





'''simple docstring'''

import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
   import onnxruntime as ort
class 							__lowerCAmelCase	(       __magic_name__		,		unittest.TestCase					):






       """simple docstring"""
       _snake_case      :	Optional[Any]									=       'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'





       def snake_case__		(				self  :						Dict			,					lowerCAmelCase__  :						str=0	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			floats_tensor((1, 3, 128, 128)			,					rng=random.Random(lowerCAmelCase__	)	)
            _UpperCamelCase							       =			np.random.RandomState(lowerCAmelCase__	)
            _UpperCamelCase							       =			{
                '''prompt''': '''A painting of a squirrel eating a burger''',
                '''image''': image,
                '''generator''': generator,
                '''num_inference_steps''': 3,
                '''strength''': 0.75,
                '''guidance_scale''': 7.5,
                '''output_type''': '''numpy''',
            }
            return inputs





       def snake_case__		(				self  :						str	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1].flatten()

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.69643, 0.58484, 0.50314, 0.58760, 0.55368, 0.59643, 0.51529, 0.41217, 0.49087]	)
            assert np.abs(image_slice - expected_slice	).max() < 1e-1





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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			PNDMScheduler.from_config(pipe.scheduler.config			,					skip_prk_steps=lowerCAmelCase__	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.61737, 0.54642, 0.53183, 0.54465, 0.52742, 0.60525, 0.49969, 0.40655, 0.48154]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1





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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			LMSDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            # warmup pass to apply optimizations
            _UpperCamelCase							       =			pipe(**self.get_dummy_inputs()	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.52761, 0.59977, 0.49033, 0.49619, 0.54282, 0.50311, 0.47600, 0.40918, 0.45203]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1





       def snake_case__		(				self  :						str	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			EulerDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1





       def snake_case__		(				self  :						Tuple	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.52911, 0.60004, 0.49229, 0.49805, 0.54502, 0.50680, 0.47777, 0.41028, 0.45304]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1







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

            '''simple docstring'''




            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint			,					provider='''CPUExecutionProvider'''	)
            _UpperCamelCase							       =			DPMSolverMultistepScheduler.from_config(pipe.scheduler.config	)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			self.get_dummy_inputs()
            _UpperCamelCase							       =			pipe(**lowerCAmelCase__	).images
            _UpperCamelCase							       =			image[0, -3:, -3:, -1]

            assert image.shape == (1, 128, 128, 3)
            _UpperCamelCase							       =			np.array([0.65331, 0.58277, 0.48204, 0.56059, 0.53665, 0.56235, 0.50969, 0.40009, 0.46552]	)

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-1

@nightly
@require_onnxruntime
@require_torch_gpu
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @property
       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            return (
                "CUDAExecutionProvider",
                {
                    "gpu_mem_limit": "15000000000",  # 15GB
                    "arena_extend_strategy": "kSameAsRequested",
                },
            )





       @property
       def snake_case__		(				self  :						List[Any]	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			ort.SessionOptions()
            _UpperCamelCase							       =			False
            return options





       def snake_case__		(				self  :						str	)       ->	Any:

            '''simple docstring'''




            _UpperCamelCase							       =			load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/img2img/sketch-mountains-input.jpg'''	)
            _UpperCamelCase							       =			init_image.resize((768, 512)	)
            # using the PNDM scheduler by default
            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
                '''CompVis/stable-diffusion-v1-4'''			,					revision='''onnx'''			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''A fantasy landscape, trending on artstation'''

            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			pipe(
                prompt=lowerCAmelCase__			,					image=lowerCAmelCase__			,					strength=0.75			,					guidance_scale=7.5			,					num_inference_steps=10			,					generator=lowerCAmelCase__			,					output_type='''np'''			,					)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			images[0, 255:258, 383:386, -1]

            assert images.shape == (1, 512, 768, 3)
            _UpperCamelCase							       =			np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]	)
            # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 2e-2







       def snake_case__		(				self  :						Any	)       ->	Tuple:

            '''simple docstring'''




            _UpperCamelCase							       =			load_image(
                '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                '''/img2img/sketch-mountains-input.jpg'''	)
            _UpperCamelCase							       =			init_image.resize((768, 512)	)
            _UpperCamelCase							       =			LMSDiscreteScheduler.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					subfolder='''scheduler'''			,					revision='''onnx'''	)
            _UpperCamelCase							       =			OnnxStableDiffusionImgaImgPipeline.from_pretrained(
                '''runwayml/stable-diffusion-v1-5'''			,					revision='''onnx'''			,					scheduler=lowerCAmelCase__			,					safety_checker=lowerCAmelCase__			,					feature_extractor=lowerCAmelCase__			,					provider=self.gpu_provider			,					sess_options=self.gpu_options			,					)
            pipe.set_progress_bar_config(disable=lowerCAmelCase__	)

            _UpperCamelCase							       =			'''A fantasy landscape, trending on artstation'''

            _UpperCamelCase							       =			np.random.RandomState(0	)
            _UpperCamelCase							       =			pipe(
                prompt=lowerCAmelCase__			,					image=lowerCAmelCase__			,					strength=0.75			,					guidance_scale=7.5			,					num_inference_steps=20			,					generator=lowerCAmelCase__			,					output_type='''np'''			,					)
            _UpperCamelCase							       =			output.images
            _UpperCamelCase							       =			images[0, 255:258, 383:386, -1]

            assert images.shape == (1, 512, 768, 3)
            _UpperCamelCase							       =			np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]	)
            # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

            assert np.abs(image_slice.flatten() - expected_slice	).max() < 2e-2









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        __lowerCAmelCase					(			unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			MODEL_FOR_MASKED_LM_MAPPING
			snake_case_  						=			TF_MODEL_FOR_MASKED_LM_MAPPING


			def  lowercase_       ( 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  lowercase_       ( self				)       -> Any:



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

							__lowerCamelCase									=       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': 38_015,
							            'token_str': ' grouped',
							        },
							        {
							            'sequence': 'The largest city in France is accuser',
							            'score': 2.1e-05,
							            'token': 25_506,
							            'token_str': ' accuser',
							        },
							    ]     ,		)

							__lowerCamelCase									=       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': 13_606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Patrick', 'score': 2e-05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 1.9e-05, 'token': 2_941, 'token_str': ' Te'},
							    ]     ,		)


			@require_torch
			def  lowercase_       ( self				)       -> str:



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

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

							__lowerCamelCase									=       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': 35_676,
							            'token_str': ' Maul',
							        },
							        {'sequence': 'The largest city in France isELS', 'score': 2.2e-05, 'token': 16_416, 'token_str': 'ELS'},
							    ]     ,		)

							__lowerCamelCase									=       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': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Te', 'score': 2e-05, 'token': 2_941, 'token_str': ' Te'},
							        {'sequence': 'My name is Clara', 'score': 2e-05, 'token': 13_606, 'token_str': ' Clara'},
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask> <mask>'     ,		top_k=2				)

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


			@require_torch_gpu
			def  lowercase_       ( self				)       -> int:



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

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

							__lowerCamelCase									=       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  lowercase_       ( self				)       -> str:



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


			@slow
			@require_tf
			def  lowercase_       ( self				)       -> str:



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


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       unmasker('My name is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {'sequence': 'My name is John', 'score': 0.0_08, 'token': 610, 'token_str': ' John'},
							        {'sequence': 'My name is Chris', 'score': 0.0_07, 'token': 1_573, 'token_str': ' Chris'},
							    ]     ,		)
							__lowerCamelCase									=       unmasker('The largest city in France is <mask>'				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {
							            'sequence': 'The largest city in France is Paris',
							            'score': 0.2_51,
							            'token': 2_201,
							            'token_str': ' Paris',
							        },
							        {
							            'sequence': 'The largest city in France is Lyon',
							            'score': 0.2_14,
							            'token': 12_790,
							            'token_str': ' Lyon',
							        },
							    ]     ,		)

							__lowerCamelCase									=       unmasker('My name is <mask>'     ,		targets=[' Patrick', ' Clara', ' Teven']     ,		top_k=3				)
							self.assertEqual(
							    nested_simplify(lowerCamelCase__				)     ,		[
							        {'sequence': 'My name is Patrick', 'score': 0.0_05, 'token': 3_499, 'token_str': ' Patrick'},
							        {'sequence': 'My name is Clara', 'score': 0.0_00, 'token': 13_606, 'token_str': ' Clara'},
							        {'sequence': 'My name is Te', 'score': 0.0_00, 'token': 2_941, 'token_str': ' Te'},
							    ]     ,		)


			@require_torch
			def  lowercase_       ( self				)       -> int:



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


			@require_tf
			def  lowercase_       ( self				)       -> str:



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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[str]:



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

							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       [
							    f"""This is another {tokenizer.mask_token} test""",
							]
							return fill_masker, examples


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



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

							__lowerCamelCase									=       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__				)},
							    ]     ,		)

							__lowerCamelCase									=       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__				)},
							    ]     ,		)

							__lowerCamelCase									=       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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Dict:



							'''simple docstring'''
							__lowerCamelCase									=       tokenizer.get_vocab()
							__lowerCamelCase									=       sorted(vocab.keys()				)[:2]
							# Pipeline argument
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__     ,		targets=lowerCamelCase__				)
							__lowerCamelCase									=       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__				)},
							    ]     ,		)
							__lowerCamelCase									=       {vocab[el] for el in targets}
							self.assertEqual({el['token'] for el in outputs}     ,		lowerCamelCase__				)
							__lowerCamelCase									=       [tokenizer.decode([x]				) for x in target_ids]
							self.assertEqual({el['token_str'] for el in outputs}     ,		set(lowerCamelCase__				)				)

							# Call argument
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       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__				)},
							    ]     ,		)
							__lowerCamelCase									=       {vocab[el] for el in targets}
							self.assertEqual({el['token'] for el in outputs}     ,		lowerCamelCase__				)
							__lowerCamelCase									=       [tokenizer.decode([x]				) for x in target_ids]
							self.assertEqual({el['token_str'] for el in outputs}     ,		set(lowerCamelCase__				)				)

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

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


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[int]:



							'''simple docstring'''
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__     ,		top_k=2				)
							__lowerCamelCase									=       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__				)},
							    ]     ,		)

							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       tokenizer.get_vocab()
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)

							# top_k=2, ntargets=3
							__lowerCamelCase									=       sorted(vocab.keys()				)[:3]
							__lowerCamelCase									=       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
							__lowerCamelCase									=       [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__				):
											__lowerCamelCase									=       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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)
							__lowerCamelCase									=       tokenizer.get_vocab()
							# String duplicates + id duplicates
							__lowerCamelCase									=       sorted(vocab.keys()				)[:3]
							__lowerCamelCase									=       [targets[0], targets[1], targets[0], targets[2], targets[1]]
							__lowerCamelCase									=       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  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Union[str, Any]:



							'''simple docstring'''
							__lowerCamelCase									=       FillMaskPipeline(model=lowerCamelCase__     ,		tokenizer=lowerCamelCase__				)

							__lowerCamelCase									=       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__				)},
							        ],
							    ]     ,		)


