




from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union

import torch
import torch.nn as nn

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder







@dataclass
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			42







class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__					):

			"""simple docstring"""







			snake_case_  						=			True


			@register_to_config
			def __init__( self     ,		lowerCamelCase__ = 3     ,		lowerCamelCase__ = 3     ,		lowerCamelCase__ = ("DownEncoderBlock2D",)     ,		lowerCamelCase__ = ("UpDecoderBlock2D",)     ,		lowerCamelCase__ = (64,)     ,		lowerCamelCase__ = 1     ,		lowerCamelCase__ = "silu"     ,		lowerCamelCase__ = 4     ,		lowerCamelCase__ = 32     ,		lowerCamelCase__ = 32     ,		lowerCamelCase__ = 0.1_82_15     ,		)       -> str:



							'''simple docstring'''
							super().__init__()

							# pass init params to Encoder
							__lowerCamelCase									=       Encoder(
							    in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		down_block_types=lowerCamelCase__     ,		block_out_channels=lowerCamelCase__     ,		layers_per_block=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		norm_num_groups=lowerCamelCase__     ,		double_z=lowerCamelCase__     ,		)

							# pass init params to Decoder
							__lowerCamelCase									=       Decoder(
							    in_channels=lowerCamelCase__     ,		out_channels=lowerCamelCase__     ,		up_block_types=lowerCamelCase__     ,		block_out_channels=lowerCamelCase__     ,		layers_per_block=lowerCamelCase__     ,		norm_num_groups=lowerCamelCase__     ,		act_fn=lowerCamelCase__     ,		)

							__lowerCamelCase									=       nn.Convad(2 * latent_channels     ,		2 * latent_channels     ,		1				)
							__lowerCamelCase									=       nn.Convad(lowerCamelCase__     ,		lowerCamelCase__     ,		1				)

							__lowerCamelCase									=       False
							__lowerCamelCase									=       False

							# only relevant if vae tiling is enabled
							__lowerCamelCase									=       self.config.sample_size
							__lowerCamelCase									=       (
							    self.config.sample_size[0]
							    if isinstance(self.config.sample_size     ,		(list, tuple)				)
							    else self.config.sample_size
							)
							__lowerCamelCase									=       int(sample_size / (2 ** (len(self.config.block_out_channels				) - 1))				)
							__lowerCamelCase									=       0.25


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__=False				)       -> str:



							'''simple docstring'''
							if isinstance(lowerCamelCase__     ,		(Encoder, Decoder)				):
											__lowerCamelCase									=       value


			def  lowercase_       ( self     ,		lowerCamelCase__ = True				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       use_tiling


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



							'''simple docstring'''
							self.enable_tiling(lowerCamelCase__				)


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



							'''simple docstring'''
							__lowerCamelCase									=       True


			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       False


			@property
			# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
			def  lowercase_       ( self				)       -> Dict[str, AttentionProcessor]:



							'''simple docstring'''
							__lowerCamelCase									=       {}

							def fn_recursive_add_processors(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				):
											if hasattr(lowerCamelCase__     ,		'set_processor'				):
															__lowerCamelCase									=       module.processor

											for sub_name, child in module.named_children():
															fn_recursive_add_processors(f"""{name}.{sub_name}"""     ,		lowerCamelCase__     ,		lowerCamelCase__				)

											return processors

							for name, module in self.named_children():
											fn_recursive_add_processors(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)

							return processors


			def  lowercase_       ( self     ,		lowerCamelCase__				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       len(self.attn_processors.keys()				)

							if isinstance(lowerCamelCase__     ,		lowerCamelCase__				) and len(lowerCamelCase__				) != count:
											raise ValueError(
											    f"""A dict of processors was passed, but the number of processors {len(lowerCamelCase__				)} does not match the"""
											    f""" number of attention layers: {count}. Please make sure to pass {count} processor classes."""				)

							def fn_recursive_attn_processor(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				):
											if hasattr(lowerCamelCase__     ,		'set_processor'				):
															if not isinstance(lowerCamelCase__     ,		lowerCamelCase__				):
																			module.set_processor(lowerCamelCase__				)
															else:
																			module.set_processor(processor.pop(f"""{name}.processor"""				)				)

											for sub_name, child in module.named_children():
															fn_recursive_attn_processor(f"""{name}.{sub_name}"""     ,		lowerCamelCase__     ,		lowerCamelCase__				)

							for name, module in self.named_children():
											fn_recursive_attn_processor(lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							self.set_attn_processor(AttnProcessor()				)


			@apply_forward_hook
			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = True				)       -> AutoencoderKLOutput:



							'''simple docstring'''
							if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
											return self.tiled_encode(lowerCamelCase__     ,		return_dict=lowerCamelCase__				)

							if self.use_slicing and x.shape[0] > 1:
											__lowerCamelCase									=       [self.encoder(lowerCamelCase__				) for x_slice in x.split(1				)]
											__lowerCamelCase									=       torch.cat(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       self.encoder(lowerCamelCase__				)

							__lowerCamelCase									=       self.quant_conv(lowerCamelCase__				)
							__lowerCamelCase									=       DiagonalGaussianDistribution(lowerCamelCase__				)

							if not return_dict:
											return (posterior,)

							return AutoencoderKLOutput(latent_dist=lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = True				)       -> Union[DecoderOutput, torch.FloatTensor]:



							'''simple docstring'''
							if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
											return self.tiled_decode(lowerCamelCase__     ,		return_dict=lowerCamelCase__				)

							__lowerCamelCase									=       self.post_quant_conv(lowerCamelCase__				)
							__lowerCamelCase									=       self.decoder(lowerCamelCase__				)

							if not return_dict:
											return (dec,)

							return DecoderOutput(sample=lowerCamelCase__				)


			@apply_forward_hook
			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = True				)       -> Union[DecoderOutput, torch.FloatTensor]:



							'''simple docstring'''
							if self.use_slicing and z.shape[0] > 1:
											__lowerCamelCase									=       [self._decode(lowerCamelCase__				).sample for z_slice in z.split(1				)]
											__lowerCamelCase									=       torch.cat(lowerCamelCase__				)
							else:
											__lowerCamelCase									=       self._decode(lowerCamelCase__				).sample

							if not return_dict:
											return (decoded,)

							return DecoderOutput(sample=lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       min(a.shape[2]     ,		b.shape[2]     ,		lowerCamelCase__				)
							for y in range(lowerCamelCase__				):
											__lowerCamelCase									=       a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
							return b


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



							'''simple docstring'''
							__lowerCamelCase									=       min(a.shape[3]     ,		b.shape[3]     ,		lowerCamelCase__				)
							for x in range(lowerCamelCase__				):
											__lowerCamelCase									=       a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
							return b


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = True				)       -> AutoencoderKLOutput:



							'''simple docstring'''
							__lowerCamelCase									=       int(self.tile_sample_min_size * (1 - self.tile_overlap_factor)				)
							__lowerCamelCase									=       int(self.tile_latent_min_size * self.tile_overlap_factor				)
							__lowerCamelCase									=       self.tile_latent_min_size - blend_extent

							# Split the image into 512x512 tiles and encode them separately.
							__lowerCamelCase									=       []
							for i in range(0     ,		x.shape[2]     ,		lowerCamelCase__				):
											__lowerCamelCase									=       []
											for j in range(0     ,		x.shape[3]     ,		lowerCamelCase__				):
															__lowerCamelCase									=       x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
															__lowerCamelCase									=       self.encoder(lowerCamelCase__				)
															__lowerCamelCase									=       self.quant_conv(lowerCamelCase__				)
															row.append(lowerCamelCase__				)
											rows.append(lowerCamelCase__				)
							__lowerCamelCase									=       []
							for i, row in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       []
											for j, tile in enumerate(lowerCamelCase__				):
															# blend the above tile and the left tile
															# to the current tile and add the current tile to the result row
															if i > 0:
																			__lowerCamelCase									=       self.blend_v(rows[i - 1][j]     ,		lowerCamelCase__     ,		lowerCamelCase__				)
															if j > 0:
																			__lowerCamelCase									=       self.blend_h(row[j - 1]     ,		lowerCamelCase__     ,		lowerCamelCase__				)
															result_row.append(tile[:, :, :row_limit, :row_limit]				)
											result_rows.append(torch.cat(lowerCamelCase__     ,		dim=3				)				)

							__lowerCamelCase									=       torch.cat(lowerCamelCase__     ,		dim=2				)
							__lowerCamelCase									=       DiagonalGaussianDistribution(lowerCamelCase__				)

							if not return_dict:
											return (posterior,)

							return AutoencoderKLOutput(latent_dist=lowerCamelCase__				)


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = True				)       -> Union[DecoderOutput, torch.FloatTensor]:



							'''simple docstring'''
							__lowerCamelCase									=       int(self.tile_latent_min_size * (1 - self.tile_overlap_factor)				)
							__lowerCamelCase									=       int(self.tile_sample_min_size * self.tile_overlap_factor				)
							__lowerCamelCase									=       self.tile_sample_min_size - blend_extent

							# Split z into overlapping 64x64 tiles and decode them separately.
							# The tiles have an overlap to avoid seams between tiles.
							__lowerCamelCase									=       []
							for i in range(0     ,		z.shape[2]     ,		lowerCamelCase__				):
											__lowerCamelCase									=       []
											for j in range(0     ,		z.shape[3]     ,		lowerCamelCase__				):
															__lowerCamelCase									=       z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
															__lowerCamelCase									=       self.post_quant_conv(lowerCamelCase__				)
															__lowerCamelCase									=       self.decoder(lowerCamelCase__				)
															row.append(lowerCamelCase__				)
											rows.append(lowerCamelCase__				)
							__lowerCamelCase									=       []
							for i, row in enumerate(lowerCamelCase__				):
											__lowerCamelCase									=       []
											for j, tile in enumerate(lowerCamelCase__				):
															# blend the above tile and the left tile
															# to the current tile and add the current tile to the result row
															if i > 0:
																			__lowerCamelCase									=       self.blend_v(rows[i - 1][j]     ,		lowerCamelCase__     ,		lowerCamelCase__				)
															if j > 0:
																			__lowerCamelCase									=       self.blend_h(row[j - 1]     ,		lowerCamelCase__     ,		lowerCamelCase__				)
															result_row.append(tile[:, :, :row_limit, :row_limit]				)
											result_rows.append(torch.cat(lowerCamelCase__     ,		dim=3				)				)

							__lowerCamelCase									=       torch.cat(lowerCamelCase__     ,		dim=2				)
							if not return_dict:
											return (dec,)

							return DecoderOutput(sample=lowerCamelCase__				)






			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__ = False     ,		lowerCamelCase__ = True     ,		lowerCamelCase__ = None     ,		)       -> Union[DecoderOutput, torch.FloatTensor]:



							'''simple docstring'''
							__lowerCamelCase									=       sample
							__lowerCamelCase									=       self.encode(lowerCamelCase__				).latent_dist
							if sample_posterior:
											__lowerCamelCase									=       posterior.sample(generator=lowerCamelCase__				)
							else:
											__lowerCamelCase									=       posterior.mode()
							__lowerCamelCase									=       self.decode(lowerCamelCase__				).sample

							if not return_dict:
											return (dec,)

							return DecoderOutput(sample=lowerCamelCase__				)








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__				)},
							        ],
							    ]     ,		)


