lowerCAmelCase_							)		,				[
                {"""sequence""": """My name is Patrick""", """score""": 0.005, """token""": 34_99, """token_str""": """ Patrick"""},
                {"""sequence""": """My name is Clara""", """score""": 0.000, """token""": 1_36_06, """token_str""": """ Clara"""},
                {"""sequence""": """My name is Te""", """score""": 0.000, """token""": 29_41, """token_str""": """ Te"""},
            ]		,				)



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



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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[str]:
        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=lowerCAmelCase_		,				tokenizer=lowerCAmelCase_							)
        _A        =  [
            F'''This is another {tokenizer.mask_token} test''',
        ]
        return fill_masker, examples



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        _A        =  fill_masker.tokenizer
        _A        =  fill_masker.model

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

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

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

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

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



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

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

        # Score equivalence
        _A        =  fill_masker(F'''This is a {tokenizer.mask_token}'''		,				targets=lowerCAmelCase_							)
        _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(lowerCAmelCase_							) == set(lowerCAmelCase_							):
               _A        =  fill_masker(F'''This is a {tokenizer.mask_token}'''		,				targets=lowerCAmelCase_							)
               _A        =  [top_mask["""score"""] for top_mask in unmasked_targets]
               self.assertEqual(nested_simplify(lowerCAmelCase_							)		,				nested_simplify(lowerCAmelCase_							)							)

        # Raises with invalid
        with self.assertRaises(lowerCAmelCase_							):
               _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(lowerCAmelCase_							):
                      _A        =  fill_masker(F'''This is a {tokenizer.mask_token}'''		,				targets=[""""""]							)
               with self.assertRaises(lowerCAmelCase_							):
                      _A        =  fill_masker(F'''This is a {tokenizer.mask_token}'''		,				targets=""""""							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					int:
        _A        =  FillMaskPipeline(model=lowerCAmelCase_		,				tokenizer=lowerCAmelCase_		,				top_k=2							)
        _A        =  fill_masker(F'''This is a {tokenizer.mask_token}'''							)
        self.assertEqual(
            lowerCAmelCase_		,				[
                {"""sequence""": ANY(lowerCAmelCase_							), """score""": ANY(lowerCAmelCase_							), """token""": ANY(lowerCAmelCase_							), """token_str""": ANY(lowerCAmelCase_							)},
                {"""sequence""": ANY(lowerCAmelCase_							), """score""": ANY(lowerCAmelCase_							), """token""": ANY(lowerCAmelCase_							), """token_str""": ANY(lowerCAmelCase_							)},
            ]		,				)

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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					str:
        _A        =  tokenizer.get_vocab()
        _A        =  FillMaskPipeline(model=lowerCAmelCase_		,				tokenizer=lowerCAmelCase_							)

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

        # 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(lowerCAmelCase_		,				key=lambda lowerCAmelCase_							: x["score"]		,				reverse=lowerCAmelCase_							)]
        # For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
        if set(lowerCAmelCase_							).issubset(lowerCAmelCase_							):
               _A        =  fill_masker(F'''This is a {tokenizer.mask_token}'''		,				top_k=3		,				targets=lowerCAmelCase_							)
               # They should yield exactly the same result
               self.assertEqual(nested_simplify(lowerCAmelCase_							)		,				nested_simplify(lowerCAmelCase_							)							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					List[Any]:
        _A        =  FillMaskPipeline(model=lowerCAmelCase_		,				tokenizer=lowerCAmelCase_							)
        _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=lowerCAmelCase_		,				top_k=10							)

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



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					Tuple:
        _A        =  FillMaskPipeline(model=lowerCAmelCase_		,				tokenizer=lowerCAmelCase_							)

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





from __future__ import annotations

_SCREAMING_SNAKE_CASE										=						{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}



class 				a							:







 """simple docstring"""






 def __init__(					self		,				lowerCAmelCase_		,				lowerCAmelCase_							)  ->					None:
        _A        =  graph
        # mapping node to its parent in resulting breadth first tree
        _A        =  {}
        _A        =  source_vertex



 def      UpperCAmelCase							(					self							)  ->					None:
        _A        =  {self.source_vertex}
        _A        =  None
        _A        =  [self.source_vertex]  # first in first out queue

        while queue:
               _A        =  queue.pop(0							)
               for adjacent_vertex in self.graph[vertex]:
                      if adjacent_vertex not in visited:
                             visited.add(lowerCAmelCase_							)
                             _A        =  vertex
                             queue.append(lowerCAmelCase_							)



 def      UpperCAmelCase							(					self		,				lowerCAmelCase_							)  ->					str:
        if target_vertex == self.source_vertex:
               return self.source_vertex

        _A        =  self.parent.get(lowerCAmelCase_							)
        if target_vertex_parent is None:
               _A        =  (
                   F'''No path from vertex: {self.source_vertex} to vertex: {target_vertex}'''
               )
               raise ValueError(lowerCAmelCase_							)

        return self.shortest_path(lowerCAmelCase_							) + F'''->{target_vertex}'''


if __name__ == "__main__":
    _SCREAMING_SNAKE_CASE										=						Graph(graph, 'G')
    g.breath_first_search()
    print(g.shortest_path('D'))
    print(g.shortest_path('G'))
    print(g.shortest_path('Foo'))




