





from dataclasses import dataclass, field
from typing import Optional







@dataclass
class __a :
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot',   metadata={'help': 'Model name or path of model to be trained.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='./',   metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot-clean-train',   metadata={'help': 'Name or path of training dataset.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot-clean-valid',   metadata={'help': 'Name or path of validation dataset.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=2,   metadata={'help': 'Batch size for training.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=2,   metadata={'help': 'Batch size for evaluation.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(default=0.1,   metadata={'help': 'Value of weight decay.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=1_0000,   metadata={'help': 'Size of buffer used to shuffle streaming dataset.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(default=2E-4,   metadata={'help': 'Learning rate fo training.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(default='cosine',   metadata={'help': 'Learning rate.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=750,   metadata={'help': 'Number of warmup steps in the learning rate schedule.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=16,   metadata={'help': 'Number of gradient accumulation steps.'}			)
   __UpperCamelCase   : Optional[bool]	      =	field(
       default=_snake_case,   metadata={'help': 'Use gradient checkpointing to reduce memory footprint.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=5_0000,   metadata={'help': 'Maximum number of training steps.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=-1,   metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=1024,   metadata={'help': 'Sequence lengths used for training.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=1,   metadata={'help': 'Training seed.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=1024,   metadata={'help': 'Interval to save checkpoints. Measured as number of forward passes not training steps.'},   )
   __UpperCamelCase   : Optional[str]	      =	field(
       default=_snake_case,   metadata={'help': 'States path if the training should continue from a checkpoint folder.'}			)
   __UpperCamelCase   : Optional[bool]	      =	field(default=_snake_case,   metadata={'help': 'If True the data is pretokenized.'}			)







@dataclass
class __a :
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot',   metadata={'help': 'Model name or path of model to be evaluated.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot-clean-valid',   metadata={'help': 'Name or path of validation dataset.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=2,   metadata={'help': 'Batch size used for evaluation.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=-1,   metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=1024,   metadata={'help': 'Length of sequences to be evaluated.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=1,   metadata={'help': 'Random seed used for evaluation.'}			)







@dataclass
class __a :
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot',   metadata={'help': 'Model name or path of model to be evaluated.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=_snake_case,   metadata={'help': 'Number of workers used for code evaluation.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=_snake_case,   metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'},   )
   __UpperCamelCase   : Optional[bool]	      =	field(
       default=_snake_case,   metadata={'help': 'Sample from the language model\'s output distribution.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(default=0.2,   metadata={'help': 'Sampling temperature used for generation.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=256,   metadata={'help': 'Maximum number of newly generated tokens.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=0,   metadata={'help': 'Top-k parameter used for generation.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(default=0.95,   metadata={'help': 'Top-p parameter used for nucleus sampling.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=10,   metadata={'help': 'Number of generations to run in parallel.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=200,   metadata={'help': 'Number of completions to generate for each sample.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=1,   metadata={'help': 'Random seed used for evaluation.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='eval_results.json',   metadata={'help': 'Random seed used for evaluation.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='0',   metadata={'help': 'Allow `code_eval` to execute Python code on machine'}			)
   __UpperCamelCase   : Optional[int]	      =	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 __a :
   __UpperCamelCase   : Optional[int]	      =	field(
       default=_snake_case,   metadata={
           'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
       },   )
   __UpperCamelCase   : Optional[str]	      =	field(
       default='transformersbook/codeparrot',   metadata={'help': 'Folder or name of dataset to process.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot-clean',   metadata={'help': 'Folder to save processed processed dataset.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=10_0000,   metadata={'help': 'Number of files to save per JSON output file.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(default='content',   metadata={'help': 'Column containing text data to process.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(
       default=1000,   metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(
       default=100,   metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(
       default=0.25,   metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(
       default=1.5,   metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(
       default=0.7,   metadata={'help': 'Probability for filtering config, test and uncommon files.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot',   metadata={'help': 'Name or path to the tokenizer.'},   )
   __UpperCamelCase   : Optional[bool]	      =	field(
       default=_snake_case,   metadata={'help': 'If True, near-duplicate samples are removed.'}			)
   __UpperCamelCase   : Optional[float]	      =	field(
       default=0.85,   metadata={'help': 'Jaccard threshold for near-duplicate samples.'}			)







@dataclass
class __a :
   __UpperCamelCase   : Optional[str]	      =	field(
       default='gpt2',   metadata={'help': 'Base tokenizer to build new tokenizer from.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='transformersbook/codeparrot-train',   metadata={'help': 'Dataset to train tokenizer on.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(default='content',   metadata={'help': 'Column containing text data to process.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=20_0000,   metadata={'help': 'Number of examples to train tokenizer on.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(
       default=3_2768,   metadata={'help': 'Number of examples to train the tokenizer on.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(default='codeparrot',   metadata={'help': 'Name of new tokenizer.'}			)
   __UpperCamelCase   : Optional[bool]	      =	field(default=_snake_case,   metadata={'help': 'Push saved tokenizer to the hub.'}			)







@dataclass
class __a :
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot',   metadata={'help': 'Name or path to the tokenizer.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot-clean-train',   metadata={'help': 'Name or path to the dataset to pretokenize.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='tokenized-codeparrot-train',   metadata={'help': 'Repo name of the pretokenized data.'}			)
   __UpperCamelCase   : Optional[int]	      =	field(default=_snake_case,   metadata={'help': 'Number of workers used for code evaluation.'}			)







@dataclass
class __a :
   __UpperCamelCase   : Optional[str]	      =	field(
       default='gpt2-large',   metadata={'help': 'Configuration to use for model initialization.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(
       default='codeparrot/codeparrot',   metadata={'help': 'Tokenizer attached to model.'}			)
   __UpperCamelCase   : Optional[str]	      =	field(default='codeparrot',   metadata={'help': 'Name of the created model.'}			)
   __UpperCamelCase   : Optional[bool]	      =	field(default=_snake_case,   metadata={'help': 'Push saved tokenizer to the hub.'}			)












'''simple docstring'''

import flax.linen as nn
import jax
import jax.numpy as jnp







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			List[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[Any]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE							,     __SCREAMING_SNAKE_CASE      							=       hidden_states.shape
      __SCREAMING_SNAKE_CASE      							=       jax.image.resize(
          lowerCamelCase				,shape=(batch, height * 2, width * 2, channels)				,method="""nearest"""				,)
      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Optional[int]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          self.out_channels				,kernel_size=(3, 3)				,strides=(2, 2)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.conv(lowerCamelCase    )
      return hidden_states







class __a (   nn.Module			):
   __UpperCamelCase   : int
   __UpperCamelCase   : int	      =	None
   __UpperCamelCase   : float	      =	0.0
   __UpperCamelCase   : bool	      =	None
   __UpperCamelCase   : jnp.dtype	      =	jnp.floataa





   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.in_channels if self.out_channels is None else self.out_channels

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       nn.Dense(lowerCamelCase				,dtype=self.dtype    )

      __SCREAMING_SNAKE_CASE      							=       nn.GroupNorm(num_groups=32				,epsilon=1E-5    )
      __SCREAMING_SNAKE_CASE      							=       nn.Dropout(self.dropout_prob    )
      __SCREAMING_SNAKE_CASE      							=       nn.Conv(
          lowerCamelCase				,kernel_size=(3, 3)				,strides=(1, 1)				,padding=((1, 1), (1, 1))				,dtype=self.dtype				,)

      __SCREAMING_SNAKE_CASE      							=       self.in_channels != out_channels if self.use_nin_shortcut is None else self.use_nin_shortcut

      __SCREAMING_SNAKE_CASE      							=       None
      if use_nin_shortcut:
         __SCREAMING_SNAKE_CASE      							=       nn.Conv(
             lowerCamelCase				,kernel_size=(1, 1)				,strides=(1, 1)				,padding="""VALID"""				,dtype=self.dtype				,)





   def __call__(					self				:			List[str]				,lowerCamelCase				:			Optional[int]				,lowerCamelCase				:			Tuple				,lowerCamelCase				:			Union[str, Any]=True    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       hidden_states
      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      __SCREAMING_SNAKE_CASE      							=       self.time_emb_proj(nn.swish(lowerCamelCase    )    )
      __SCREAMING_SNAKE_CASE      							=       jnp.expand_dims(jnp.expand_dims(lowerCamelCase				,1    )				,1    )
      __SCREAMING_SNAKE_CASE      							=       hidden_states + temb

      __SCREAMING_SNAKE_CASE      							=       self.norma(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       nn.swish(lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.dropout(lowerCamelCase				,lowerCamelCase    )
      __SCREAMING_SNAKE_CASE      							=       self.conva(lowerCamelCase    )

      if self.conv_shortcut is not None:
         __SCREAMING_SNAKE_CASE      							=       self.conv_shortcut(lowerCamelCase    )

      return hidden_states + residual





