



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'''






    __lowerCamelCase      :			Tuple =				MODEL_FOR_MASKED_LM_MAPPING
    __lowerCamelCase      :			List[Any] =				TF_MODEL_FOR_MASKED_LM_MAPPING






    def       _lowerCAmelCase (	self		):
        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		):
        A			:	Optional[int]										=       pipeline(task="""fill-mask""",		model="""sshleifer/tiny-distilroberta-base""",		top_k=2,		framework="""tf"""		)
        A			:	Optional[int]										=       unmasker("""My name is <mask>"""		)
        self.assertEqual(
            nested_simplify(lowerCamelCase__,		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"""},
            ],		)

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

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

        A			:	Any										=       unmasker("""My name is <mask>"""		)
        self.assertEqual(
            nested_simplify(lowerCamelCase__,		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"""},
            ],		)

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

        A			:	int										=       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""": 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"""},
            ],		)

        A			:	Any										=       unmasker("""My name is <mask> <mask>""",		top_k=2		)

        self.assertEqual(
            nested_simplify(lowerCamelCase__,		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		):
        A			:	Any										=       pipeline("""fill-mask""",		model="""hf-internal-testing/tiny-random-distilbert""",		device=0,		framework="""pt"""		)

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

        A			:	Dict										=       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       _lowerCAmelCase (	self		):
        A			:	List[str]										=       pipeline(task="""fill-mask""",		model="""distilroberta-base""",		top_k=2,		framework="""pt"""		)
        self.run_large_test(lowerCamelCase__		)






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






    def       _lowerCAmelCase (	self,		lowerCamelCase__		):
        A			:	Union[str, Any]										=       unmasker("""My name is <mask>"""		)
        self.assertEqual(
            nested_simplify(lowerCamelCase__		),		[
                {"""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			:	List[str]										=       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""": 2201,
                    """token_str""": """ Paris""",
                },
                {
                    """sequence""": """The largest city in France is Lyon""",
                    """score""": 0.214,
                    """token""": 1_2790,
                    """token_str""": """ Lyon""",
                },
            ],		)

        A			:	Tuple										=       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""": 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		):
        A			:	Any										=       pipeline(task="""fill-mask""",		model="""sshleifer/tiny-distilroberta-base""",		framework="""pt"""		)
        A			:	Union[str, Any]										=       None
        A			:	Any										=       None
        self.run_pipeline_test(lowerCamelCase__,		[]		)






    @require_tf
    def       _lowerCAmelCase (	self		):
        A			:	List[str]										=       pipeline(task="""fill-mask""",		model="""sshleifer/tiny-distilroberta-base""",		framework="""tf"""		)
        A			:	List[Any]										=       None
        A			:	Optional[Any]										=       None
        self.run_pipeline_test(lowerCamelCase__,		[]		)






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__,		lowerCamelCase__		):
        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			:	Any										=       FillMaskPipeline(model=lowerCamelCase__,		tokenizer=lowerCamelCase__		)
        A			:	Any										=       [
            f'''This is another {tokenizer.mask_token} test''',
        ]
        return fill_masker, examples






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	List[str]										=       fill_masker.tokenizer
        A			:	Union[str, Any]										=       fill_masker.model

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

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

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






    def       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Tuple										=       FillMaskPipeline(model=lowerCamelCase__,		tokenizer=lowerCamelCase__,		top_k=2		)
        A			:	str										=       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			:	Any										=       FillMaskPipeline(model=lowerCamelCase__,		tokenizer=lowerCamelCase__		)
        A			:	Optional[Any]										=       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       _lowerCAmelCase (	self,		lowerCamelCase__,		lowerCamelCase__		):
        A			:	Tuple										=       tokenizer.get_vocab()
        A			:	str										=       FillMaskPipeline(model=lowerCamelCase__,		tokenizer=lowerCamelCase__		)

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

        A			:	List[str]										=       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 argparse import ArgumentParser

from .env import EnvironmentCommand




def      __UpperCamelCase     (  )	->  Dict:



    """simple docstring"""






    A			:	str										=       ArgumentParser("""Diffusers CLI tool"""				,					usage="""diffusers-cli <command> [<args>]"""		)
    A			:	int										=       parser.add_subparsers(help="""diffusers-cli command helpers"""		)

    # Register commands
    EnvironmentCommand.register_subcommand(_lowerCAmelCase		)

    # Let's go
    A			:	str										=       parser.parse_args()

    if not hasattr(_lowerCAmelCase				,					"""func"""		):
        parser.print_help()
        exit(1		)

    # Run
    A			:	Any										=       args.func(_lowerCAmelCase		)
    service.run()


if __name__ == "__main__":
     main()


