
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


a											=							logging.get_logger(__name__)


@add_end_docstrings(
    snake_case__			,  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 	UpperCAmelCase_ (snake_case__							):
	"""simple docstring"""

	def       SCREAMING_SNAKE_CASE__     (     self:     Optional[int]							,   _UpperCAmelCase:     GenericTensor			):
								if self.framework == "tf":
															_lowerCAmelCase :List[str]      	=						tf.where(input_ids == self.tokenizer.mask_token_id			).numpy()
								elif self.framework == "pt":
															_lowerCAmelCase :Optional[int]      	=						torch.nonzero(input_ids == self.tokenizer.mask_token_id							,   as_tuple=snake_case_			)
								else:
															raise ValueError('Unsupported framework'			)
								return masked_index







	def       SCREAMING_SNAKE_CASE__     (     self:     List[Any]							,   _UpperCAmelCase:     GenericTensor			):
								_lowerCAmelCase :List[Any]      	=						self.get_masked_index(snake_case_			)
								_lowerCAmelCase :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       SCREAMING_SNAKE_CASE__     (     self:     int							,   _UpperCAmelCase:     GenericTensor			):
								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       SCREAMING_SNAKE_CASE__     (     self:     Optional[Any]							,   _UpperCAmelCase:     Union[str, Any]							,   _UpperCAmelCase:     str=None							,   **_UpperCAmelCase:     Optional[int]			):
								if return_tensors is None:
															_lowerCAmelCase :Tuple      	=						self.framework
								_lowerCAmelCase :Any      	=						self.tokenizer(snake_case_							,   return_tensors=snake_case_			)
								self.ensure_exactly_one_mask_token(snake_case_			)
								return model_inputs







	def       SCREAMING_SNAKE_CASE__     (     self:     Tuple							,   _UpperCAmelCase:     List[Any]			):
								_lowerCAmelCase :Optional[int]      	=						self.model(**snake_case_			)
								_lowerCAmelCase :str      	=						model_inputs['input_ids']
								return model_outputs







	def       SCREAMING_SNAKE_CASE__     (     self:     Optional[int]							,   _UpperCAmelCase:     List[Any]							,   _UpperCAmelCase:     str=5							,   _UpperCAmelCase:     List[str]=None			):
								# Cap top_k if there are targets
								if target_ids is not None and target_ids.shape[0] < top_k:
															_lowerCAmelCase :Optional[Any]      	=						target_ids.shape[0]
								_lowerCAmelCase :List[str]      	=						model_outputs['input_ids'][0]
								_lowerCAmelCase :List[str]      	=						model_outputs['logits']

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

															_lowerCAmelCase :Dict      	=						outputs.numpy()

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

															_lowerCAmelCase :str      	=						tf.math.top_k(snake_case_							,   k=snake_case_			)
															_lowerCAmelCase	,  _lowerCAmelCase :Tuple      	=						topk.values.numpy(), topk.indices.numpy()
								else:
															_lowerCAmelCase :int      	=						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

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

															_lowerCAmelCase	,  _lowerCAmelCase :Any      	=						probs.topk(snake_case_			)

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

																						_lowerCAmelCase :Any      	=						p
																						# Filter padding out:
																						_lowerCAmelCase :Optional[int]      	=						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
																						_lowerCAmelCase :Tuple      	=						self.tokenizer.decode(snake_case_							,   skip_special_tokens=snake_case_			)
																						_lowerCAmelCase :Optional[Any]      	=						{'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       SCREAMING_SNAKE_CASE__     (     self:     Any							,   _UpperCAmelCase:     Optional[Any]							,   _UpperCAmelCase:     int=None			):
								if isinstance(snake_case_							,   snake_case_			):
															_lowerCAmelCase :List[Any]      	=						[targets]
								try:
															_lowerCAmelCase :List[Any]      	=						self.tokenizer.get_vocab()
								except Exception:
															_lowerCAmelCase :int      	=						{}
								_lowerCAmelCase :Optional[int]      	=						[]
								for target in targets:
															_lowerCAmelCase :Dict      	=						vocab.get(snake_case_							,   snake_case_			)
															if id_ is None:
																						_lowerCAmelCase :List[Any]      	=						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
																						_lowerCAmelCase :List[str]      	=						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_			)
								_lowerCAmelCase :Optional[int]      	=						list(set(snake_case_			)			)
								if len(snake_case_			) == 0:
															raise ValueError('At least one target must be provided when passed.'			)
								_lowerCAmelCase :List[str]      	=						np.array(snake_case_			)
								return target_ids







	def       SCREAMING_SNAKE_CASE__     (     self:     Union[str, Any]							,   _UpperCAmelCase:     str=None							,   _UpperCAmelCase:     List[Any]=None			):
								_lowerCAmelCase :Tuple      	=						{}

								if targets is not None:
															_lowerCAmelCase :Union[str, Any]      	=						self.get_target_ids(snake_case_							,   snake_case_			)
															_lowerCAmelCase :Tuple      	=						target_ids

								if top_k is not None:
															_lowerCAmelCase :str      	=						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:     List[Any]							,   _UpperCAmelCase:     List[str]							,   *_UpperCAmelCase:     Dict							,   **_UpperCAmelCase:     Any			):
								_lowerCAmelCase :int      	=						super().__call__(snake_case_							,   **snake_case_			)
								if isinstance(snake_case_							,   snake_case_			) and len(snake_case_			) == 1:
															return outputs[0]
								return outputs




from __future__ import annotations






class     __a			:



	def __init__(    self				:     Optional[int]					,						snake_case_				:     int = 0)->				List[str]:
						__lowerCAmelCase						 =key



	def 		UpperCamelCase				(    self				:     Any					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				list[str]:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key is an appropriate size
						key %= 2_55

						return [chr(ord(snake_case_) ^ key) for ch in content]



	def 		UpperCamelCase				(    self				:     str					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				str:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						__lowerCAmelCase						 =key or self.__key or 1

						# make sure key can be any size
						while key > 2_55:
											key -= 2_55

						# This will be returned
						__lowerCAmelCase						 =""""""

						for ch in content:
											ans += chr(ord(snake_case_) ^ key)

						return ans



	def 		UpperCamelCase				(    self				:     int					,						snake_case_				:     str					,						snake_case_				:     int = 0)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""encrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.encrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True



	def 		UpperCamelCase				(    self				:     Tuple					,						snake_case_				:     str					,						snake_case_				:     int)->				bool:
						assert isinstance(snake_case_					,						snake_case_) and isinstance(snake_case_					,						snake_case_)

						try:
											with open(snake_case_) as fin, open("""decrypt.out"""					,						"""w+""") as fout:
																# actual encrypt-process
																for line in fin:
																					fout.write(self.decrypt_string(snake_case_					,						snake_case_))

						except OSError:
											return False

						return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




