


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


_snake_case  :	Optional[int]			      =       logging.get_logger(__name__)

@add_end_docstrings(
    _lowerCAmelCase  ,       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        a	(_lowerCAmelCase   ):




							"""simple docstring"""


							def 					__snake_case      (							self :						Dict					,			lowerCamelCase :						GenericTensor       )			->					np.ndarray:
														if self.framework == "tf":
																					__snake_case	:    Any											=     tf.where(input_ids == self.tokenizer.mask_token_id       ).numpy()
														elif self.framework == "pt":
																					__snake_case	:    Any											=     torch.nonzero(input_ids == self.tokenizer.mask_token_id					,			as_tuple=lowerCamelCase       )
														else:
																					raise ValueError("Unsupported framework"       )
														return masked_index

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						GenericTensor       )			->					np.ndarray:
														__snake_case	:    List[str]											=     self.get_masked_index(lowerCamelCase       )
														__snake_case	:    Optional[int]											=     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 					__snake_case      (							self :						List[Any]					,			lowerCamelCase :						GenericTensor       )			->					Union[str, Any]:
														if isinstance(lowerCamelCase					,			lowerCamelCase       ):
																					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(lowerCamelCase       )

							def 					__snake_case      (							self :						Optional[Any]					,			lowerCamelCase :						List[Any]					,			lowerCamelCase :						str=None					,			**lowerCamelCase :						Any       )			->					Dict[str, GenericTensor]:
														if return_tensors is None:
																					__snake_case	:    int											=     self.framework
														__snake_case	:    Dict											=     self.tokenizer(lowerCamelCase					,			return_tensors=lowerCamelCase       )
														self.ensure_exactly_one_mask_token(lowerCamelCase       )
														return model_inputs

							def 					__snake_case      (							self :						Union[str, Any]					,			lowerCamelCase :						Dict       )			->					Tuple:
														__snake_case	:    Tuple											=     self.model(**lowerCamelCase       )
														__snake_case	:    Any											=     model_inputs["input_ids"]
														return model_outputs

							def 					__snake_case      (							self :						Optional[int]					,			lowerCamelCase :						Union[str, Any]					,			lowerCamelCase :						Optional[int]=5					,			lowerCamelCase :						str=None       )			->					Union[str, Any]:
														# Cap top_k if there are targets
														if target_ids is not None and target_ids.shape[0] < top_k:
																					__snake_case	:    List[Any]											=     target_ids.shape[0]
														__snake_case	:    int											=     model_outputs["input_ids"][0]
														__snake_case	:    Any											=     model_outputs["logits"]

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

																					__snake_case	:    Dict											=     outputs.numpy()

																					__snake_case	:    int											=     outputs[0, masked_index, :]
																					__snake_case	:    Dict											=     stable_softmax(lowerCamelCase					,			axis=-1       )
																					if target_ids is not None:
																												__snake_case	:    List[Any]											=     tf.gather_nd(tf.squeeze(lowerCamelCase					,			0       )					,			target_ids.reshape(-1					,			1       )       )
																												__snake_case	:    str											=     tf.expand_dims(lowerCamelCase					,			0       )

																					__snake_case	:    Optional[Any]											=     tf.math.top_k(lowerCamelCase					,			k=lowerCamelCase       )
																					__snake_case				,    __snake_case	:    Union[str, Any]											=     topk.values.numpy(), topk.indices.numpy()
														else:
																					__snake_case	:    Optional[Any]											=     torch.nonzero(input_ids == self.tokenizer.mask_token_id					,			as_tuple=lowerCamelCase       ).squeeze(-1       )
																					# Fill mask pipeline supports only one ${mask_token} per sample

																					__snake_case	:    List[Any]											=     outputs[0, masked_index, :]
																					__snake_case	:    int											=     logits.softmax(dim=-1       )
																					if target_ids is not None:
																												__snake_case	:    List[str]											=     probs[..., target_ids]

																					__snake_case				,    __snake_case	:    Optional[Any]											=     probs.topk(lowerCamelCase       )

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

																												__snake_case	:    List[Any]											=     p
																												# Filter padding out:
																												__snake_case	:    List[str]											=     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
																												__snake_case	:    Any											=     self.tokenizer.decode(lowerCamelCase					,			skip_special_tokens=lowerCamelCase       )
																												__snake_case	:    Any											=     {"score": v, "token": p, "token_str": self.tokenizer.decode([p]       ), "sequence": sequence}
																												row.append(lowerCamelCase       )
																					result.append(lowerCamelCase       )
														if single_mask:
																					return result[0]
														return result

							def 					__snake_case      (							self :						Tuple					,			lowerCamelCase :						str					,			lowerCamelCase :						List[Any]=None       )			->					Dict:
														if isinstance(lowerCamelCase					,			lowerCamelCase       ):
																					__snake_case	:    Dict											=     [targets]
														try:
																					__snake_case	:    Union[str, Any]											=     self.tokenizer.get_vocab()
														except Exception:
																					__snake_case	:    Optional[Any]											=     {}
														__snake_case	:    List[str]											=     []
														for target in targets:
																					__snake_case	:    Optional[Any]											=     vocab.get(lowerCamelCase					,			lowerCamelCase       )
																					if id_ is None:
																												__snake_case	:    Dict											=     self.tokenizer(
																												    lowerCamelCase					,			add_special_tokens=lowerCamelCase					,			return_attention_mask=lowerCamelCase					,			return_token_type_ids=lowerCamelCase					,			max_length=1					,			truncation=lowerCamelCase					,			)["input_ids"]
																												if len(lowerCamelCase       ) == 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
																												__snake_case	:    Tuple											=     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_       )
														__snake_case	:    int											=     list(set(lowerCamelCase       )       )
														if len(lowerCamelCase       ) == 0:
																					raise ValueError("At least one target must be provided when passed."       )
														__snake_case	:    List[Any]											=     np.array(lowerCamelCase       )
														return target_ids

							def 					__snake_case      (							self :						List[str]					,			lowerCamelCase :						List[str]=None					,			lowerCamelCase :						Tuple=None       )			->					List[Any]:
														__snake_case	:    Dict											=     {}

														if targets is not None:
																					__snake_case	:    List[Any]											=     self.get_target_ids(lowerCamelCase					,			lowerCamelCase       )
																					__snake_case	:    str											=     target_ids

														if top_k is not None:
																					__snake_case	:    Optional[int]											=     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 :						Tuple					,			lowerCamelCase :						Tuple					,			*lowerCamelCase :						Tuple					,			**lowerCamelCase :						Optional[Any]       )			->					Optional[Any]:
														__snake_case	:    Tuple											=     super().__call__(lowerCamelCase					,			**lowerCamelCase       )
														if isinstance(lowerCamelCase					,			lowerCamelCase       ) and len(lowerCamelCase       ) == 1:
																					return outputs[0]
														return outputs





import math







def        lowerCAmelCase_  (      __lowerCamelCase							,			__lowerCamelCase			):

							if initial_intensity < 0:
														raise ValueError("The value of intensity cannot be negative"			)
														# handling of negative values of initial intensity
							if angle < 0 or angle > 3_6_0:
														raise ValueError("In Malus Law, the angle is in the range 0-360 degrees"			)
														# handling of values out of allowed range
							return initial_intensity * (math.cos(math.radians(__lowerCamelCase			)			) ** 2)


if __name__ == "__main__":
			import doctest

			doctest.testmod(name="malus_law")

