


'''simple docstring'''



from dataclasses import dataclass, field
from typing import Optional





@dataclass
class 							__UpperCamelCase				:
	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=10000		,      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=750		,      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=lowerCAmelCase_		,      metadata={"help": "Use gradient checkpointing to reduce memory footprint."}					)
	A_				=    field(default=50000		,      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=1024		,      metadata={"help": "Sequence lengths used for training."}					)
	A_				=    field(default=1		,      metadata={"help": "Training seed."}					)
	A_				=    field(
	    default=1024		,      metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}		,      )
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "States path if the training should continue from a checkpoint folder."}					)
	A_				=    field(default=lowerCAmelCase_		,      metadata={"help": "If True the data is pretokenized."}					)





@dataclass
class 							__UpperCamelCase				:
	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=1024		,      metadata={"help": "Length of sequences to be evaluated."}					)
	A_				=    field(default=1		,      metadata={"help": "Random seed used for evaluation."}					)





@dataclass
class 							__UpperCamelCase				:
	A_				=    field(
	    default="codeparrot/codeparrot"		,      metadata={"help": "Model name or path of model to be evaluated."}					)
	A_				=    field(default=lowerCAmelCase_		,      metadata={"help": "Number of workers used for code evaluation."}					)
	A_				=    field(
	    default=lowerCAmelCase_		,      metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}		,      )
	A_				=    field(
	    default=lowerCAmelCase_		,      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=256		,      metadata={"help": "Maximum number of newly generated tokens."}					)
	A_				=    field(default=0		,      metadata={"help": "Top-k parameter used for generation."}					)
	A_				=    field(default=0.95		,      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=200		,      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				:
	A_				=    field(
	    default=lowerCAmelCase_		,      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=100000		,      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=1000		,      metadata={"help": "Maximum line length in file, otherwise file is filtered."}					)
	A_				=    field(
	    default=100		,      metadata={"help": "Maximum mean line length in file, otherwise file is filtered."}					)
	A_				=    field(
	    default=0.25		,      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=lowerCAmelCase_		,      metadata={"help": "If True, near-duplicate samples are removed."}					)
	A_				=    field(
	    default=0.85		,      metadata={"help": "Jaccard threshold for near-duplicate samples."}					)





@dataclass
class 							__UpperCamelCase				:
	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=200000		,      metadata={"help": "Number of examples to train tokenizer on."}					)
	A_				=    field(
	    default=32768		,      metadata={"help": "Number of examples to train the tokenizer on."}					)
	A_				=    field(default="codeparrot"		,      metadata={"help": "Name of new tokenizer."}					)
	A_				=    field(default=lowerCAmelCase_		,      metadata={"help": "Push saved tokenizer to the hub."}					)





@dataclass
class 							__UpperCamelCase				:
	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=lowerCAmelCase_		,      metadata={"help": "Number of workers used for code evaluation."}					)





@dataclass
class 							__UpperCamelCase				:
	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=lowerCAmelCase_		,      metadata={"help": "Push saved tokenizer to the hub."}					)







'''simple docstring'''



import torch

from transformers import AutoModel





class 							__UpperCamelCase				(		torch.nn.Module					):




	def __init__(   self						,				__a="sayef/fsner-bert-base-uncased"							):

						'''simple docstring'''





						super(__a						,				self							).__init__()

						__a			:       Tuple					    =  AutoModel.from_pretrained(__a						,				return_dict=__a							)
						__a			:       int					    =  torch.nn.CosineSimilarity(3						,				1E-0_8							)
						__a			:       Union[str, Any]					    =  torch.nn.Softmax(dim=1							)




	def       __UpperCAmelCase							(   self						,				**__a							):

						'''simple docstring'''





						return self.bert(**__a							).last_hidden_state




	def       __UpperCAmelCase							(   self						,				__a							):

						'''simple docstring'''





						return token_embeddings.sum(2						,				keepdim=__a							)




	def       __UpperCAmelCase							(   self						,				__a						,				__a						,				__a=1							):

						'''simple docstring'''





						return self.softmax(T * self.cos(__a						,				__a							)							)





	def       __UpperCAmelCase							(   self						,				__a						,				__a							):

						'''simple docstring'''





						__a			:       str					    =  W_supports['sizes'].tolist()
						__a			:       Union[str, Any]					    =  W_supports['start_token_id'].item()
						__a			:       Any					    =  W_supports['end_token_id'].item()

						del W_supports["sizes"]
						del W_supports["start_token_id"]
						del W_supports["end_token_id"]

						__a			:       Tuple					    =  self.BERT(**__a							)
						__a			:       str					    =  self.BERT(**__a							)

						__a			:       Any					    =  None
						__a			:       Dict					    =  None

						__a			:       Dict					    =  W_supports['input_ids'] == start_token_id
						__a			:       Union[str, Any]					    =  W_supports['input_ids'] == end_token_id

						for i, size in enumerate(__a							):
											if i == 0:
																__a			:       Optional[int]					    =  0
											else:
																__a			:       Union[str, Any]					    =  support_sizes[i - 1]

											__a			:       int					    =  S[s : s + size][start_token_masks[s : s + size]]
											__a			:       Union[str, Any]					    =  S[s : s + size][end_token_masks[s : s + size]]

											__a			:       Tuple					    =  torch.matmul(q[i]						,				s_start.T							).sum(1							).softmax(0							)
											__a			:       Dict					    =  torch.matmul(q[i]						,				s_end.T							).sum(1							).softmax(0							)

											if p_starts is not None:
																__a			:       str					    =  torch.vstack((p_starts, p_start)							)
																__a			:       str					    =  torch.vstack((p_ends, p_end)							)
											else:
																__a			:       List[str]					    =  p_start
																__a			:       int					    =  p_end

						return p_starts, p_ends



