


"""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      :Any	    =		logging.get_logger(__name__)


class 			A__	:


     """simple docstring"""

     def __init__(	self:						Optional[int]     ,		__a:						Optional[int] = None     ,		__a:						Union[str, Any] = None     ,		__a:						Optional[int]=None     ,		__a:						Tuple=None	)->    Tuple:
           if not conversation_id:
                 lowerCamelCase					:					int        =	uuid.uuida()
           if past_user_inputs is None:
                 lowerCamelCase					:					int        =	[]
           if generated_responses is None:
                 lowerCamelCase					:					List[Any]        =	[]

           lowerCamelCase					:					uuid.UUID        =	conversation_id
           lowerCamelCase					:					List[str]        =	past_user_inputs
           lowerCamelCase					:					List[str]        =	generated_responses
           lowerCamelCase					:					Optional[str]        =	text

     def __eq__(	self:						str     ,		__a:						List[str]	)->    Union[str, Any]:
           if not isinstance(lowercase__     ,		lowercase__	):
                 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 		a__						(	self:						Optional[Any]     ,		__a:						List[str]     ,		__a:						str = False	)->    Any:
           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					:					Any        =	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					:					List[Any]        =	text

     def 		a__						(	self:						List[Any]	)->    Any:
           if self.new_user_input:
                 self.past_user_inputs.append(self.new_user_input	)
           lowerCamelCase					:					Dict        =	None

     def 		a__						(	self:						List[Any]     ,		__a:						Optional[int]	)->    List[str]:
           self.generated_responses.append(lowercase__	)

     def 		a__						(	self:						List[Any]	)->    Any:
           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:						str	)->    Dict:
           lowerCamelCase					:					List[str]        =	f'Conversation id: {self.uuid} \n'
           for is_user, text in self.iter_texts():
                 lowerCamelCase					:					Optional[Any]        =	"""user""" if is_user else """bot"""
                 output += f'{name} >> {text} \n'
           return output


@add_end_docstrings(
    _UpperCAmelCase ,     R'''
        min_length_for_response (`int`, *optional*, defaults to 32):
            The minimum length (in number of tokens) for a response.
        minimum_tokens (`int`, *optional*, defaults to 10):
            The minimum length of tokens to leave for a response.
    ''' ,     )
class 			A__	(						_UpperCAmelCase):


     """simple docstring"""

     def __init__(	self:						Optional[Any]     ,		*__a:						Tuple     ,		**__a:						int	)->    Dict:
           super().__init__(*lowercase__     ,		**lowercase__	)
           if self.tokenizer.pad_token_id is None:
                 lowerCamelCase					:					Union[str, Any]        =	self.tokenizer.eos_token

     def 		a__						(	self:						Optional[Any]     ,		__a:						Optional[Any]=None     ,		__a:						List[str]=None     ,		__a:						str=None     ,		**__a:						Any	)->    int:
           lowerCamelCase					:					Tuple        =	{}
           lowerCamelCase					:					Optional[Any]        =	{}
           lowerCamelCase					:					Optional[Any]        =	{}

           if min_length_for_response is not None:
                 lowerCamelCase					:					Dict        =	min_length_for_response
           if minimum_tokens is not None:
                 lowerCamelCase					:					Union[str, Any]        =	minimum_tokens

           if "max_length" in generate_kwargs:
                 lowerCamelCase					:					List[str]        =	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					:					Union[str, Any]        =	clean_up_tokenization_spaces

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

     def __call__(	self:						Union[str, Any]     ,		__a:						Any     ,		__a:						Any=0     ,		**__a:						Dict	)->    List[Any]:
           lowerCamelCase					:					List[str]        =	super().__call__(lowercase__     ,		num_workers=lowercase__     ,		**lowercase__	)
           if isinstance(lowercase__     ,		lowercase__	) and len(lowercase__	) == 1:
                 return outputs[0]
           return outputs

     def 		a__						(	self:						Optional[int]     ,		__a:						Union[str, Any]     ,		__a:						str=32	)->    str:
           if not isinstance(lowercase__     ,		lowercase__	):
                 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					:					str        =	self.tokenizer._build_conversation_input_ids(lowercase__	)
           else:
                 # If the tokenizer cannot handle conversations, we default to only the old version
                 lowerCamelCase					:					Any        =	self._legacy_parse_and_tokenize(lowercase__	)

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

     def 		a__						(	self:						Tuple     ,		__a:						List[str]     ,		__a:						str=10     ,		**__a:						Optional[Any]	)->    Union[str, Any]:
           lowerCamelCase					:					Optional[int]        =	generate_kwargs.get("""max_length"""     ,		self.model.config.max_length	)

           lowerCamelCase					:					str        =	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					:					int        =	max_length - minimum_tokens
                 lowerCamelCase					:					List[Any]        =	model_inputs["""input_ids"""][:, -trim:]
                 if "attention_mask" in model_inputs:
                       lowerCamelCase					:					Union[str, Any]        =	model_inputs["""attention_mask"""][:, -trim:]
           lowerCamelCase					:					int        =	model_inputs.pop("""conversation"""	)
           lowerCamelCase					:					Dict        =	max_length
           lowerCamelCase					:					Tuple        =	self.model.generate(**lowercase__     ,		**lowercase__	)
           if self.model.config.is_encoder_decoder:
                 lowerCamelCase					:					str        =	1
           else:
                 lowerCamelCase					:					List[Any]        =	n
           return {"output_ids": output_ids[:, start_position:], "conversation": conversation}

     def 		a__						(	self:						Optional[int]     ,		__a:						str     ,		__a:						Any=True	)->    Any:
           lowerCamelCase					:					int        =	model_outputs["""output_ids"""]
           lowerCamelCase					:					List[Any]        =	self.tokenizer.decode(
               output_ids[0]     ,		skip_special_tokens=lowercase__     ,		clean_up_tokenization_spaces=lowercase__     ,		)
           lowerCamelCase					:					Optional[Any]        =	model_outputs["""conversation"""]
           conversation.mark_processed()
           conversation.append_response(lowercase__	)
           return conversation







     def 		a__						(	self:						Dict     ,		__a:						Dict	)->    Union[str, Any]:
           lowerCamelCase					:					Dict        =	self.tokenizer.eos_token_id
           lowerCamelCase					:					Optional[int]        =	[]
           for is_user, text in conversation.iter_texts():
                 if eos_token_id is not None:
                       input_ids.extend(self.tokenizer.encode(lowercase__     ,		add_special_tokens=lowercase__	) + [eos_token_id]	)
                 else:
                       input_ids.extend(self.tokenizer.encode(lowercase__     ,		add_special_tokens=lowercase__	)	)

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








"""simple docstring"""


import os




def   snake_case		(     )   ->      Optional[Any]:
      with open(os.path.dirname(UpperCamelCase__    ) + """/grid.txt"""    ) as f:
            lowerCamelCase					:					int        =	[]  # noqa: E741
            for _ in range(20    ):
                  l.append([int(UpperCamelCase__    ) for x in f.readline().split()]    )

            lowerCamelCase					:					Union[str, Any]        =	0

            # right
            for i in range(20    ):
                  for j in range(17    ):
                        lowerCamelCase					:					Dict        =	l[i][j] * l[i][j + 1] * l[i][j + 2] * l[i][j + 3]
                        if temp > maximum:
                              lowerCamelCase					:					Tuple        =	temp

        # down
            for i in range(17    ):
                  for j in range(20    ):
                        lowerCamelCase					:					Any        =	l[i][j] * l[i + 1][j] * l[i + 2][j] * l[i + 3][j]
                        if temp > maximum:
                              lowerCamelCase					:					Optional[Any]        =	temp

        # diagonal 1
            for i in range(17    ):
                  for j in range(17    ):
                        lowerCamelCase					:					List[Any]        =	l[i][j] * l[i + 1][j + 1] * l[i + 2][j + 2] * l[i + 3][j + 3]
                        if temp > maximum:
                              lowerCamelCase					:					List[str]        =	temp

        # diagonal 2
            for i in range(17    ):
                  for j in range(3			,				20    ):
                        lowerCamelCase					:					List[str]        =	l[i][j] * l[i + 1][j - 1] * l[i + 2][j - 2] * l[i + 3][j - 3]
                        if temp > maximum:
                              lowerCamelCase					:					List[Any]        =	temp
            return maximum


if __name__ == "__main__":
    print(solution())




