

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
			from transformers.pipelines.conversational import Conversation


a_						     =    logging.get_logger(__name__)

a_						     =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

a_						     =    {
    'vocab_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/vocab.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/vocab.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/vocab.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/vocab.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/vocab.json',
    },
    'merges_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/merges.txt',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/merges.txt',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/merges.txt',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/merges.txt',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/merges.txt',
    },
    'tokenizer_file': {
        'gpt2': 'https://huggingface.co/gpt2/resolve/main/tokenizer.json',
        'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json',
        'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/tokenizer.json',
        'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json',
        'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/tokenizer.json',
    },
}

a_						     =    {
    'gpt2': 1024,
    'gpt2-medium': 1024,
    'gpt2-large': 1024,
    'gpt2-xl': 1024,
    'distilgpt2': 1024,
}






class _UpperCamelCase							(							__A   ):
		'''simple docstring'''







		lowerCamelCase__           =VOCAB_FILES_NAMES
		lowerCamelCase__           =PRETRAINED_VOCAB_FILES_MAP
		lowerCamelCase__           =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		lowerCamelCase__           =['input_ids', 'attention_mask']
		lowerCamelCase__           =GPTaTokenizer
		def __init__(			self							: str      ,      a							: Tuple=None      ,      a							: List[Any]=None      ,      a							: List[Any]=None      ,      a							: Any="<|endoftext|>"      ,      a							: str="<|endoftext|>"      ,      a							: Any="<|endoftext|>"      ,      a							: Union[str, Any]=False      ,      **a							: int      ,      )							->       Union[str, Any]:

			"""simple docstring"""

			super().__init__(
			    a      ,      a      ,      tokenizer_file=a      ,      unk_token=a      ,      bos_token=a      ,      eos_token=a      ,      add_prefix_space=a      ,      **a      ,      )

			SCREAMING_SNAKE_CASE   :      Tuple				     =       kwargs.pop("add_bos_token"      ,      a	)

			SCREAMING_SNAKE_CASE   :      Dict				     =       json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
			if pre_tok_state.get("add_prefix_space"      ,      a	) != add_prefix_space:
				SCREAMING_SNAKE_CASE   :      Tuple				     =       getattr(a      ,      pre_tok_state.pop("type"	)	)
				SCREAMING_SNAKE_CASE   :      List[str]				     =       add_prefix_space
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       pre_tok_class(**a	)

			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       add_prefix_space
		def 							__UpperCamelCase (			self							: Optional[int]      ,      *a							: Tuple      ,      **a							: List[Any]	)							->       BatchEncoding:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       kwargs.get("is_split_into_words"      ,      a	)
			assert self.add_prefix_space or not is_split_into_words, (
			    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
			    "to use it with pretokenized inputs."
			)

			return super()._batch_encode_plus(*a      ,      **a	)
		def 							__UpperCamelCase (			self							: List[str]      ,      *a							: Any      ,      **a							: Dict	)							->       BatchEncoding:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      str				     =       kwargs.get("is_split_into_words"      ,      a	)

			assert self.add_prefix_space or not is_split_into_words, (
			    F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
			    "to use it with pretokenized inputs."
			)

			return super()._encode_plus(*a      ,      **a	)
		def 							__UpperCamelCase (			self							: str      ,      a							: str      ,      a							: Optional[str] = None	)							->       Tuple[str]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      int				     =       self._tokenizer.model.save(a      ,      name=a	)
			return tuple(a	)






		def 							__UpperCamelCase (			self							: Union[str, Any]      ,      a							: "Conversation"	)							->       List[int]:

			"""simple docstring"""

			SCREAMING_SNAKE_CASE   :      Optional[int]				     =       []
			for is_user, text in conversation.iter_texts():
				input_ids.extend(self.encode(a      ,      add_special_tokens=a	) + [self.eos_token_id]	)

			if len(a	) > self.model_max_length:
				SCREAMING_SNAKE_CASE   :      List[Any]				     =       input_ids[-self.model_max_length :]
			return input_ids


def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	global f  # a global dp table for knapsack
	if f[i][j] < 0:
		if j < wt[i - 1]:
			SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       mf_knapsack(i - 1							,			_a							,			_a							,			_a)
		else:
			SCREAMING_SNAKE_CASE   :      Any				     =       max(
			    mf_knapsack(i - 1							,			_a							,			_a							,			_a)							,			mf_knapsack(i - 1							,			_a							,			_a							,			j - wt[i - 1]) + val[i - 1]							,			)
		SCREAMING_SNAKE_CASE   :      Tuple				     =       val
	return f[i][j]

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a):
	SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       [[0] * (w + 1) for _ in range(n + 1)]

	for i in range(1							,			n + 1):
		for w_ in range(1							,			w + 1):
			if wt[i - 1] <= w_:
				SCREAMING_SNAKE_CASE   :      Union[str, Any]				     =       max(val[i - 1] + dp[i - 1][w_ - wt[i - 1]]							,			dp[i - 1][w_])
			else:
				SCREAMING_SNAKE_CASE   :      int				     =       dp[i - 1][w_]

	return dp[n][w_], dp

def  lowerCamelCase__ (					_a							,			_a							,			_a):
	if not (isinstance(_a							,			(list, tuple)) and isinstance(_a							,			(list, tuple))):
		raise ValueError(
		    "Both the weights and values vectors must be either lists or tuples")

	SCREAMING_SNAKE_CASE   :      int				     =       len(_a)
	if num_items != len(_a):
		SCREAMING_SNAKE_CASE   :      List[str]				     =       (
		    "The number of weights must be the same as the number of values.\n"
		    f"But got {num_items} weights and {len(_a)} values"
		)
		raise ValueError(_a)
	for i in range(_a):
		if not isinstance(wt[i]							,			_a):
			SCREAMING_SNAKE_CASE   :      int				     =       (
			    "All weights must be integers but got weight of "
			    f"type {type(wt[i])} at index {i}"
			)
			raise TypeError(_a)

	SCREAMING_SNAKE_CASE     ,SCREAMING_SNAKE_CASE   :      Tuple				     =       knapsack(_a							,			_a							,			_a							,			_a)
	SCREAMING_SNAKE_CASE   :      set				     =       set()
	_construct_solution(_a							,			_a							,			_a							,			_a							,			_a)

	return optimal_val, example_optional_set

def  lowerCamelCase__ (					_a							,			_a							,			_a							,			_a							,			_a):
	# for the current item i at a maximum weight j to be part of an optimal subset,
	# the optimal value at (i, j) must be greater than the optimal value at (i-1, j).
	# where i - 1 means considering only the previous items at the given maximum weight
	if i > 0 and j > 0:
		if dp[i - 1][j] == dp[i][j]:
			_construct_solution(_a							,			_a							,			i - 1							,			_a							,			_a)
		else:
			optimal_set.add(_a)
			_construct_solution(_a							,			_a							,			i - 1							,			j - wt[i - 1]							,			_a)


if __name__ == "__main__":
			a_						     =    [3, 2, 4, 4]
			a_						     =    [4, 3, 2, 3]
			a_						     =    4
			a_						     =    6
			a_						     =    [[0] * (w + 1)] + [[0] + [-1] * (w + 1) for _ in range(n + 1)]
			a_     ,	a_						     =    knapsack(w, wt, val, n)
			print(optimal_solution)
			print(mf_knapsack(n, wt, val, w))  # switched the n and w

			# testing the dynamic programming problem with example
			# the optimal subset for the above example are items 3 and 4
			a_     ,	a_						     =    knapsack_with_example_solution(w, wt, val)
			assert optimal_solution == 8
			assert optimal_subset == {3, 4}
			print('optimal_value = ', optimal_solution)
			print('An optimal subset corresponding to the optimal value', optimal_subset)