
import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest







class        A_   (			__a   ):




	'''simple docstring'''


	_UpperCamelCase     :     Optional[Any]							 =       (DDIMParallelScheduler,)
	_UpperCamelCase     :     List[str]							 =       (("""eta""", 0.0), ("""num_inference_steps""", 50))





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		lowercase       =    {
		    """num_train_timesteps""": 1000,
		    """beta_start""": 0.0_001,
		    """beta_end""": 0.02,
		    """beta_schedule""": """linear""",
		    """clip_sample""": True,
		}

		config.update(**a_       )
		return config





	def 	SCREAMING_SNAKE_CASE__						(       self		,    **snake_case       ):
		lowercase       =    self.scheduler_classes[0]
		lowercase       =    self.get_scheduler_config(**a_       )
		lowercase       =    scheduler_class(**a_       )

		lowercase       =    10, 0.0

		lowercase       =    self.dummy_model()
		lowercase       =    self.dummy_sample_deter

		scheduler.set_timesteps(a_       )

		for t in scheduler.timesteps:
			lowercase       =    model(a_		,    a_       )
			lowercase       =    scheduler.step(a_		,    a_		,    a_		,    a_       ).prev_sample

		return sample





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for timesteps in [100, 500, 1000]:
			self.check_over_configs(num_train_timesteps=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for steps_offset in [0, 1]:
			self.check_over_configs(steps_offset=a_       )

		lowercase       =    self.scheduler_classes[0]
		lowercase       =    self.get_scheduler_config(steps_offset=1       )
		lowercase       =    scheduler_class(**a_       )
		scheduler.set_timesteps(5       )
		assert torch.equal(scheduler.timesteps		,    torch.LongTensor([801, 601, 401, 201, 1]       )       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]		,    [0.002, 0.02, 0.2, 2]       ):
			self.check_over_configs(beta_start=a_		,    beta_end=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for schedule in ["linear", "squaredcos_cap_v2"]:
			self.check_over_configs(beta_schedule=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



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





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for clip_sample in [True, False]:
			self.check_over_configs(clip_sample=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for timestep_spacing in ["trailing", "leading"]:
			self.check_over_configs(timestep_spacing=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for rescale_betas_zero_snr in [True, False]:
			self.check_over_configs(rescale_betas_zero_snr=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		self.check_over_configs(thresholding=a_       )
		for threshold in [0.5, 1.0, 2.0]:
			for prediction_type in ["epsilon", "v_prediction"]:
				self.check_over_configs(
				    thresholding=a_		,    prediction_type=a_		,    sample_max_value=a_		,    )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for t in [1, 10, 49]:
			self.check_over_forward(time_step=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for t, num_inference_steps in zip([1, 10, 50]		,    [10, 50, 500]       ):
			self.check_over_forward(time_step=a_		,    num_inference_steps=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):



		for t, eta in zip([1, 10, 49]		,    [0.0, 0.5, 1.0]       ):
			self.check_over_forward(time_step=a_		,    eta=a_       )





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.scheduler_classes[0]
		lowercase       =    self.get_scheduler_config()
		lowercase       =    scheduler_class(**a_       )

		assert torch.sum(torch.abs(scheduler._get_variance(0		,    0       ) - 0.0       )       ) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(420		,    400       ) - 0.14_771       )       ) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(980		,    960       ) - 0.32_460       )       ) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(0		,    0       ) - 0.0       )       ) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(487		,    486       ) - 0.00_979       )       ) < 1E-5
		assert torch.sum(torch.abs(scheduler._get_variance(999		,    998       ) - 0.02       )       ) < 1E-5





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.scheduler_classes[0]
		lowercase       =    self.get_scheduler_config()
		lowercase       =    scheduler_class(**a_       )

		lowercase       =    10, 0.0
		scheduler.set_timesteps(a_       )

		lowercase       =    self.dummy_model()
		lowercase       =    self.dummy_sample_deter
		lowercase       =    self.dummy_sample_deter + 0.1
		lowercase       =    self.dummy_sample_deter - 0.1

		lowercase       =    samplea.shape[0]
		lowercase       =    torch.stack([samplea, samplea, samplea]		,    dim=0       )
		lowercase       =    torch.arange(a_       )[0:3, None].repeat(1		,    a_       )

		lowercase       =    model(samples.flatten(0		,    1       )		,    timesteps.flatten(0		,    1       )       )
		lowercase       =    scheduler.batch_step_no_noise(a_		,    timesteps.flatten(0		,    1       )		,    samples.flatten(0		,    1       )		,    a_       )

		lowercase       =    torch.sum(torch.abs(a_       )       )
		lowercase       =    torch.mean(torch.abs(a_       )       )

		assert abs(result_sum.item() - 1147.7904       ) < 1E-2
		assert abs(result_mean.item() - 0.4_982       ) < 1E-3





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.full_loop()

		lowercase       =    torch.sum(torch.abs(a_       )       )
		lowercase       =    torch.mean(torch.abs(a_       )       )

		assert abs(result_sum.item() - 172.0_067       ) < 1E-2
		assert abs(result_mean.item() - 0.223_967       ) < 1E-3





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.full_loop(prediction_type='v_prediction'       )

		lowercase       =    torch.sum(torch.abs(a_       )       )
		lowercase       =    torch.mean(torch.abs(a_       )       )

		assert abs(result_sum.item() - 52.5_302       ) < 1E-2
		assert abs(result_mean.item() - 0.0_684       ) < 1E-3





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.full_loop(set_alpha_to_one=a_		,    beta_start=0.01       )
		lowercase       =    torch.sum(torch.abs(a_       )       )
		lowercase       =    torch.mean(torch.abs(a_       )       )

		assert abs(result_sum.item() - 149.8_295       ) < 1E-2
		assert abs(result_mean.item() - 0.1_951       ) < 1E-3





	def 	SCREAMING_SNAKE_CASE__						(       self       ):
		lowercase       =    self.full_loop(set_alpha_to_one=a_		,    beta_start=0.01       )
		lowercase       =    torch.sum(torch.abs(a_       )       )
		lowercase       =    torch.mean(torch.abs(a_       )       )

		assert abs(result_sum.item() - 149.0_784       ) < 1E-2
		assert abs(result_mean.item() - 0.1_941       ) < 1E-3







"""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


A_						=				logging.get_logger(__name__)

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

A_						=				{
    '''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'''
        ),
    },
}

A_						=				{
    '''Salesforce/codegen-350M-mono''': 20_48,
}






class 					lowercase(     __a ):







 '''simple docstring'''





 lowercase__ 					=					VOCAB_FILES_NAMES
 lowercase__ 					=					PRETRAINED_VOCAB_FILES_MAP
 lowercase__ 					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 lowercase__ 					=					["input_ids", "attention_mask"]
 lowercase__ 					=					CodeGenTokenizer


 def __init__(				self:		Union[str, Any],      a_:		List[Any]=None,      a_:		str=None,      a_:		str=None,      a_:		Dict="<|endoftext|>",      a_:		Tuple="<|endoftext|>",      a_:		str="<|endoftext|>",      a_:		List[Any]=False,      **a_:		List[str],      ):




   '''simple docstring'''



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

   if kwargs.pop("""add_bos_token""",      a_		):
     _snake_case							:		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."""		)

   _snake_case							:		Tuple								=  json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
   if pre_tok_state.get("""add_prefix_space""",      a_		) != add_prefix_space:
     _snake_case							:		Dict								=  getattr(a_,      pre_tok_state.pop("""type"""		)		)
     _snake_case							:		Dict								=  add_prefix_space
     _snake_case							:		str								=  pre_tok_class(**a_		)

   _snake_case							:		List[Any]								=  add_prefix_space


 def  UpperCamelCase_   (				self:		Any,      *a_:		Any,      **a_:		int		):




   '''simple docstring'''



   _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:		Optional[Any],      *a_:		Any,      **a_:		List[str]		):




   '''simple docstring'''



   _snake_case							:		Dict								=  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:		Optional[int],      a_:		str,      a_:		Optional[str] = None		):




   '''simple docstring'''



   _snake_case							:		List[Any]								=  self._tokenizer.model.save(a_,      name=a_		)
   return tuple(a_		)


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




   '''simple docstring'''



   _snake_case							:		Any								=  super().decode(
       token_ids=a_,      skip_special_tokens=a_,      clean_up_tokenization_spaces=a_,      **a_,      )

   if truncate_before_pattern is not None and len(a_		) > 0:
     _snake_case							:		List[str]								=  self.truncate(a_,      a_		)

   return decoded_text


 def  UpperCamelCase_   (				self:		Dict,      a_:		Tuple,      a_:		Optional[Any]		):




   '''simple docstring'''



   def find_re(a_:		Dict,      a_:		str,      a_:		Union[str, Any]		):
     _snake_case							:		Any								=  pattern.search(a_,      a_		)
     return m.start() if m else -1

   _snake_case							:		Tuple								=  [re.compile(a_,      re.MULTILINE		) for pattern in truncate_before_pattern]

   _snake_case							:		List[Any]								=  list(re.finditer("""^print""",      a_,      re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		int								=  completion[: prints[1].start()]

   _snake_case							:		List[str]								=  list(re.finditer("""^def""",      a_,      re.MULTILINE		)		)

   if len(a_		) > 1:
     _snake_case							:		List[Any]								=  completion[: defs[1].start()]

   _snake_case							:		int								=  0

   _snake_case							:		List[Any]								=  [
       pos for pos in [find_re(a_,      a_,      a_		) for terminal in terminals] if pos != -1
   ]

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


