

import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
       import torch






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      "char"
  a     =      "bpe"
  a     =      "wp"



_SCREAMING_SNAKE_CASE  :						Dict         =    (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class  UpperCAmelCase__	( A__ ):






  """simple docstring"""


  a     =      ["image_processor", "char_tokenizer"]
  a     =      "ViTImageProcessor"
  a     =      "MgpstrTokenizer"






  def __init__(							self		:	int			,     __lowerCamelCase		:	Optional[int]=None			,     __lowerCamelCase		:	Dict=None			,     **__lowerCamelCase		:	Tuple      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					None
       if "feature_extractor" in kwargs:
            warnings.warn(
                '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
                ''' instead.'''			,     __lowerCamelCase			,     )
            SCREAMING_SNAKE_CASE__					=					kwargs.pop('''feature_extractor'''      )

       SCREAMING_SNAKE_CASE__					=					image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
            raise ValueError('''You need to specify an `image_processor`.'''      )
       if tokenizer is None:
            raise ValueError('''You need to specify a `tokenizer`.'''      )

       SCREAMING_SNAKE_CASE__					=					tokenizer
       SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained('''gpt2'''      )
       SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained('''bert-base-uncased'''      )

       super().__init__(__lowerCamelCase			,     __lowerCamelCase      )






  def __call__(							self		:	str			,     __lowerCamelCase		:	Tuple=None			,     __lowerCamelCase		:	Any=None			,     __lowerCamelCase		:	List[Any]=None			,     **__lowerCamelCase		:	Optional[Any]      )	->							Tuple:
       if images is None and text is None:
            raise ValueError('''You need to specify either an `images` or `text` input to process.'''      )

       if images is not None:
            SCREAMING_SNAKE_CASE__					=					self.image_processor(__lowerCamelCase			,     return_tensors=__lowerCamelCase			,     **__lowerCamelCase      )
       if text is not None:
            SCREAMING_SNAKE_CASE__					=					self.char_tokenizer(__lowerCamelCase			,     return_tensors=__lowerCamelCase			,     **__lowerCamelCase      )

       if text is None:
            return inputs
       elif images is None:
            return encodings
       else:
            SCREAMING_SNAKE_CASE__					=					encodings['''input_ids''']
            return inputs






  def    lowercase_	(							self		:	Tuple			,     __lowerCamelCase		:	Any      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					sequences
       SCREAMING_SNAKE_CASE__					=					char_preds.size(0      )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self._decode_helper(__lowerCamelCase			,     '''char'''      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self._decode_helper(__lowerCamelCase			,     '''bpe'''      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					self._decode_helper(__lowerCamelCase			,     '''wp'''      )

       SCREAMING_SNAKE_CASE__					=					[]
       SCREAMING_SNAKE_CASE__					=					[]
       for i in range(__lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					[char_scores[i], bpe_scores[i], wp_scores[i]]
            SCREAMING_SNAKE_CASE__					=					[char_strs[i], bpe_strs[i], wp_strs[i]]
            SCREAMING_SNAKE_CASE__					=					scores.index(max(__lowerCamelCase      )      )
            final_strs.append(strs[max_score_index]      )
            final_scores.append(scores[max_score_index]      )

       SCREAMING_SNAKE_CASE__					=					{}
       SCREAMING_SNAKE_CASE__					=					final_strs
       SCREAMING_SNAKE_CASE__					=					final_scores
       SCREAMING_SNAKE_CASE__					=					char_strs
       SCREAMING_SNAKE_CASE__					=					bpe_strs
       SCREAMING_SNAKE_CASE__					=					wp_strs
       return out






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]      )	->							str:
       if format == DecodeType.CHARACTER:
            SCREAMING_SNAKE_CASE__					=					self.char_decode
            SCREAMING_SNAKE_CASE__					=					1
            SCREAMING_SNAKE_CASE__					=					'''[s]'''
       elif format == DecodeType.BPE:
            SCREAMING_SNAKE_CASE__					=					self.bpe_decode
            SCREAMING_SNAKE_CASE__					=					2
            SCREAMING_SNAKE_CASE__					=					'''#'''
       elif format == DecodeType.WORDPIECE:
            SCREAMING_SNAKE_CASE__					=					self.wp_decode
            SCREAMING_SNAKE_CASE__					=					102
            SCREAMING_SNAKE_CASE__					=					'''[SEP]'''
       else:
            raise ValueError(f'''Format {format} is not supported.'''      )

       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					[], []
       SCREAMING_SNAKE_CASE__					=					pred_logits.size(0      )
       SCREAMING_SNAKE_CASE__					=					pred_logits.size(1      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					pred_logits.topk(1			,     dim=-1			,     largest=__lowerCamelCase			,     sorted=__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					preds_index.view(-1			,     __lowerCamelCase      )[:, 1:]
       SCREAMING_SNAKE_CASE__					=					decoder(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__,SCREAMING_SNAKE_CASE__					=					torch.nn.functional.softmax(__lowerCamelCase			,     dim=2      ).max(dim=2      )
       SCREAMING_SNAKE_CASE__					=					preds_max_prob[:, 1:]

       for index in range(__lowerCamelCase      ):
            SCREAMING_SNAKE_CASE__					=					preds_str[index].find(__lowerCamelCase      )
            SCREAMING_SNAKE_CASE__					=					preds_str[index][:pred_eos]
            SCREAMING_SNAKE_CASE__					=					preds_index[index].cpu().tolist()
            SCREAMING_SNAKE_CASE__					=					pred_index.index(__lowerCamelCase      ) if eos_token in pred_index else -1
            SCREAMING_SNAKE_CASE__					=					preds_max_prob[index][: pred_eos_index + 1]
            SCREAMING_SNAKE_CASE__					=					pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
            dec_strs.append(__lowerCamelCase      )
            conf_scores.append(__lowerCamelCase      )

       return dec_strs, conf_scores






  def    lowercase_	(							self		:	Optional[int]			,     __lowerCamelCase		:	Tuple      )	->							str:
       SCREAMING_SNAKE_CASE__					=					[seq.replace(''' '''			,     ''''''      ) for seq in self.char_tokenizer.batch_decode(__lowerCamelCase      )]
       return decode_strs






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Any      )	->							int:
       return self.bpe_tokenizer.batch_decode(__lowerCamelCase      )







  def    lowercase_	(							self		:	Dict			,     __lowerCamelCase		:	int      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					[seq.replace(''' '''			,     ''''''      ) for seq in self.wp_tokenizer.batch_decode(__lowerCamelCase      )]
       return decode_strs





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Model name or path of model to be evaluated."} )
  a     =      field(default=A__		,  metadata={"help": "Number of workers used for code evaluation."} )
  a     =      field(
      default=A__		,  metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "Sample from the language model's output distribution."} )
  a     =      field(default=0.2		,  metadata={"help": "Sampling temperature used for generation."} )
  a     =      field(default=2_56		,  metadata={"help": "Maximum number of newly generated tokens."} )
  a     =      field(default=0		,  metadata={"help": "Top-k parameter used for generation."} )
  a     =      field(default=0.9_5		,  metadata={"help": "Top-p parameter used for nucleus sampling."} )
  a     =      field(default=10		,  metadata={"help": "Number of generations to run in parallel."} )
  a     =      field(
      default=2_00		,  metadata={"help": "Number of completions to generate for each sample."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="eval_results.json"		,  metadata={"help": "Random seed used for evaluation."} )
  a     =      field(
      default="0"		,  metadata={"help": "Allow `code_eval` to execute Python code on machine"} )
  a     =      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  UpperCAmelCase__	:






  """simple docstring"""


  a     =      field(
      default=A__		,  metadata={
          "help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
      }		,  )
  a     =      field(
      default="transformersbook/codeparrot"		,  metadata={"help": "Folder or name of dataset to process."} )
  a     =      field(
      default="codeparrot-clean"		,  metadata={"help": "Folder to save processed processed dataset."} )
  a     =      field(
      default=10_00_00		,  metadata={"help": "Number of files to save per JSON output file."} )
  a     =      field(default="content"		,  metadata={"help": "Column containing text data to process."} )
  a     =      field(
      default=10_00		,  metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
  a     =      field(
      default=1_00		,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
  a     =      field(
      default=0.2_5		,  metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."} )
  a     =      field(
      default=1.5		,  metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."} )
  a     =      field(
      default=0.7		,  metadata={"help": "Probability for filtering config, test and uncommon files."} )
  a     =      field(
      default="codeparrot/codeparrot"		,  metadata={"help": "Name or path to the tokenizer."}		,  )
  a     =      field(
      default=A__		,  metadata={"help": "If True, near-duplicate samples are removed."} )
  a     =      field(
      default=0.8_5		,  metadata={"help": "Jaccard threshold for near-duplicate samples."} )






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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






@dataclass
class  UpperCAmelCase__	:






  """simple docstring"""


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


