





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class   A							:
      _snake_case				  =field(
          default='''codeparrot/codeparrot'''		,   metadata={'''help''': '''Model name or path of model to be trained.'''}    )
      _snake_case				  =field(
          default='''./'''		,   metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}    )
      _snake_case				  =field(
          default='''codeparrot/codeparrot-clean-train'''		,   metadata={'''help''': '''Name or path of training dataset.'''}    )
      _snake_case				  =field(
          default='''codeparrot/codeparrot-clean-valid'''		,   metadata={'''help''': '''Name or path of validation dataset.'''}    )
      _snake_case				  =field(default=2		,   metadata={'''help''': '''Batch size for training.'''}    )
      _snake_case				  =field(default=2		,   metadata={'''help''': '''Batch size for evaluation.'''}    )
      _snake_case				  =field(default=0.1		,   metadata={'''help''': '''Value of weight decay.'''}    )
      _snake_case				  =field(
          default=10000		,   metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}    )
      _snake_case				  =field(default=2E-4		,   metadata={'''help''': '''Learning rate fo training.'''}    )
      _snake_case				  =field(default='''cosine'''		,   metadata={'''help''': '''Learning rate.'''}    )
      _snake_case				  =field(
          default=750		,   metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}    )
      _snake_case				  =field(
          default=16		,   metadata={'''help''': '''Number of gradient accumulation steps.'''}    )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}    )
      _snake_case				  =field(default=50000		,   metadata={'''help''': '''Maximum number of training steps.'''}    )
      _snake_case				  =field(
          default=-1		,   metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}    )
      _snake_case				  =field(default=1024		,   metadata={'''help''': '''Sequence lengths used for training.'''}    )
      _snake_case				  =field(default=1		,   metadata={'''help''': '''Training seed.'''}    )
      _snake_case				  =field(
          default=1024		,   metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}		,   )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}    )
      _snake_case				  =field(default=__lowercase		,   metadata={'''help''': '''If True the data is pretokenized.'''}    )

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

@dataclass
class   A							:
      _snake_case				  =field(
          default='''codeparrot/codeparrot'''		,   metadata={'''help''': '''Model name or path of model to be evaluated.'''}    )
      _snake_case				  =field(default=__lowercase		,   metadata={'''help''': '''Number of workers used for code evaluation.'''}    )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}		,   )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''Sample from the language model\'s output distribution.'''}    )
      _snake_case				  =field(default=0.2		,   metadata={'''help''': '''Sampling temperature used for generation.'''}    )
      _snake_case				  =field(default=256		,   metadata={'''help''': '''Maximum number of newly generated tokens.'''}    )
      _snake_case				  =field(default=0		,   metadata={'''help''': '''Top-k parameter used for generation.'''}    )
      _snake_case				  =field(default=0.95		,   metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}    )
      _snake_case				  =field(default=10		,   metadata={'''help''': '''Number of generations to run in parallel.'''}    )
      _snake_case				  =field(
          default=200		,   metadata={'''help''': '''Number of completions to generate for each sample.'''}    )
      _snake_case				  =field(default=1		,   metadata={'''help''': '''Random seed used for evaluation.'''}    )
      _snake_case				  =field(
          default='''eval_results.json'''		,   metadata={'''help''': '''Random seed used for evaluation.'''}    )
      _snake_case				  =field(
          default='''0'''		,   metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}    )
      _snake_case				  =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							:
      _snake_case				  =field(
          default=__lowercase		,   metadata={
              '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
          }		,   )
      _snake_case				  =field(
          default='''transformersbook/codeparrot'''		,   metadata={'''help''': '''Folder or name of dataset to process.'''}    )
      _snake_case				  =field(
          default='''codeparrot-clean'''		,   metadata={'''help''': '''Folder to save processed processed dataset.'''}    )
      _snake_case				  =field(
          default=100000		,   metadata={'''help''': '''Number of files to save per JSON output file.'''}    )
      _snake_case				  =field(default='''content'''		,   metadata={'''help''': '''Column containing text data to process.'''}    )
      _snake_case				  =field(
          default=1000		,   metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=100		,   metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=0.25		,   metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=1.5		,   metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}    )
      _snake_case				  =field(
          default=0.7		,   metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}    )
      _snake_case				  =field(
          default='''codeparrot/codeparrot'''		,   metadata={'''help''': '''Name or path to the tokenizer.'''}		,   )
      _snake_case				  =field(
          default=__lowercase		,   metadata={'''help''': '''If True, near-duplicate samples are removed.'''}    )
      _snake_case				  =field(
          default=0.85		,   metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}    )

@dataclass
class   A							:
      _snake_case				  =field(
          default='''gpt2'''		,   metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}    )
      _snake_case				  =field(
          default='''transformersbook/codeparrot-train'''		,   metadata={'''help''': '''Dataset to train tokenizer on.'''}    )
      _snake_case				  =field(default='''content'''		,   metadata={'''help''': '''Column containing text data to process.'''}    )
      _snake_case				  =field(default=200000		,   metadata={'''help''': '''Number of examples to train tokenizer on.'''}    )
      _snake_case				  =field(
          default=32768		,   metadata={'''help''': '''Number of examples to train the tokenizer on.'''}    )
      _snake_case				  =field(default='''codeparrot'''		,   metadata={'''help''': '''Name of new tokenizer.'''}    )
      _snake_case				  =field(default=__lowercase		,   metadata={'''help''': '''Push saved tokenizer to the hub.'''}    )

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

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












import itertools
import random
import unittest

import numpy as np

from transformers import ASTFeatureExtractor
from transformers.testing_utils import require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase    				=   random.Random()

if is_torch_available():
	import torch





def 		__UpperCAmelCase     (	a_				,	a_=1.0				,	a_=None				,	a_=None):
							if rng is None:
														snake_case_           =				global_rng

							snake_case_           =				[]
							for batch_idx in range(shape[0]):
														values.append([])
														for _ in range(shape[1]):
																					values[-1].append(rng.random() * scale)

							return values




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def __init__(			self			,     a			,     a=7			,     a=4_00			,     a=20_00			,     a=1			,     a=0.0			,     a=1_60_00			,     a=True			,     a=True			,     )			->       Tuple:
								snake_case_           =				parent
								snake_case_           =				batch_size
								snake_case_           =				min_seq_length
								snake_case_           =				max_seq_length
								snake_case_           =				(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								snake_case_           =				feature_size
								snake_case_           =				padding_value
								snake_case_           =				sampling_rate
								snake_case_           =				return_attention_mask
								snake_case_           =				do_normalize




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								return {
								    "feature_size": self.feature_size,
								    "padding_value": self.padding_value,
								    "sampling_rate": self.sampling_rate,
								    "return_attention_mask": self.return_attention_mask,
								    "do_normalize": self.do_normalize,
								}






	def 				_UpperCamelCase     (			self			,     a=False			,     a=False   )			->       Optional[int]:
								def _flatten(a   ):
															return list(itertools.chain(*a   )   )

								if equal_length:
															snake_case_           =				floats_list((self.batch_size, self.max_seq_length)   )
								else:
															# make sure that inputs increase in size
															snake_case_           =				[
															    _flatten(floats_list((x, self.feature_size)   )   )
															    for x in range(self.min_seq_length			,     self.max_seq_length			,     self.seq_length_diff   )
															]

								if numpify:
															snake_case_           =				[np.asarray(a   ) for x in speech_inputs]

								return speech_inputs






@require_torch
@require_torchaudio
class 					UpperCamelCase_ (     snake_case_			,   unittest.TestCase							):



	'''simple docstring'''




	lowerCAmelCase						=					ASTFeatureExtractor




	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								snake_case_           =				ASTFeatureExtractionTester(self   )




	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								# Tests that all call wrap to encode_plus and batch_encode_plus
								snake_case_           =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								# create three inputs of length 800, 1000, and 1200
								snake_case_           =				[floats_list((1, x)   )[0] for x in range(8_00			,     14_00			,     2_00   )]
								snake_case_           =				[np.asarray(a   ) for speech_input in speech_inputs]

								# Test not batched input
								snake_case_           =				feat_extract(speech_inputs[0]			,     return_tensors='np'   ).input_values
								snake_case_           =				feat_extract(np_speech_inputs[0]			,     return_tensors='np'   ).input_values
								self.assertTrue(np.allclose(a			,     a			,     atol=1E-3   )   )

								# Test batched
								snake_case_           =				feat_extract(a			,     padding=a			,     return_tensors='np'   ).input_values
								snake_case_           =				feat_extract(a			,     padding=a			,     return_tensors='np'   ).input_values
								for enc_seq_a, enc_seq_a in zip(a			,     a   ):
															self.assertTrue(np.allclose(a			,     a			,     atol=1E-3   )   )

								# Test 2-D numpy arrays are batched.
								snake_case_           =				[floats_list((1, x)   )[0] for x in (8_00, 8_00, 8_00)]
								snake_case_           =				np.asarray(a   )
								snake_case_           =				feat_extract(a			,     return_tensors='np'   ).input_values
								snake_case_           =				feat_extract(a			,     return_tensors='np'   ).input_values
								for enc_seq_a, enc_seq_a in zip(a			,     a   ):
															self.assertTrue(np.allclose(a			,     a			,     atol=1E-3   )   )




	@require_torch
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								import torch

								snake_case_           =				self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()   )
								snake_case_           =				np.random.rand(1_00   ).astype(np.floataa   )
								snake_case_           =				np_speech_inputs.tolist()

								for inputs in [py_speech_inputs, np_speech_inputs]:
															snake_case_           =				feature_extractor.pad([{'input_values': inputs}]			,     return_tensors='np'   )
															self.assertTrue(np_processed.input_values.dtype == np.floataa   )
															snake_case_           =				feature_extractor.pad([{'input_values': inputs}]			,     return_tensors='pt'   )
															self.assertTrue(pt_processed.input_values.dtype == torch.floataa   )




	def 				_UpperCamelCase     (			self			,     a   )			->       Tuple:
								from datasets import load_dataset

								snake_case_           =				load_dataset('hf-internal-testing/librispeech_asr_dummy'			,     'clean'			,     split='validation'   )
								# automatic decoding with librispeech
								snake_case_           =				ds.sort('id'   ).select(range(a   )   )[:num_samples]['audio']

								return [x["array"] for x in speech_samples]






	@require_torch
	def 				_UpperCamelCase     (			self   )			->       Optional[int]:
								# fmt: off
								snake_case_           =				torch.tensor(
								    [-0.9_894, -1.2_776, -0.9_066, -1.2_776, -0.9_349, -1.2_609, -1.0_386, -1.2_776,
								     -1.1_561, -1.2_776, -1.2_052, -1.2_723, -1.2_190, -1.2_132, -1.2_776, -1.1_133,
								     -1.1_953, -1.1_343, -1.1_584, -1.2_203, -1.1_770, -1.2_474, -1.2_381, -1.1_936,
								     -0.9_270, -0.8_317, -0.8_049, -0.7_706, -0.7_565, -0.7_869]   )
								# fmt: on

								snake_case_           =				self._load_datasamples(1   )
								snake_case_           =				ASTFeatureExtractor()
								snake_case_           =				feature_extractor(a			,     return_tensors='pt'   ).input_values
								self.assertEquals(input_values.shape			,     (1, 10_24, 1_28)   )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]			,     a			,     atol=1E-4   )   )





