





'''simple docstring'''




from dataclasses import dataclass, field
from typing import Optional


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




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




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




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




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




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



import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


_lowercase							:   Union[str, Any]						=						{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}






class    _UpperCamelCase     (       nn.Module					):

				"""simple docstring"""







				def __init__(    self					, a__ )						->					Dict:
					super().__init__()
					A             =					torchvision.models.resnetaaa(pretrained=a__ )
					A             =					list(model.children() )[:-2]
					A             =					nn.Sequential(*a__ )
					A             =					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )

				def   _UpperCAmelCase       (    self					, a__ )						->					List[str]:
					# Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
					A             =					self.pool(self.model(a__ ) )
					A             =					torch.flatten(a__					, start_dim=2 )
					A             =					out.transpose(1					, 2 ).contiguous()
					return out  # BxNx2048






class    _UpperCamelCase     (       __snake_case					):

				"""simple docstring"""







				def __init__(    self					, a__					, a__					, a__					, a__					, a__ )						->					Union[str, Any]:
					A             =					[json.loads(a__ ) for l in open(a__ )]
					A             =					os.path.dirname(a__ )
					A             =					tokenizer
					A             =					labels
					A             =					len(a__ )
					A             =					max_seq_length

					A             =					transforms

				def __len__(    self )						->					Tuple:
					return len(self.data )

				def __getitem__(    self					, a__ )						->					Optional[int]:
					A             =					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]					, add_special_tokens=a__ ) )
					A       ,      A       ,      A             =					sentence[0], sentence[1:-1], sentence[-1]
					A             =					sentence[: self.max_seq_length]

					A             =					torch.zeros(self.n_classes )
					A             =					1

					A             =					Image.open(os.path.join(self.data_dir					, self.data[index]["""img"""] ) ).convert("""RGB""" )
					A             =					self.transforms(a__ )

					return {
					    "image_start_token": start_token,
					    "image_end_token": end_token,
					    "sentence": sentence,
					    "image": image,
					    "label": label,
					}

				def   _UpperCAmelCase       (    self )						->					Union[str, Any]:
					A             =					Counter()
					for row in self.data:
						label_freqs.update(row["""label"""] )
					return label_freqs





def 	_lowerCAmelCase					( UpperCamelCase__:  int    )			-> List[str]:





	"""simple docstring"""







	A             =					[len(row["""sentence"""]    ) for row in batch]
	A       ,      A             =					len(UpperCamelCase__    ), max(UpperCamelCase__    )

	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )
	A             =					torch.zeros(UpperCamelCase__				,       UpperCamelCase__				,       dtype=torch.long    )

	for i_batch, (input_row, length) in enumerate(zip(UpperCamelCase__				,       UpperCamelCase__    )    ):
		A             =					input_row["""sentence"""]
		A             =					1

	A             =					torch.stack([row["""image"""] for row in batch]    )
	A             =					torch.stack([row["""label"""] for row in batch]    )
	A             =					torch.stack([row["""image_start_token"""] for row in batch]    )
	A             =					torch.stack([row["""image_end_token"""] for row in batch]    )

	return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor





def 	_lowerCAmelCase					( )			-> Union[str, Any]:





	"""simple docstring"""







	return [
	    "Crime",
	    "Drama",
	    "Thriller",
	    "Action",
	    "Comedy",
	    "Romance",
	    "Documentary",
	    "Short",
	    "Mystery",
	    "History",
	    "Family",
	    "Adventure",
	    "Fantasy",
	    "Sci-Fi",
	    "Western",
	    "Horror",
	    "Sport",
	    "War",
	    "Music",
	    "Musical",
	    "Animation",
	    "Biography",
	    "Film-Noir",
	]





def 	_lowerCAmelCase					( )			-> Optional[Any]:





	"""simple docstring"""







	return transforms.Compose(
	    [
	        transforms.Resize(2_56    ),
	        transforms.CenterCrop(2_24    ),
	        transforms.ToTensor(),
	        transforms.Normalize(
	            mean=[0.46_77_70_44, 0.44_53_14_29, 0.40_66_10_17]				,       std=[0.12_22_19_94, 0.12_14_58_35, 0.14_38_04_69]				,       ),
	    ]    )



