






"""simple docstring"""
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union

import numpy as np

from ...utils import is_tf_available, is_torch_available, logging


if TYPE_CHECKING:
			if is_torch_available():
						import torch
			if is_tf_available():
						import tensorflow as tf

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer


lowerCamelCase_					=      logging.get_logger(__name__)

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

lowerCamelCase_					=      {
    '''vocab_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''Salesforce/codegen-350M-mono''': (
            '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
        ),
    },
}

lowerCamelCase_					=      {
    '''Salesforce/codegen-350M-mono''': 2048,
}







class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				VOCAB_FILES_NAMES
	__magic_name__	      =				PRETRAINED_VOCAB_FILES_MAP
	__magic_name__	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__magic_name__	      =				['''input_ids''', '''attention_mask''']
	__magic_name__	      =				CodeGenTokenizer





	def __init__(      self					: Optional[int]	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: List[Any]=None	,	lowerCAmelCase_					: List[str]=None	,	lowerCAmelCase_					: List[str]="<|endoftext|>"	,	lowerCAmelCase_					: Dict="<|endoftext|>"	,	lowerCAmelCase_					: List[Any]="<|endoftext|>"	,	lowerCAmelCase_					: List[Any]=False	,	**lowerCAmelCase_					: Tuple	,	)						->			Any:
				super().__init__(
				    lowerCAmelCase_	,	lowerCAmelCase_	,	tokenizer_file=lowerCAmelCase_	,	unk_token=lowerCAmelCase_	,	bos_token=lowerCAmelCase_	,	eos_token=lowerCAmelCase_	,	add_prefix_space=lowerCAmelCase_	,	**lowerCAmelCase_	,	)

				if kwargs.pop("add_bos_token"	,	lowerCAmelCase_						):
							UpperCAmelCase_     : str			     =					kwargs.pop("name_or_path"	,	""						)
							raise ValueError(
							    "Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
							    "Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
							    f"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
							    f"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
							    "This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
							    " so that the fast tokenizer works correctly."						)

				UpperCAmelCase_     : int			     =					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
				if pre_tok_state.get("add_prefix_space"	,	lowerCAmelCase_						) != add_prefix_space:
							UpperCAmelCase_     : str			     =					getattr(lowerCAmelCase_	,	pre_tok_state.pop("type"						)						)
							UpperCAmelCase_     : Union[str, Any]			     =					add_prefix_space
							UpperCAmelCase_     : Tuple			     =					pre_tok_class(**lowerCAmelCase_						)

				UpperCAmelCase_     : Dict			     =					add_prefix_space





	def      _SCREAMING_SNAKE_CASE      (      self					: Optional[Any]	,	*lowerCAmelCase_					: Union[str, Any]	,	**lowerCAmelCase_					: List[str]						)						->			BatchEncoding:
				UpperCAmelCase_     : List[str]			     =					kwargs.get("is_split_into_words"	,	lowerCAmelCase_						)
				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(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Tuple	,	*lowerCAmelCase_					: List[str]	,	**lowerCAmelCase_					: Any						)						->			BatchEncoding:
				UpperCAmelCase_     : int			     =					kwargs.get("is_split_into_words"	,	lowerCAmelCase_						)

				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(*lowerCAmelCase_	,	**lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Any	,	lowerCAmelCase_					: str	,	lowerCAmelCase_					: Optional[str] = None						)						->			Tuple[str]:
				UpperCAmelCase_     : List[Any]			     =					self._tokenizer.model.save(lowerCAmelCase_	,	name=lowerCAmelCase_						)
				return tuple(lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]	,	lowerCAmelCase_					: bool = False	,	lowerCAmelCase_					: bool = None	,	lowerCAmelCase_					: Optional[List[str]] = None	,	**lowerCAmelCase_					: List[str]	,	)						->			str:
				UpperCAmelCase_     : str			     =					super().decode(
				    token_ids=lowerCAmelCase_	,	skip_special_tokens=lowerCAmelCase_	,	clean_up_tokenization_spaces=lowerCAmelCase_	,	**lowerCAmelCase_	,	)

				if truncate_before_pattern is not None and len(lowerCAmelCase_						) > 0:
							UpperCAmelCase_     : List[Any]			     =					self.truncate(lowerCAmelCase_	,	lowerCAmelCase_						)

				return decoded_text





	def      _SCREAMING_SNAKE_CASE      (      self					: Dict	,	lowerCAmelCase_					: Any	,	lowerCAmelCase_					: int						)						->			List[Any]:
				def find_re(lowerCAmelCase_					: int	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple						):
							UpperCAmelCase_     : List[Any]			     =					pattern.search(lowerCAmelCase_	,	lowerCAmelCase_						)
							return m.start() if m else -1

				UpperCAmelCase_     : Union[str, Any]			     =					[re.compile(lowerCAmelCase_	,	re.MULTILINE						) for pattern in truncate_before_pattern]

				UpperCAmelCase_     : Union[str, Any]			     =					list(re.finditer("^print"	,	lowerCAmelCase_	,	re.MULTILINE						)						)

				if len(lowerCAmelCase_						) > 1:
							UpperCAmelCase_     : Optional[int]			     =					completion[: prints[1].start()]

				UpperCAmelCase_     : Union[str, Any]			     =					list(re.finditer("^def"	,	lowerCAmelCase_	,	re.MULTILINE						)						)

				if len(lowerCAmelCase_						) > 1:
							UpperCAmelCase_     : List[str]			     =					completion[: defs[1].start()]

				UpperCAmelCase_     : Tuple			     =					0

				UpperCAmelCase_     : Tuple			     =					[
				    pos for pos in [find_re(lowerCAmelCase_	,	lowerCAmelCase_	,	lowerCAmelCase_						) for terminal in terminals] if pos != -1
				]

				if len(lowerCAmelCase_						) > 0:
							return completion[: min(lowerCAmelCase_						)]
				else:
							return completion











"""simple docstring"""
def 				snake_case      (   A__	,A__						):
			# "extended trapezoidal rule"
			# int(f) = dx/2 * (f1 + 2f2 + ... + fn)
			UpperCAmelCase_     : Dict			     =					(boundary[1] - boundary[0]) / steps
			UpperCAmelCase_     : Optional[int]			     =					boundary[0]
			UpperCAmelCase_     : str			     =					boundary[1]
			UpperCAmelCase_     : Tuple			     =					make_points(A__	,A__	,A__						)
			UpperCAmelCase_     : List[str]			     =					0.0
			y += (h / 2.0) * f(A__						)
			for i in x_i:
						# print(i)
						y += h * f(A__						)
			y += (h / 2.0) * f(A__						)
			return y

def 				snake_case      (   A__	,A__	,A__						):
			UpperCAmelCase_     : Union[str, Any]			     =					a + h
			while x < (b - h):
						yield x
						UpperCAmelCase_     : Optional[Any]			     =					x + h

def 				snake_case      (   A__						):  # enter your function here
			UpperCAmelCase_     : Dict			     =					(x - 0) * (x - 0)
			return y

def 				snake_case      (   ):
			UpperCAmelCase_     : Dict			     =					0.0  # Lower bound of integration
			UpperCAmelCase_     : Optional[int]			     =					1.0  # Upper bound of integration
			UpperCAmelCase_     : Dict			     =					10.0  # define number of steps or resolution
			UpperCAmelCase_     : List[Any]			     =					[a, b]  # define boundary of integration
			UpperCAmelCase_     : Union[str, Any]			     =					method_a(A__	,A__						)
			print(F"""y = {y}"""						)


if __name__ == "__main__":
			main()



