





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							:					Tuple		    =		logging.get_logger(__name__)

_lowerCAmelCase							:					Any		    =		{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

_lowerCAmelCase							:					Optional[int]		    =		{
    "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							:					List[str]		    =		{
    "Salesforce/codegen-350M-mono": 2_048,
}


class     __magic_name__     (							UpperCamelCase__							):





     """simple docstring"""



     __UpperCamelCase    			=   VOCAB_FILES_NAMES
     __UpperCamelCase    			=   PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase    			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase    			=   ['''input_ids''', '''attention_mask''']
     __UpperCamelCase    			=   CodeGenTokenizer



     def __init__(							self				:int	,					snake_case				:List[Any]=None	,					snake_case				:Optional[int]=None	,					snake_case				:Optional[Any]=None	,					snake_case				:Optional[int]="<|endoftext|>"	,					snake_case				:Optional[Any]="<|endoftext|>"	,					snake_case				:int="<|endoftext|>"	,					snake_case				:Optional[Any]=False	,					**snake_case				:List[Any]	,					):





        '''simple docstring'''




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

        if kwargs.pop("add_bos_token"	,					lowercase_     ):
           A_    : Optional[int]								=	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."     )

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

        A_    : Any								=	add_prefix_space



     def   SCREAMING_SNAKE_CASE						(							self				:Union[str, Any]	,					*snake_case				:List[Any]	,					**snake_case				:Optional[Any]     ):





        '''simple docstring'''




        A_    : Optional[Any]								=	kwargs.get("is_split_into_words"	,					lowercase_     )
        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(*lowercase_	,					**lowercase_     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					*snake_case				:Dict	,					**snake_case				:str     ):





        '''simple docstring'''




        A_    : Dict								=	kwargs.get("is_split_into_words"	,					lowercase_     )

        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(*lowercase_	,					**lowercase_     )



     def   SCREAMING_SNAKE_CASE						(							self				:int	,					snake_case				:str	,					snake_case				:Optional[str] = None     ):





        '''simple docstring'''




        A_    : Any								=	self._tokenizer.model.save(lowercase_	,					name=lowercase_     )
        return tuple(lowercase_     )



     def   SCREAMING_SNAKE_CASE						(							self				:List[str]	,					snake_case				:Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"]	,					snake_case				:bool = False	,					snake_case				:bool = None	,					snake_case				:Optional[List[str]] = None	,					**snake_case				:List[Any]	,					):





        '''simple docstring'''




        A_    : int								=	super().decode(
            token_ids=lowercase_	,					skip_special_tokens=lowercase_	,					clean_up_tokenization_spaces=lowercase_	,					**lowercase_	,					)

        if truncate_before_pattern is not None and len(lowercase_     ) > 0:
           A_    : Optional[Any]								=	self.truncate(lowercase_	,					lowercase_     )

        return decoded_text





     def   SCREAMING_SNAKE_CASE						(							self				:Tuple	,					snake_case				:str	,					snake_case				:str     ):





        '''simple docstring'''




        def find_re(snake_case				:Optional[Any]	,					snake_case				:List[str]	,					snake_case				:Optional[int]     ):
           A_    : Union[str, Any]								=	pattern.search(lowercase_	,					lowercase_     )
           return m.start() if m else -1

        A_    : List[str]								=	[re.compile(lowercase_	,					re.MULTILINE     ) for pattern in truncate_before_pattern]

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

        if len(lowercase_     ) > 1:
           A_    : Union[str, Any]								=	completion[: prints[1].start()]

        A_    : int								=	list(re.finditer("^def"	,					lowercase_	,					re.MULTILINE     )     )

        if len(lowercase_     ) > 1:
           A_    : str								=	completion[: defs[1].start()]

        A_    : int								=	0

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

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







'''simple docstring'''

import torch

from diffusers import DPMSolverSDEScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import require_torchsde

from .test_schedulers import SchedulerCommonTest






@require_torchsde
class    UpperCAmelCase	(       UpperCamelCase__				):
 __lowercase   		=				(DPMSolverSDEScheduler,)
 __lowercase   		=				10
 def      UpperCAmelCase_							(				self      :List[Any]						,       **lowercase_      :Optional[int]							)->							str:
     A__				=			{
         "num_train_timesteps": 11_00,
         "beta_start": 0.0_0_0_1,
         "beta_end": 0.0_2,
         "beta_schedule": "linear",
         "noise_sampler_seed": 0,
     }

     config.update(**lowercase_							)
     return config
 def      UpperCAmelCase_							(				self      :int							)->							Dict:
     for timesteps in [10, 50, 1_00, 10_00]:
         self.check_over_configs(num_train_timesteps=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Tuple:
     for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]						,       [0.0_0_0_2, 0.0_0_2, 0.0_2]							):
         self.check_over_configs(beta_start=lowercase_						,       beta_end=lowercase_							)
 def      UpperCAmelCase_							(				self      :Any							)->							Optional[Any]:
     for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[Any]							)->							Dict:
     for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=lowercase_							)
 def      UpperCAmelCase_							(				self      :List[str]							)->							Union[str, Any]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

     for i, t in enumerate(scheduler.timesteps							):
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_7_8_2_1_0_4_4_9_2_1_8_7_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_7_8_7_0_5_9_6_4_5_6_5_2_7_7							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_2_1_1_1_8_1_6_4_0_6							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_6_8_9_2_2_9_9_6_5_2							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config(prediction_type="v_prediction"							)
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

     for i, t in enumerate(scheduler.timesteps							):
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_2_4.7_7_1_4_9_2_0_0_4_3_9_4_5_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_2_2_6_2_8_9_0_1_4_8_1_6_2_8_4							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_2_8.1_6_6_3_3_6_0_5_9_5_7_0_3							) < 1E-2
         assert abs(result_mean.item() - 0.1_6_6_8_8_3_2_6_0_0_1_1_6_7_2_9_7							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_1_9.8_4_8_7_5_4_8_8_2_8_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.1_5_6_0_5_3_0_6_6_2_5_3_6_6_2_1							) < 1E-3
 def      UpperCAmelCase_							(				self      :Optional[int]							)->							List[str]:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps						,       device=lowercase_							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma

     for t in scheduler.timesteps:
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_6_7.4_6_9_5_7_3_9_7_4_6_0_9_3_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_8_0_5_9_3_4_6_0_7_9_8_2_6_3_5							) < 1E-3
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_1.5_9_3_5_3_6_3_7_6_9_5_3_1_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_2_3_4_2_9_0_8_3_8_2_4_1_5_7_7_1							) < 1E-3
     else:
         assert abs(result_sum.item() - 1_6_2.5_2_3_8_3_4_2_2_8_5_1_5_6_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_1_1_6_1_9_5_7_0_8_5_1_3_2_6							) < 1E-3



 def      UpperCAmelCase_							(				self      :Tuple							)->							Dict:
     A__				=			self.scheduler_classes[0]
     A__				=			self.get_scheduler_config()
     A__				=			scheduler_class(**lowercase_						,       use_karras_sigmas=lowercase_							)

     scheduler.set_timesteps(self.num_inference_steps						,       device=lowercase_							)

     A__				=			self.dummy_model()
     A__				=			self.dummy_sample_deter.to(lowercase_							) * scheduler.init_noise_sigma
     A__				=			sample.to(lowercase_							)

     for t in scheduler.timesteps:
         A__				=			scheduler.scale_model_input(lowercase_						,       lowercase_							)

         A__				=			model(lowercase_						,       lowercase_							)

         A__				=			scheduler.step(lowercase_						,       lowercase_						,       lowercase_							)
         A__				=			output.prev_sample

     A__				=			torch.sum(torch.abs(lowercase_							)							)
     A__				=			torch.mean(torch.abs(lowercase_							)							)

     if torch_device in ["mps"]:
         assert abs(result_sum.item() - 1_7_6.6_6_9_7_4_1_3_5_7_4_2_1_8_8							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     elif torch_device in ["cuda"]:
         assert abs(result_sum.item() - 1_7_7.6_3_6_5_3_5_6_4_4_5_3_1_2_5							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2
     else:
         assert abs(result_sum.item() - 1_7_0.3_1_3_5_2_2_3_3_8_8_6_7_2							) < 1E-2
         assert abs(result_mean.item() - 0.2_3_0_0_3_8_7_2_7_3_0_9_8_1_8_1_1							) < 1E-2



