






import copy
import unittest

from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, 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, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
         MODEL_FOR_QUESTION_ANSWERING_MAPPING,
         MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
         MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
         LayoutLMvaConfig,
         LayoutLMvaForQuestionAnswering,
         LayoutLMvaForSequenceClassification,
         LayoutLMvaForTokenClassification,
         LayoutLMvaModel,
     )
     from transformers.models.layoutlmva.modeling_layoutlmva import LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST

if is_vision_available():
     from PIL import Image

     from transformers import LayoutLMvaImageProcessor

class   __a:






    """simple docstring"""





    def __init__(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=7       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=True       ,_SCREAMING_SNAKE_CASE=99       ,_SCREAMING_SNAKE_CASE=36       ,_SCREAMING_SNAKE_CASE=3       ,_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=512       ,_SCREAMING_SNAKE_CASE=16       ,_SCREAMING_SNAKE_CASE=2       ,_SCREAMING_SNAKE_CASE=0.02       ,_SCREAMING_SNAKE_CASE=6       ,_SCREAMING_SNAKE_CASE=6       ,_SCREAMING_SNAKE_CASE=3       ,_SCREAMING_SNAKE_CASE=4       ,_SCREAMING_SNAKE_CASE=None       ,_SCREAMING_SNAKE_CASE=1_000       ,)			->		List[Any]:
         UpperCAmelCase_      :							int       =      parent
         UpperCAmelCase_      :							List[str]       =      batch_size
         UpperCAmelCase_      :							Union[str, Any]       =      num_channels
         UpperCAmelCase_      :							Dict       =      image_size
         UpperCAmelCase_      :							Dict       =      patch_size
         UpperCAmelCase_      :							Any       =      text_seq_length
         UpperCAmelCase_      :							Optional[int]       =      is_training
         UpperCAmelCase_      :							List[Any]       =      use_input_mask
         UpperCAmelCase_      :							Optional[Any]       =      use_token_type_ids
         UpperCAmelCase_      :							Dict       =      use_labels
         UpperCAmelCase_      :							int       =      vocab_size
         UpperCAmelCase_      :							List[str]       =      hidden_size
         UpperCAmelCase_      :							int       =      num_hidden_layers
         UpperCAmelCase_      :							Any       =      num_attention_heads
         UpperCAmelCase_      :							Any       =      intermediate_size
         UpperCAmelCase_      :							int       =      hidden_act
         UpperCAmelCase_      :							Dict       =      hidden_dropout_prob
         UpperCAmelCase_      :							Tuple       =      attention_probs_dropout_prob
         UpperCAmelCase_      :							Optional[Any]       =      max_position_embeddings
         UpperCAmelCase_      :							Dict       =      type_vocab_size
         UpperCAmelCase_      :							Any       =      type_sequence_label_size
         UpperCAmelCase_      :							List[str]       =      initializer_range
         UpperCAmelCase_      :							int       =      coordinate_size
         UpperCAmelCase_      :							Union[str, Any]       =      shape_size
         UpperCAmelCase_      :							List[str]       =      num_labels
         UpperCAmelCase_      :							List[str]       =      num_choices
         UpperCAmelCase_      :							Union[str, Any]       =      scope
         UpperCAmelCase_      :							Dict       =      range_bbox

         # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
         UpperCAmelCase_      :							List[Any]       =      text_seq_length
         UpperCAmelCase_      :							Optional[Any]       =      (image_size // patch_size) ** 2 + 1
         UpperCAmelCase_      :							Optional[int]       =      self.text_seq_length + self.image_seq_length





    def 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							List[str]       =      ids_tensor([self.batch_size, self.text_seq_length]       ,self.vocab_size  )

         UpperCAmelCase_      :							Tuple       =      ids_tensor([self.batch_size, self.text_seq_length, 4]       ,self.range_bbox  )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]  ):
              for j in range(bbox.shape[1]  ):
                   if bbox[i, j, 3] < bbox[i, j, 1]:
                        UpperCAmelCase_      :							int       =      bbox[i, j, 3]
                        UpperCAmelCase_      :							List[Any]       =      bbox[i, j, 1]
                        UpperCAmelCase_      :							Optional[Any]       =      t
                   if bbox[i, j, 2] < bbox[i, j, 0]:
                        UpperCAmelCase_      :							Any       =      bbox[i, j, 2]
                        UpperCAmelCase_      :							Dict       =      bbox[i, j, 0]
                        UpperCAmelCase_      :							Dict       =      t

         UpperCAmelCase_      :							Any       =      floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

         UpperCAmelCase_      :							Dict       =      None
         if self.use_input_mask:
              UpperCAmelCase_      :							int       =      random_attention_mask([self.batch_size, self.text_seq_length]  )

         UpperCAmelCase_      :							List[Any]       =      None
         if self.use_token_type_ids:
              UpperCAmelCase_      :							Tuple       =      ids_tensor([self.batch_size, self.text_seq_length]       ,self.type_vocab_size  )

         UpperCAmelCase_      :							List[Any]       =      None
         UpperCAmelCase_      :							Optional[int]       =      None
         if self.use_labels:
              UpperCAmelCase_      :							List[str]       =      ids_tensor([self.batch_size]       ,self.type_sequence_label_size  )
              UpperCAmelCase_      :							int       =      ids_tensor([self.batch_size, self.text_seq_length]       ,self.num_labels  )

         UpperCAmelCase_      :							int       =      LayoutLMvaConfig(
             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       ,initializer_range=self.initializer_range       ,coordinate_size=self.coordinate_size       ,shape_size=self.shape_size       ,input_size=self.image_size       ,patch_size=self.patch_size       ,)

         return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							str       =      LayoutLMvaModel(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()

         # text + image
         UpperCAmelCase_      :							Optional[int]       =      model(_SCREAMING_SNAKE_CASE       ,pixel_values=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							Tuple       =      model(
             _SCREAMING_SNAKE_CASE       ,bbox=_SCREAMING_SNAKE_CASE       ,pixel_values=_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							List[str]       =      model(_SCREAMING_SNAKE_CASE       ,bbox=_SCREAMING_SNAKE_CASE       ,pixel_values=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE  )
         UpperCAmelCase_      :							str       =      model(_SCREAMING_SNAKE_CASE       ,bbox=_SCREAMING_SNAKE_CASE       ,pixel_values=_SCREAMING_SNAKE_CASE  )

         self.parent.assertEqual(result.last_hidden_state.shape       ,(self.batch_size, self.seq_length, self.hidden_size)  )

         # text only
         UpperCAmelCase_      :							Optional[Any]       =      model(_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,(self.batch_size, self.text_seq_length, self.hidden_size)  )

         # image only
         UpperCAmelCase_      :							Dict       =      model(pixel_values=_SCREAMING_SNAKE_CASE  )
         self.parent.assertEqual(
             result.last_hidden_state.shape       ,(self.batch_size, self.image_seq_length, self.hidden_size)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Optional[Any]:
         UpperCAmelCase_      :							Optional[int]       =      self.num_labels
         UpperCAmelCase_      :							List[Any]       =      LayoutLMvaForSequenceClassification(_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Tuple       =      model(
             _SCREAMING_SNAKE_CASE       ,bbox=_SCREAMING_SNAKE_CASE       ,pixel_values=_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE       ,)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.num_labels)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Tuple:
         UpperCAmelCase_      :							Optional[int]       =      self.num_labels
         UpperCAmelCase_      :							List[str]       =      LayoutLMvaForTokenClassification(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							str       =      model(
             _SCREAMING_SNAKE_CASE       ,bbox=_SCREAMING_SNAKE_CASE       ,pixel_values=_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,labels=_SCREAMING_SNAKE_CASE       ,)
         self.parent.assertEqual(result.logits.shape       ,(self.batch_size, self.text_seq_length, self.num_labels)  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         UpperCAmelCase_      :							int       =      LayoutLMvaForQuestionAnswering(config=_SCREAMING_SNAKE_CASE  )
         model.to(_SCREAMING_SNAKE_CASE  )
         model.eval()
         UpperCAmelCase_      :							Dict       =      model(
             _SCREAMING_SNAKE_CASE       ,bbox=_SCREAMING_SNAKE_CASE       ,pixel_values=_SCREAMING_SNAKE_CASE       ,attention_mask=_SCREAMING_SNAKE_CASE       ,token_type_ids=_SCREAMING_SNAKE_CASE       ,start_positions=_SCREAMING_SNAKE_CASE       ,end_positions=_SCREAMING_SNAKE_CASE       ,)
         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 							a__				(				self  )			->		Dict:
         UpperCAmelCase_      :							Any       =      self.prepare_config_and_inputs()
         (
             UpperCAmelCase_   
         )      :							Optional[int]       =      config_and_inputs
         UpperCAmelCase_      :							Optional[Any]       =      {
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''pixel_values''': pixel_values,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict


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






    """simple docstring"""





    lowerCAmelCase        =				False
    lowerCAmelCase        =				False
    lowerCAmelCase        =				False

    lowerCAmelCase        =				(
        (
            LayoutLMvaModel,
            LayoutLMvaForSequenceClassification,
            LayoutLMvaForTokenClassification,
            LayoutLMvaForQuestionAnswering,
        )
        if is_torch_available()
        else ()
    )
    lowerCAmelCase        =				(
        {'''document-question-answering''': LayoutLMvaForQuestionAnswering, '''feature-extraction''': LayoutLMvaModel}
        if is_torch_available()
        else {}
    )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE  )			->		Dict:
         # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
         # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
         # the sequence dimension of the text embedding only.
         # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
         return True





    def 							a__				(				self  )			->		List[str]:
         UpperCAmelCase_      :							Optional[int]       =      LayoutLMvaModelTester(self  )
         UpperCAmelCase_      :							List[str]       =      ConfigTester(self       ,config_class=_SCREAMING_SNAKE_CASE       ,hidden_size=37  )





    def 							a__				(				self       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE       ,_SCREAMING_SNAKE_CASE=False  )			->		Union[str, Any]:
         UpperCAmelCase_      :							Dict       =      copy.deepcopy(_SCREAMING_SNAKE_CASE  )
         if model_class in get_values(_SCREAMING_SNAKE_CASE  ):
              UpperCAmelCase_      :							str       =      {
                  k: v.unsqueeze(1  ).expand(-1       ,self.model_tester.num_choices       ,-1  ).contiguous()
                  if isinstance(_SCREAMING_SNAKE_CASE       ,torch.Tensor  ) and v.ndim > 1
                  else v
                  for k, v in inputs_dict.items()
              }
         if return_labels:
              if model_class in get_values(_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							Dict       =      torch.ones(self.model_tester.batch_size       ,dtype=torch.long       ,device=_SCREAMING_SNAKE_CASE  )
              elif model_class in get_values(_SCREAMING_SNAKE_CASE  ):
                   UpperCAmelCase_      :							str       =      torch.zeros(
                       self.model_tester.batch_size       ,dtype=torch.long       ,device=_SCREAMING_SNAKE_CASE  )
                   UpperCAmelCase_      :							str       =      torch.zeros(
                       self.model_tester.batch_size       ,dtype=torch.long       ,device=_SCREAMING_SNAKE_CASE  )
              elif model_class in [
                  *get_values(_SCREAMING_SNAKE_CASE  ),
              ]:
                   UpperCAmelCase_      :							int       =      torch.zeros(
                       self.model_tester.batch_size       ,dtype=torch.long       ,device=_SCREAMING_SNAKE_CASE  )
              elif model_class in [
                  *get_values(_SCREAMING_SNAKE_CASE  ),
              ]:
                   UpperCAmelCase_      :							int       =      torch.zeros(
                       (self.model_tester.batch_size, self.model_tester.text_seq_length)       ,dtype=torch.long       ,device=_SCREAMING_SNAKE_CASE       ,)

         return inputs_dict





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





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





    def 							a__				(				self  )			->		Any:
         UpperCAmelCase_      :							int       =      self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
              UpperCAmelCase_      :							Dict       =      type
              self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )





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





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





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





    @slow
    def 							a__				(				self  )			->		Any:
         for model_name in LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              UpperCAmelCase_      :							Dict       =      LayoutLMvaModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
              self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )







def lowerCamelCase__       (      ):

     '''simple docstring'''


     UpperCAmelCase_      :							int       =      Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''				)
     return image



@require_torch
class   __a(					unittest.TestCase       ):






    """simple docstring"""





    @cached_property
    def 							a__				(				self  )			->		int:
         return LayoutLMvaImageProcessor(apply_ocr=_SCREAMING_SNAKE_CASE  ) if is_vision_available() else None





    @slow
    def 							a__				(				self  )			->		Tuple:
         UpperCAmelCase_      :							Optional[int]       =      LayoutLMvaModel.from_pretrained('''microsoft/layoutlmv3-base'''  ).to(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							Dict       =      self.default_image_processor
         UpperCAmelCase_      :							int       =      prepare_img()
         UpperCAmelCase_      :							Union[str, Any]       =      image_processor(images=_SCREAMING_SNAKE_CASE       ,return_tensors='''pt'''  ).pixel_values.to(_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							List[str]       =      torch.tensor([[1, 2]]  )
         UpperCAmelCase_      :							Optional[Any]       =      torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]  ).unsqueeze(0  )

         # forward pass
         UpperCAmelCase_      :							Optional[int]       =      model(
             input_ids=input_ids.to(_SCREAMING_SNAKE_CASE  )       ,bbox=bbox.to(_SCREAMING_SNAKE_CASE  )       ,pixel_values=pixel_values.to(_SCREAMING_SNAKE_CASE  )       ,)

         # verify the logits
         UpperCAmelCase_      :							Dict       =      torch.Size((1, 199, 768)  )
         self.assertEqual(outputs.last_hidden_state.shape       ,_SCREAMING_SNAKE_CASE  )

         UpperCAmelCase_      :							int       =      torch.tensor(
             [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]  ).to(_SCREAMING_SNAKE_CASE  )

         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3]       ,_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.'''}       )