


'''simple docstring'''




import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

 if is_vision_available():
  from transformers import MaskaFormerImageProcessor

if is_vision_available():
 from PIL import Image







class 					a			:
 '''simple docstring'''




 def __init__(    self		,      lowerCamelCase_		,      lowerCamelCase_=2		,      lowerCamelCase_=True		,      lowerCamelCase_=False		,      lowerCamelCase_=1_0		,      lowerCamelCase_=3		,      lowerCamelCase_=3_2 * 8		,      lowerCamelCase_=3_2 * 8		,      lowerCamelCase_=4		,      lowerCamelCase_=6_4		,      )      ->    Optional[int]:
    _a					:  Optional[Any]						=  parent
    _a					:  Any						=  batch_size
    _a					:  Any						=  is_training
    _a					:  List[Any]						=  use_auxiliary_loss
    _a					:  Optional[Any]						=  num_queries
    _a					:  Optional[int]						=  num_channels
    _a					:  List[str]						=  min_size
    _a					:  Union[str, Any]						=  max_size
    _a					:  Optional[Any]						=  num_labels
    _a					:  Tuple						=  hidden_dim
    _a					:  Tuple						=  hidden_dim




 def   __UpperCamelCase   (    self      )      ->    Optional[int]:
    _a					:  int						=  floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]      ).to(
        lowerCamelCase_      )

    _a					:  str						=  torch.ones([self.batch_size, self.min_size, self.max_size]		,      device=lowerCamelCase_      )

    _a					:  Tuple						=  (
        torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size]		,      device=lowerCamelCase_      ) > 0.5
    ).float()
    _a					:  Optional[int]						=  (torch.rand((self.batch_size, self.num_labels)		,      device=lowerCamelCase_      ) > 0.5).long()

    _a					:  str						=  self.get_config()
    return config, pixel_values, pixel_mask, mask_labels, class_labels




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  Tuple						=  MaskaFormerConfig(
        hidden_size=self.hidden_dim		,      )
    _a					:  Dict						=  self.num_queries
    _a					:  Tuple						=  self.num_labels

    _a					:  Optional[Any]						=  [1, 1, 1, 1]
    _a					:  List[str]						=  self.num_channels

    _a					:  Tuple						=  6_4
    _a					:  int						=  1_2_8
    _a					:  List[Any]						=  self.hidden_dim
    _a					:  Dict						=  self.hidden_dim
    _a					:  List[str]						=  self.hidden_dim
    return config




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a		,						_a		,						_a		,						_a		,						_a					:  str						=  self.prepare_config_and_inputs()
    _a					:  Optional[Any]						=  {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
    return config, inputs_dict




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Union[str, Any]:
    _a					:  List[str]						=  output.encoder_hidden_states
    _a					:  int						=  output.pixel_decoder_hidden_states
    _a					:  Union[str, Any]						=  output.transformer_decoder_hidden_states

    self.parent.assertTrue(len(lowerCamelCase_      )		,      len(config.backbone_config.depths      )      )
    self.parent.assertTrue(len(lowerCamelCase_      )		,      len(config.backbone_config.depths      )      )
    self.parent.assertTrue(len(lowerCamelCase_      )		,      config.decoder_layers      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_=False      )      ->    int:
    with torch.no_grad():
       _a					:  Tuple						=  MaskaFormerModel(config=lowerCamelCase_      )
       model.to(lowerCamelCase_      )
       model.eval()

       _a					:  str						=  model(pixel_values=lowerCamelCase_		,      pixel_mask=lowerCamelCase_      )
       _a					:  Any						=  model(lowerCamelCase_		,      output_hidden_states=lowerCamelCase_      )

    self.parent.assertEqual(
        output.transformer_decoder_last_hidden_state.shape		,      (self.batch_size, self.num_queries, self.hidden_dim)		,      )
    # let's ensure the other two hidden state exists
    self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None      )
    self.parent.assertTrue(output.encoder_last_hidden_state is not None      )

    if output_hidden_states:
       self.check_output_hidden_state(lowerCamelCase_		,      lowerCamelCase_      )




 def   __UpperCamelCase   (    self		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_		,      lowerCamelCase_      )      ->    Optional[Any]:
    _a					:  List[str]						=  MaskaFormerForUniversalSegmentation(config=lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.eval()

    def comm_check_on_output(lowerCamelCase_      ):
       # let's still check that all the required stuff is there
       self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None      )
       self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None      )
       self.parent.assertTrue(result.encoder_last_hidden_state is not None      )
       # okay, now we need to check the logits shape
       # due to the encoder compression, masks have a //4 spatial size
       self.parent.assertEqual(
           result.masks_queries_logits.shape		,      (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4)		,      )
       # + 1 for null class
       self.parent.assertEqual(
           result.class_queries_logits.shape		,      (self.batch_size, self.num_queries, self.num_labels + 1)      )

    with torch.no_grad():
       _a					:  Any						=  model(pixel_values=lowerCamelCase_		,      pixel_mask=lowerCamelCase_      )
       _a					:  Optional[Any]						=  model(lowerCamelCase_      )

       comm_check_on_output(lowerCamelCase_      )

       _a					:  int						=  model(
           pixel_values=lowerCamelCase_		,      pixel_mask=lowerCamelCase_		,      mask_labels=lowerCamelCase_		,      class_labels=lowerCamelCase_      )

    comm_check_on_output(lowerCamelCase_      )

    self.parent.assertTrue(result.loss is not None      )
    self.parent.assertEqual(result.loss.shape		,      torch.Size([1]      )      )







@require_torch
class 					a			(				snake_case__			,							snake_case__			,							unittest.TestCase  ):
 '''simple docstring'''




 __lowerCAmelCase						: int							  =		(MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
 __lowerCAmelCase						: Optional[Any]							  =		{"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

 __lowerCAmelCase						: int							  =		False
 __lowerCAmelCase						: Optional[int]							  =		False
 __lowerCAmelCase						: int							  =		False
 __lowerCAmelCase						: int							  =		False




 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    _a					:  str						=  MaskaFormerModelTester(self      )
    _a					:  int						=  ConfigTester(self		,      config_class=lowerCamelCase_		,      has_text_modality=lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    str:
    self.config_tester.run_common_tests()




 def   __UpperCamelCase   (    self      )      ->    Dict:
    _a		,						_a					:  Optional[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_		,      **lowerCamelCase_		,      output_hidden_states=lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  List[str]						=  self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowerCamelCase_      )




 @unittest.skip(reason='Mask2Former does not use inputs_embeds'      )
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    pass




 @unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'      )
 def   __UpperCamelCase   (    self      )      ->    int:
    pass




 @unittest.skip(reason='Mask2Former is not a generative model'      )
 def   __UpperCamelCase   (    self      )      ->    str:
    pass




 @unittest.skip(reason='Mask2Former does not use token embeddings'      )
 def   __UpperCamelCase   (    self      )      ->    Dict:
    pass




 @require_torch_multi_gpu
 @unittest.skip(
     reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'      )
 def   __UpperCamelCase   (    self      )      ->    Tuple:
    pass




 @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
 def   __UpperCamelCase   (    self      )      ->    List[Any]:
    pass




 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    _a		,						_a					:  int						=  self.model_tester.prepare_config_and_inputs_for_common()

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

       _a					:  Optional[int]						=  ['pixel_values']
       self.assertListEqual(arg_names[:1]		,      lowerCamelCase_      )




 @slow
 def   __UpperCamelCase   (    self      )      ->    Any:
    for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
       _a					:  List[str]						=  MaskaFormerModel.from_pretrained(lowerCamelCase_      )
       self.assertIsNotNone(lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a					:  Tuple						=  (self.model_tester.min_size,) * 2
    _a					:  Optional[Any]						=  {
        'pixel_values': torch.randn((2, 3, *size)		,      device=lowerCamelCase_      ),
        'mask_labels': torch.randn((2, 1_0, *size)		,      device=lowerCamelCase_      ),
        'class_labels': torch.zeros(2		,      1_0		,      device=lowerCamelCase_      ).long(),
    }
    _a					:  List[str]						=  self.model_tester.get_config()

    _a					:  Union[str, Any]						=  MaskaFormerForUniversalSegmentation(lowerCamelCase_      ).to(lowerCamelCase_      )
    _a					:  Optional[int]						=  model(**lowerCamelCase_      )
    self.assertTrue(outputs.loss is not None      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a		,						_a					:  List[Any]						=  self.model_tester.prepare_config_and_inputs_for_common()
    self.model_tester.create_and_check_maskaformer_model(lowerCamelCase_		,      **lowerCamelCase_		,      output_hidden_states=lowerCamelCase_      )




 def   __UpperCamelCase   (    self      )      ->    Any:
    _a		,						_a					:  Any						=  self.model_tester.prepare_config_and_inputs_for_common()

    for model_class in self.all_model_classes:
       _a					:  Union[str, Any]						=  model_class(lowerCamelCase_      ).to(lowerCamelCase_      )
       _a					:  Dict						=  model(**lowerCamelCase_		,      output_attentions=lowerCamelCase_      )
       self.assertTrue(outputs.attentions is not None      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    if not self.model_tester.is_training:
       return

    _a					:  List[Any]						=  self.all_model_classes[1]
    _a		,						_a		,						_a		,						_a		,						_a					:  Union[str, Any]						=  self.model_tester.prepare_config_and_inputs()

    _a					:  Optional[int]						=  model_class(lowerCamelCase_      )
    model.to(lowerCamelCase_      )
    model.train()

    _a					:  int						=  model(lowerCamelCase_		,      mask_labels=lowerCamelCase_		,      class_labels=lowerCamelCase_      ).loss
    loss.backward()




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    _a					:  Dict						=  self.all_model_classes[1]
    _a		,						_a		,						_a		,						_a		,						_a					:  str						=  self.model_tester.prepare_config_and_inputs()
    _a					:  Any						=  True
    _a					:  List[str]						=  True

    _a					:  Optional[Any]						=  model_class(lowerCamelCase_      ).to(lowerCamelCase_      )
    model.train()

    _a					:  int						=  model(lowerCamelCase_		,      mask_labels=lowerCamelCase_		,      class_labels=lowerCamelCase_      )

    _a					:  List[str]						=  outputs.encoder_hidden_states[0]
    encoder_hidden_states.retain_grad()

    _a					:  Tuple						=  outputs.pixel_decoder_hidden_states[0]
    pixel_decoder_hidden_states.retain_grad()

    _a					:  Optional[int]						=  outputs.transformer_decoder_hidden_states[0]
    transformer_decoder_hidden_states.retain_grad()

    _a					:  str						=  outputs.attentions[0]
    attentions.retain_grad()

    outputs.loss.backward(retain_graph=lowerCamelCase_      )

    self.assertIsNotNone(encoder_hidden_states.grad      )
    self.assertIsNotNone(pixel_decoder_hidden_states.grad      )
    self.assertIsNotNone(transformer_decoder_hidden_states.grad      )
    self.assertIsNotNone(attentions.grad      )





UpperCAmelCase_      :		Tuple          =		1E-4


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Union[str, Any]						=  Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'      )
   return image







@require_vision
@slow
class 					a			(				unittest.TestCase  ):
 '''simple docstring'''




 @cached_property
 def   __UpperCamelCase   (    self      )      ->    Optional[Any]:
    return "facebook/mask2former-swin-small-coco-instance"




 @cached_property
 def   __UpperCamelCase   (    self      )      ->    str:
    return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints      ) if is_vision_available() else None




 def   __UpperCamelCase   (    self      )      ->    int:
    _a					:  Union[str, Any]						=  MaskaFormerModel.from_pretrained(self.model_checkpoints      ).to(lowerCamelCase_      )
    _a					:  Dict						=  self.default_image_processor
    _a					:  Any						=  prepare_img()
    _a					:  int						=  image_processor(lowerCamelCase_		,      return_tensors='pt'      ).to(lowerCamelCase_      )
    _a					:  Tuple						=  inputs['pixel_values'].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0      )
    # check size
    self.assertEqual(lowerCamelCase_		,      (1, 3, 3_8_4, 3_8_4)      )

    with torch.no_grad():
       _a					:  int						=  model(**lowerCamelCase_      )

    _a					:  Union[str, Any]						=  torch.tensor(
        [[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]]      ).to(lowerCamelCase_      )
    self.assertTrue(
        torch.allclose(
            outputs.encoder_last_hidden_state[0, 0, :3, :3]		,      lowerCamelCase_		,      atol=lowerCamelCase_      )      )

    _a					:  str						=  torch.tensor(
        [[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]]      ).to(lowerCamelCase_      )
    self.assertTrue(
        torch.allclose(
            outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3]		,      lowerCamelCase_		,      atol=lowerCamelCase_      )      )

    _a					:  Optional[Any]						=  torch.tensor(
        [[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]]      ).to(lowerCamelCase_      )
    self.assertTrue(
        torch.allclose(
            outputs.transformer_decoder_last_hidden_state[0, :3, :3]		,      lowerCamelCase_		,      atol=lowerCamelCase_      )      )




 def   __UpperCamelCase   (    self      )      ->    Union[str, Any]:
    _a					:  List[str]						=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(lowerCamelCase_      ).eval()
    _a					:  Optional[Any]						=  self.default_image_processor
    _a					:  Optional[Any]						=  prepare_img()
    _a					:  str						=  image_processor(lowerCamelCase_		,      return_tensors='pt'      ).to(lowerCamelCase_      )
    _a					:  Dict						=  inputs['pixel_values'].shape
    # check size is divisible by 32
    self.assertTrue((inputs_shape[-1] % 3_2) == 0 and (inputs_shape[-2] % 3_2) == 0      )
    # check size
    self.assertEqual(lowerCamelCase_		,      (1, 3, 3_8_4, 3_8_4)      )

    with torch.no_grad():
       _a					:  Optional[int]						=  model(**lowerCamelCase_      )
    # masks_queries_logits
    _a					:  Any						=  outputs.masks_queries_logits
    self.assertEqual(
        masks_queries_logits.shape		,      (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)      )
    _a					:  Dict						=  [
        [-8.7839, -9.0056, -8.8121],
        [-7.4104, -7.0313, -6.5401],
        [-6.6105, -6.3427, -6.4675],
    ]
    _a					:  Tuple						=  torch.tensor(lowerCamelCase_      ).to(lowerCamelCase_      )
    self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3]		,      lowerCamelCase_		,      atol=lowerCamelCase_      )      )
    # class_queries_logits
    _a					:  Any						=  outputs.class_queries_logits
    self.assertEqual(class_queries_logits.shape		,      (1, model.config.num_queries, model.config.num_labels + 1)      )
    _a					:  Tuple						=  torch.tensor(
        [
            [1.8324, -8.0835, -4.1922],
            [0.8450, -9.0050, -3.6053],
            [0.3045, -7.7293, -3.0275],
        ]      ).to(lowerCamelCase_      )
    self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3]		,      lowerCamelCase_		,      atol=lowerCamelCase_      )      )




 def   __UpperCamelCase   (    self      )      ->    str:
    _a					:  Tuple						=  MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints      ).to(lowerCamelCase_      ).eval()
    _a					:  List[Any]						=  self.default_image_processor

    _a					:  Optional[int]						=  image_processor(
        [np.zeros((3, 8_0_0, 1_3_3_3)      ), np.zeros((3, 8_0_0, 1_3_3_3)      )]		,      segmentation_maps=[np.zeros((3_8_4, 3_8_4)      ).astype(np.floataa      ), np.zeros((3_8_4, 3_8_4)      ).astype(np.floataa      )]		,      return_tensors='pt'		,      )

    _a					:  Optional[Any]						=  inputs['pixel_values'].to(lowerCamelCase_      )
    _a					:  int						=  [el.to(lowerCamelCase_      ) for el in inputs['mask_labels']]
    _a					:  Optional[int]						=  [el.to(lowerCamelCase_      ) for el in inputs['class_labels']]

    with torch.no_grad():
       _a					:  int						=  model(**lowerCamelCase_      )

    self.assertTrue(outputs.loss is not None      )






'''simple docstring'''




import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

from datasets import Dataset, load_dataset

import transformers
from transformers import (
    CONFIG_MAPPING,
    MODEL_FOR_MASKED_LM_MAPPING,
    AutoConfig,
    AutoModelForMaskedLM,
    AutoTokenizer,
    DataCollatorForWholeWordMask,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process


UpperCAmelCase_      :		str          =		logging.getLogger(__name__)
UpperCAmelCase_      :		List[Any]          =		list(MODEL_FOR_MASKED_LM_MAPPING.keys())
UpperCAmelCase_      :		List[Any]          =		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class 					a			:
 '''simple docstring'''




 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={
         """help""": (
             """The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."""
         )
     }			,							)
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(snake_case__  )}			,							)
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={
         """help""": (
             """Override some existing default config settings when a model is trained from scratch. Example: """
             """n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"""
         )
     }			,							)
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}			,							)
 __lowerCAmelCase						: bool							  =		field(
     default=snake_case__			,							metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}			,							)
 __lowerCAmelCase						: str							  =		field(
     default="""main"""			,							metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}			,							)
 __lowerCAmelCase						: bool							  =		field(
     default=snake_case__			,							metadata={
         """help""": (
             """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
             """with private models)."""
         )
     }			,							)




 def   __UpperCamelCase   (    self      )      ->    Tuple:
    if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
       raise ValueError(
           '--config_overrides can\'t be used in combination with --config_name or --model_name_or_path'      )







@dataclass
class 					a			:
 '''simple docstring'''




 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """The name of the dataset to use (via the datasets library)."""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(default=snake_case__			,							metadata={"""help""": """The input training data file (a text file)."""}  )
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """An optional input evaluation data file to evaluate the perplexity on (a text file)."""}			,							)
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """An optional input train ref data file for whole word masking in Chinese."""}			,							)
 __lowerCAmelCase						: Optional[str]							  =		field(
     default=snake_case__			,							metadata={"""help""": """An optional input validation ref data file for whole word masking in Chinese."""}			,							)
 __lowerCAmelCase						: bool							  =		field(
     default=snake_case__			,							metadata={"""help""": """Overwrite the cached training and evaluation sets"""}  )
 __lowerCAmelCase						: Optional[int]							  =		field(
     default=5			,							metadata={
         """help""": """The percentage of the train set used as validation set in case there's no validation split"""
     }			,							)
 __lowerCAmelCase						: Optional[int]							  =		field(
     default=snake_case__			,							metadata={
         """help""": (
             """The maximum total input sequence length after tokenization. Sequences longer """
             """than this will be truncated. Default to the max input length of the model."""
         )
     }			,							)
 __lowerCAmelCase						: Optional[int]							  =		field(
     default=snake_case__			,							metadata={"""help""": """The number of processes to use for the preprocessing."""}			,							)
 __lowerCAmelCase						: float							  =		field(
     default=0.1_5			,							metadata={"""help""": """Ratio of tokens to mask for masked language modeling loss"""}  )
 __lowerCAmelCase						: bool							  =		field(
     default=snake_case__			,							metadata={
         """help""": (
             """Whether to pad all samples to `max_seq_length`. """
             """If False, will pad the samples dynamically when batching to the maximum length in the batch."""
         )
     }			,							)




 def   __UpperCamelCase   (    self      )      ->    str:
    if self.train_file is not None:
       _a					:  int						=  self.train_file.split('.'      )[-1]
       assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
    if self.validation_file is not None:
       _a					:  List[Any]						=  self.validation_file.split('.'      )[-1]
       assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."





def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





   with open(A		,	'r'		,	encoding='utf-8'      ) as f:
      _a					:  Dict						=  [json.loads(A      ) for line in f.read().splitlines() if (len(A      ) > 0 and not line.isspace())]
   assert len(A      ) == len(A      )

   _a					:  Any						=  {c: dataset[c] for c in dataset.column_names}
   _a					:  Optional[Any]						=  refs
   return Dataset.from_dict(A      )


def    UpperCAmelCase_  (       ):
   '''simple docstring'''





   _a					:  Optional[int]						=  HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
   if len(sys.argv      ) == 2 and sys.argv[1].endswith('.json'      ):
      # If we pass only one argument to the script and it's the path to a json file,
      # let's parse it to get our arguments.
      _a		,						_a		,						_a					:  List[Any]						=  parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]      )      )
   else:
      _a		,						_a		,						_a					:  Dict						=  parser.parse_args_into_dataclasses()

   # Detecting last checkpoint.
   _a					:  Dict						=  None
   if os.path.isdir(training_args.output_dir      ) and training_args.do_train and not training_args.overwrite_output_dir:
      _a					:  List[str]						=  get_last_checkpoint(training_args.output_dir      )
      if last_checkpoint is None and len(os.listdir(training_args.output_dir      )      ) > 0:
         raise ValueError(
             f'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
             'Use --overwrite_output_dir to overcome.'      )
      elif last_checkpoint is not None:
         logger.info(
             f'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
             'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'      )

    # Setup logging
   logging.basicConfig(
       format='%(asctime)s - %(levelname)s - %(name)s - %(message)s'		,	datefmt='%m/%d/%Y %H:%M:%S'		,	handlers=[logging.StreamHandler(sys.stdout      )]		,	)
   logger.setLevel(logging.INFO if is_main_process(training_args.local_rank      ) else logging.WARN      )

   # Log on each process the small summary:
   logger.warning(
       f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
       + f'''distributed training: {bool(training_args.local_rank != -1      )}, 16-bits training: {training_args.fpaa}'''      )
   # Set the verbosity to info of the Transformers logger (on main process only):
   if is_main_process(training_args.local_rank      ):
      transformers.utils.logging.set_verbosity_info()
      transformers.utils.logging.enable_default_handler()
      transformers.utils.logging.enable_explicit_format()
   logger.info('Training/evaluation parameters %s'		,	A      )

   # Set seed before initializing model.
   set_seed(training_args.seed      )

   # Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
   # or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
   # (the dataset will be downloaded automatically from the datasets Hub).
   #
   # For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
   # 'text' is found. You can easily tweak this behavior (see below).
   #
   # In distributed training, the load_dataset function guarantee that only one local process can concurrently
   # download the dataset.
   if data_args.dataset_name is not None:
      # Downloading and loading a dataset from the hub.
      _a					:  Tuple						=  load_dataset(data_args.dataset_name		,	data_args.dataset_config_name      )
      if "validation" not in datasets.keys():
         _a					:  Tuple						=  load_dataset(
             data_args.dataset_name		,	data_args.dataset_config_name		,	split=f'''train[:{data_args.validation_split_percentage}%]'''		,	)
         _a					:  List[str]						=  load_dataset(
             data_args.dataset_name		,	data_args.dataset_config_name		,	split=f'''train[{data_args.validation_split_percentage}%:]'''		,	)
   else:
      _a					:  int						=  {}
      if data_args.train_file is not None:
         _a					:  Tuple						=  data_args.train_file
      if data_args.validation_file is not None:
         _a					:  List[Any]						=  data_args.validation_file
      _a					:  Optional[Any]						=  data_args.train_file.split('.'      )[-1]
      if extension == "txt":
         _a					:  Optional[Any]						=  'text'
      _a					:  int						=  load_dataset(A		,	data_files=A      )
   # See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
   # https://huggingface.co/docs/datasets/loading_datasets.html.

   # Load pretrained model and tokenizer
   #
   # Distributed training:
   # The .from_pretrained methods guarantee that only one local process can concurrently
   # download model & vocab.
   _a					:  List[str]						=  {
       'cache_dir': model_args.cache_dir,
       'revision': model_args.model_revision,
       'use_auth_token': True if model_args.use_auth_token else None,
   }
   if model_args.config_name:
      _a					:  List[str]						=  AutoConfig.from_pretrained(model_args.config_name		,	**A      )
   elif model_args.model_name_or_path:
      _a					:  Any						=  AutoConfig.from_pretrained(model_args.model_name_or_path		,	**A      )
   else:
      _a					:  Optional[Any]						=  CONFIG_MAPPING[model_args.model_type]()
      logger.warning('You are instantiating a new config instance from scratch.'      )
      if model_args.config_overrides is not None:
         logger.info(f'''Overriding config: {model_args.config_overrides}'''      )
         config.update_from_string(model_args.config_overrides      )
         logger.info(f'''New config: {config}'''      )

   _a					:  Any						=  {
       'cache_dir': model_args.cache_dir,
       'use_fast': model_args.use_fast_tokenizer,
       'revision': model_args.model_revision,
       'use_auth_token': True if model_args.use_auth_token else None,
   }
   if model_args.tokenizer_name:
      _a					:  Tuple						=  AutoTokenizer.from_pretrained(model_args.tokenizer_name		,	**A      )
   elif model_args.model_name_or_path:
      _a					:  Dict						=  AutoTokenizer.from_pretrained(model_args.model_name_or_path		,	**A      )
   else:
      raise ValueError(
          'You are instantiating a new tokenizer from scratch. This is not supported by this script.'
          'You can do it from another script, save it, and load it from here, using --tokenizer_name.'      )

   if model_args.model_name_or_path:
      _a					:  Optional[Any]						=  AutoModelForMaskedLM.from_pretrained(
          model_args.model_name_or_path		,	from_tf=bool('.ckpt' in model_args.model_name_or_path      )		,	config=A		,	cache_dir=model_args.cache_dir		,	revision=model_args.model_revision		,	use_auth_token=True if model_args.use_auth_token else None		,	)
   else:
      logger.info('Training new model from scratch'      )
      _a					:  List[Any]						=  AutoModelForMaskedLM.from_config(A      )

   model.resize_token_embeddings(len(A      )      )

   # Preprocessing the datasets.
   # First we tokenize all the texts.
   if training_args.do_train:
      _a					:  str						=  datasets['train'].column_names
   else:
      _a					:  int						=  datasets['validation'].column_names
   _a					:  Union[str, Any]						=  'text' if 'text' in column_names else column_names[0]

   _a					:  List[Any]						=  'max_length' if data_args.pad_to_max_length else False

   def tokenize_function(A      ):
      # Remove empty lines
      _a					:  Union[str, Any]						=  [line for line in examples['text'] if len(A      ) > 0 and not line.isspace()]
      return tokenizer(examples['text']		,	padding=A		,	truncation=A		,	max_length=data_args.max_seq_length      )

   _a					:  Any						=  datasets.map(
       A		,	batched=A		,	num_proc=data_args.preprocessing_num_workers		,	remove_columns=[text_column_name]		,	load_from_cache_file=not data_args.overwrite_cache		,	)

   # Add the chinese references if provided
   if data_args.train_ref_file is not None:
      _a					:  str						=  add_chinese_references(tokenized_datasets['train']		,	data_args.train_ref_file      )
   if data_args.validation_ref_file is not None:
      _a					:  Tuple						=  add_chinese_references(
          tokenized_datasets['validation']		,	data_args.validation_ref_file      )
   # If we have ref files, need to avoid it removed by trainer
   _a					:  Union[str, Any]						=  data_args.train_ref_file or data_args.validation_ref_file
   if has_ref:
      _a					:  Optional[int]						=  False

   # Data collator
   # This one will take care of randomly masking the tokens.
   _a					:  Tuple						=  DataCollatorForWholeWordMask(tokenizer=A		,	mlm_probability=data_args.mlm_probability      )

   # Initialize our Trainer
   _a					:  Union[str, Any]						=  Trainer(
       model=A		,	args=A		,	train_dataset=tokenized_datasets['train'] if training_args.do_train else None		,	eval_dataset=tokenized_datasets['validation'] if training_args.do_eval else None		,	tokenizer=A		,	data_collator=A		,	)

   # Training
   if training_args.do_train:
      if last_checkpoint is not None:
         _a					:  Any						=  last_checkpoint
      elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path      ):
         _a					:  Optional[int]						=  model_args.model_name_or_path
      else:
         _a					:  int						=  None
      _a					:  Tuple						=  trainer.train(resume_from_checkpoint=A      )
      trainer.save_model()  # Saves the tokenizer too for easy upload

      _a					:  Union[str, Any]						=  os.path.join(training_args.output_dir		,	'train_results.txt'      )
      if trainer.is_world_process_zero():
         with open(A		,	'w'      ) as writer:
            logger.info('***** Train results *****'      )
            for key, value in sorted(train_result.metrics.items()      ):
               logger.info(f'''  {key} = {value}'''      )
               writer.write(f'''{key} = {value}\n'''      )

            # Need to save the state, since Trainer.save_model saves only the tokenizer with the model
         trainer.state.save_to_json(os.path.join(training_args.output_dir		,	'trainer_state.json'      )      )

    # Evaluation
   _a					:  Optional[int]						=  {}
   if training_args.do_eval:
      logger.info('*** Evaluate ***'      )

      _a					:  Union[str, Any]						=  trainer.evaluate()

      _a					:  Any						=  math.exp(eval_output['eval_loss']      )
      _a					:  List[str]						=  perplexity

      _a					:  Optional[int]						=  os.path.join(training_args.output_dir		,	'eval_results_mlm_wwm.txt'      )
      if trainer.is_world_process_zero():
         with open(A		,	'w'      ) as writer:
            logger.info('***** Eval results *****'      )
            for key, value in sorted(results.items()      ):
               logger.info(f'''  {key} = {value}'''      )
               writer.write(f'''{key} = {value}\n'''      )

   return results


def    UpperCAmelCase_  (       A      ):
   '''simple docstring'''





   main()


if __name__ == "__main__":
 main()


