



from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
							import tensorflow as tf

							from ..tf_utils import stable_softmax


if is_torch_available():
							import torch


SCREAMING_SNAKE_CASE__       =      logging.get_logger(__name__)


@add_end_docstrings(
    UpperCAmelCase_							,     R"\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    "							,     )
class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Union[str, Any]				,					_snake_case					:       GenericTensor					):






								"""simple docstring"""


								if self.framework == "tf":
											A__       =    tf.where(input_ids == self.tokenizer.mask_token_id					).numpy()
								elif self.framework == "pt":
											A__       =    torch.nonzero(input_ids == self.tokenizer.mask_token_id				,					as_tuple=_snake_case					)
								else:
											raise ValueError('Unsupported framework'					)
								return masked_index



					def     _a							(     self					:       List[Any]				,					_snake_case					:       GenericTensor					):






								"""simple docstring"""


								A__       =    self.get_masked_index(_snake_case					)
								A__       =    np.prod(masked_index.shape					)
								if numel < 1:
											raise PipelineException(
											    'fill-mask'				,					self.model.base_model_prefix				,					F'''No mask_token ({self.tokenizer.mask_token}) found on the input'''				,					)



					def     _a							(     self					:       Any				,					_snake_case					:       GenericTensor					):






								"""simple docstring"""


								if isinstance(_snake_case				,					_snake_case					):
											for model_input in model_inputs:
														self._ensure_exactly_one_mask_token(model_input['input_ids'][0]					)
								else:
											for input_ids in model_inputs["input_ids"]:
														self._ensure_exactly_one_mask_token(_snake_case					)



					def     _a							(     self					:       str				,					_snake_case					:       Tuple				,					_snake_case					:       Union[str, Any]=None				,					**_snake_case					:       Dict					):






								"""simple docstring"""


								if return_tensors is None:
											A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case					)
								self.ensure_exactly_one_mask_token(_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Union[str, Any]					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								A__       =    model_inputs['input_ids']
								return model_outputs



					def     _a							(     self					:       int				,					_snake_case					:       Any				,					_snake_case					:       Dict=5				,					_snake_case					:       List[str]=None					):






								"""simple docstring"""


								if target_ids is not None and target_ids.shape[0] < top_k:
											A__       =    target_ids.shape[0]
								A__       =    model_outputs['input_ids'][0]
								A__       =    model_outputs['logits']

								if self.framework == "tf":
											A__       =    tf.where(input_ids == self.tokenizer.mask_token_id					).numpy()[:, 0]

											A__       =    outputs.numpy()

											A__       =    outputs[0, masked_index, :]
											A__       =    stable_softmax(_snake_case				,					axis=-1					)
											if target_ids is not None:
														A__       =    tf.gather_nd(tf.squeeze(_snake_case				,					0					)				,					target_ids.reshape(-1				,					1					)					)
														A__       =    tf.expand_dims(_snake_case				,					0					)

											A__       =    tf.math.top_k(_snake_case				,					k=_snake_case					)
											A__     , A__       =    topk.values.numpy(), topk.indices.numpy()
								else:
											A__       =    torch.nonzero(input_ids == self.tokenizer.mask_token_id				,					as_tuple=_snake_case					).squeeze(-1					)
											# Fill mask pipeline supports only one ${mask_token} per sample

											A__       =    outputs[0, masked_index, :]
											A__       =    logits.softmax(dim=-1					)
											if target_ids is not None:
														A__       =    probs[..., target_ids]

											A__     , A__       =    probs.topk(_snake_case					)

								A__       =    []
								A__       =    values.shape[0] == 1
								for i, (_values, _predictions) in enumerate(zip(values.tolist()				,					predictions.tolist()					)					):
											A__       =    []
											for v, p in zip(_values				,					_predictions					):
														# Copy is important since we're going to modify this array in place
														A__       =    input_ids.numpy().copy()
														if target_ids is not None:
																	A__       =    target_ids[p].tolist()

														A__       =    p
														# Filter padding out:
														A__       =    tokens[np.where(tokens != self.tokenizer.pad_token_id					)]
														# Originally we skip special tokens to give readable output.
														# For multi masks though, the other [MASK] would be removed otherwise
														# making the output look odd, so we add them back
														A__       =    self.tokenizer.decode(_snake_case				,					skip_special_tokens=_snake_case					)
														A__       =    {'score': v, 'token': p, 'token_str': self.tokenizer.decode([p]					), 'sequence': sequence}
														row.append(_snake_case					)
											result.append(_snake_case					)
								if single_mask:
											return result[0]
								return result



					def     _a							(     self					:       List[str]				,					_snake_case					:       List[str]				,					_snake_case					:       List[Any]=None					):






								"""simple docstring"""


								if isinstance(_snake_case				,					_snake_case					):
											A__       =    [targets]
								try:
											A__       =    self.tokenizer.get_vocab()
								except Exception:
											A__       =    {}
								A__       =    []
								for target in targets:
											A__       =    vocab.get(_snake_case				,					_snake_case					)
											if id_ is None:
														A__       =    self.tokenizer(
														    _snake_case				,					add_special_tokens=_snake_case				,					return_attention_mask=_snake_case				,					return_token_type_ids=_snake_case				,					max_length=1				,					truncation=_snake_case				,					)['input_ids']
														if len(_snake_case					) == 0:
																	logger.warning(
																	    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
																	    'We cannot replace it with anything meaningful, ignoring it'					)
																	continue
														A__       =    input_ids[0]
														# XXX: If users encounter this pass
														# it becomes pretty slow, so let's make sure
														# The warning enables them to fix the input to
														# get faster performance.
														logger.warning(
														    F'''The specified target token `{target}` does not exist in the model vocabulary. '''
														    F'''Replacing with `{self.tokenizer.convert_ids_to_tokens(id_					)}`.'''					)
											target_ids.append(id_					)
								A__       =    list(set(_snake_case					)					)
								if len(_snake_case					) == 0:
											raise ValueError('At least one target must be provided when passed.'					)
								A__       =    np.array(_snake_case					)
								return target_ids



					def     _a							(     self					:       List[Any]				,					_snake_case					:       List[Any]=None				,					_snake_case					:       Any=None					):






								"""simple docstring"""


								A__       =    {}

								if targets is not None:
											A__       =    self.get_target_ids(_snake_case				,					_snake_case					)
											A__       =    target_ids

								if top_k is not None:
											A__       =    top_k

								if self.tokenizer.mask_token_id is None:
											raise PipelineException(
											    'fill-mask'				,					self.model.base_model_prefix				,					'The tokenizer does not define a `mask_token`.'					)
								return {}, {}, postprocess_params



					def __call__(     self					:       Any				,					_snake_case					:       Optional[int]				,					*_snake_case					:       List[Any]				,					**_snake_case					:       Any					):






								"""simple docstring"""


								A__       =    super().__call__(_snake_case				,					**_snake_case					)
								if isinstance(_snake_case				,					_snake_case					) and len(_snake_case					) == 1:
											return outputs[0]
								return outputs









import pytest

import datasets


# Import fixture modules as plugins
SCREAMING_SNAKE_CASE__       =      ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']


def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Optional[int]:
			# Mark tests as "unit" by default if not marked as "integration" (or already marked as "unit")
			for item in items:
						if any(marker in item.keywords for marker in ['integration', 'unit']					):
									continue
						item.add_marker(pytest.mark.unit					)


def A						(       __UpperCamelCase					)						->      str:
			config.addinivalue_line('markers' ,	'torchaudio_latest: mark test to run with torchaudio>=0.12'					)


@pytest.fixture(autouse=__UpperCamelCase					)
def A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Union[str, Any]:
			# test_hf_cache_home = tmp_path_factory.mktemp("cache")  # TODO: why a cache dir per test function does not work?
			A__       =    tmp_path_factory.getbasetemp() / 'cache'
			A__       =    test_hf_cache_home / 'datasets'
			A__       =    test_hf_cache_home / 'metrics'
			A__       =    test_hf_cache_home / 'modules'
			monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' ,	str(__UpperCamelCase					)					)
			monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' ,	str(__UpperCamelCase					)					)
			monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' ,	str(__UpperCamelCase					)					)
			A__       =    test_hf_datasets_cache / 'downloads'
			monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' ,	str(__UpperCamelCase					)					)
			A__       =    test_hf_datasets_cache / 'downloads' / 'extracted'
			monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' ,	str(__UpperCamelCase					)					)


@pytest.fixture(autouse=__UpperCamelCase ,	scope='session'					)
def A						(       )						->      Union[str, Any]:
			datasets.disable_progress_bar()


@pytest.fixture(autouse=__UpperCamelCase					)
def A						(       __UpperCamelCase					)						->      int:
			# don't take tests into account when counting downloads
			monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' ,	__UpperCamelCase					)






@pytest.fixture
def A						(       __UpperCamelCase					)						->      Any:
			# Required to suppress RemovedIn20Warning when feature(s) are not compatible with SQLAlchemy 2.0
			# To be removed once SQLAlchemy 2.0 supported
			monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' ,	__UpperCamelCase					)




