

from dataclasses import dataclass, field
from typing import Optional







@dataclass
class 	lowercase    :


		"""simple docstring"""




		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=1_0_0_0_0    ,							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=7_5_0    ,							metadata={"help": "Number of warmup steps in the learning rate schedule."}							)
		a__					       =  field(
		    default=1_6    ,							metadata={"help": "Number of gradient accumulation steps."}							)
		a__					       =  field(
		    default=__a    ,							metadata={"help": "Use gradient checkpointing to reduce memory footprint."}							)
		a__					       =  field(default=5_0_0_0_0    ,							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=1_0_2_4    ,							metadata={"help": "Sequence lengths used for training."}							)
		a__					       =  field(default=1    ,							metadata={"help": "Training seed."}							)
		a__					       =  field(
		    default=1_0_2_4    ,							metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."}    ,							)
		a__					       =  field(
		    default=__a    ,							metadata={"help": "States path if the training should continue from a checkpoint folder."}							)
		a__					       =  field(default=__a    ,							metadata={"help": "If True the data is pretokenized."}							)







@dataclass
class 	lowercase    :


		"""simple docstring"""




		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=1_0_2_4    ,							metadata={"help": "Length of sequences to be evaluated."}							)
		a__					       =  field(default=1    ,							metadata={"help": "Random seed used for evaluation."}							)







@dataclass
class 	lowercase    :


		"""simple docstring"""




		a__					       =  field(
		    default="codeparrot/codeparrot"    ,							metadata={"help": "Model name or path of model to be evaluated."}							)
		a__					       =  field(default=__a    ,							metadata={"help": "Number of workers used for code evaluation."}							)
		a__					       =  field(
		    default=__a    ,							metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."}    ,							)
		a__					       =  field(
		    default=__a    ,							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=2_5_6    ,							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=1_0    ,							metadata={"help": "Number of generations to run in parallel."}							)
		a__					       =  field(
		    default=2_0_0    ,							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    :


		"""simple docstring"""




		a__					       =  field(
		    default=__a    ,							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=1_0_0_0_0_0    ,							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=1_0_0_0    ,							metadata={"help": "Maximum line length in file, otherwise file is filtered."}							)
		a__					       =  field(
		    default=1_0_0    ,							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=__a    ,							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    :


		"""simple docstring"""




		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=2_0_0_0_0_0    ,							metadata={"help": "Number of examples to train tokenizer on."}							)
		a__					       =  field(
		    default=3_2_7_6_8    ,							metadata={"help": "Number of examples to train the tokenizer on."}							)
		a__					       =  field(default="codeparrot"    ,							metadata={"help": "Name of new tokenizer."}							)
		a__					       =  field(default=__a    ,							metadata={"help": "Push saved tokenizer to the hub."}							)







@dataclass
class 	lowercase    :


		"""simple docstring"""




		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=__a    ,							metadata={"help": "Number of workers used for code evaluation."}							)







@dataclass
class 	lowercase    :


		"""simple docstring"""




		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=__a    ,							metadata={"help": "Push saved tokenizer to the hub."}							)









import unittest
from pathlib import Path
from tempfile import TemporaryDirectory

from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow


if is_tf_available():
							import tensorflow as tf

if is_tensorflow_text_available():
							from transformers.models.bert import TFBertTokenizer


lowerCAmelCase__								=		["""bert-base-uncased""", """bert-base-cased"""]
lowerCAmelCase__								=		"""hf-internal-testing/tiny-bert-tf-only"""

if is_tf_available():







							class 	lowercase    (				tf.keras.Model							):


									"""simple docstring"""




									def __init__(				self	,       __snake_case):
										super().__init__()
										_UpperCamelCase   :		List[Any]			=						tokenizer
										_UpperCamelCase   :		List[Any]			=						AutoConfig.from_pretrained(__snake_case)
										_UpperCamelCase   :		Dict			=						TFAutoModel.from_config(__snake_case)






									def 		A__					(				self	,       __snake_case):
										_UpperCamelCase   :		Any			=						self.tokenizer(__snake_case)
										_UpperCamelCase   :		Dict			=						self.bert(**__snake_case)
										return out["pooler_output"]







@require_tf
@require_tensorflow_text
class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self):
			super().setUp()

			_UpperCamelCase   :		Optional[Any]			=						[
			    BertTokenizer.from_pretrained(__snake_case) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
			]  # repeat for when fast_bert_tokenizer=false
			_UpperCamelCase   :		Optional[Any]			=						[TFBertTokenizer.from_pretrained(__snake_case) for checkpoint in TOKENIZER_CHECKPOINTS] + [
			    TFBertTokenizer.from_pretrained(__snake_case	,       use_fast_bert_tokenizer=__snake_case)
			    for checkpoint in TOKENIZER_CHECKPOINTS
			]
			assert len(self.tokenizers) == len(self.tf_tokenizers)

			_UpperCamelCase   :		Optional[Any]			=						[
			    'This is a straightforward English test sentence.',
			    'This one has some weird characters\rto\nsee\r\nif  those\u00E9break things.',
			    'Now we\'re going to add some Chinese: 一 二 三 一二三',
			    'And some much more rare Chinese: 齉 堃 齉堃',
			    'Je vais aussi écrire en français pour tester les accents',
			    'Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ',
			]
			_UpperCamelCase   :		Dict			=						list(zip(self.test_sentences	,       self.test_sentences[::-1]))


		def 		A__					(				self):
			for tokenizer, tf_tokenizer in zip(self.tokenizers	,       self.tf_tokenizers):
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		List[str]			=						tokenizer(__snake_case	,       return_tensors='tf'	,       padding='longest')
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					for key in python_outputs.keys():
						self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape))
						self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key]	,       tf.intaa) == tf_outputs[key]))


		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf_tokenizer(self.paired_sentences)
				_UpperCamelCase   :		Optional[Any]			=						tf_tokenizer(
				    text=[sentence[0] for sentence in self.paired_sentences]	,       text_pair=[sentence[1] for sentence in self.paired_sentences]	,       )
				for key in merged_outputs.keys():
					self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key]	,       tf.intaa) == separated_outputs[key]))


		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Tuple			=						tf.function(__snake_case)
				for test_inputs in (self.test_sentences, self.paired_sentences):
					_UpperCamelCase   :		Optional[int]			=						tf.constant(__snake_case)
					_UpperCamelCase   :		Union[str, Any]			=						compiled_tokenizer(__snake_case)
					_UpperCamelCase   :		Tuple			=						tf_tokenizer(__snake_case)

					for key in eager_outputs.keys():
						self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))






		@slow
		def 		A__					(				self):
			for tf_tokenizer in self.tf_tokenizers:
				_UpperCamelCase   :		Any			=						ModelToSave(tokenizer=__snake_case)
				_UpperCamelCase   :		Any			=						tf.convert_to_tensor(self.test_sentences)
				_UpperCamelCase   :		Union[str, Any]			=						model(__snake_case)  # Build model with some sample inputs
				with TemporaryDirectory() as tempdir:
					_UpperCamelCase   :		int			=						Path(__snake_case) / 'saved.model'
					model.save(__snake_case)
					_UpperCamelCase   :		Optional[int]			=						tf.keras.models.load_model(__snake_case)
				_UpperCamelCase   :		int			=						loaded_model(__snake_case)
				# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
				self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output))	,       1e-5)




