"""simple docstring"""




from dataclasses import dataclass, field
from typing import Optional


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






@dataclass
class 						a__							:
			A__    :       Optional[str]        =      field(
			    default='codeparrot/codeparrot'				,						metadata={'help': 'Model name or path of model to be evaluated.'}		)
			A__    :       Optional[str]        =      field(
			    default='codeparrot/codeparrot-clean-valid'				,						metadata={'help': 'Name or path of validation dataset.'}		)
			A__    :       Optional[int]        =      field(default=2				,						metadata={'help': 'Batch size used for evaluation.'}		)
			A__    :       Optional[int]        =      field(
			    default=-1				,						metadata={'help': 'Maximum number of evaluation steps. If -1 the full dataset is evaluated.'}		)
			A__    :       Optional[int]        =      field(default=10_24				,						metadata={'help': 'Length of sequences to be evaluated.'}		)
			A__    :       Optional[int]        =      field(default=1				,						metadata={'help': 'Random seed used for evaluation.'}		)






@dataclass
class 						a__							:
			A__    :       Optional[str]        =      field(
			    default='codeparrot/codeparrot'				,						metadata={'help': 'Model name or path of model to be evaluated.'}		)
			A__    :       Optional[int]        =      field(default=__snake_case				,						metadata={'help': 'Number of workers used for code evaluation.'}		)
			A__    :       Optional[int]        =      field(
			    default=__snake_case				,						metadata={'help': 'The number of human-eval tasks to run. If not included all tasks are evaluated.'}				,						)
			A__    :       Optional[bool]        =      field(
			    default=__snake_case				,						metadata={'help': 'Sample from the language model\'s output distribution.'}		)
			A__    :       Optional[float]        =      field(default=0.2				,						metadata={'help': 'Sampling temperature used for generation.'}		)
			A__    :       Optional[int]        =      field(default=2_56				,						metadata={'help': 'Maximum number of newly generated tokens.'}		)
			A__    :       Optional[int]        =      field(default=0				,						metadata={'help': 'Top-k parameter used for generation.'}		)
			A__    :       Optional[float]        =      field(default=0.95				,						metadata={'help': 'Top-p parameter used for nucleus sampling.'}		)
			A__    :       Optional[int]        =      field(default=10				,						metadata={'help': 'Number of generations to run in parallel.'}		)
			A__    :       Optional[int]        =      field(
			    default=2_00				,						metadata={'help': 'Number of completions to generate for each sample.'}		)
			A__    :       Optional[int]        =      field(default=1				,						metadata={'help': 'Random seed used for evaluation.'}		)
			A__    :       Optional[str]        =      field(
			    default='eval_results.json'				,						metadata={'help': 'Random seed used for evaluation.'}		)
			A__    :       Optional[str]        =      field(
			    default='0'				,						metadata={'help': 'Allow `code_eval` to execute Python code on machine'}		)
			A__    :       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__							:
			A__    :       Optional[int]        =      field(
			    default=__snake_case				,						metadata={
			        'help': 'The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'
			    }				,						)
			A__    :       Optional[str]        =      field(
			    default='transformersbook/codeparrot'				,						metadata={'help': 'Folder or name of dataset to process.'}		)
			A__    :       Optional[str]        =      field(
			    default='codeparrot-clean'				,						metadata={'help': 'Folder to save processed processed dataset.'}		)
			A__    :       Optional[int]        =      field(
			    default=10_00_00				,						metadata={'help': 'Number of files to save per JSON output file.'}		)
			A__    :       Optional[str]        =      field(default='content'				,						metadata={'help': 'Column containing text data to process.'}		)
			A__    :       Optional[float]        =      field(
			    default=10_00				,						metadata={'help': 'Maximum line length in file, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=1_00				,						metadata={'help': 'Maximum mean line length in file, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=0.25				,						metadata={'help': 'Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=1.5				,						metadata={'help': 'Minimum character token ratio for the file, otherwise file is filtered.'}		)
			A__    :       Optional[float]        =      field(
			    default=0.7				,						metadata={'help': 'Probability for filtering config, test and uncommon files.'}		)
			A__    :       Optional[str]        =      field(
			    default='codeparrot/codeparrot'				,						metadata={'help': 'Name or path to the tokenizer.'}				,						)
			A__    :       Optional[bool]        =      field(
			    default=__snake_case				,						metadata={'help': 'If True, near-duplicate samples are removed.'}		)
			A__    :       Optional[float]        =      field(
			    default=0.85				,						metadata={'help': 'Jaccard threshold for near-duplicate samples.'}		)






@dataclass
class 						a__							:
			A__    :       Optional[str]        =      field(
			    default='gpt2'				,						metadata={'help': 'Base tokenizer to build new tokenizer from.'}		)
			A__    :       Optional[str]        =      field(
			    default='transformersbook/codeparrot-train'				,						metadata={'help': 'Dataset to train tokenizer on.'}		)
			A__    :       Optional[str]        =      field(default='content'				,						metadata={'help': 'Column containing text data to process.'}		)
			A__    :       Optional[int]        =      field(default=20_00_00				,						metadata={'help': 'Number of examples to train tokenizer on.'}		)
			A__    :       Optional[int]        =      field(
			    default=3_27_68				,						metadata={'help': 'Number of examples to train the tokenizer on.'}		)
			A__    :       Optional[str]        =      field(default='codeparrot'				,						metadata={'help': 'Name of new tokenizer.'}		)
			A__    :       Optional[bool]        =      field(default=__snake_case				,						metadata={'help': 'Push saved tokenizer to the hub.'}		)






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






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





from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
	from PIL import Image

	from ..image_utils import load_image

if is_torch_available():
	import torch

	from transformers.modeling_outputs import BaseModelOutput

	from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

lowerCamelCase_    :		Dict							=	logging.get_logger(__name__)


@add_end_docstrings(__snake_case		)
class 						a__							(       __snake_case		):



			def __init__(						self				,						**UpperCAmelCase   )			->   List[str]:
								super().__init__(**UpperCAmelCase   )

								if self.framework == "tf":
													raise ValueError(f'''The {self.__class__} is only available in PyTorch.'''   )

								requires_backends(self				,						'vision'   )
								self.check_model_type(UpperCAmelCase   )



			def __call__(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   Tuple:
								if "text_queries" in kwargs:
													__a		 =       kwargs.pop('text_queries'   )

								if isinstance(UpperCAmelCase				,						(str, Image.Image)   ):
													__a		 =       {'image': image, 'candidate_labels': candidate_labels}
								else:
													__a		 =       image
								__a		 =       super().__call__(UpperCAmelCase				,						**UpperCAmelCase   )
								return results



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   List[str]:
								__a		 =       {}
								if "threshold" in kwargs:
													__a		 =       kwargs['threshold']
								if "top_k" in kwargs:
													__a		 =       kwargs['top_k']
								return {}, {}, postprocess_params



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       load_image(inputs['image']   )
								__a		 =       inputs['candidate_labels']
								if isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       candidate_labels.split(','   )

								__a		 =       torch.tensor([[image.height, image.width]]				,						dtype=torch.intaa   )
								for i, candidate_label in enumerate(UpperCAmelCase   ):
													__a		 =       self.tokenizer(UpperCAmelCase				,						return_tensors=self.framework   )
													__a		 =       self.image_processor(UpperCAmelCase				,						return_tensors=self.framework   )
													yield {
													    "is_last": i == len(UpperCAmelCase   ) - 1,
													    "target_size": target_size,
													    "candidate_label": candidate_label,
													    **text_inputs,
													    **image_features,
													}



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   str:
								__a		 =       model_inputs.pop('target_size'   )
								__a		 =       model_inputs.pop('candidate_label'   )
								__a		 =       model_inputs.pop('is_last'   )

								__a		 =       self.model(**UpperCAmelCase   )

								__a		 =       {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
								return model_outputs



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=0.1				,						UpperCAmelCase=None   )			->   Tuple:
								__a		 =       []
								for model_output in model_outputs:
													__a		 =       model_output['candidate_label']
													__a		 =       BaseModelOutput(UpperCAmelCase   )
													__a		 =       self.image_processor.post_process_object_detection(
													    outputs=UpperCAmelCase				,						threshold=UpperCAmelCase				,						target_sizes=model_output['target_size']   )[0]

													for index in outputs["scores"].nonzero():
																		__a		 =       outputs['scores'][index].item()
																		__a		 =       self._get_bounding_box(outputs['boxes'][index][0]   )

																		__a		 =       {'score': score, 'label': label, 'box': box}
																		results.append(UpperCAmelCase   )

								__a		 =       sorted(UpperCAmelCase				,						key=lambda UpperCAmelCase   : x["score"]				,						reverse=UpperCAmelCase   )
								if top_k:
													__a		 =       results[:top_k]

								return results







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Dict[str, int]:
								if self.framework != "pt":
													raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'   )
								__a ,				__a ,				__a ,				__a		 =       box.int().tolist()
								__a		 =       {
								    'xmin': xmin,
								    'ymin': ymin,
								    'xmax': xmax,
								    'ymax': ymax,
								}
								return bbox




