




"""simple docstring"""




import string

import numpy



def 					a__				(       snake_case__ ,				snake_case__       )		->      int:
			return b if a == 0 else greatest_common_divisor(b % a ,				snake_case__       )






class 					__magic_name__							:






				'''simple docstring'''
				__UpperCamelCase    						=		string.ascii_uppercase + string.digits
				# This cipher takes alphanumerics into account
				# i.e. a total of 36 characters

				# take x and return x % len(key_string)
				__UpperCamelCase    						=		numpy.vectorize(lambda UpperCAmelCase__       : x % 36       )

				__UpperCamelCase    						=		numpy.vectorize(UpperCAmelCase__       )





				def __init__( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.modulus(_a				)  # mod36 calc's on the encrypt key
							self.check_determinant()  # validate the determinant of the encryption key
							lowerCamelCase       =      encrypt_key.shape[0]





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							return self.key_string.index(_a				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							return self.key_string[round(_a				)]





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      round(numpy.linalg.det(self.encrypt_key				)				)

							if det < 0:
										lowerCamelCase       =      det % len(self.key_string				)

							lowerCamelCase       =      len(self.key_string				)
							if greatest_common_divisor(_a	,				len(self.key_string				)				) != 1:
										lowerCamelCase       =      (
										    f'determinant modular {req_l} of encryption key({det}) '
										    f'is not co prime w.r.t {req_l}.\nTry another key.'
										)
										raise ValueError(_a				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      [char for char in text.upper() if char in self.key_string]

							lowerCamelCase       =      chars[-1]
							while len(_a				) % self.break_key != 0:
										chars.append(_a				)

							return "".join(_a				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.process_text(text.upper()				)
							lowerCamelCase       =      """"""

							for i in range(0	,				len(_a				) - self.break_key + 1	,				self.break_key				):
										lowerCamelCase       =      text[i : i + self.break_key]
										lowerCamelCase       =      [self.replace_letters(_a				) for char in batch]
										lowerCamelCase       =      numpy.array([vec]				).T
										lowerCamelCase       =      self.modulus(self.encrypt_key.dot(_a				)				).T.tolist()[
										    0
										]
										lowerCamelCase       =      """""".join(
										    self.replace_digits(_a				) for num in batch_encrypted				)
										encrypted += encrypted_batch

							return encrypted





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							lowerCamelCase       =      round(numpy.linalg.det(self.encrypt_key				)				)

							if det < 0:
										lowerCamelCase       =      det % len(self.key_string				)
							lowerCamelCase       =      None
							for i in range(len(self.key_string				)				):
										if (det * i) % len(self.key_string				) == 1:
													lowerCamelCase       =      i
													break

							lowerCamelCase       =      (
							    det_inv
							    * numpy.linalg.det(self.encrypt_key				)
							    * numpy.linalg.inv(self.encrypt_key				)
							)

							return self.to_int(self.modulus(_a				)				)





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.make_decrypt_key()
							lowerCamelCase       =      self.process_text(text.upper()				)
							lowerCamelCase       =      """"""

							for i in range(0	,				len(_a				) - self.break_key + 1	,				self.break_key				):
										lowerCamelCase       =      text[i : i + self.break_key]
										lowerCamelCase       =      [self.replace_letters(_a				) for char in batch]
										lowerCamelCase       =      numpy.array([vec]				).T
										lowerCamelCase       =      self.modulus(decrypt_key.dot(_a				)				).T.tolist()[0]
										lowerCamelCase       =      """""".join(
										    self.replace_digits(_a				) for num in batch_decrypted				)
										decrypted += decrypted_batch

							return decrypted




def 					a__				(       )		->      None:
			lowerCamelCase       =      int(input("""Enter the order of the encryption key: """       )       )
			lowerCamelCase       =      []

			print("""Enter each row of the encryption key with space separated integers"""       )
			for _ in range(snake_case__       ):
						lowerCamelCase       =      [int(snake_case__       ) for x in input().split()]
						hill_matrix.append(snake_case__       )

			lowerCamelCase       =      HillCipher(numpy.array(snake_case__       )       )

			print("""Would you like to encrypt or decrypt some text? (1 or 2)"""       )
			lowerCamelCase       =      input("""\n1. Encrypt\n2. Decrypt\n"""       )
			if option == "1":
						lowerCamelCase       =      input("""What text would you like to encrypt?: """       )
						print("""Your encrypted text is:"""       )
						print(hc.encrypt(snake_case__       )       )
			elif option == "2":
						lowerCamelCase       =      input("""What text would you like to decrypt?: """       )
						print("""Your decrypted text is:"""       )
						print(hc.decrypt(snake_case__       )       )


if __name__ == "__main__":
					import doctest

					doctest.testmod()

					main()









"""simple docstring"""




import uuid
from typing import Any, Dict, List, Optional, Union

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


if is_tf_available():
					import tensorflow as tf

if is_torch_available():
					import torch


lowerCAmelCase							:    List[str]  							=							logging.get_logger(__name__)






class 					__magic_name__							:






				'''simple docstring'''





				def __init__( self	,				_a = None	,				_a = None	,				_a=None	,				_a=None				):





							"""simple docstring"""
							if not conversation_id:
										lowerCamelCase       =      uuid.uuida()
							if past_user_inputs is None:
										lowerCamelCase       =      []
							if generated_responses is None:
										lowerCamelCase       =      []

							lowerCamelCase       =      conversation_id
							lowerCamelCase       =      past_user_inputs
							lowerCamelCase       =      generated_responses
							lowerCamelCase       =      text





				def __eq__( self	,				_a				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										return False
							if self.uuid == other.uuid:
										return True
							return (
							    self.new_user_input == other.new_user_input
							    and self.past_user_inputs == other.past_user_inputs
							    and self.generated_responses == other.generated_responses
							)





				def      _lowerCAmelCase       ( self	,				_a	,				_a = False				):





							"""simple docstring"""
							if self.new_user_input:
										if overwrite:
													logger.warning(
													    f'User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '
													    f'with: "{text}".'				)
													lowerCamelCase       =      text
										else:
													logger.warning(
													    f'User input added while unprocessed input was existing: "{self.new_user_input}" new input '
													    f'ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input'				)
							else:
										lowerCamelCase       =      text





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							if self.new_user_input:
										self.past_user_inputs.append(self.new_user_input				)
							lowerCamelCase       =      None





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							self.generated_responses.append(_a				)





				def      _lowerCAmelCase       ( self				):





							"""simple docstring"""
							for user_input, generated_response in zip(self.past_user_inputs	,				self.generated_responses				):
										yield True, user_input
										yield False, generated_response
							if self.new_user_input:
										yield True, self.new_user_input





				def __repr__( self				):





							"""simple docstring"""
							lowerCamelCase       =      f'Conversation id: {self.uuid} \n'
							for is_user, text in self.iter_texts():
										lowerCamelCase       =      """user""" if is_user else """bot"""
										output += f'{name} >> {text} \n'
							return output






@add_end_docstrings(
    UpperCAmelCase__		,      R"\n        min_length_for_response (`int`, *optional*, defaults to 32):\n            The minimum length (in number of tokens) for a response.\n        minimum_tokens (`int`, *optional*, defaults to 10):\n            The minimum length of tokens to leave for a response.\n    "		,      )
class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def __init__( self	,				*_a	,				**_a				):





							"""simple docstring"""
							super().__init__(*_a	,				**_a				)
							if self.tokenizer.pad_token_id is None:
										lowerCamelCase       =      self.tokenizer.eos_token





				def      _lowerCAmelCase       ( self	,				_a=None	,				_a=None	,				_a=None	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}
							lowerCamelCase       =      {}

							if min_length_for_response is not None:
										lowerCamelCase       =      min_length_for_response
							if minimum_tokens is not None:
										lowerCamelCase       =      minimum_tokens

							if "max_length" in generate_kwargs:
										lowerCamelCase       =      generate_kwargs["""max_length"""]
										# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
							if clean_up_tokenization_spaces is not None:
										lowerCamelCase       =      clean_up_tokenization_spaces

							if generate_kwargs:
										forward_params.update(_a				)
							return preprocess_params, forward_params, postprocess_params





				def __call__( self	,				_a	,				_a=0	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      super().__call__(_a	,				num_workers=_a	,				**_a				)
							if isinstance(_a	,				_a				) and len(_a				) == 1:
										return outputs[0]
							return outputs





				def      _lowerCAmelCase       ( self	,				_a	,				_a=32				):





							"""simple docstring"""
							if not isinstance(_a	,				_a				):
										raise ValueError("""ConversationalPipeline, expects Conversation as inputs"""				)
							if conversation.new_user_input is None:
										raise ValueError(
										    f'Conversation with UUID {type(conversation.uuid				)} does not contain new user input to process. '
										    """Add user inputs with the conversation's `add_user_input` method"""				)
							if hasattr(self.tokenizer	,				"""_build_conversation_input_ids"""				):
										lowerCamelCase       =      self.tokenizer._build_conversation_input_ids(_a				)
							else:
										# If the tokenizer cannot handle conversations, we default to only the old version
										lowerCamelCase       =      self._legacy_parse_and_tokenize(_a				)

							if self.framework == "pt":
										lowerCamelCase       =      torch.LongTensor([input_ids]				)
							elif self.framework == "tf":
										lowerCamelCase       =      tf.constant([input_ids]				)
							return {"input_ids": input_ids, "conversation": conversation}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=10	,				**_a				):





							"""simple docstring"""
							lowerCamelCase       =      generate_kwargs.get("""max_length"""	,				self.model.config.max_length				)

							lowerCamelCase       =      model_inputs["""input_ids"""].shape[1]
							if max_length - minimum_tokens < n:
										logger.warning(f'Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})'				)
										lowerCamelCase       =      max_length - minimum_tokens
										lowerCamelCase       =      model_inputs["""input_ids"""][:, -trim:]
										if "attention_mask" in model_inputs:
													lowerCamelCase       =      model_inputs["""attention_mask"""][:, -trim:]
							lowerCamelCase       =      model_inputs.pop("""conversation"""				)
							lowerCamelCase       =      max_length
							lowerCamelCase       =      self.model.generate(**_a	,				**_a				)
							if self.model.config.is_encoder_decoder:
										lowerCamelCase       =      1
							else:
										lowerCamelCase       =      n
							return {"output_ids": output_ids[:, start_position:], "conversation": conversation}





				def      _lowerCAmelCase       ( self	,				_a	,				_a=True				):





							"""simple docstring"""
							lowerCamelCase       =      model_outputs["""output_ids"""]
							lowerCamelCase       =      self.tokenizer.decode(
							    output_ids[0]	,				skip_special_tokens=_a	,				clean_up_tokenization_spaces=_a	,				)
							lowerCamelCase       =      model_outputs["""conversation"""]
							conversation.mark_processed()
							conversation.append_response(_a				)
							return conversation





				def      _lowerCAmelCase       ( self	,				_a				):





							"""simple docstring"""
							lowerCamelCase       =      self.tokenizer.eos_token_id
							lowerCamelCase       =      []
							for is_user, text in conversation.iter_texts():
										if eos_token_id is not None:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				) + [eos_token_id]				)
										else:
													input_ids.extend(self.tokenizer.encode(_a	,				add_special_tokens=_a				)				)

							if len(_a				) > self.tokenizer.model_max_length:
										lowerCamelCase       =      input_ids[-self.tokenizer.model_max_length :]
							return input_ids



