
import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

							if is_vision_available():
														from transformers import OneFormerImageProcessor
														from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
														from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
							from PIL import Image



def 		UpperCamelCase     (       _UpperCAmelCase  :   Any ,			_UpperCAmelCase  :   str="shi-labs/oneformer_demo"  )   ->			Any:






						'''simple docstring'''

						with open(hf_hub_download(_UpperCAmelCase ,			_UpperCAmelCase ,			repo_type="dataset"  ) ,			"r"  ) as f:
												_lowercase       :	Tuple           =   json.load(_UpperCAmelCase  )
						_lowercase       :	Any           =   {}
						_lowercase       :	Dict           =   []
						_lowercase       :	Union[str, Any]           =   []
						for key, info in class_info.items():
												_lowercase       :	Tuple           =   info["name"]
												class_names.append(info["name"]  )
												if info["isthing"]:
																		thing_ids.append(int(_UpperCAmelCase  )  )
						_lowercase       :	int           =   thing_ids
						_lowercase       :	int           =   class_names
						return metadata




class   __lowercase						(						unittest.TestCase       ):






	def __init__(self				: List[str]      ,	snake_case				: List[str]      ,	snake_case				: str=7      ,	snake_case				: Dict=3      ,	snake_case				: str=30      ,	snake_case				: str=400      ,	snake_case				: List[Any]=None      ,	snake_case				: Tuple=True      ,	snake_case				: Optional[Any]=True      ,	snake_case				: int=[0.5, 0.5, 0.5]      ,	snake_case				: Any=[0.5, 0.5, 0.5]      ,	snake_case				: Dict=10      ,	snake_case				: List[str]=False      ,	snake_case				: List[str]=255      ,	snake_case				: Optional[int]="shi-labs/oneformer_demo"      ,	snake_case				: Tuple="ade20k_panoptic.json"      ,	snake_case				: str=10      ,	) ->			Any:
							_lowercase       :	Optional[int]           =   parent
							_lowercase       :	Optional[int]           =   batch_size
							_lowercase       :	List[str]           =   num_channels
							_lowercase       :	List[Any]           =   min_resolution
							_lowercase       :	Optional[int]           =   max_resolution
							_lowercase       :	Any           =   do_resize
							_lowercase       :	Any           =   {"shortest_edge": 32, "longest_edge": 1333} if size is None else size
							_lowercase       :	Any           =   do_normalize
							_lowercase       :	Any           =   image_mean
							_lowercase       :	List[Any]           =   image_std
							_lowercase       :	str           =   class_info_file
							_lowercase       :	Optional[int]           =   prepare_metadata(snake_case      ,	snake_case    )
							_lowercase       :	Optional[int]           =   num_text
							_lowercase       :	List[Any]           =   repo_path

							# for the post_process_functions
							_lowercase       :	int           =   2
							_lowercase       :	Dict           =   10
							_lowercase       :	Optional[Any]           =   10
							_lowercase       :	Union[str, Any]           =   3
							_lowercase       :	List[str]           =   4
							_lowercase       :	List[str]           =   num_labels
							_lowercase       :	Dict           =   do_reduce_labels
							_lowercase       :	Union[str, Any]           =   ignore_index






	def       _a(self				: int    ) ->			Dict:
							return {
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "do_normalize": self.do_normalize,
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "num_labels": self.num_labels,
							    "do_reduce_labels": self.do_reduce_labels,
							    "ignore_index": self.ignore_index,
							    "class_info_file": self.class_info_file,
							    "metadata": self.metadata,
							    "num_text": self.num_text,
							}






	def       _a(self				: Union[str, Any]      ,	snake_case				: Any      ,	snake_case				: Dict=False    ) ->			Tuple:
							if not batched:
													_lowercase       :	Any           =   image_inputs[0]
													if isinstance(snake_case      ,	Image.Image    ):
																			_lowercase      ,    _lowercase       :	List[Any]           =   image.size
													else:
																			_lowercase      ,    _lowercase       :	Optional[Any]           =   image.shape[1], image.shape[2]
													if w < h:
																			_lowercase       :	Any           =   int(self.size["shortest_edge"] * h / w    )
																			_lowercase       :	str           =   self.size["shortest_edge"]
													elif w > h:
																			_lowercase       :	int           =   self.size["shortest_edge"]
																			_lowercase       :	Tuple           =   int(self.size["shortest_edge"] * w / h    )
													else:
																			_lowercase       :	int           =   self.size["shortest_edge"]
																			_lowercase       :	Optional[Any]           =   self.size["shortest_edge"]

							else:
													_lowercase       :	List[str]           =   []
													for image in image_inputs:
																			_lowercase      ,    _lowercase       :	str           =   self.get_expected_values([image]    )
																			expected_values.append((expected_height, expected_width)    )
													_lowercase       :	str           =   max(snake_case      ,	key=lambda snake_case    : item[0]    )[0]
													_lowercase       :	int           =   max(snake_case      ,	key=lambda snake_case    : item[1]    )[1]

							return expected_height, expected_width






	def       _a(self				: str    ) ->			List[Any]:
							return OneFormerForUniversalSegmentationOutput(
							    # +1 for null class
							    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)    )      ,	masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)    )      ,	)




@require_torch
@require_vision
class   __lowercase						(						__snake_case   , unittest.TestCase       ):
	_A       =   OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	_A       =   image_processing_class






	def       _a(self				: Dict    ) ->			Union[str, Any]:
							_lowercase       :	Optional[int]           =   OneFormerImageProcessorTester(self    )






	@property
	def       _a(self				: Optional[int]    ) ->			Any:
							return self.image_processing_tester.prepare_image_processor_dict()






	def       _a(self				: Dict    ) ->			Any:
							_lowercase       :	Union[str, Any]           =   self.image_processing_class(**self.image_processor_dict    )
							self.assertTrue(hasattr(snake_case      ,	"image_mean"    )    )
							self.assertTrue(hasattr(snake_case      ,	"image_std"    )    )
							self.assertTrue(hasattr(snake_case      ,	"do_normalize"    )    )
							self.assertTrue(hasattr(snake_case      ,	"do_resize"    )    )
							self.assertTrue(hasattr(snake_case      ,	"size"    )    )
							self.assertTrue(hasattr(snake_case      ,	"ignore_index"    )    )
							self.assertTrue(hasattr(snake_case      ,	"class_info_file"    )    )
							self.assertTrue(hasattr(snake_case      ,	"num_text"    )    )
							self.assertTrue(hasattr(snake_case      ,	"repo_path"    )    )
							self.assertTrue(hasattr(snake_case      ,	"metadata"    )    )
							self.assertTrue(hasattr(snake_case      ,	"do_reduce_labels"    )    )






	def       _a(self				: Optional[int]    ) ->			Optional[Any]:
							pass






	def       _a(self				: List[str]    ) ->			List[Any]:
							# Initialize image_processor
							_lowercase       :	Union[str, Any]           =   self.image_processing_class(**self.image_processor_dict    )
							# create random PIL images
							_lowercase       :	List[str]           =   prepare_image_inputs(self.image_processing_tester      ,	equal_resolution=snake_case    )
							for image in image_inputs:
													self.assertIsInstance(snake_case      ,	Image.Image    )

							# Test not batched input
							_lowercase       :	Optional[Any]           =   image_processor(image_inputs[0]      ,	["semantic"]      ,	return_tensors="pt"    ).pixel_values

							_lowercase      ,    _lowercase       :	Dict           =   self.image_processing_tester.get_expected_values(snake_case    )

							self.assertEqual(
							    encoded_images.shape      ,	(1, self.image_processing_tester.num_channels, expected_height, expected_width)      ,	)

							# Test batched
							_lowercase      ,    _lowercase       :	Tuple           =   self.image_processing_tester.get_expected_values(snake_case      ,	batched=snake_case    )

							_lowercase       :	str           =   image_processor(
							    snake_case      ,	["semantic"] * len(snake_case    )      ,	return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,	(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,	)






	def       _a(self				: int    ) ->			Dict:
							# Initialize image_processor
							_lowercase       :	List[Any]           =   self.image_processing_class(**self.image_processor_dict    )
							# create random numpy tensors
							_lowercase       :	int           =   prepare_image_inputs(self.image_processing_tester      ,	equal_resolution=snake_case      ,	numpify=snake_case    )
							for image in image_inputs:
													self.assertIsInstance(snake_case      ,	np.ndarray    )

							# Test not batched input
							_lowercase       :	Optional[int]           =   image_processor(image_inputs[0]      ,	["semantic"]      ,	return_tensors="pt"    ).pixel_values

							_lowercase      ,    _lowercase       :	Optional[Any]           =   self.image_processing_tester.get_expected_values(snake_case    )

							self.assertEqual(
							    encoded_images.shape      ,	(1, self.image_processing_tester.num_channels, expected_height, expected_width)      ,	)

							# Test batched
							_lowercase      ,    _lowercase       :	Optional[int]           =   self.image_processing_tester.get_expected_values(snake_case      ,	batched=snake_case    )

							_lowercase       :	Any           =   image_processor(
							    snake_case      ,	["semantic"] * len(snake_case    )      ,	return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,	(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,	)






	def       _a(self				: List[Any]    ) ->			List[str]:
							# Initialize image_processor
							_lowercase       :	Any           =   self.image_processing_class(**self.image_processor_dict    )
							# create random PyTorch tensors
							_lowercase       :	Tuple           =   prepare_image_inputs(self.image_processing_tester      ,	equal_resolution=snake_case      ,	torchify=snake_case    )
							for image in image_inputs:
													self.assertIsInstance(snake_case      ,	torch.Tensor    )

							# Test not batched input
							_lowercase       :	Optional[int]           =   image_processor(image_inputs[0]      ,	["semantic"]      ,	return_tensors="pt"    ).pixel_values

							_lowercase      ,    _lowercase       :	Tuple           =   self.image_processing_tester.get_expected_values(snake_case    )

							self.assertEqual(
							    encoded_images.shape      ,	(1, self.image_processing_tester.num_channels, expected_height, expected_width)      ,	)

							# Test batched
							_lowercase      ,    _lowercase       :	str           =   self.image_processing_tester.get_expected_values(snake_case      ,	batched=snake_case    )

							_lowercase       :	Tuple           =   image_processor(
							    snake_case      ,	["semantic"] * len(snake_case    )      ,	return_tensors="pt"    ).pixel_values
							self.assertEqual(
							    encoded_images.shape      ,	(
							        self.image_processing_tester.batch_size,
							        self.image_processing_tester.num_channels,
							        expected_height,
							        expected_width,
							    )      ,	)






	def       _a(self				: List[str]      ,	snake_case				: List[str]=False      ,	snake_case				: int=False      ,	snake_case				: Optional[Any]="np"    ) ->			Optional[int]:
							_lowercase       :	Union[str, Any]           =   self.image_processing_class(**self.image_processor_dict    )
							# prepare image and target
							_lowercase       :	str           =   self.image_processing_tester.num_labels
							_lowercase       :	str           =   None
							_lowercase       :	Dict           =   None
							_lowercase       :	Union[str, Any]           =   prepare_image_inputs(self.image_processing_tester      ,	equal_resolution=snake_case    )
							if with_segmentation_maps:
													_lowercase       :	Dict           =   num_labels
													if is_instance_map:
																			_lowercase       :	List[Any]           =   list(range(snake_case    )    ) * 2
																			_lowercase       :	int           =   dict(enumerate(snake_case    )    )
													_lowercase       :	List[str]           =   [
													    np.random.randint(0      ,	high * 2      ,	(img.size[1], img.size[0])    ).astype(np.uinta    ) for img in image_inputs
													]
													if segmentation_type == "pil":
																			_lowercase       :	List[str]           =   [Image.fromarray(snake_case    ) for annotation in annotations]

							_lowercase       :	List[Any]           =   image_processor(
							    snake_case      ,	["semantic"] * len(snake_case    )      ,	snake_case      ,	return_tensors="pt"      ,	instance_id_to_semantic_id=snake_case      ,	pad_and_return_pixel_mask=snake_case      ,	)

							return inputs






	def       _a(self				: List[str]    ) ->			int:
							pass






	def       _a(self				: Optional[Any]    ) ->			List[Any]:
							def common(snake_case				: str=False      ,	snake_case				: Dict=None    ):
													_lowercase       :	List[str]           =   self.comm_get_image_processor_inputs(
													    with_segmentation_maps=snake_case      ,	is_instance_map=snake_case      ,	segmentation_type=snake_case    )

													_lowercase       :	Optional[Any]           =   inputs["mask_labels"]
													_lowercase       :	int           =   inputs["class_labels"]
													_lowercase       :	str           =   inputs["pixel_values"]
													_lowercase       :	Optional[Any]           =   inputs["text_inputs"]

													# check the batch_size
													for mask_label, class_label, text_input in zip(snake_case      ,	snake_case      ,	snake_case    ):
																			self.assertEqual(mask_label.shape[0]      ,	class_label.shape[0]    )
																			# this ensure padding has happened
																			self.assertEqual(mask_label.shape[1:]      ,	pixel_values.shape[2:]    )
																			self.assertEqual(len(snake_case    )      ,	self.image_processing_tester.num_text    )

							common()
							common(is_instance_map=snake_case    )
							common(is_instance_map=snake_case      ,	segmentation_type="pil"    )
							common(is_instance_map=snake_case      ,	segmentation_type="pil"    )






	def       _a(self				: Dict    ) ->			str:
							_lowercase       :	int           =   np.zeros((20, 50)    )
							_lowercase       :	str           =   1
							_lowercase       :	List[str]           =   1
							_lowercase       :	List[Any]           =   1

							_lowercase       :	Optional[int]           =   binary_mask_to_rle(snake_case    )
							self.assertEqual(len(snake_case    )      ,	4    )
							self.assertEqual(rle[0]      ,	21    )
							self.assertEqual(rle[1]      ,	45    )






	def       _a(self				: List[Any]    ) ->			str:
							_lowercase       :	List[Any]           =   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes      ,	max_seq_length=77      ,	task_seq_length=77      ,	class_info_file="ade20k_panoptic.json"      ,	num_text=self.image_processing_tester.num_text      ,	repo_path="shi-labs/oneformer_demo"      ,	)
							_lowercase       :	int           =   self.image_processing_tester.get_fake_oneformer_outputs()

							_lowercase       :	str           =   fature_extractor.post_process_semantic_segmentation(snake_case    )

							self.assertEqual(len(snake_case    )      ,	self.image_processing_tester.batch_size    )
							self.assertEqual(
							    segmentation[0].shape      ,	(
							        self.image_processing_tester.height,
							        self.image_processing_tester.width,
							    )      ,	)

							_lowercase       :	Optional[int]           =   [(1, 4) for i in range(self.image_processing_tester.batch_size    )]
							_lowercase       :	int           =   fature_extractor.post_process_semantic_segmentation(snake_case      ,	target_sizes=snake_case    )

							self.assertEqual(segmentation[0].shape      ,	target_sizes[0]    )






	def       _a(self				: Tuple    ) ->			str:
							_lowercase       :	Union[str, Any]           =   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes      ,	max_seq_length=77      ,	task_seq_length=77      ,	class_info_file="ade20k_panoptic.json"      ,	num_text=self.image_processing_tester.num_text      ,	repo_path="shi-labs/oneformer_demo"      ,	)
							_lowercase       :	Optional[int]           =   self.image_processing_tester.get_fake_oneformer_outputs()
							_lowercase       :	Dict           =   image_processor.post_process_instance_segmentation(snake_case      ,	threshold=0    )

							self.assertTrue(len(snake_case    ) == self.image_processing_tester.batch_size    )
							for el in segmentation:
													self.assertTrue("segmentation" in el    )
													self.assertTrue("segments_info" in el    )
													self.assertEqual(type(el["segments_info"]    )      ,	snake_case    )
													self.assertEqual(
													    el["segmentation"].shape      ,	(self.image_processing_tester.height, self.image_processing_tester.width)    )






	def       _a(self				: Union[str, Any]    ) ->			int:
							_lowercase       :	Dict           =   self.image_processing_class(
							    num_labels=self.image_processing_tester.num_classes      ,	max_seq_length=77      ,	task_seq_length=77      ,	class_info_file="ade20k_panoptic.json"      ,	num_text=self.image_processing_tester.num_text      ,	repo_path="shi-labs/oneformer_demo"      ,	)
							_lowercase       :	Dict           =   self.image_processing_tester.get_fake_oneformer_outputs()
							_lowercase       :	Union[str, Any]           =   image_processor.post_process_panoptic_segmentation(snake_case      ,	threshold=0    )

							self.assertTrue(len(snake_case    ) == self.image_processing_tester.batch_size    )
							for el in segmentation:
													self.assertTrue("segmentation" in el    )
													self.assertTrue("segments_info" in el    )
													self.assertEqual(type(el["segments_info"]    )      ,	snake_case    )
													self.assertEqual(
													    el["segmentation"].shape      ,	(self.image_processing_tester.height, self.image_processing_tester.width)    )






from dataclasses import dataclass, field
from typing import Optional




@dataclass
class   __lowercase						:
	_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=__snake_case   , 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=__snake_case   , metadata={"help": "States path if the training should continue from a checkpoint folder."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "If True the data is pretokenized."}       )




@dataclass
class   __lowercase						:
	_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   __lowercase						:
	_A       =   field(
	    default="codeparrot/codeparrot"   , metadata={"help": "Model name or path of model to be evaluated."}       )
	_A       =   field(default=__snake_case   , metadata={"help": "Number of workers used for code evaluation."}       )
	_A       =   field(
	    default=__snake_case   , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}   , )
	_A       =   field(
	    default=__snake_case   , 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   __lowercase						:
	_A       =   field(
	    default=__snake_case   , 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=__snake_case   , metadata={"help": "If True, near-duplicate samples are removed."}       )
	_A       =   field(
	    default=0.85   , metadata={"help": "Jaccard threshold for near-duplicate samples."}       )




@dataclass
class   __lowercase						:
	_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=__snake_case   , metadata={"help": "Push saved tokenizer to the hub."}       )




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




@dataclass
class   __lowercase						:
	_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=__snake_case   , metadata={"help": "Push saved tokenizer to the hub."}       )




