





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 					lowercase__ (			snake_case__       ):

			'''simple docstring'''


			a		:							int     	=	(DPMSolverSDEScheduler,)
			a		:							Optional[Any]     	=	10




			def    UpperCamelCase__						(					self,	**__magic_name__							)				->   int:






							"""simple docstring"""
							UpperCamelCase__							:       str      =   {
							    '''num_train_timesteps''': 1100,
							    '''beta_start''': 0.0001,
							    '''beta_end''': 0.02,
							    '''beta_schedule''': '''linear''',
							    '''noise_sampler_seed''': 0,
							}

							config.update(**UpperCAmelCase_							)
							return config




			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""



							for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=UpperCAmelCase_							)




			def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






							"""simple docstring"""



							for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001],	[0.0002, 0.002, 0.02]							):
											self.check_over_configs(beta_start=UpperCAmelCase_,	beta_end=UpperCAmelCase_							)




			def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






							"""simple docstring"""



							for schedule in ["linear", "scaled_linear"]:
											self.check_over_configs(beta_schedule=UpperCAmelCase_							)




			def    UpperCamelCase__						(					self							)				->   Optional[Any]:






							"""simple docstring"""



							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=UpperCAmelCase_							)




			def    UpperCamelCase__						(					self							)				->   Dict:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   self.scheduler_classes[0]
							UpperCamelCase__							:       Union[str, Any]      =   self.get_scheduler_config()
							UpperCamelCase__							:       List[Any]      =   scheduler_class(**UpperCAmelCase_							)

							scheduler.set_timesteps(self.num_inference_steps							)

							UpperCamelCase__							:       Tuple      =   self.dummy_model()
							UpperCamelCase__							:       Tuple      =   self.dummy_sample_deter * scheduler.init_noise_sigma
							UpperCamelCase__							:       int      =   sample.to(UpperCAmelCase_							)

							for i, t in enumerate(scheduler.timesteps							):
											UpperCamelCase__							:       int      =   scheduler.scale_model_input(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       Dict      =   model(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       List[Any]      =   scheduler.step(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_							)
											UpperCamelCase__							:       List[str]      =   output.prev_sample

							UpperCamelCase__							:       Tuple      =   torch.sum(torch.abs(UpperCAmelCase_							)							)
							UpperCamelCase__							:       Any      =   torch.mean(torch.abs(UpperCAmelCase_							)							)

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 167.47_8210_4492_1875							) < 1E-2
											assert abs(result_mean.item() - 0.2178_7059_6456_5277							) < 1E-3
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 171.59_3521_1181_6406							) < 1E-2
											assert abs(result_mean.item() - 0.2_2342_9068_9229_9652							) < 1E-3
							else:
											assert abs(result_sum.item() - 162.52_3834_2285_1562							) < 1E-2
											assert abs(result_mean.item() - 0.211_6195_7085_1326							) < 1E-3




			def    UpperCamelCase__						(					self							)				->   Optional[int]:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   self.scheduler_classes[0]
							UpperCamelCase__							:       List[str]      =   self.get_scheduler_config(prediction_type='''v_prediction'''							)
							UpperCamelCase__							:       int      =   scheduler_class(**UpperCAmelCase_							)

							scheduler.set_timesteps(self.num_inference_steps							)

							UpperCamelCase__							:       List[Any]      =   self.dummy_model()
							UpperCamelCase__							:       List[str]      =   self.dummy_sample_deter * scheduler.init_noise_sigma
							UpperCamelCase__							:       Dict      =   sample.to(UpperCAmelCase_							)

							for i, t in enumerate(scheduler.timesteps							):
											UpperCamelCase__							:       List[str]      =   scheduler.scale_model_input(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       Optional[Any]      =   model(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       List[Any]      =   scheduler.step(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_							)
											UpperCamelCase__							:       List[str]      =   output.prev_sample

							UpperCamelCase__							:       str      =   torch.sum(torch.abs(UpperCAmelCase_							)							)
							UpperCamelCase__							:       Dict      =   torch.mean(torch.abs(UpperCAmelCase_							)							)

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 124.77_1492_0043_9453							) < 1E-2
											assert abs(result_mean.item() - 0.1_6226_2890_1481_6284							) < 1E-3
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 128.1_6633_6059_5703							) < 1E-2
											assert abs(result_mean.item() - 0.1_6688_3260_0116_7297							) < 1E-3
							else:
											assert abs(result_sum.item() - 119.8_4875_4882_8125							) < 1E-2
											assert abs(result_mean.item() - 0.1560_5306_6253_6621							) < 1E-3




			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							UpperCamelCase__							:       List[str]      =   self.scheduler_classes[0]
							UpperCamelCase__							:       Tuple      =   self.get_scheduler_config()
							UpperCamelCase__							:       int      =   scheduler_class(**UpperCAmelCase_							)

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

							UpperCamelCase__							:       List[str]      =   self.dummy_model()
							UpperCamelCase__							:       List[str]      =   self.dummy_sample_deter.to(UpperCAmelCase_							) * scheduler.init_noise_sigma

							for t in scheduler.timesteps:
											UpperCamelCase__							:       Tuple      =   scheduler.scale_model_input(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       str      =   model(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       int      =   scheduler.step(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_							)
											UpperCamelCase__							:       int      =   output.prev_sample

							UpperCamelCase__							:       List[str]      =   torch.sum(torch.abs(UpperCAmelCase_							)							)
							UpperCamelCase__							:       Union[str, Any]      =   torch.mean(torch.abs(UpperCAmelCase_							)							)

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 167.46_9573_9746_0938							) < 1E-2
											assert abs(result_mean.item() - 0.2_1805_9346_0798_2635							) < 1E-3
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 171.59_3536_3769_5312							) < 1E-2
											assert abs(result_mean.item() - 0.2_2342_9083_8241_5771							) < 1E-3
							else:
											assert abs(result_sum.item() - 162.52_3834_2285_1562							) < 1E-2
											assert abs(result_mean.item() - 0.211_6195_7085_1326							) < 1E-3




			def    UpperCamelCase__						(					self							)				->   Optional[int]:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[Any]      =   self.scheduler_classes[0]
							UpperCamelCase__							:       Union[str, Any]      =   self.get_scheduler_config()
							UpperCamelCase__							:       List[Any]      =   scheduler_class(**UpperCAmelCase_,	use_karras_sigmas=UpperCAmelCase_							)

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

							UpperCamelCase__							:       Tuple      =   self.dummy_model()
							UpperCamelCase__							:       Optional[int]      =   self.dummy_sample_deter.to(UpperCAmelCase_							) * scheduler.init_noise_sigma
							UpperCamelCase__							:       Optional[Any]      =   sample.to(UpperCAmelCase_							)

							for t in scheduler.timesteps:
											UpperCamelCase__							:       str      =   scheduler.scale_model_input(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       int      =   model(UpperCAmelCase_,	UpperCAmelCase_							)

											UpperCamelCase__							:       Dict      =   scheduler.step(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_							)
											UpperCamelCase__							:       List[Any]      =   output.prev_sample

							UpperCamelCase__							:       Any      =   torch.sum(torch.abs(UpperCAmelCase_							)							)
							UpperCamelCase__							:       List[Any]      =   torch.mean(torch.abs(UpperCAmelCase_							)							)

							if torch_device in ["mps"]:
											assert abs(result_sum.item() - 176.66_9741_3574_2188							) < 1E-2
											assert abs(result_mean.item() - 0.2_3003_8727_3098_1811							) < 1E-2
							elif torch_device in ["cuda"]:
											assert abs(result_sum.item() - 177.63_6535_6445_3125							) < 1E-2
											assert abs(result_mean.item() - 0.2_3003_8727_3098_1811							) < 1E-2
							else:
											assert abs(result_sum.item() - 170.3_1352_2338_8672							) < 1E-2
											assert abs(result_mean.item() - 0.2_3003_8727_3098_1811							) < 1E-2








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


UpperCAmelCase_  							=				logging.get_logger(__name__)

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

UpperCAmelCase_  							=				{
    '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'
        ),
    },
}

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






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Union[str, Any]     	=	VOCAB_FILES_NAMES
   a		:							List[str]     	=	PRETRAINED_VOCAB_FILES_MAP
   a		:							Optional[int]     	=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   a		:							List[Any]     	=	["input_ids", "attention_mask"]
   a		:							Any     	=	CodeGenTokenizer




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






       """simple docstring"""
       super().__init__(
           __magic_name__,	__magic_name__,	tokenizer_file=__magic_name__,	unk_token=__magic_name__,	bos_token=__magic_name__,	eos_token=__magic_name__,	add_prefix_space=__magic_name__,	**__magic_name__,	)

       if kwargs.pop('''add_bos_token''',	__magic_name__							):
           UpperCamelCase__							:       List[Any]      =   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__							:       Dict      =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()							)
       if pre_tok_state.get('''add_prefix_space''',	__magic_name__							) != add_prefix_space:
           UpperCamelCase__							:       List[str]      =   getattr(__magic_name__,	pre_tok_state.pop('''type'''							)							)
           UpperCamelCase__							:       int      =   add_prefix_space
           UpperCamelCase__							:       List[str]      =   pre_tok_class(**__magic_name__							)

       UpperCamelCase__							:       List[Any]      =   add_prefix_space




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   kwargs.get('''is_split_into_words''',	__magic_name__							)
       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(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	*__magic_name__,	**__magic_name__							)				->   BatchEncoding:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   kwargs.get('''is_split_into_words''',	__magic_name__							)

       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(*__magic_name__,	**__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   Tuple[str]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self._tokenizer.model.save(__magic_name__,	name=__magic_name__							)
       return tuple(__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = False,	__magic_name__ = None,	__magic_name__ = None,	**__magic_name__,	)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   super().decode(
           token_ids=__magic_name__,	skip_special_tokens=__magic_name__,	clean_up_tokenization_spaces=__magic_name__,	**__magic_name__,	)

       if truncate_before_pattern is not None and len(__magic_name__							) > 0:
           UpperCamelCase__							:       str      =   self.truncate(__magic_name__,	__magic_name__							)

       return decoded_text




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   List[str]:






       """simple docstring"""
       def find_re(__magic_name__,	__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Dict      =   pattern.search(__magic_name__,	__magic_name__							)
           return m.start() if m else -1

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

       UpperCamelCase__							:       List[Any]      =   list(re.finditer('''^print''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Tuple      =   completion[: prints[1].start()]

       UpperCamelCase__							:       Optional[int]      =   list(re.finditer('''^def''',	__magic_name__,	re.MULTILINE							)							)

       if len(__magic_name__							) > 1:
           UpperCamelCase__							:       Optional[Any]      =   completion[: defs[1].start()]

       UpperCamelCase__							:       str      =   0

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

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

