




"""simple docstring"""


from dataclasses import dataclass, field
from typing import Optional






@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Model name or path of model to be trained."}				)
 lowercase__				      =      field(
     default="./" ,    metadata={"help": "Save dir where model repo is cloned and models updates are saved to."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-train" ,    metadata={"help": "Name or path of training dataset."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-valid" ,    metadata={"help": "Name or path of validation dataset."}				)
 lowercase__				      =      field(default=2 ,    metadata={"help": "Batch size for training."}				)
 lowercase__				      =      field(default=2 ,    metadata={"help": "Batch size for evaluation."}				)
 lowercase__				      =      field(default=0.1 ,    metadata={"help": "Value of weight decay."}				)
 lowercase__				      =      field(
     default=1_00_00 ,    metadata={"help": "Size of buffer used to shuffle streaming dataset."}				)
 lowercase__				      =      field(default=2e-4 ,    metadata={"help": "Learning rate fo training."}				)
 lowercase__				      =      field(default="cosine" ,    metadata={"help": "Learning rate."}				)
 lowercase__				      =      field(
     default=7_50 ,    metadata={"help": "Number of warmup steps in the learning rate schedule."}				)
 lowercase__				      =      field(
     default=16 ,    metadata={"help": "Number of gradient accumulation steps."}				)
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Use gradient checkpointing to reduce memory footprint."}				)
 lowercase__				      =      field(default=5_00_00 ,    metadata={"help": "Maximum number of training steps."}				)
 lowercase__				      =      field(
     default=-1 ,    metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}				)
 lowercase__				      =      field(default=10_24 ,    metadata={"help": "Sequence lengths used for training."}				)
 lowercase__				      =      field(default=1 ,    metadata={"help": "Training seed."}				)
 lowercase__				      =      field(
     default=10_24 ,    metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."} ,    )
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "States path if the training should continue from a checkpoint folder."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "If True the data is pretokenized."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Model name or path of model to be evaluated."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-valid" ,    metadata={"help": "Name or path of validation dataset."}				)
 lowercase__				      =      field(default=2 ,    metadata={"help": "Batch size used for evaluation."}				)
 lowercase__				      =      field(
     default=-1 ,    metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."}				)
 lowercase__				      =      field(default=10_24 ,    metadata={"help": "Length of sequences to be evaluated."}				)
 lowercase__				      =      field(default=1 ,    metadata={"help": "Random seed used for evaluation."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Model name or path of model to be evaluated."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Number of workers used for code evaluation."}				)
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."} ,    )
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "Sample from the language model's output distribution."}				)
 lowercase__				      =      field(default=0.2 ,    metadata={"help": "Sampling temperature used for generation."}				)
 lowercase__				      =      field(default=2_56 ,    metadata={"help": "Maximum number of newly generated tokens."}				)
 lowercase__				      =      field(default=0 ,    metadata={"help": "Top-k parameter used for generation."}				)
 lowercase__				      =      field(default=0.95 ,    metadata={"help": "Top-p parameter used for nucleus sampling."}				)
 lowercase__				      =      field(default=10 ,    metadata={"help": "Number of generations to run in parallel."}				)
 lowercase__				      =      field(
     default=2_00 ,    metadata={"help": "Number of completions to generate for each sample."}				)
 lowercase__				      =      field(default=1 ,    metadata={"help": "Random seed used for evaluation."}				)
 lowercase__				      =      field(
     default="eval_results.json" ,    metadata={"help": "Random seed used for evaluation."}				)
 lowercase__				      =      field(
     default="0" ,    metadata={"help": "Allow `code_eval` to execute Python code on machine"}				)
 lowercase__				      =      field(
     default=-1 ,    metadata={
         "help": (
             "Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
             " number corresponds to which GPU device id to run on."
         )
     } ,    )







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default=__a ,    metadata={
         "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
     } ,    )
 lowercase__				      =      field(
     default="transformersbook/codeparrot" ,    metadata={"help": "Folder or name of dataset to process."}				)
 lowercase__				      =      field(
     default="codeparrot-clean" ,    metadata={"help": "Folder to save processed processed dataset."}				)
 lowercase__				      =      field(
     default=10_00_00 ,    metadata={"help": "Number of files to save per JSON output file."}				)
 lowercase__				      =      field(default="content" ,    metadata={"help": "Column containing text data to process."}				)
 lowercase__				      =      field(
     default=10_00 ,    metadata={"help": "Maximum line length in file, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=1_00 ,    metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=0.25 ,    metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=1.5 ,    metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."}				)
 lowercase__				      =      field(
     default=0.7 ,    metadata={"help": "Probability for filtering config, test and uncommon files."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Name or path to the tokenizer."} ,    )
 lowercase__				      =      field(
     default=__a ,    metadata={"help": "If True, near-duplicate samples are removed."}				)
 lowercase__				      =      field(
     default=0.85 ,    metadata={"help": "Jaccard threshold for near-duplicate samples."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="gpt2" ,    metadata={"help": "Base tokenizer to build new tokenizer from."}				)
 lowercase__				      =      field(
     default="transformersbook/codeparrot-train" ,    metadata={"help": "Dataset to train tokenizer on."}				)
 lowercase__				      =      field(default="content" ,    metadata={"help": "Column containing text data to process."}				)
 lowercase__				      =      field(default=20_00_00 ,    metadata={"help": "Number of examples to train tokenizer on."}				)
 lowercase__				      =      field(
     default=3_27_68 ,    metadata={"help": "Number of examples to train the tokenizer on."}				)
 lowercase__				      =      field(default="codeparrot" ,    metadata={"help": "Name of new tokenizer."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Push saved tokenizer to the hub."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Name or path to the tokenizer."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot-clean-train" ,    metadata={"help": "Name or path to the dataset to pretokenize."}				)
 lowercase__				      =      field(
     default="tokenized-codeparrot-train" ,    metadata={"help": "Repo name of the pretokenized data."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Number of workers used for code evaluation."}				)







@dataclass
class       lowercase:







 '''simple docstring'''





 lowercase__				      =      field(
     default="gpt2-large" ,    metadata={"help": "Configuration to use for model initialization."}				)
 lowercase__				      =      field(
     default="codeparrot/codeparrot" ,    metadata={"help": "Tokenizer attached to model."}				)
 lowercase__				      =      field(default="codeparrot" ,    metadata={"help": "Name of the created model."}				)
 lowercase__				      =      field(default=__a ,    metadata={"help": "Push saved tokenizer to the hub."}				)








"""simple docstring"""


import tempfile

import torch

from diffusers import IPNDMScheduler

from .test_schedulers import SchedulerCommonTest






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      (IPNDMScheduler,)
 lowercase__				      =      (("num_inference_steps", 50),)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   **a_:	Union[str, Any]	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						{"""num_train_timesteps""": 1_000}
   config.update(**a_	)
   return config


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	Optional[int]=0,   **a_:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						dict(self.forward_default_kwargs	)
   _snake_case							:	Optional[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[Any]			=						self.dummy_sample
   _snake_case							:	Dict			=						0.1 * sample
   _snake_case							:	Any			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	int			=						self.get_scheduler_config(**a_	)
     _snake_case							:	Dict			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)
     # copy over dummy past residuals
     _snake_case							:	int			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Union[str, Any]			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	Tuple			=						scheduler_class.from_pretrained(a_	)
       new_scheduler.set_timesteps(a_	)
       # copy over dummy past residuals
       _snake_case							:	Optional[Any]			=						dummy_past_residuals[:]

     _snake_case							:	List[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	str			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	Optional[Any]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Optional[int]			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   pass


 def 				UpperCamelCase_     (       self:	str,   a_:	Any=0,   **a_:	Tuple	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)
   _snake_case							:	List[Any]			=						kwargs.pop("""num_inference_steps""",   a_	)
   _snake_case							:	Optional[int]			=						self.dummy_sample
   _snake_case							:	Tuple			=						0.1 * sample
   _snake_case							:	Optional[int]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Any			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)
     scheduler.set_timesteps(a_	)

     # copy over dummy past residuals (must be after setting timesteps)
     _snake_case							:	Union[str, Any]			=						dummy_past_residuals[:]

     if time_step is None:
       _snake_case							:	Tuple			=						scheduler.timesteps[len(scheduler.timesteps	) // 2]

     with tempfile.TemporaryDirectory() as tmpdirname:
       scheduler.save_config(a_	)
       _snake_case							:	List[str]			=						scheduler_class.from_pretrained(a_	)
       # copy over dummy past residuals
       new_scheduler.set_timesteps(a_	)

       # copy over dummy past residual (must be after setting timesteps)
       _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	int			=						new_scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     assert torch.sum(torch.abs(output - new_output	)	) < 1E-5, "Scheduler outputs are not identical"


 def 				UpperCamelCase_     (       self:	List[Any],   **a_:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.scheduler_classes[0]
   _snake_case							:	Any			=						self.get_scheduler_config(**a_	)
   _snake_case							:	List[Any]			=						scheduler_class(**a_	)

   _snake_case							:	Union[str, Any]			=						10
   _snake_case							:	Union[str, Any]			=						self.dummy_model()
   _snake_case							:	List[Any]			=						self.dummy_sample_deter
   scheduler.set_timesteps(a_	)

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Optional[Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   for i, t in enumerate(scheduler.timesteps	):
     _snake_case							:	Union[str, Any]			=						model(a_,   a_	)
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_	).prev_sample

   return sample


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						dict(self.forward_default_kwargs	)

   _snake_case							:	int			=						kwargs.pop("""num_inference_steps""",   a_	)

   for scheduler_class in self.scheduler_classes:
     _snake_case							:	Union[str, Any]			=						self.get_scheduler_config()
     _snake_case							:	Tuple			=						scheduler_class(**a_	)

     _snake_case							:	Dict			=						self.dummy_sample
     _snake_case							:	List[str]			=						0.1 * sample

     if num_inference_steps is not None and hasattr(a_,   """set_timesteps"""	):
       scheduler.set_timesteps(a_	)
     elif num_inference_steps is not None and not hasattr(a_,   """set_timesteps"""	):
       _snake_case							:	Dict			=						num_inference_steps

     # copy over dummy past residuals (must be done after set_timesteps)
     _snake_case							:	Union[str, Any]			=						[residual + 0.2, residual + 0.15, residual + 0.1, residual + 0.05]
     _snake_case							:	List[str]			=						dummy_past_residuals[:]

     _snake_case							:	Optional[int]			=						scheduler.timesteps[5]
     _snake_case							:	Optional[Any]			=						scheduler.timesteps[6]

     _snake_case							:	str			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	List[str]			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)

     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample
     _snake_case							:	Any			=						scheduler.step(a_,   a_,   a_,   **a_	).prev_sample

     self.assertEqual(output_a.shape,   sample.shape	)
     self.assertEqual(output_a.shape,   output_a.shape	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   for timesteps in [100, 1_000]:
     self.check_over_configs(num_train_timesteps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   for t, num_inference_steps in zip([1, 5, 10],   [10, 50, 100]	):
     self.check_over_forward(num_inference_steps=a_,   time_step=a_	)


 def 				UpperCamelCase_     (       self:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.full_loop()
   _snake_case							:	Optional[int]			=						torch.mean(torch.abs(a_	)	)

   assert abs(result_mean.item() - 2_540_529	) < 10


