




import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch
     from torch import nn

     from transformers import (
         MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
         VideoMAEForPreTraining,
         VideoMAEForVideoClassification,
         VideoMAEModel,
     )
     from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
     from transformers import VideoMAEImageProcessor

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=13       ,_SCREAMING_SNAKE_CASE=10       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=32       ,_SCREAMING_SNAKE_CASE=5       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=37       ,_SCREAMING_SNAKE_CASE="gelu"       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=0.1       ,_SCREAMING_SNAKE_CASE=10       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=0.9       ,_SCREAMING_SNAKE_CASE=None       ,)			->		int:
         UpperCAmelCase_      :							Tuple       =      parent
         UpperCAmelCase_      :							Any       =      batch_size
         UpperCAmelCase_      :							Any       =      image_size
         UpperCAmelCase_      :							Dict       =      num_channels
         UpperCAmelCase_      :							Optional[int]       =      patch_size
         UpperCAmelCase_      :							int       =      tubelet_size
         UpperCAmelCase_      :							Union[str, Any]       =      num_frames
         UpperCAmelCase_      :							Any       =      is_training
         UpperCAmelCase_      :							Optional[int]       =      use_labels
         UpperCAmelCase_      :							Optional[Any]       =      hidden_size
         UpperCAmelCase_      :							Dict       =      num_hidden_layers
         UpperCAmelCase_      :							List[Any]       =      num_attention_heads
         UpperCAmelCase_      :							Any       =      intermediate_size
         UpperCAmelCase_      :							Union[str, Any]       =      hidden_act
         UpperCAmelCase_      :							Dict       =      hidden_dropout_prob
         UpperCAmelCase_      :							Any       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							int       =      type_sequence_label_size
         UpperCAmelCase_      :							Optional[int]       =      initializer_range
         UpperCAmelCase_      :							Union[str, Any]       =      mask_ratio
         UpperCAmelCase_      :							Union[str, Any]       =      scope

         # in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
         UpperCAmelCase_      :							List[str]       =      (image_size // patch_size) ** 2
         UpperCAmelCase_      :							List[str]       =      (num_frames // tubelet_size) * self.num_patches_per_frame

         # use this variable to define bool_masked_pos
         UpperCAmelCase_      :							Tuple       =      int(mask_ratio * self.seq_length  )





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_      :							Union[str, Any]       =      floats_tensor(
             [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]  )

         UpperCAmelCase_      :							Union[str, Any]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							List[str]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )

         UpperCAmelCase_      :							int       =      self.get_config()

         return config, pixel_values, labels





    def 							a__				(				self  )			->		Tuple:
         return VideoMAEConfig(
             image_size=self.image_size       ,patch_size=self.patch_size       ,num_channels=self.num_channels       ,num_frames=self.num_frames       ,tubelet_size=self.tubelet_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       ,is_decoder=_SCREAMING_SNAKE_CASE       ,initializer_range=self.initializer_range       ,)





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Union[str, Any]:
         UpperCAmelCase_      :							int       =      VideoMAEModel(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Any       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							List[Any]       =      VideoMAEForPreTraining(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         # important: each video needs to have the same number of masked patches
         # hence we define a single mask, which we then repeat for each example in the batch
         UpperCAmelCase_      :							List[str]       =      torch.ones((self.num_masks,)  )
         UpperCAmelCase_      :							List[Any]       =      torch.cat([mask, torch.zeros(self.seq_length - mask.size(0  )  )]  )
         UpperCAmelCase_      :							List[Any]       =      mask.expand(self.batch_size       ,-1  ).bool()

         UpperCAmelCase_      :							Union[str, Any]       =      model(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )
         # model only returns predictions for masked patches
         UpperCAmelCase_      :							List[str]       =      mask.sum().item()
         UpperCAmelCase_      :							Optional[int]       =      3 * self.tubelet_size * self.patch_size**2
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, num_masked_patches, decoder_num_labels)  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Any       =      self.prepare_config_and_inputs()
         UpperCAmelCase_,   UpperCAmelCase_,   UpperCAmelCase_      :							Optional[int]       =      config_and_inputs
         UpperCAmelCase_      :							Dict       =      {'''pixel_values''': pixel_values}
         return config, inputs_dict


@require_torch
class   __a(					_a    ,						_a    ,						unittest.TestCase       ):






    """simple docstring"""





    lowerCAmelCase        =				(
        (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
    )
    lowerCAmelCase        =				(
        {'''feature-extraction''': VideoMAEModel, '''video-classification''': VideoMAEForVideoClassification}
        if is_torch_available()
        else {}
    )

    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Dict       =      VideoMAEModelTester(self  )
         UpperCAmelCase_      :							Dict       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,has_text_modality=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		int:
         UpperCAmelCase_      :							Union[str, Any]       =      copy.deepcopy(_SCREAMING_SNAKE_CASE  )

         if model_class == VideoMAEForPreTraining:
              # important: each video needs to have the same number of masked patches
              # hence we define a single mask, which we then repeat for each example in the batch
              UpperCAmelCase_      :							Tuple       =      torch.ones((self.model_tester.num_masks,)  )
              UpperCAmelCase_      :							Union[str, Any]       =      torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0  )  )]  )
              UpperCAmelCase_      :							int       =      mask.expand(self.model_tester.batch_size       ,-1  ).bool()
              UpperCAmelCase_      :							Union[str, Any]       =      bool_masked_pos.to(_SCREAMING_SNAKE_CASE  )

         if return_labels:
              if model_class in [
                  *get_values(_SCREAMING_SNAKE_CASE  ),
              ]:
                   UpperCAmelCase_      :							Any       =      torch.zeros(
                       self.model_tester.batch_size       ,dtype=torch.long       ,device=_SCREAMING_SNAKE_CASE  )

         return inputs_dict





    def 							a__				(				self  )			->		Any:
         self.config_tester.run_common_tests()





    @unittest.skip(reason='''VideoMAE does not use inputs_embeds'''  )
    def 							a__				(				self  )			->		int:
         pass





    def 							a__				(				self  )			->		List[Any]:
         UpperCAmelCase_,   UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Any       =      model_class(_SCREAMING_SNAKE_CASE  )
              self.assertIsInstance(model.get_input_embeddings()       ,(nn.Module)  )
              UpperCAmelCase_      :							Any       =      model.get_output_embeddings()
              self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE       ,nn.Linear  )  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_,   UpperCAmelCase_      :							Optional[Any]       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Tuple       =      model_class(_SCREAMING_SNAKE_CASE  )
              UpperCAmelCase_      :							int       =      inspect.signature(model.forward  )
              # signature.parameters is an OrderedDict => so arg_names order is deterministic
              UpperCAmelCase_      :							Any       =      [*signature.parameters.keys()]

              UpperCAmelCase_      :							Any       =      ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Optional[int]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							List[str]       =      self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_pretraining(*_SCREAMING_SNAKE_CASE  )





    @slow
    def 							a__				(				self  )			->		Optional[Any]:
         for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_      :							List[str]       =      VideoMAEModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )





    def 							a__				(				self  )			->		Any:
         if not self.has_attentions:
              pass

         else:
              UpperCAmelCase_,   UpperCAmelCase_      :							Tuple       =      self.model_tester.prepare_config_and_inputs_for_common()
              UpperCAmelCase_      :							Union[str, Any]       =      True

              for model_class in self.all_model_classes:
                   UpperCAmelCase_      :							Any       =      self.model_tester.seq_length - self.model_tester.num_masks
                   UpperCAmelCase_      :							Tuple       =      (
                       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
                   )

                   UpperCAmelCase_      :							Optional[int]       =      True
                   UpperCAmelCase_      :							Optional[int]       =      False
                   UpperCAmelCase_      :							int       =      True
                   UpperCAmelCase_      :							Optional[Any]       =      model_class(_SCREAMING_SNAKE_CASE  )
                   model.to(_SCREAMING_SNAKE_CASE  )
                   model.eval()
                   with torch.no_grad():
                        UpperCAmelCase_      :							Tuple       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )
                   UpperCAmelCase_      :							int       =      outputs.attentions
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,self.model_tester.num_hidden_layers  )

                   # check that output_attentions also work using config
                   del inputs_dict["output_attentions"]
                   UpperCAmelCase_      :							Optional[int]       =      True
                   UpperCAmelCase_      :							Any       =      model_class(_SCREAMING_SNAKE_CASE  )
                   model.to(_SCREAMING_SNAKE_CASE  )
                   model.eval()
                   with torch.no_grad():
                        UpperCAmelCase_      :							List[Any]       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )
                   UpperCAmelCase_      :							Tuple       =      outputs.attentions
                   self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,self.model_tester.num_hidden_layers  )

                   self.assertListEqual(
                       list(attentions[0].shape[-3:]  )       ,[self.model_tester.num_attention_heads, seq_len, seq_len]       ,)
                   UpperCAmelCase_      :							Optional[Any]       =      len(_SCREAMING_SNAKE_CASE  )

                   # Check attention is always last and order is fine
                   UpperCAmelCase_      :							List[str]       =      True
                   UpperCAmelCase_      :							int       =      True
                   UpperCAmelCase_      :							int       =      model_class(_SCREAMING_SNAKE_CASE  )
                   model.to(_SCREAMING_SNAKE_CASE  )
                   model.eval()
                   with torch.no_grad():
                        UpperCAmelCase_      :							str       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )

                   self.assertEqual(out_len + 1       ,len(_SCREAMING_SNAKE_CASE  )  )

                   UpperCAmelCase_      :							List[str]       =      outputs.attentions

                   self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,self.model_tester.num_hidden_layers  )
                   self.assertListEqual(
                       list(self_attentions[0].shape[-3:]  )       ,[self.model_tester.num_attention_heads, seq_len, seq_len]       ,)





    def 							a__				(				self  )			->		str:
         def check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							int       =      model_class(_SCREAMING_SNAKE_CASE  )
              model.to(_SCREAMING_SNAKE_CASE  )
              model.eval()

              with torch.no_grad():
                   UpperCAmelCase_      :							Any       =      model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )  )

              UpperCAmelCase_      :							Optional[int]       =      outputs.hidden_states
              UpperCAmelCase_      :							List[Any]       =      self.model_tester.num_hidden_layers + 1
              self.assertEqual(len(_SCREAMING_SNAKE_CASE  )       ,_SCREAMING_SNAKE_CASE  )

              UpperCAmelCase_      :							List[Any]       =      self.model_tester.seq_length - self.model_tester.num_masks
              UpperCAmelCase_      :							List[Any]       =      num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

              self.assertListEqual(
                  list(hidden_states[0].shape[-2:]  )       ,[seq_length, self.model_tester.hidden_size]       ,)

         UpperCAmelCase_,   UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs_for_common()

         for model_class in self.all_model_classes:
              UpperCAmelCase_      :							Union[str, Any]       =      True
              check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )

              # check that output_hidden_states also work using config
              del inputs_dict["output_hidden_states"]
              UpperCAmelCase_      :							Any       =      True

              check_hidden_states_output(_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )





    @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''  )
    def 							a__				(				self  )			->		Dict:
         pass







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							Tuple       =      hf_hub_download(
         repo_id='''hf-internal-testing/spaghetti-video'''			,						filename='''eating_spaghetti.npy'''			,						repo_type='''dataset'''				)
     UpperCAmelCase_      :							Optional[int]       =      np.load(_lowercase				)
     return list(_lowercase				)



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






    """simple docstring"""





    @cached_property
    def 							a__				(				self  )			->		Dict:
         # logits were tested with a different mean and std, so we use the same here
         return (
             VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]       ,image_std=[0.5, 0.5, 0.5]  )
             if is_vision_available()
             else None
         )





    @slow
    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							Optional[int]       =      VideoMAEForVideoClassification.from_pretrained('''MCG-NJU/videomae-base-finetuned-kinetics'''  ).to(
             _SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      self.default_image_processor
         UpperCAmelCase_      :							Optional[Any]       =      prepare_video()
         UpperCAmelCase_      :							int       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).to(_SCREAMING_SNAKE_CASE  )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_      :							int       =      model(**_SCREAMING_SNAKE_CASE  )

         # verify the logits
         UpperCAmelCase_      :							List[str]       =      torch.Size((1, 400)  )
         self.assertEqual(outputs.logits.shape       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Optional[Any]       =      torch.tensor([0.36_69, -0.06_88, -0.24_21]  ).to(_SCREAMING_SNAKE_CASE  )

         self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )





    @slow
    def 							a__				(				self  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Optional[Any]       =      VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''  ).to(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Tuple       =      self.default_image_processor
         UpperCAmelCase_      :							Dict       =      prepare_video()
         UpperCAmelCase_      :							List[str]       =      image_processor(_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).to(_SCREAMING_SNAKE_CASE  )

         # add boolean mask, indicating which patches to mask
         UpperCAmelCase_      :							Dict       =      hf_hub_download(repo_id='''hf-internal-testing/bool-masked-pos'''       ,filename='''bool_masked_pos.pt'''  )
         UpperCAmelCase_      :							str       =      torch.load(_SCREAMING_SNAKE_CASE  )

         # forward pass
         with torch.no_grad():
              UpperCAmelCase_      :							List[str]       =      model(**_SCREAMING_SNAKE_CASE  )

         # verify the logits
         UpperCAmelCase_      :							str       =      torch.Size([1, 1_408, 1_536]  )
         UpperCAmelCase_      :							Union[str, Any]       =      torch.tensor(
             [[0.79_94, 0.96_12, 0.85_08], [0.74_01, 0.89_58, 0.83_02], [0.58_62, 0.74_68, 0.73_25]]       ,device=_SCREAMING_SNAKE_CASE  )
         self.assertEqual(outputs.logits.shape       ,_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )

         # verify the loss (`config.norm_pix_loss` = `True`)
         UpperCAmelCase_      :							Dict       =      torch.tensor([0.51_42]       ,device=_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(outputs.loss       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )

         # verify the loss (`config.norm_pix_loss` = `False`)
         UpperCAmelCase_      :							Optional[int]       =      VideoMAEForPreTraining.from_pretrained('''MCG-NJU/videomae-base-short'''       ,norm_pix_loss=_SCREAMING_SNAKE_CASE  ).to(
             _SCREAMING_SNAKE_CASE  )

         with torch.no_grad():
              UpperCAmelCase_      :							List[Any]       =      model(**_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      torch.tensor(torch.tensor([0.64_69]  )       ,device=_SCREAMING_SNAKE_CASE  )
         self.assertTrue(torch.allclose(outputs.loss       ,_SCREAMING_SNAKE_CASE       ,atol=1e-4  )  )





from dataclasses import dataclass, field
from typing import Optional

@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be trained.'''}       )
    lowerCAmelCase        =				field(
        default='''./'''    ,						metadata={'''help''': '''Save dir where model repo is cloned and models updates are saved to.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-train'''    ,						metadata={'''help''': '''Name or path of training dataset.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot-clean-valid'''    ,						metadata={'''help''': '''Name or path of validation dataset.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for training.'''}       )
    lowerCAmelCase        =				field(default=2    ,						metadata={'''help''': '''Batch size for evaluation.'''}       )
    lowerCAmelCase        =				field(default=0.1    ,						metadata={'''help''': '''Value of weight decay.'''}       )
    lowerCAmelCase        =				field(
        default=1_0000    ,						metadata={'''help''': '''Size of buffer used to shuffle streaming dataset.'''}       )
    lowerCAmelCase        =				field(default=2E-4    ,						metadata={'''help''': '''Learning rate fo training.'''}       )
    lowerCAmelCase        =				field(default='''cosine'''    ,						metadata={'''help''': '''Learning rate.'''}       )
    lowerCAmelCase        =				field(
        default=750    ,						metadata={'''help''': '''Number of warmup steps in the learning rate schedule.'''}       )
    lowerCAmelCase        =				field(
        default=16    ,						metadata={'''help''': '''Number of gradient accumulation steps.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Use gradient checkpointing to reduce memory footprint.'''}       )
    lowerCAmelCase        =				field(default=5_0000    ,						metadata={'''help''': '''Maximum number of training steps.'''}       )
    lowerCAmelCase        =				field(
        default=-1    ,						metadata={'''help''': '''Maximum number of evaluation steps. If -1 the full dataset is evaluated.'''}       )
    lowerCAmelCase        =				field(default=1024    ,						metadata={'''help''': '''Sequence lengths used for training.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Training seed.'''}       )
    lowerCAmelCase        =				field(
        default=1024    ,						metadata={'''help''': '''Interval to save checkpoints. Measured as number of forward passes not training steps.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''States path if the training should continue from a checkpoint folder.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''If True the data is pretokenized.'''}       )


@dataclass
class   __a:






    """simple docstring"""





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


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Model name or path of model to be evaluated.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Number of workers used for code evaluation.'''}       )
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''The number of human-eval tasks to run. If not included all tasks are evaluated.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''Sample from the language model\'s output distribution.'''}       )
    lowerCAmelCase        =				field(default=0.2    ,						metadata={'''help''': '''Sampling temperature used for generation.'''}       )
    lowerCAmelCase        =				field(default=256    ,						metadata={'''help''': '''Maximum number of newly generated tokens.'''}       )
    lowerCAmelCase        =				field(default=0    ,						metadata={'''help''': '''Top-k parameter used for generation.'''}       )
    lowerCAmelCase        =				field(default=0.95    ,						metadata={'''help''': '''Top-p parameter used for nucleus sampling.'''}       )
    lowerCAmelCase        =				field(default=10    ,						metadata={'''help''': '''Number of generations to run in parallel.'''}       )
    lowerCAmelCase        =				field(
        default=200    ,						metadata={'''help''': '''Number of completions to generate for each sample.'''}       )
    lowerCAmelCase        =				field(default=1    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''eval_results.json'''    ,						metadata={'''help''': '''Random seed used for evaluation.'''}       )
    lowerCAmelCase        =				field(
        default='''0'''    ,						metadata={'''help''': '''Allow `code_eval` to execute Python code on machine'''}       )
    lowerCAmelCase        =				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:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default=_a    ,						metadata={
            '''help''': '''The number of CPU cores to use for parallel preprocessing. Default uses the maximum available.'''
        }    ,						)
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot'''    ,						metadata={'''help''': '''Folder or name of dataset to process.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot-clean'''    ,						metadata={'''help''': '''Folder to save processed processed dataset.'''}       )
    lowerCAmelCase        =				field(
        default=10_0000    ,						metadata={'''help''': '''Number of files to save per JSON output file.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(
        default=1000    ,						metadata={'''help''': '''Maximum line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=100    ,						metadata={'''help''': '''Maximum mean line length in file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.25    ,						metadata={'''help''': '''Maximum fraction of non-alphanumeric characters, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=1.5    ,						metadata={'''help''': '''Minimum character token ratio for the file, otherwise file is filtered.'''}       )
    lowerCAmelCase        =				field(
        default=0.7    ,						metadata={'''help''': '''Probability for filtering config, test and uncommon files.'''}       )
    lowerCAmelCase        =				field(
        default='''codeparrot/codeparrot'''    ,						metadata={'''help''': '''Name or path to the tokenizer.'''}    ,						)
    lowerCAmelCase        =				field(
        default=_a    ,						metadata={'''help''': '''If True, near-duplicate samples are removed.'''}       )
    lowerCAmelCase        =				field(
        default=0.85    ,						metadata={'''help''': '''Jaccard threshold for near-duplicate samples.'''}       )


@dataclass
class   __a:






    """simple docstring"""





    lowerCAmelCase        =				field(
        default='''gpt2'''    ,						metadata={'''help''': '''Base tokenizer to build new tokenizer from.'''}       )
    lowerCAmelCase        =				field(
        default='''transformersbook/codeparrot-train'''    ,						metadata={'''help''': '''Dataset to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''content'''    ,						metadata={'''help''': '''Column containing text data to process.'''}       )
    lowerCAmelCase        =				field(default=20_0000    ,						metadata={'''help''': '''Number of examples to train tokenizer on.'''}       )
    lowerCAmelCase        =				field(
        default=3_2768    ,						metadata={'''help''': '''Number of examples to train the tokenizer on.'''}       )
    lowerCAmelCase        =				field(default='''codeparrot'''    ,						metadata={'''help''': '''Name of new tokenizer.'''}       )
    lowerCAmelCase        =				field(default=_a    ,						metadata={'''help''': '''Push saved tokenizer to the hub.'''}       )


@dataclass
class   __a:






    """simple docstring"""





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


@dataclass
class   __a:






    """simple docstring"""





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