

import unittest

from parameterized import parameterized

from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
       import torch

       from transformers import (
           GPTNeoXForCausalLM,
           GPTNeoXForQuestionAnswering,
           GPTNeoXForSequenceClassification,
           GPTNeoXForTokenClassification,
           GPTNeoXModel,
       )






class  UpperCAmelCase__	:






  """simple docstring"""


  def __init__(							self		:	Any			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	List[str]=13			,     __lowerCamelCase		:	Dict=7			,     __lowerCamelCase		:	Dict=True			,     __lowerCamelCase		:	List[str]=True			,     __lowerCamelCase		:	Optional[Any]=True			,     __lowerCamelCase		:	Optional[int]=True			,     __lowerCamelCase		:	Dict=99			,     __lowerCamelCase		:	Union[str, Any]=64			,     __lowerCamelCase		:	Union[str, Any]=5			,     __lowerCamelCase		:	str=4			,     __lowerCamelCase		:	Optional[int]=37			,     __lowerCamelCase		:	List[Any]="gelu"			,     __lowerCamelCase		:	str=0.1			,     __lowerCamelCase		:	List[Any]=0.1			,     __lowerCamelCase		:	Optional[int]=512			,     __lowerCamelCase		:	Union[str, Any]=16			,     __lowerCamelCase		:	str=2			,     __lowerCamelCase		:	Optional[Any]=0.02			,     __lowerCamelCase		:	List[str]=3			,     __lowerCamelCase		:	Any=4			,     __lowerCamelCase		:	Tuple=None			,     )	->							str:
       SCREAMING_SNAKE_CASE__					=					parent
       SCREAMING_SNAKE_CASE__					=					batch_size
       SCREAMING_SNAKE_CASE__					=					seq_length
       SCREAMING_SNAKE_CASE__					=					is_training
       SCREAMING_SNAKE_CASE__					=					use_input_mask
       SCREAMING_SNAKE_CASE__					=					use_token_type_ids
       SCREAMING_SNAKE_CASE__					=					use_labels
       SCREAMING_SNAKE_CASE__					=					vocab_size
       SCREAMING_SNAKE_CASE__					=					hidden_size
       SCREAMING_SNAKE_CASE__					=					num_hidden_layers
       SCREAMING_SNAKE_CASE__					=					num_attention_heads
       SCREAMING_SNAKE_CASE__					=					intermediate_size
       SCREAMING_SNAKE_CASE__					=					hidden_act
       SCREAMING_SNAKE_CASE__					=					hidden_dropout_prob
       SCREAMING_SNAKE_CASE__					=					attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE__					=					max_position_embeddings
       SCREAMING_SNAKE_CASE__					=					type_vocab_size
       SCREAMING_SNAKE_CASE__					=					type_sequence_label_size
       SCREAMING_SNAKE_CASE__					=					initializer_range
       SCREAMING_SNAKE_CASE__					=					num_labels
       SCREAMING_SNAKE_CASE__					=					num_choices
       SCREAMING_SNAKE_CASE__					=					scope
       SCREAMING_SNAKE_CASE__					=					vocab_size - 1






  def    lowercase_	(							self		:	Any      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size, self.seq_length]			,     self.vocab_size      )

       SCREAMING_SNAKE_CASE__					=					None
       if self.use_input_mask:
            SCREAMING_SNAKE_CASE__					=					random_attention_mask([self.batch_size, self.seq_length]      )

       SCREAMING_SNAKE_CASE__					=					None
       if self.use_labels:
            SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size, self.seq_length]			,     self.num_labels      )

       SCREAMING_SNAKE_CASE__					=					self.get_config()

       return config, input_ids, input_mask, token_labels






  def    lowercase_	(							self		:	Optional[int]      )	->							str:
       return GPTNeoXConfig(
           vocab_size=self.vocab_size			,     hidden_size=self.hidden_size			,     num_hidden_layers=self.num_hidden_layers			,     num_attention_heads=self.num_attention_heads			,     intermediate_size=self.intermediate_size			,     hidden_act=self.hidden_act			,     hidden_dropout_prob=self.hidden_dropout_prob			,     attention_probs_dropout_prob=self.attention_probs_dropout_prob			,     max_position_embeddings=self.max_position_embeddings			,     type_vocab_size=self.type_vocab_size			,     is_decoder=lowerCamelCase_			,     initializer_range=self.initializer_range			,     pad_token_id=self.pad_token_id			,     )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()

       SCREAMING_SNAKE_CASE__					=					True

       return config, input_ids, input_mask, token_labels






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	int      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					GPTNeoXModel(config=lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_      )
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_      )
       self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)      )






  def    lowercase_	(							self		:	str			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	List[str]      )	->							List[str]:
       SCREAMING_SNAKE_CASE__					=					True
       SCREAMING_SNAKE_CASE__					=					GPTNeoXModel(lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_      )
       self.parent.assertEqual(result.last_hidden_state.shape			,     (self.batch_size, self.seq_length, self.hidden_size)      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	List[Any]			,     __lowerCamelCase		:	Optional[Any]			,     __lowerCamelCase		:	List[str]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					GPTNeoXForCausalLM(config=lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_			,     labels=lowerCamelCase_      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.vocab_size)      )






  def    lowercase_	(							self		:	Any			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	Optional[int]			,     __lowerCamelCase		:	Optional[Any]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					GPTNeoXForQuestionAnswering(lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_      )
       self.parent.assertEqual(result.start_logits.shape			,     (self.batch_size, self.seq_length)      )
       self.parent.assertEqual(result.end_logits.shape			,     (self.batch_size, self.seq_length)      )






  def    lowercase_	(							self		:	Union[str, Any]			,     __lowerCamelCase		:	Any			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Union[str, Any]			,     __lowerCamelCase		:	List[str]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					GPTNeoXForSequenceClassification(lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					ids_tensor([self.batch_size]			,     self.type_sequence_label_size      )
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_			,     labels=lowerCamelCase_      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.num_labels)      )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	int			,     __lowerCamelCase		:	str			,     __lowerCamelCase		:	Tuple			,     __lowerCamelCase		:	List[str]      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.num_labels
       SCREAMING_SNAKE_CASE__					=					GPTNeoXForTokenClassification(lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_			,     labels=lowerCamelCase_      )
       self.parent.assertEqual(result.logits.shape			,     (self.batch_size, self.seq_length, self.num_labels)      )






  def    lowercase_	(							self		:	List[str]			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Dict			,     __lowerCamelCase		:	Dict      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					True
       SCREAMING_SNAKE_CASE__					=					GPTNeoXForCausalLM(config=lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()

       # first forward pass
       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_			,     use_cache=lowerCamelCase_      )
       SCREAMING_SNAKE_CASE__					=					outputs.past_key_values

       # create hypothetical multiple next token and extent to next_input_ids
       SCREAMING_SNAKE_CASE__					=					ids_tensor((self.batch_size, 3)			,     config.vocab_size      )
       SCREAMING_SNAKE_CASE__					=					ids_tensor((self.batch_size, 3)			,     vocab_size=2      )

       # append to next input_ids and
       SCREAMING_SNAKE_CASE__					=					torch.cat([input_ids, next_tokens]			,     dim=-1      )
       SCREAMING_SNAKE_CASE__					=					torch.cat([input_mask, next_mask]			,     dim=-1      )

       SCREAMING_SNAKE_CASE__					=					model(lowerCamelCase_			,     attention_mask=lowerCamelCase_			,     output_hidden_states=lowerCamelCase_      )
       SCREAMING_SNAKE_CASE__					=					output_from_no_past["""hidden_states"""][0]
       SCREAMING_SNAKE_CASE__					=					model(
           lowerCamelCase_			,     attention_mask=lowerCamelCase_			,     past_key_values=lowerCamelCase_			,     output_hidden_states=lowerCamelCase_			,     )["""hidden_states"""][0]

       # select random slice
       SCREAMING_SNAKE_CASE__					=					ids_tensor((1,)			,     output_from_past.shape[-1]      ).item()
       SCREAMING_SNAKE_CASE__					=					output_from_no_past[:, -3:, random_slice_idx].detach()
       SCREAMING_SNAKE_CASE__					=					output_from_past[:, :, random_slice_idx].detach()

       self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]      )

       # test that outputs are equal for slice
       self.parent.assertTrue(torch.allclose(lowerCamelCase_			,     lowerCamelCase_			,     atol=1e-3      )      )







  def    lowercase_	(							self		:	Optional[int]      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.prepare_config_and_inputs()
       SCREAMING_SNAKE_CASE__					=					config_and_inputs
       SCREAMING_SNAKE_CASE__					=					{"""input_ids""": input_ids, """attention_mask""": input_mask}
       return config, inputs_dict






@require_torch
class  UpperCAmelCase__	( lowercase_		,  lowercase_		,  lowercase_		,  unittest.TestCase ):






  """simple docstring"""


  a     =      (
      (
          GPTNeoXModel,
          GPTNeoXForCausalLM,
          GPTNeoXForQuestionAnswering,
          GPTNeoXForSequenceClassification,
          GPTNeoXForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  a     =      (GPTNeoXForCausalLM,) if is_torch_available() else ()
  a     =      (
      {
          "feature-extraction": GPTNeoXModel,
          "question-answering": GPTNeoXForQuestionAnswering,
          "text-classification": GPTNeoXForSequenceClassification,
          "text-generation": GPTNeoXForCausalLM,
          "token-classification": GPTNeoXForTokenClassification,
          "zero-shot": GPTNeoXForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  a     =      False
  a     =      False
  a     =      False
  a     =      False






  def    lowercase_	(							self		:	List[Any]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					GPTNeoXModelTester(self      )
       SCREAMING_SNAKE_CASE__					=					ConfigTester(self			,     config_class=lowerCamelCase_			,     hidden_size=64			,     num_attention_heads=8      )






  def    lowercase_	(							self		:	List[str]      )	->							str:
       self.config_tester.run_common_tests()






  def    lowercase_	(							self		:	int      )	->							int:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(lowerCamelCase_			,     lowerCamelCase_			,     lowerCamelCase_      )






  def    lowercase_	(							self		:	Dict      )	->							List[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_decoder()
       self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_			,     lowerCamelCase_			,     lowerCamelCase_      )






  def    lowercase_	(							self		:	Dict      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_decoder()

       SCREAMING_SNAKE_CASE__					=					None

       self.model_tester.create_and_check_model_as_decoder(lowerCamelCase_			,     lowerCamelCase_			,     lowerCamelCase_      )






  def    lowercase_	(							self		:	int      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase_			,     lowerCamelCase_			,     lowerCamelCase_      )






  def    lowercase_	(							self		:	List[Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase_      )






  def    lowercase_	(							self		:	str      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_question_answering(*lowerCamelCase_      )






  def    lowercase_	(							self		:	Union[str, Any]      )	->							Tuple:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase_      )






  def    lowercase_	(							self		:	Optional[Any]      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_for_token_classification(*lowerCamelCase_      )






  @unittest.skip(reason='''Feed forward chunking is not implemented'''      )
  def    lowercase_	(							self		:	str      )	->							Union[str, Any]:
       pass







  @parameterized.expand([('''linear''',), ('''dynamic''',)]      )
  def    lowercase_	(							self		:	int			,     __lowerCamelCase		:	Tuple      )	->							Optional[int]:
       SCREAMING_SNAKE_CASE__					=					self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE__					=					ids_tensor([1, 10]			,     config.vocab_size      )
       SCREAMING_SNAKE_CASE__					=					ids_tensor([1, int(config.max_position_embeddings * 1.5      )]			,     config.vocab_size      )

       set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE__					=					GPTNeoXModel(lowerCamelCase_      )
       original_model.to(lowerCamelCase_      )
       original_model.eval()
       SCREAMING_SNAKE_CASE__					=					original_model(lowerCamelCase_      ).last_hidden_state
       SCREAMING_SNAKE_CASE__					=					original_model(lowerCamelCase_      ).last_hidden_state

       set_seed(42      )  # Fixed seed at init time so the two models get the same random weights
       SCREAMING_SNAKE_CASE__					=					{"""type""": scaling_type, """factor""": 10.0}
       SCREAMING_SNAKE_CASE__					=					GPTNeoXModel(lowerCamelCase_      )
       scaled_model.to(lowerCamelCase_      )
       scaled_model.eval()
       SCREAMING_SNAKE_CASE__					=					scaled_model(lowerCamelCase_      ).last_hidden_state
       SCREAMING_SNAKE_CASE__					=					scaled_model(lowerCamelCase_      ).last_hidden_state

       # Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
       # maximum sequence length, so the outputs for the short input should match.
       if scaling_type == "dynamic":
            self.assertTrue(torch.allclose(lowerCamelCase_			,     lowerCamelCase_			,     atol=1e-5      )      )
       else:
            self.assertFalse(torch.allclose(lowerCamelCase_			,     lowerCamelCase_			,     atol=1e-5      )      )

       # The output should be different for long inputs
       self.assertFalse(torch.allclose(lowerCamelCase_			,     lowerCamelCase_			,     atol=1e-5      )      )






@require_torch
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @slow
  def    lowercase_	(							self		:	Optional[Any]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained('''EleutherAI/pythia-410m-deduped'''      )
       for checkpointing in [True, False]:
            SCREAMING_SNAKE_CASE__					=					GPTNeoXForCausalLM.from_pretrained('''EleutherAI/pythia-410m-deduped'''      )

            if checkpointing:
                 model.gradient_checkpointing_enable()
            else:
                 model.gradient_checkpointing_disable()
            model.to(lowerCamelCase_      )

            SCREAMING_SNAKE_CASE__					=					tokenizer('''My favorite food is'''			,     return_tensors='''pt'''      ).to(lowerCamelCase_      )
            # The hub repo. is updated on 2023-04-04, resulting in poor outputs.
            # See: https://github.com/huggingface/transformers/pull/24193
            SCREAMING_SNAKE_CASE__					=					"""My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""

            SCREAMING_SNAKE_CASE__					=					model.generate(**lowerCamelCase_			,     do_sample=lowerCamelCase_			,     max_new_tokens=20      )
            SCREAMING_SNAKE_CASE__					=					tokenizer.batch_decode(lowerCamelCase_      )[0]

            self.assertEqual(lowerCamelCase_			,     lowerCamelCase_      )





from dataclasses import dataclass, field
from typing import Optional






@dataclass
class  UpperCAmelCase__	:






  """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_00_00		,  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_50		,  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=A__		,  metadata={"help": "Use gradient checkpointing to reduce memory footprint."} )
  a     =      field(default=5_00_00		,  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=10_24		,  metadata={"help": "Sequence lengths used for training."} )
  a     =      field(default=1		,  metadata={"help": "Training seed."} )
  a     =      field(
      default=10_24		,  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  UpperCAmelCase__	:






  """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=10_24		,  metadata={"help": "Length of sequences to be evaluated."} )
  a     =      field(default=1		,  metadata={"help": "Random seed used for evaluation."} )






@dataclass
class  UpperCAmelCase__	:






  """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_56		,  metadata={"help": "Maximum number of newly generated tokens."} )
  a     =      field(default=0		,  metadata={"help": "Top-k parameter used for generation."} )
  a     =      field(default=0.9_5		,  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=2_00		,  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  UpperCAmelCase__	:






  """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=10_00_00		,  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=10_00		,  metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
  a     =      field(
      default=1_00		,  metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
  a     =      field(
      default=0.2_5		,  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=0.8_5		,  metadata={"help": "Jaccard threshold for near-duplicate samples."} )






@dataclass
class  UpperCAmelCase__	:






  """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=20_00_00		,  metadata={"help": "Number of examples to train tokenizer on."} )
  a     =      field(
      default=3_27_68		,  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  UpperCAmelCase__	:






  """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  UpperCAmelCase__	:






  """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."} )


