




from dataclasses import dataclass, field
from typing import Optional







@dataclass
class __snake_case			:
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot/codeparrot''' ,  metadata={'''help''': '''Model name or path of model to be trained.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''./''' ,  metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot/codeparrot-clean-train''' ,  metadata={'''help''': '''Name or path of training dataset.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot/codeparrot-clean-valid''' ,  metadata={'''help''': '''Name or path of validation dataset.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=2 ,  metadata={'''help''': '''Batch size for training.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=2 ,  metadata={'''help''': '''Batch size for evaluation.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(default=0.1 ,  metadata={'''help''': '''Value of weight decay.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=1_0_0_0_0 ,  metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(default=2E-4 ,  metadata={'''help''': '''Learning rate fo training.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(default='''cosine''' ,  metadata={'''help''': '''Learning rate.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=7_5_0 ,  metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=1_6 ,  metadata={'''help''': '''Number of gradient accumulation steps.'''}		)
   UpperCAmelCase__  :    Optional[bool]          =  field(
       default=lowerCamelCase_ ,  metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=5_0_0_0_0 ,  metadata={'''help''': '''Maximum number of training steps.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=-1 ,  metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=1_0_2_4 ,  metadata={'''help''': '''Sequence lengths used for training.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=1 ,  metadata={'''help''': '''Training seed.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=1_0_2_4 ,  metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''} ,  )
   UpperCAmelCase__  :    Optional[str]          =  field(
       default=lowerCamelCase_ ,  metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}		)
   UpperCAmelCase__  :    Optional[bool]          =  field(default=lowerCamelCase_ ,  metadata={'''help''': '''If True the data is pretokenized.'''}		)







@dataclass
class __snake_case			:
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot/codeparrot''' ,  metadata={'''help''': '''Model name or path of model to be evaluated.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot/codeparrot-clean-valid''' ,  metadata={'''help''': '''Name or path of validation dataset.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=2 ,  metadata={'''help''': '''Batch size used for evaluation.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=-1 ,  metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=1_0_2_4 ,  metadata={'''help''': '''Length of sequences to be evaluated.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=1 ,  metadata={'''help''': '''Random seed used for evaluation.'''}		)







@dataclass
class __snake_case			:
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot/codeparrot''' ,  metadata={'''help''': '''Model name or path of model to be evaluated.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=lowerCamelCase_ ,  metadata={'''help''': '''Number of workers used for code evaluation.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=lowerCamelCase_ ,  metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''} ,  )
   UpperCAmelCase__  :    Optional[bool]          =  field(
       default=lowerCamelCase_ ,  metadata={'''help''': '''Sample from the language model\'s output distribution.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(default=0.2 ,  metadata={'''help''': '''Sampling temperature used for generation.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=2_5_6 ,  metadata={'''help''': '''Maximum number of newly generated tokens.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=0 ,  metadata={'''help''': '''Top-k parameter used for generation.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(default=0.95 ,  metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=1_0 ,  metadata={'''help''': '''Number of generations to run in parallel.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=2_0_0 ,  metadata={'''help''': '''Number of completions to generate for each sample.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=1 ,  metadata={'''help''': '''Random seed used for evaluation.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''eval_results.json''' ,  metadata={'''help''': '''Random seed used for evaluation.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''0''' ,  metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}		)
   UpperCAmelCase__  :    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 __snake_case			:
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=lowerCamelCase_ ,  metadata={
           '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
       } ,  )
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''transformersbook/codeparrot''' ,  metadata={'''help''': '''Folder or name of dataset to process.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot-clean''' ,  metadata={'''help''': '''Folder to save processed processed dataset.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=1_0_0_0_0_0 ,  metadata={'''help''': '''Number of files to save per JSON output file.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(default='''content''' ,  metadata={'''help''': '''Column containing text data to process.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(
       default=1_0_0_0 ,  metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(
       default=1_0_0 ,  metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(
       default=0.25 ,  metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(
       default=1.5 ,  metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(
       default=0.7 ,  metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''codeparrot/codeparrot''' ,  metadata={'''help''': '''Name or path to the tokenizer.'''} ,  )
   UpperCAmelCase__  :    Optional[bool]          =  field(
       default=lowerCamelCase_ ,  metadata={'''help''': '''If True, near-duplicate samples are removed.'''}		)
   UpperCAmelCase__  :    Optional[float]          =  field(
       default=0.85 ,  metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}		)







@dataclass
class __snake_case			:
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''gpt2''' ,  metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(
       default='''transformersbook/codeparrot-train''' ,  metadata={'''help''': '''Dataset to train tokenizer on.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(default='''content''' ,  metadata={'''help''': '''Column containing text data to process.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(default=2_0_0_0_0_0 ,  metadata={'''help''': '''Number of examples to train tokenizer on.'''}		)
   UpperCAmelCase__  :    Optional[int]          =  field(
       default=3_2_7_6_8 ,  metadata={'''help''': '''Number of examples to train the tokenizer on.'''}		)
   UpperCAmelCase__  :    Optional[str]          =  field(default='''codeparrot''' ,  metadata={'''help''': '''Name of new tokenizer.'''}		)
   UpperCAmelCase__  :    Optional[bool]          =  field(default=lowerCamelCase_ ,  metadata={'''help''': '''Push saved tokenizer to the hub.'''}		)







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







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










"""simple docstring"""




import json
import os
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

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

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import ImageGPTImageProcessor
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		def __init__(	self						,       lowerCAmelCase__						,       lowerCAmelCase__=7						,       lowerCAmelCase__=3						,       lowerCAmelCase__=18						,       lowerCAmelCase__=30						,       lowerCAmelCase__=400						,       lowerCAmelCase__=True						,       lowerCAmelCase__=None						,       lowerCAmelCase__=True						,       )     ->							int:
								SCREAMING_SNAKE_CASE													=					size if size is not None else {'height': 18, 'width': 18}
								SCREAMING_SNAKE_CASE													=					parent
								SCREAMING_SNAKE_CASE													=					batch_size
								SCREAMING_SNAKE_CASE													=					num_channels
								SCREAMING_SNAKE_CASE													=					image_size
								SCREAMING_SNAKE_CASE													=					min_resolution
								SCREAMING_SNAKE_CASE													=					max_resolution
								SCREAMING_SNAKE_CASE													=					do_resize
								SCREAMING_SNAKE_CASE													=					size
								SCREAMING_SNAKE_CASE													=					do_normalize







		def 							__A						(	self				)     ->							Optional[Any]:
								return {
								    # here we create 2 clusters for the sake of simplicity
								    "clusters": np.asarray(
								        [
								            [0.88_66_44_36_34_03_32_03, 0.66_18_82_93_69_54_49_83, 0.38_91_74_64_01_78_68_04],
								            [-0.60_42_55_91_46_88_11_04, -0.0_22_95_00_88_60_52_84_69, 0.54_23_79_73_69_00_32_96],
								        ]				),
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_normalize": self.do_normalize,
								}





@require_torch
@require_vision
class 					lowerCAmelCase	(					lowerCamelCase_	,    unittest.TestCase    ):




		'''simple docstring'''







		SCREAMING_SNAKE_CASE_						:     Union[str, Any]								=     ImageGPTImageProcessor if is_vision_available() else None







		def 							__A						(	self				)     ->							Optional[int]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessingTester(self				)







		@property
		def 							__A						(	self				)     ->							Any:
								return self.image_processor_tester.prepare_image_processor_dict()







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'clusters'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_resize'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'size'				)				)
								self.assertTrue(hasattr(lowerCAmelCase__						,       'do_normalize'				)				)







		def 							__A						(	self				)     ->							Tuple:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict				)
								self.assertEqual(image_processor.size						,       {'height': 18, 'width': 18}				)

								SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_dict(self.image_processor_dict						,       size=42				)
								self.assertEqual(image_processor.size						,       {'height': 42, 'width': 42}				)







		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)
								SCREAMING_SNAKE_CASE													=					json.loads(image_processor.to_json_string()				)
								for key, value in self.image_processor_dict.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       obj[key]				)				)
														else:
																				self.assertEqual(obj[key]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							List[str]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
														SCREAMING_SNAKE_CASE													=					os.path.join(lowerCAmelCase__						,       'image_processor.json'				)
														image_processor_first.to_json_file(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_json_file(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       image_processor_second[key]				)				)
														else:
																				self.assertEqual(image_processor_first[key]						,       lowerCAmelCase__				)







		def 							__A						(	self				)     ->							Optional[Any]:
								SCREAMING_SNAKE_CASE													=					self.image_processing_class(**self.image_processor_dict				)

								with tempfile.TemporaryDirectory() as tmpdirname:
														image_processor_first.save_pretrained(lowerCAmelCase__				)
														SCREAMING_SNAKE_CASE													=					self.image_processing_class.from_pretrained(lowerCAmelCase__				).to_dict()

								SCREAMING_SNAKE_CASE													=					image_processor_first.to_dict()
								for key, value in image_processor_first.items():
														if key == "clusters":
																				self.assertTrue(np.array_equal(lowerCAmelCase__						,       image_processor_second[key]				)				)
														else:
																				self.assertEqual(image_processor_first[key]						,       lowerCAmelCase__				)







		@unittest.skip('ImageGPT requires clusters at initialization'				)
		def 							__A						(	self				)     ->							Optional[Any]:
								pass





def     lowercase			()   ->						Union[str, Any]:
						SCREAMING_SNAKE_CASE													=					load_dataset('hf-internal-testing/fixtures_image_utils' ,			split='test'  )

						SCREAMING_SNAKE_CASE													=					Image.open(dataset[4]['file']  )
						SCREAMING_SNAKE_CASE													=					Image.open(dataset[5]['file']  )

						SCREAMING_SNAKE_CASE													=					[imagea, imagea]

						return images


@require_vision
@require_torch
class 					lowerCAmelCase	(					unittest.TestCase    ):




		'''simple docstring'''







		@slow
		def 							__A						(	self				)     ->							List[Any]:
								SCREAMING_SNAKE_CASE													=					ImageGPTImageProcessor.from_pretrained('openai/imagegpt-small'				)

								SCREAMING_SNAKE_CASE													=					prepare_images()

								# test non-batched
								SCREAMING_SNAKE_CASE													=					image_processing(images[0]						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (1, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[306, 191, 191]
								self.assertEqual(encoding.input_ids[0, :3].tolist()						,       lowerCAmelCase__				)

								# test batched
								SCREAMING_SNAKE_CASE													=					image_processing(lowerCAmelCase__						,       return_tensors='pt'				)

								self.assertIsInstance(encoding.input_ids						,       torch.LongTensor				)
								self.assertEqual(encoding.input_ids.shape						,       (2, 1_024)				)

								SCREAMING_SNAKE_CASE													=					[303, 13, 13]
								self.assertEqual(encoding.input_ids[1, -3:].tolist()						,       lowerCAmelCase__				)


