


'''simple docstring'''

import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

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


if is_torch_available():
							import torch

							from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel
class _lowerCamelCase			:



		'''simple docstring'''







		def __init__(   self						:		List[str]    ,	_A						:		Tuple    ,	_A						:		List[str]=99    ,	_A						:		List[Any]=13    ,	_A						:		int=7    ,	_A						:		Optional[int]=9    ,	_A						:		Optional[int]=True    ,	_A						:		Any=True    ,	_A						:		Optional[Any]=False    ,	_A						:		Tuple=32    ,	_A						:		Union[str, Any]=5    ,	_A						:		Union[str, Any]=4    ,	_A						:		Optional[Any]=37    ,	_A						:		Union[str, Any]=8    ,	_A						:		List[Any]=0.1    ,	_A						:		str=0.002    ,	_A						:		Optional[int]=1    ,	_A						:		Any=0    ,	_A						:		List[Any]=0    ,	_A						:		List[Any]=None    ,	_A						:		List[Any]=None    ,	)    ->					List[str]:
			__magic_name__			:       Tuple	      =	parent
			__magic_name__			:       str	      =	batch_size
			__magic_name__			:       Optional[int]	      =	encoder_seq_length
			__magic_name__			:       Dict	      =	decoder_seq_length
			# For common tests
			__magic_name__			:       str	      =	self.decoder_seq_length
			__magic_name__			:       int	      =	is_training
			__magic_name__			:       str	      =	use_attention_mask
			__magic_name__			:       int	      =	use_labels
			__magic_name__			:       List[str]	      =	vocab_size
			__magic_name__			:       int	      =	hidden_size
			__magic_name__			:       str	      =	num_hidden_layers
			__magic_name__			:       Union[str, Any]	      =	num_attention_heads
			__magic_name__			:       Any	      =	d_ff
			__magic_name__			:       List[str]	      =	relative_attention_num_buckets
			__magic_name__			:       Optional[Any]	      =	dropout_rate
			__magic_name__			:       Optional[int]	      =	initializer_factor
			__magic_name__			:       int	      =	eos_token_id
			__magic_name__			:       int	      =	pad_token_id
			__magic_name__			:       Any	      =	decoder_start_token_id
			__magic_name__			:       Any	      =	None
			__magic_name__			:       Optional[Any]	      =	decoder_layers







		def   __lowerCAmelCase						(   self						:		Optional[Any]     )    ->					Tuple:
			return TaConfig.from_pretrained('google/umt5-base'     )







		def   __lowerCAmelCase						(   self						:		List[str]    ,	_A						:		Dict    ,	_A						:		int    ,	_A						:		Tuple    ,	_A						:		str=None    ,	_A						:		List[str]=None    ,	_A						:		Any=None    ,	_A						:		str=None    ,	_A						:		Dict=None    ,	)    ->					Optional[int]:





			if attention_mask is None:
				__magic_name__			:       Optional[int]	      =	input_ids.ne(config.pad_token_id     )
			if decoder_attention_mask is None:
				__magic_name__			:       str	      =	decoder_input_ids.ne(config.pad_token_id     )
			if head_mask is None:
				__magic_name__			:       Union[str, Any]	      =	torch.ones(config.num_hidden_layers    ,	config.num_attention_heads    ,	device=__snake_case     )
			if decoder_head_mask is None:
				__magic_name__			:       Tuple	      =	torch.ones(config.num_decoder_layers    ,	config.num_attention_heads    ,	device=__snake_case     )
			if cross_attn_head_mask is None:
				__magic_name__			:       Optional[int]	      =	torch.ones(
				    config.num_decoder_layers    ,	config.num_attention_heads    ,	device=__snake_case     )
			return {
			    "input_ids": input_ids,
			    "decoder_input_ids": decoder_input_ids,
			    "attention_mask": attention_mask,
			    "decoder_attention_mask": decoder_attention_mask,
			    "head_mask": head_mask,
			    "decoder_head_mask": decoder_head_mask,
			    "cross_attn_head_mask": cross_attn_head_mask,
			}







		def   __lowerCAmelCase						(   self						:		int     )    ->					Union[str, Any]:
			__magic_name__			:       Optional[int]	      =	ids_tensor([self.batch_size, self.encoder_seq_length]    ,	self.vocab_size     )
			__magic_name__			:       Optional[int]	      =	ids_tensor([self.batch_size, self.decoder_seq_length]    ,	self.vocab_size     )

			# we need to clamp the input ids here to avoid having pad token in between
			# this is because for NllbMoe the position_ids are prepared such that
			# all pad tokens have pos id = 2 and rest are between 2..seq_length
			# and the seq_length here is seq_length - num_pad_tokens
			# but when using past, there is no way of knowing if the past input ids had
			# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
			# position_ids being off by num_pad_tokens in past input
			__magic_name__			:       Union[str, Any]	      =	input_ids.clamp(self.pad_token_id + 1     )
			__magic_name__			:       Any	      =	decoder_input_ids.clamp(self.pad_token_id + 1     )

			__magic_name__			:       str	      =	self.get_config()
			__magic_name__			:       Union[str, Any]	      =	config.num_attention_heads
			__magic_name__			:       Tuple	      =	self.prepare_inputs_dict(__snake_case    ,	__snake_case    ,	__snake_case     )
			return config, input_dict







		def   __lowerCAmelCase						(   self						:		Any     )    ->					Optional[int]:
			__magic_name__      ,							__magic_name__			:       int	      =	self.prepare_config_and_inputs()
			return config, inputs_dict







		def   __lowerCAmelCase						(   self						:		Optional[Any]     )    ->					Dict:
			return TaConfig(
			    vocab_size=166    ,	d_model=self.hidden_size    ,	d_ff=self.d_ff    ,	d_kv=self.hidden_size // self.num_attention_heads    ,	num_layers=self.num_hidden_layers    ,	num_decoder_layers=self.decoder_layers    ,	num_heads=self.num_attention_heads    ,	relative_attention_num_buckets=self.relative_attention_num_buckets    ,	dropout_rate=self.dropout_rate    ,	initializer_factor=self.initializer_factor    ,	eos_token_id=self.eos_token_id    ,	bos_token_id=self.pad_token_id    ,	pad_token_id=self.pad_token_id    ,	decoder_start_token_id=self.decoder_start_token_id    ,	)







		def   __lowerCAmelCase						(   self						:		int     )    ->					Any:
			return TaConfig(
			    vocab_size=self.vocab_size    ,	d_model=self.hidden_size    ,	d_ff=self.d_ff    ,	d_kv=self.hidden_size // self.num_attention_heads    ,	num_layers=self.num_hidden_layers    ,	num_decoder_layers=self.decoder_layers    ,	num_heads=self.num_attention_heads    ,	relative_attention_num_buckets=self.relative_attention_num_buckets    ,	dropout_rate=self.dropout_rate    ,	initializer_factor=self.initializer_factor    ,	eos_token_id=self.eos_token_id    ,	bos_token_id=self.pad_token_id    ,	pad_token_id=self.pad_token_id    ,	decoder_start_token_id=self.decoder_start_token_id    ,	)







		def   __lowerCAmelCase						(   self						:		Any    ,	_A						:		Optional[int]    ,	_A						:		int    ,	_A						:		Optional[Any]    ,	_A						:		Optional[int]    ,	_A						:		Any    ,	_A						:		str    ,	)    ->					Union[str, Any]:
			__magic_name__			:       str	      =	UMTaModel(config=__snake_case     )
			model.to(__snake_case     )
			model.eval()
			__magic_name__			:       Any	      =	model(
			    input_ids=__snake_case    ,	decoder_input_ids=__snake_case    ,	attention_mask=__snake_case    ,	decoder_attention_mask=__snake_case    ,	)
			__magic_name__			:       List[Any]	      =	model(input_ids=__snake_case    ,	decoder_input_ids=__snake_case     )
			__magic_name__			:       int	      =	result.last_hidden_state
			__magic_name__			:       int	      =	result.past_key_values
			__magic_name__			:       Optional[Any]	      =	result.encoder_last_hidden_state

			self.parent.assertEqual(encoder_output.size()    ,	(self.batch_size, self.encoder_seq_length, self.hidden_size)     )
			self.parent.assertEqual(decoder_output.size()    ,	(self.batch_size, self.decoder_seq_length, self.hidden_size)     )
			# There should be `num_layers` key value embeddings stored in decoder_past
			self.parent.assertEqual(len(__snake_case     )    ,	config.num_layers     )
			# There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
			self.parent.assertEqual(len(decoder_past[0]     )    ,	4     )







		def   __lowerCAmelCase						(   self						:		Any    ,	_A						:		Optional[Any]    ,	_A						:		List[Any]    ,	_A						:		str    ,	_A						:		Optional[Any]    ,	_A						:		int    ,	_A						:		Any    ,	)    ->					str:
			__magic_name__			:       Optional[int]	      =	UMTaModel(config=__snake_case     ).get_decoder().to(__snake_case     ).eval()
			# first forward pass
			__magic_name__			:       List[str]	      =	model(__snake_case    ,	use_cache=__snake_case     )
			__magic_name__			:       Union[str, Any]	      =	model(__snake_case     )
			__magic_name__			:       List[str]	      =	model(__snake_case    ,	use_cache=__snake_case     )

			self.parent.assertTrue(len(__snake_case     ) == len(__snake_case     )     )
			self.parent.assertTrue(len(__snake_case     ) == len(__snake_case     ) + 1     )

			__magic_name__      ,							__magic_name__			:       Any	      =	outputs.to_tuple()

			# create hypothetical next token and extent to next_input_ids
			__magic_name__			:       Dict	      =	ids_tensor((self.batch_size, 1)    ,	config.vocab_size     )

			# append to next input_ids and
			__magic_name__			:       List[str]	      =	torch.cat([input_ids, next_tokens]    ,	dim=-1     )

			__magic_name__			:       List[Any]	      =	model(__snake_case     )['last_hidden_state']
			__magic_name__			:       str	      =	model(__snake_case    ,	past_key_values=__snake_case     )['last_hidden_state']

			# select random slice
			__magic_name__			:       Optional[Any]	      =	ids_tensor((1,)    ,	output_from_past.shape[-1]     ).item()
			__magic_name__			:       str	      =	output_from_no_past[:, -1, random_slice_idx].detach()
			__magic_name__			:       Optional[int]	      =	output_from_past[:, 0, random_slice_idx].detach()

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







		def   __lowerCAmelCase						(   self						:		Optional[Any]    ,	_A						:		List[str]    ,	_A						:		Optional[int]    ,	)    ->					Dict:
			__magic_name__			:       Any	      =	UMTaModel(config=__snake_case     ).to(__snake_case     ).half().eval()
			__magic_name__			:       Any	      =	model(**__snake_case     )['last_hidden_state']
			self.parent.assertFalse(torch.isnan(__snake_case     ).any().item()     )







@require_torch
class _lowerCamelCase			(		_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	_SCREAMING_SNAKE_CASE						,	unittest.TestCase				):



		'''simple docstring'''







		A_      :     int        =							(
		    (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
		)
		A_      :     Optional[int]        =							(UMTaForConditionalGeneration,) if is_torch_available() else ()
		A_      :     Optional[int]        =							(
		    {
		        """conversational""": UMTaForConditionalGeneration,
		        """feature-extraction""": UMTaModel,
		        """summarization""": UMTaForConditionalGeneration,
		        """text2text-generation""": UMTaForConditionalGeneration,
		        """translation""": UMTaForConditionalGeneration,
		        """question-answering""": UMTaForQuestionAnswering,
		    }
		    if is_torch_available()
		    else {}
		)
		A_      :     List[str]        =							True
		A_      :     Tuple        =							False
		A_      :     List[str]        =							False
		A_      :     List[Any]        =							True
		A_      :     List[Any]        =							True
		# The small UMT5 model needs higher percentages for CPU/MP tests
		A_      :     List[str]        =							[0.8, 0.9]







		def   __lowerCAmelCase						(   self						:		Optional[int]     )    ->					List[Any]:
			__magic_name__			:       Optional[Any]	      =	UMTaModelTester(self     )







		@unittest.skip('Test has a segmentation fault on torch 1.8.0'     )
		def   __lowerCAmelCase						(   self						:		List[Any]     )    ->					str:
			__magic_name__			:       Optional[Any]	      =	self.model_tester.prepare_config_and_inputs()
			__magic_name__			:       Dict	      =	UMTaModel(config_and_inputs[0]     ).to(__snake_case     )
			with tempfile.TemporaryDirectory() as tmpdirname:
				torch.onnx.export(
				    __snake_case    ,	(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2])    ,	F'{tmpdirname}/t5_test.onnx'    ,	export_params=__snake_case    ,	opset_version=9    ,	input_names=['input_ids', 'decoder_input_ids']    ,	)







		@unittest.skipIf(torch_device == 'cpu'    ,	'Cant do half precision'     )
		def   __lowerCAmelCase						(   self						:		Dict     )    ->					List[str]:
			__magic_name__			:       Optional[Any]	      =	self.model_tester.prepare_config_and_inputs()
			self.model_tester.create_and_check_model_fpaa_forward(*__snake_case     )







		def   __lowerCAmelCase						(   self						:		Union[str, Any]     )    ->					str:
			__magic_name__			:       int	      =	['encoder_attentions', 'decoder_attentions', 'cross_attentions']
			__magic_name__			:       Tuple	      =	self.model_tester.prepare_config_and_inputs()
			__magic_name__			:       Dict	      =	config_and_inputs[0]
			__magic_name__			:       int	      =	UMTaForConditionalGeneration(__snake_case     ).eval()
			model.to(__snake_case     )

			__magic_name__			:       Dict	      =	{
			    'head_mask': torch.zeros(config.num_layers    ,	config.num_heads    ,	device=__snake_case     ),
			    'decoder_head_mask': torch.zeros(config.num_decoder_layers    ,	config.num_heads    ,	device=__snake_case     ),
			    'cross_attn_head_mask': torch.zeros(config.num_decoder_layers    ,	config.num_heads    ,	device=__snake_case     ),
			}

			for attn_name, (name, mask) in zip(__snake_case    ,	head_masking.items()     ):
				__magic_name__			:       List[str]	      =	{name: mask}
				# Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
				if name == "head_mask":
					__magic_name__			:       Any	      =	torch.ones(
					    config.num_decoder_layers    ,	config.num_heads    ,	device=__snake_case     )

				__magic_name__			:       Union[str, Any]	      =	model.generate(
				    config_and_inputs[1]['input_ids']    ,	num_beams=1    ,	max_length=3    ,	output_attentions=__snake_case    ,	return_dict_in_generate=__snake_case    ,	**__snake_case    ,	)
				# We check the state of decoder_attentions and cross_attentions just from the last step
				__magic_name__			:       Union[str, Any]	      =	out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
				self.assertEqual(sum([w.sum().item() for w in attn_weights]     )    ,	0.0     )







		@unittest.skip('Does not work on the tiny model as we keep hitting edge cases.'     )
		def   __lowerCAmelCase						(   self						:		Any     )    ->					str:
			pass







@require_torch
@require_sentencepiece
@require_tokenizers
class _lowerCamelCase			(		unittest.TestCase				):



		'''simple docstring'''







		@slow
		@unittest.skip(
		    'Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'     )
		def   __lowerCAmelCase						(   self						:		int     )    ->					Union[str, Any]:
			__magic_name__			:       Optional[int]	      =	UMTaForConditionalGeneration.from_pretrained('google/umt5-small'    ,	return_dict=__snake_case     ).to(__snake_case     )
			__magic_name__			:       Any	      =	AutoTokenizer.from_pretrained('google/umt5-small'    ,	use_fast=__snake_case    ,	legacy=__snake_case     )
			__magic_name__			:       Dict	      =	[
			    'Bonjour monsieur <extra_id_0> bien <extra_id_1>.',
			    'No se como puedo <extra_id_0>.',
			    'This is the reason why we <extra_id_0> them.',
			    'The <extra_id_0> walks in <extra_id_1>, seats',
			    'A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.',
			]
			__magic_name__			:       List[str]	      =	tokenizer(__snake_case    ,	return_tensors='pt'    ,	padding=__snake_case     ).input_ids
			# fmt: off
			__magic_name__			:       Any	      =	torch.tensor(
			    [
			        [ 38530, 210703, 256299, 1410, 256298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   826, 321, 671, 25922, 256299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [  1460, 339, 312, 19014, 10620, 758, 256299, 2355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
			        [   517, 256299, 14869, 281, 301, 256298, 275, 119983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
			        [   320, 256299, 14869, 281, 2234, 289, 2275, 333,61391, 289, 256298, 543, 256297, 168714, 329, 256296,274, 1],
			    ]     )
			# fmt: on
			torch.testing.assert_allclose(__snake_case    ,	__snake_case     )

			__magic_name__			:       Optional[Any]	      =	model.generate(input_ids.to(__snake_case     )     )
			__magic_name__			:       Union[str, Any]	      =	[
			    '<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>',
			    '<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
			    '<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
			    '<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
			    '<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>',
			]
			__magic_name__			:       Optional[Any]	      =	tokenizer.batch_decode(__snake_case     )
			self.assertEqual(__snake_case    ,	__snake_case     )






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

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a__      :     Dict         =		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt')





@dataclass
class UpperCAmelCase_				:
  __lowerCAmelCase   :						Optional[str]							=   field(
      default="""cifar10"""     ,		metadata={"""help""": """Name of a dataset from the datasets package"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """The column name of the images in the files."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """A folder containing the training data."""} )
  __lowerCAmelCase   :						Optional[str]							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """A folder containing the validation data."""} )
  __lowerCAmelCase   :						Optional[float]							=   field(
      default=0.15     ,		metadata={"""help""": """Percent to split off of train for validation."""} )
  __lowerCAmelCase   :						Optional[int]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of training examples to this """
              """value if set."""
          )
      }     ,		)
  __lowerCAmelCase   :						Optional[int]							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
              """value if set."""
          )
      }     ,		)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       {}
       if self.train_dir is not None:
            A_          =       self.train_dir
       if self.validation_dir is not None:
            A_          =       self.validation_dir
       A_          =       data_files if data_files else None





@dataclass
class UpperCAmelCase_				:
  __lowerCAmelCase   :						str							=   field(
      default=_SCREAMING_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=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Pretrained config name or path if not the same as model_name_or_path"""} )
  __lowerCAmelCase   :						Optional[str]							=   field(
      default=_SCREAMING_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=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""} )
  __lowerCAmelCase   :						str							=   field(
      default="""main"""     ,		metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}     ,		)
  __lowerCAmelCase   :						str							=   field(default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Name or path of preprocessor config."""} )
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={
          """help""": (
              """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
              """with private models)."""
          )
      }     ,		)
  __lowerCAmelCase   :						float							=   field(
      default=0.75     ,		metadata={"""help""": """The ratio of the number of masked tokens in the input sequence."""} )
  __lowerCAmelCase   :						bool							=   field(
      default=_SCREAMING_SNAKE_CASE     ,		metadata={"""help""": """Whether or not to train with normalized pixel values as target."""} )





@dataclass
class UpperCAmelCase_				( _SCREAMING_SNAKE_CASE ):
  __lowerCAmelCase   :						float							=   field(
      default=1E-3     ,		metadata={"""help""": """Base learning rate: absolute_lr = base_lr * total_batch_size / 256."""} )




def      UpperCAmelCase_    (    _UpperCAmelCase							:Any				)	->				List[str]:
     '''simple docstring'''


     A_          =       torch.stack([example['''pixel_values'''] for example in examples]				)
     return {"pixel_values": pixel_values}

def      UpperCAmelCase_    (    )	->				str:
     '''simple docstring'''


     A_          =       HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)				)
     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_          =       parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
     else:
          A_   ,    A_   ,    A_          =       parser.parse_args_into_dataclasses()

     # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
     # information sent is the one passed as arguments along with your Python/PyTorch versions.
     send_example_telemetry('''run_mae'''  ,						_UpperCAmelCase  ,						_UpperCAmelCase				)

     # 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				)]  ,						)

     if training_args.should_log:
          # The default of training_args.log_level is passive, so we set log level at info here to have that default.
          transformers.utils.logging.set_verbosity_info()

     A_          =       training_args.get_process_log_level()
     logger.setLevel(_UpperCAmelCase				)
     transformers.utils.logging.set_verbosity(_UpperCAmelCase				)
     transformers.utils.logging.enable_default_handler()
     transformers.utils.logging.enable_explicit_format()

     # 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}'				)
     logger.info(f'Training/evaluation parameters {training_args}'				)

     # Detecting last checkpoint.
     A_          =       None
     if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
          A_          =       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 and training_args.resume_from_checkpoint is 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.'''				)

    # Initialize our dataset.
     A_          =       load_dataset(
         data_args.dataset_name  ,						data_args.dataset_config_name  ,						data_files=data_args.data_files  ,						cache_dir=model_args.cache_dir  ,						use_auth_token=True if model_args.use_auth_token else None  ,						)

     # If we don't have a validation split, split off a percentage of train as validation.
     A_          =       None if '''validation''' in ds.keys() else data_args.train_val_split
     if isinstance(data_args.train_val_split  ,						_UpperCAmelCase				) and data_args.train_val_split > 0.0:
          A_          =       ds['''train'''].train_test_split(data_args.train_val_split				)
          A_          =       split['''train''']
          A_          =       split['''test''']

     # Load pretrained model and image processor
     #
     # Distributed training:
     # The .from_pretrained methods guarantee that only one local process can concurrently
     # download model & vocab.
     A_          =       {
         '''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_          =       ViTMAEConfig.from_pretrained(model_args.config_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTMAEConfig.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       ViTMAEConfig()
          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}'				)

    # adapt config
     config.update(
         {
             '''mask_ratio''': model_args.mask_ratio,
             '''norm_pix_loss''': model_args.norm_pix_loss,
         }				)

     # create image processor
     if model_args.image_processor_name:
          A_          =       ViTImageProcessor.from_pretrained(model_args.image_processor_name  ,						**_UpperCAmelCase				)
     elif model_args.model_name_or_path:
          A_          =       ViTImageProcessor.from_pretrained(model_args.model_name_or_path  ,						**_UpperCAmelCase				)
     else:
          A_          =       ViTImageProcessor()

     # create model
     if model_args.model_name_or_path:
          A_          =       ViTMAEForPreTraining.from_pretrained(
              model_args.model_name_or_path  ,						from_tf=bool('''.ckpt''' in model_args.model_name_or_path				)  ,						config=_UpperCAmelCase  ,						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_          =       ViTMAEForPreTraining(_UpperCAmelCase				)

     if training_args.do_train:
          A_          =       ds['''train'''].column_names
     else:
          A_          =       ds['''validation'''].column_names

     if data_args.image_column_name is not None:
          A_          =       data_args.image_column_name
     elif "image" in column_names:
          A_          =       '''image'''
     elif "img" in column_names:
          A_          =       '''img'''
     else:
          A_          =       column_names[0]

     # transformations as done in original MAE paper
     # source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
     if "shortest_edge" in image_processor.size:
          A_          =       image_processor.size['''shortest_edge''']
     else:
          A_          =       (image_processor.size['''height'''], image_processor.size['''width'''])
     A_          =       Compose(
         [
             Lambda(lambda _UpperCAmelCase				: img.convert('''RGB'''				) if img.mode != "RGB" else img				),
             RandomResizedCrop(_UpperCAmelCase  ,						scale=(0.2, 1.0)  ,						interpolation=InterpolationMode.BICUBIC				),
             RandomHorizontalFlip(),
             ToTensor(),
             Normalize(mean=image_processor.image_mean  ,						std=image_processor.image_std				),
         ]				)

     def preprocess_images(_UpperCAmelCase							:Optional[Any]				):
          A_          =       [transforms(_UpperCAmelCase				) for image in examples[image_column_name]]
          return examples

     if training_args.do_train:
          if "train" not in ds:
               raise ValueError('''--do_train requires a train dataset'''				)
          if data_args.max_train_samples is not None:
               A_          =       ds['''train'''].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
          # Set the training transforms
          ds["train"].set_transform(_UpperCAmelCase				)

     if training_args.do_eval:
          if "validation" not in ds:
               raise ValueError('''--do_eval requires a validation dataset'''				)
          if data_args.max_eval_samples is not None:
               A_          =       (
                   ds['''validation'''].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
               )
          # Set the validation transforms
          ds["validation"].set_transform(_UpperCAmelCase				)

     # Compute absolute learning rate
     A_          =       (
         training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
     )
     if training_args.base_learning_rate is not None:
          A_          =       training_args.base_learning_rate * total_train_batch_size / 256

     # Initialize our trainer
     A_          =       Trainer(
         model=_UpperCAmelCase  ,						args=_UpperCAmelCase  ,						train_dataset=ds['''train'''] if training_args.do_train else None  ,						eval_dataset=ds['''validation'''] if training_args.do_eval else None  ,						tokenizer=_UpperCAmelCase  ,						data_collator=_UpperCAmelCase  ,						)

     # Training
     if training_args.do_train:
          A_          =       None
          if training_args.resume_from_checkpoint is not None:
               A_          =       training_args.resume_from_checkpoint
          elif last_checkpoint is not None:
               A_          =       last_checkpoint
          A_          =       trainer.train(resume_from_checkpoint=_UpperCAmelCase				)
          trainer.save_model()
          trainer.log_metrics('''train'''  ,						train_result.metrics				)
          trainer.save_metrics('''train'''  ,						train_result.metrics				)
          trainer.save_state()

     # Evaluation
     if training_args.do_eval:
          A_          =       trainer.evaluate()
          trainer.log_metrics('''eval'''  ,						_UpperCAmelCase				)
          trainer.save_metrics('''eval'''  ,						_UpperCAmelCase				)

     # Write model card and (optionally) push to hub
     A_          =       {
         '''tasks''': '''masked-auto-encoding''',
         '''dataset''': data_args.dataset_name,
         '''tags''': ['''masked-auto-encoding'''],
     }
     if training_args.push_to_hub:
          trainer.push_to_hub(**_UpperCAmelCase				)
     else:
          trainer.create_model_card(**_UpperCAmelCase				)

def      UpperCAmelCase_    (    _UpperCAmelCase							:Union[str, Any]				)	->				Dict:
     '''simple docstring'''


     main()


if __name__ == "__main__":
      main()



