






'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional
@dataclass
class UpperCAmelCase_					:




						"""simple docstring"""




						UpperCamelCase_								=			field(
						    default='''codeparrot/codeparrot'''				,       metadata={'''help''': '''Model name or path of model to be trained.'''}				)
						UpperCamelCase_								=			field(
						    default='''./'''				,       metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}				)
						UpperCamelCase_								=			field(
						    default='''codeparrot/codeparrot-clean-train'''				,       metadata={'''help''': '''Name or path of training dataset.'''}				)
						UpperCamelCase_								=			field(
						    default='''codeparrot/codeparrot-clean-valid'''				,       metadata={'''help''': '''Name or path of validation dataset.'''}				)
						UpperCamelCase_								=			field(default=2				,       metadata={'''help''': '''Batch size for training.'''}				)
						UpperCamelCase_								=			field(default=2				,       metadata={'''help''': '''Batch size for evaluation.'''}				)
						UpperCamelCase_								=			field(default=0.1				,       metadata={'''help''': '''Value of weight decay.'''}				)
						UpperCamelCase_								=			field(
						    default=10000				,       metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}				)
						UpperCamelCase_								=			field(default=2e-4				,       metadata={'''help''': '''Learning rate fo training.'''}				)
						UpperCamelCase_								=			field(default='''cosine'''				,       metadata={'''help''': '''Learning rate.'''}				)
						UpperCamelCase_								=			field(
						    default=750				,       metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}				)
						UpperCamelCase_								=			field(
						    default=16				,       metadata={'''help''': '''Number of gradient accumulation steps.'''}				)
						UpperCamelCase_								=			field(
						    default=__A				,       metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}				)
						UpperCamelCase_								=			field(default=50000				,       metadata={'''help''': '''Maximum number of training steps.'''}				)
						UpperCamelCase_								=			field(
						    default=-1				,       metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}				)
						UpperCamelCase_								=			field(default=1024				,       metadata={'''help''': '''Sequence lengths used for training.'''}				)
						UpperCamelCase_								=			field(default=1				,       metadata={'''help''': '''Training seed.'''}				)
						UpperCamelCase_								=			field(
						    default=1024				,       metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}				,       )
						UpperCamelCase_								=			field(
						    default=__A				,       metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}				)
						UpperCamelCase_								=			field(default=__A				,       metadata={'''help''': '''If True the data is pretokenized.'''}				)





@dataclass
class UpperCAmelCase_					:




						"""simple docstring"""




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





@dataclass
class UpperCAmelCase_					:




						"""simple docstring"""




						UpperCamelCase_								=			field(
						    default='''codeparrot/codeparrot'''				,       metadata={'''help''': '''Model name or path of model to be evaluated.'''}				)
						UpperCamelCase_								=			field(default=__A				,       metadata={'''help''': '''Number of workers used for code evaluation.'''}				)
						UpperCamelCase_								=			field(
						    default=__A				,       metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}				,       )
						UpperCamelCase_								=			field(
						    default=__A				,       metadata={'''help''': '''Sample from the language model\'s output distribution.'''}				)
						UpperCamelCase_								=			field(default=0.2				,       metadata={'''help''': '''Sampling temperature used for generation.'''}				)
						UpperCamelCase_								=			field(default=256				,       metadata={'''help''': '''Maximum number of newly generated tokens.'''}				)
						UpperCamelCase_								=			field(default=0				,       metadata={'''help''': '''Top-k parameter used for generation.'''}				)
						UpperCamelCase_								=			field(default=0.95				,       metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}				)
						UpperCamelCase_								=			field(default=10				,       metadata={'''help''': '''Number of generations to run in parallel.'''}				)
						UpperCamelCase_								=			field(
						    default=200				,       metadata={'''help''': '''Number of completions to generate for each sample.'''}				)
						UpperCamelCase_								=			field(default=1				,       metadata={'''help''': '''Random seed used for evaluation.'''}				)
						UpperCamelCase_								=			field(
						    default='''eval_results.json'''				,       metadata={'''help''': '''Random seed used for evaluation.'''}				)
						UpperCamelCase_								=			field(
						    default='''0'''				,       metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}				)
						UpperCamelCase_								=			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"""




						UpperCamelCase_								=			field(
						    default=__A				,       metadata={
						        '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
						    }				,       )
						UpperCamelCase_								=			field(
						    default='''transformersbook/codeparrot'''				,       metadata={'''help''': '''Folder or name of dataset to process.'''}				)
						UpperCamelCase_								=			field(
						    default='''codeparrot-clean'''				,       metadata={'''help''': '''Folder to save processed processed dataset.'''}				)
						UpperCamelCase_								=			field(
						    default=100000				,       metadata={'''help''': '''Number of files to save per JSON output file.'''}				)
						UpperCamelCase_								=			field(default='''content'''				,       metadata={'''help''': '''Column containing text data to process.'''}				)
						UpperCamelCase_								=			field(
						    default=1000				,       metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}				)
						UpperCamelCase_								=			field(
						    default=100				,       metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}				)
						UpperCamelCase_								=			field(
						    default=0.25				,       metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}				)
						UpperCamelCase_								=			field(
						    default=1.5				,       metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}				)
						UpperCamelCase_								=			field(
						    default=0.7				,       metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}				)
						UpperCamelCase_								=			field(
						    default='''codeparrot/codeparrot'''				,       metadata={'''help''': '''Name or path to the tokenizer.'''}				,       )
						UpperCamelCase_								=			field(
						    default=__A				,       metadata={'''help''': '''If True, near-duplicate samples are removed.'''}				)
						UpperCamelCase_								=			field(
						    default=0.85				,       metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}				)





@dataclass
class UpperCAmelCase_					:




						"""simple docstring"""




						UpperCamelCase_								=			field(
						    default='''gpt2'''				,       metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}				)
						UpperCamelCase_								=			field(
						    default='''transformersbook/codeparrot-train'''				,       metadata={'''help''': '''Dataset to train tokenizer on.'''}				)
						UpperCamelCase_								=			field(default='''content'''				,       metadata={'''help''': '''Column containing text data to process.'''}				)
						UpperCamelCase_								=			field(default=200000				,       metadata={'''help''': '''Number of examples to train tokenizer on.'''}				)
						UpperCamelCase_								=			field(
						    default=32768				,       metadata={'''help''': '''Number of examples to train the tokenizer on.'''}				)
						UpperCamelCase_								=			field(default='''codeparrot'''				,       metadata={'''help''': '''Name of new tokenizer.'''}				)
						UpperCamelCase_								=			field(default=__A				,       metadata={'''help''': '''Push saved tokenizer to the hub.'''}				)





@dataclass
class UpperCAmelCase_					:




						"""simple docstring"""




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





@dataclass
class UpperCAmelCase_					:




						"""simple docstring"""




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











'''simple docstring'''



from typing import Dict, Iterable, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_pytesseract_available, is_vision_available, logging, requires_backends


if is_vision_available():
      import PIL

# soft dependency
if is_pytesseract_available():
      import pytesseract

SCREAMING_SNAKE_CASE						=	logging.get_logger(__name__)


def 							lowercase_				(    __A :       List[Any]					,      __A :       int					,      __A :       int			)       ->      Optional[int]:



 """simple docstring"""




 return [
     int(1_0_0_0 * (box[0] / width)			),
     int(1_0_0_0 * (box[1] / height)			),
     int(1_0_0_0 * (box[2] / width)			),
     int(1_0_0_0 * (box[3] / height)			),
 ]


def 							lowercase_				(    __A :       np.ndarray					,      __A :       Optional[str]					,      __A :       Optional[str]			)       ->      Optional[Any]:



 """simple docstring"""




 lowercase					:   int				  =to_pil_image(__A			)
 lowercase  ,		lowercase					:   Tuple				  =pil_image.size
 lowercase					:   Optional[Any]				  =pytesseract.image_to_data(__A					,      lang=__A					,      output_type='''dict'''					,      config=__A			)
 lowercase  ,		lowercase  ,		lowercase  ,		lowercase  ,		lowercase					:   Optional[Any]				  =data['''text'''], data['''left'''], data['''top'''], data['''width'''], data['''height''']

 # filter empty words and corresponding coordinates
 lowercase					:   Dict				  =[idx for idx, word in enumerate(__A			) if not word.strip()]
 lowercase					:   str				  =[word for idx, word in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   Optional[int]				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   List[Any]				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   str				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]
 lowercase					:   int				  =[coord for idx, coord in enumerate(__A			) if idx not in irrelevant_indices]

 # turn coordinates into (left, top, left+width, top+height) format
 lowercase					:   Tuple				  =[]
 for x, y, w, h in zip(__A					,      __A					,      __A					,      __A			):
  lowercase					:   str				  =[x, y, x + w, y + h]
  actual_boxes.append(__A			)

 # finally, normalize the bounding boxes
 lowercase					:   List[str]				  =[]
 for box in actual_boxes:
  normalized_boxes.append(normalize_box(__A					,      __A					,      __A			)			)

 assert len(__A			) == len(__A			), "Not as many words as there are bounding boxes"

 return words, normalized_boxes





class UpperCAmelCase_					(    __A				):




      """simple docstring"""




      UpperCamelCase_								=			['''pixel_values''']






      def __init__( self			:		List[Any]     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		float = 1 / 255     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = True     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = ""     ,      **UpperCAmelCase			:		Tuple     ,      )  ->  None:


       '''simple docstring'''

       super().__init__(**UpperCAmelCase		)
       lowercase					:   Tuple				  =size if size is not None else {'''height''': 224, '''width''': 224}
       lowercase					:   Optional[Any]				  =get_size_dict(UpperCAmelCase		)

       lowercase					:   Optional[Any]				  =do_resize
       lowercase					:   List[Any]				  =size
       lowercase					:   List[str]				  =resample
       lowercase					:   Dict				  =do_rescale
       lowercase					:   str				  =rescale_value
       lowercase					:   Optional[int]				  =do_normalize
       lowercase					:   Any				  =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       lowercase					:   Union[str, Any]				  =image_std if image_std is not None else IMAGENET_STANDARD_STD
       lowercase					:   List[Any]				  =apply_ocr
       lowercase					:   Union[str, Any]				  =ocr_lang
       lowercase					:   str				  =tesseract_config






      def 				A__      ( self			:		Dict     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Dict[str, int]     ,      UpperCAmelCase			:		PILImageResampling = PILImageResampling.BILINEAR     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       lowercase					:   Tuple				  =get_size_dict(UpperCAmelCase		)
       if "height" not in size or "width" not in size:
        raise ValueError(f'The size dictionary must contain the keys \'height\' and \'width\'. Got {size.keys()}'		)
       lowercase					:   Optional[Any]				  =(size['''height'''], size['''width'''])
       return resize(UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[int, float]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[Any]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       return rescale(UpperCAmelCase     ,      scale=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Union[str, Any]     ,      UpperCAmelCase			:		np.ndarray     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Union[float, Iterable[float]]     ,      UpperCAmelCase			:		Optional[Union[str, ChannelDimension]] = None     ,      **UpperCAmelCase			:		Optional[int]     ,      )  ->  np.ndarray:


       '''simple docstring'''

       return normalize(UpperCAmelCase     ,      mean=UpperCAmelCase     ,      std=UpperCAmelCase     ,      data_format=UpperCAmelCase     ,      **UpperCAmelCase		)






      def 				A__      ( self			:		Any     ,      UpperCAmelCase			:		ImageInput     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Dict[str, int] = None     ,      UpperCAmelCase			:		Union[str, Any]=None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		float = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		Union[float, Iterable[float]] = None     ,      UpperCAmelCase			:		bool = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[str] = None     ,      UpperCAmelCase			:		Optional[Union[str, TensorType]] = None     ,      UpperCAmelCase			:		ChannelDimension = ChannelDimension.FIRST     ,      **UpperCAmelCase			:		List[str]     ,      )  ->  PIL.Image.Image:


       '''simple docstring'''

       lowercase					:   Optional[int]				  =do_resize if do_resize is not None else self.do_resize
       lowercase					:   Tuple				  =size if size is not None else self.size
       lowercase					:   Optional[int]				  =get_size_dict(UpperCAmelCase		)
       lowercase					:   List[str]				  =resample if resample is not None else self.resample
       lowercase					:   List[Any]				  =do_rescale if do_rescale is not None else self.do_rescale
       lowercase					:   List[Any]				  =rescale_factor if rescale_factor is not None else self.rescale_factor
       lowercase					:   Optional[int]				  =do_normalize if do_normalize is not None else self.do_normalize
       lowercase					:   List[Any]				  =image_mean if image_mean is not None else self.image_mean
       lowercase					:   Optional[int]				  =image_std if image_std is not None else self.image_std
       lowercase					:   Any				  =apply_ocr if apply_ocr is not None else self.apply_ocr
       lowercase					:   Any				  =ocr_lang if ocr_lang is not None else self.ocr_lang
       lowercase					:   Dict				  =tesseract_config if tesseract_config is not None else self.tesseract_config

       lowercase					:   str				  =make_list_of_images(UpperCAmelCase		)

       if not valid_images(UpperCAmelCase		):
        raise ValueError(
            '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
            '''torch.Tensor, tf.Tensor or jax.ndarray.'''		)

       if do_resize and size is None:
        raise ValueError('''Size must be specified if do_resize is True.'''		)

       if do_rescale and rescale_factor is None:
        raise ValueError('''Rescale factor must be specified if do_rescale is True.'''		)

       if do_normalize and (image_mean is None or image_std is None):
        raise ValueError('''If do_normalize is True, image_mean and image_std must be specified.'''		)

       # All transformations expect numpy arrays.
       lowercase					:   Tuple				  =[to_numpy_array(UpperCAmelCase		) for image in images]

       # Tesseract OCR to get words + normalized bounding boxes
       if apply_ocr:
        requires_backends(self     ,      '''pytesseract'''		)
        lowercase					:   int				  =[]
        lowercase					:   Tuple				  =[]
        for image in images:
         lowercase  ,		lowercase					:   Dict				  =apply_tesseract(UpperCAmelCase     ,      UpperCAmelCase     ,      UpperCAmelCase		)
         words_batch.append(UpperCAmelCase		)
         boxes_batch.append(UpperCAmelCase		)

       if do_resize:
        lowercase					:   int				  =[self.resize(image=UpperCAmelCase     ,      size=UpperCAmelCase     ,      resample=UpperCAmelCase		) for image in images]

       if do_rescale:
        lowercase					:   Tuple				  =[self.rescale(image=UpperCAmelCase     ,      scale=UpperCAmelCase		) for image in images]

       if do_normalize:
        lowercase					:   str				  =[self.normalize(image=UpperCAmelCase     ,      mean=UpperCAmelCase     ,      std=UpperCAmelCase		) for image in images]

       lowercase					:   Optional[Any]				  =[to_channel_dimension_format(UpperCAmelCase     ,      UpperCAmelCase		) for image in images]

       lowercase					:   Dict				  =BatchFeature(data={'''pixel_values''': images}     ,      tensor_type=UpperCAmelCase		)

       if apply_ocr:
        lowercase					:   int				  =words_batch
        lowercase					:   List[str]				  =boxes_batch
       return data



