





import gc
import importlib.metadata
import tempfile
import unittest

from packaging import version

from transformers import (
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForSeqaSeqLM,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    BitsAndBytesConfig,
    pipeline,
)
from transformers.testing_utils import (
    is_torch_available,
    require_accelerate,
    require_bitsandbytes,
    require_torch,
    require_torch_gpu,
    require_torch_multi_gpu,
    slow,
)



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


      if model.config.model_type == "gpt2":
            return model.transformer.h[0].mlp.c_fc
      return model.transformer.h[0].mlp.dense_ah_to_h


if is_torch_available():
      import torch
      import torch.nn as nn

      class 	UpperCamelCase__				(   nn.Module     ):



           '''simple docstring'''

           def __init__(							self		:					Any		,lowerCamelCase__		:					nn.Module		,lowerCamelCase__		:					int )  ->	Optional[int]:




                 '''simple docstring'''


                 super().__init__()
                 SCREAMING_SNAKE_CASE	=		module
                 SCREAMING_SNAKE_CASE	=		nn.Sequential(
                     nn.Linear(module.in_features		,lowerCamelCase__		,bias=lowerCamelCase__ )		,nn.Linear(lowerCamelCase__		,module.out_features		,bias=lowerCamelCase__ )		,)
                 SCREAMING_SNAKE_CASE	=		(2.0 / (5 * min(module.in_features		,module.out_features ))) ** 0.5
                 nn.init.normal_(self.adapter[0].weight		,std=lowerCamelCase__ )
                 nn.init.zeros_(self.adapter[1].weight )
                 self.adapter.to(module.weight.device )






           def        SCREAMING_SNAKE_CASE__						(							self		:					int		,lowerCamelCase__		:					Optional[Any]		,*lowerCamelCase__		:					Tuple		,**lowerCamelCase__		:					Any )  ->	int:




                 '''simple docstring'''


                 return self.module(lowerCamelCase__		,*lowerCamelCase__		,**lowerCamelCase__ ) + self.adapter(lowerCamelCase__ )

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     __snake_case					:			Optional[int]				=    "bigscience/bloom-1b7"

     # Constant values
     __snake_case					:			Optional[Any]				=    2.109659552692574

     __snake_case					:			List[Any]				=    "Hello my name is"
     __snake_case					:			List[str]				=    set()
     EXPECTED_OUTPUTS.add("Hello my name is John and I am a professional photographer. I"     )
     EXPECTED_OUTPUTS.add("Hello my name is John.\nI am a friend of your father.\n"     )
     EXPECTED_OUTPUTS.add("Hello my name is John Doe, I am a student at the University"     )
     __snake_case					:			Tuple				=    10






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		AutoTokenizer.from_pretrained(self.model_name )

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Any:




           '''simple docstring'''


           super().setUp()

           # Models and tokenizer
           SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(
               self.model_name		,torch_dtype=torch.floataa		,device_map="""auto""" )
           SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(self.model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[str]:




           '''simple docstring'''


           del self.model_fpaa
           del self.model_abit

           gc.collect()
           torch.cuda.empty_cache()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.model_abit.config

           self.assertTrue(hasattr(lowerCamelCase__		,"""quantization_config""" ) )

           SCREAMING_SNAKE_CASE	=		config.to_dict()
           SCREAMING_SNAKE_CASE	=		config.to_diff_dict()

           SCREAMING_SNAKE_CASE	=		config.to_json_string()






     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	List[str]:




           '''simple docstring'''


           from bitsandbytes.nn import Paramsabit

           SCREAMING_SNAKE_CASE	=		self.model_fpaa.get_memory_footprint()
           SCREAMING_SNAKE_CASE	=		self.model_abit.get_memory_footprint()

           self.assertAlmostEqual(mem_fpaa / mem_abit		,self.EXPECTED_RELATIVE_DIFFERENCE )
           SCREAMING_SNAKE_CASE	=		get_some_linear_layer(self.model_abit )
           self.assertTrue(linear.weight.__class__ == Paramsabit )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	int:




           '''simple docstring'''


           from transformers import TaPreTrainedModel

           self.model_fpaa.get_memory_footprint()
           self.model_abit.get_memory_footprint()

           for name, module in self.model_abit.named_modules():
                 if isinstance(lowerCamelCase__		,torch.nn.Linear ):
                       if name not in ["lm_head"] + TaPreTrainedModel._keep_in_fpaa_modules:
                             # 4-bit parameters are packed in uint8 variables
                             self.assertTrue(module.weight.dtype == torch.uinta )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	int:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" )
           SCREAMING_SNAKE_CASE	=		self.model_abit.generate(input_ids=encoded_input["""input_ids"""].to(0 )		,max_new_tokens=10 )

           self.assertIn(self.tokenizer.decode(output_sequences[0]		,skip_special_tokens=lowerCamelCase__ )		,self.EXPECTED_OUTPUTS )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		BitsAndBytesConfig()
           SCREAMING_SNAKE_CASE	=		True

           SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(
               self.model_name		,quantization_config=lowerCamelCase__		,device_map="""auto""" )

           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" )
           SCREAMING_SNAKE_CASE	=		model_abit_from_config.generate(
               input_ids=encoded_input["""input_ids"""].to(0 )		,max_new_tokens=10 )

           self.assertIn(self.tokenizer.decode(output_sequences[0]		,skip_special_tokens=lowerCamelCase__ )		,self.EXPECTED_OUTPUTS )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Dict:




           '''simple docstring'''


           with self.assertRaises(lowerCamelCase__ ), tempfile.TemporaryDirectory() as tmpdirname:
                 self.model_abit.save_pretrained(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		BitsAndBytesConfig()

           with self.assertRaises(lowerCamelCase__ ):
                 SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(
                     self.model_name		,quantization_config=lowerCamelCase__		,load_in_abit=lowerCamelCase__		,device_map="""auto"""		,bnb_abit_quant_type="""nf4"""		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	List[Any]:




           '''simple docstring'''


           with self.assertRaises(lowerCamelCase__ ):
                 # Tries with `str`
                 self.model_abit.to("""cpu""" )

           with self.assertRaises(lowerCamelCase__ ):
                 # Tries with a `dtype``
                 self.model_abit.to(torch.floataa )

           with self.assertRaises(lowerCamelCase__ ):
                 # Tries with a `device`
                 self.model_abit.to(torch.device("""cuda:0""" ) )

           with self.assertRaises(lowerCamelCase__ ):
                 # Tries with a `device`
                 self.model_abit.float()

           with self.assertRaises(lowerCamelCase__ ):
                 # Tries with a `device`
                 self.model_abit.half()

           # Test if we did not break anything
           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" )

           SCREAMING_SNAKE_CASE	=		self.model_fpaa.to(torch.floataa )
           SCREAMING_SNAKE_CASE	=		self.model_fpaa.generate(input_ids=encoded_input["""input_ids"""].to(0 )		,max_new_tokens=10 )

           # Check this does not throw an error
           SCREAMING_SNAKE_CASE	=		self.model_fpaa.to("""cpu""" )

           # Check this does not throw an error
           SCREAMING_SNAKE_CASE	=		self.model_fpaa.half()

           # Check this does not throw an error
           SCREAMING_SNAKE_CASE	=		self.model_fpaa.float()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	List[Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		AutoModelForSeqaSeqLM.from_pretrained("""t5-small"""		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )
           self.assertTrue(model.decoder.block[0].layer[2].DenseReluDense.wo.weight.dtype == torch.floataa )

@require_bitsandbytes
@require_accelerate
@require_torch
@require_torch_gpu
@slow
class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     @classmethod
     def        SCREAMING_SNAKE_CASE__						(							cls		:					Tuple )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""t5-small"""
           SCREAMING_SNAKE_CASE	=		"""google/flan-t5-small"""  # flan-t5 uses dense-act instead of dense-relu-dense
           SCREAMING_SNAKE_CASE	=		AutoTokenizer.from_pretrained(cls.model_name )
           SCREAMING_SNAKE_CASE	=		"""Translate in German: Hello, my dog is cute"""






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


           gc.collect()
           torch.cuda.empty_cache()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int] )  ->	List[str]:




           '''simple docstring'''


           from transformers import TaForConditionalGeneration

           SCREAMING_SNAKE_CASE	=		TaForConditionalGeneration._keep_in_fpaa_modules
           SCREAMING_SNAKE_CASE	=		None

           # test with `t5-small`
           SCREAMING_SNAKE_CASE	=		TaForConditionalGeneration.from_pretrained(self.model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )
           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" ).to(0 )
           SCREAMING_SNAKE_CASE	=		model.generate(**lowerCamelCase__ )

           # test with `flan-t5-small`
           SCREAMING_SNAKE_CASE	=		TaForConditionalGeneration.from_pretrained(
               self.dense_act_model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )
           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" ).to(0 )
           SCREAMING_SNAKE_CASE	=		model.generate(**lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		modules






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	str:




           '''simple docstring'''


           import bitsandbytes as bnb

           from transformers import TaForConditionalGeneration

           # test with `t5-small`
           SCREAMING_SNAKE_CASE	=		TaForConditionalGeneration.from_pretrained(self.model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )

           # there was a bug with decoders - this test checks that it is fixed
           self.assertTrue(isinstance(model.decoder.block[0].layer[0].SelfAttention.q		,bnb.nn.Linearabit ) )

           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" ).to(0 )
           SCREAMING_SNAKE_CASE	=		model.generate(**lowerCamelCase__ )

           # test with `flan-t5-small`
           SCREAMING_SNAKE_CASE	=		TaForConditionalGeneration.from_pretrained(
               self.dense_act_model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )
           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" ).to(0 )
           SCREAMING_SNAKE_CASE	=		model.generate(**lowerCamelCase__ )

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[Any] )  ->	int:




           '''simple docstring'''


           super().setUp()
           # model_name
           SCREAMING_SNAKE_CASE	=		"""bigscience/bloom-560m"""
           SCREAMING_SNAKE_CASE	=		"""t5-small"""

           # Different types of model

           SCREAMING_SNAKE_CASE	=		AutoModel.from_pretrained(self.model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )
           # Sequence classification model
           SCREAMING_SNAKE_CASE	=		AutoModelForSequenceClassification.from_pretrained(
               self.model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )
           # CausalLM model
           SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(self.model_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )
           # Seq2seq model
           SCREAMING_SNAKE_CASE	=		AutoModelForSeqaSeqLM.from_pretrained(
               self.seq_to_seq_name		,load_in_abit=lowerCamelCase__		,device_map="""auto""" )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	str:




           '''simple docstring'''


           del self.base_model
           del self.sequence_model
           del self.model_abit
           del self.seq_to_seq_model

           gc.collect()
           torch.cuda.empty_cache()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	List[str]:




           '''simple docstring'''


           from bitsandbytes.nn import Paramsabit

           self.assertTrue(self.base_model.h[-1].mlp.dense_ah_to_h.weight.__class__ == Paramsabit )

           # Other heads should be nn.Parameter
           self.assertTrue(self.model_abit.lm_head.weight.__class__ == torch.nn.Parameter )
           self.assertTrue(self.sequence_model.score.weight.__class__ == torch.nn.Parameter )
           self.assertTrue(self.seq_to_seq_model.lm_head.weight.__class__ == torch.nn.Parameter )

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					str )  ->	Any:




           '''simple docstring'''


           super().setUp()






     def        SCREAMING_SNAKE_CASE__						(							self		:					int )  ->	Dict:




           '''simple docstring'''


           del self.pipe

           gc.collect()
           torch.cuda.empty_cache()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		pipeline(
               """text-generation"""		,model=self.model_name		,model_kwargs={"""device_map""": """auto""", """load_in_4bit""": True, """torch_dtype""": torch.floataa}		,max_new_tokens=self.MAX_NEW_TOKENS		,)

           # Real second forward pass
           SCREAMING_SNAKE_CASE	=		self.pipe(self.input_text )
           self.assertIn(pipeline_output[0]["""generated_text"""]		,self.EXPECTED_OUTPUTS )

@require_torch_multi_gpu
class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	str:




           '''simple docstring'''


           super().setUp()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(
               self.model_name		,load_in_abit=lowerCamelCase__		,device_map="""balanced""" )

           # Check correct device map
           self.assertEqual(set(model_parallel.hf_device_map.values() )		,{0, 1} )

           # Check that inference pass works on the model
           SCREAMING_SNAKE_CASE	=		self.tokenizer(self.input_text		,return_tensors="""pt""" )

           # Second real batch
           SCREAMING_SNAKE_CASE	=		model_parallel.generate(input_ids=encoded_input["""input_ids"""].to(0 )		,max_new_tokens=10 )
           self.assertIn(self.tokenizer.decode(output_parallel[0]		,skip_special_tokens=lowerCamelCase__ )		,self.EXPECTED_OUTPUTS )

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Any:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""facebook/opt-350m"""
           super().setUp()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	List[Any]:




           '''simple docstring'''


           if version.parse(importlib.metadata.version("""bitsandbytes""" ) ) < version.parse("""0.37.0""" ):
                 return

           # Step 1: freeze all parameters
           SCREAMING_SNAKE_CASE	=		AutoModelForCausalLM.from_pretrained(self.model_name		,load_in_abit=lowerCamelCase__ )

           self.assertEqual(set(model.hf_device_map.values() )		,{torch.cuda.current_device()} )

           for param in model.parameters():
                 SCREAMING_SNAKE_CASE	=		False  # freeze the model - train adapters later
                 if param.ndim == 1:
                       # cast the small parameters (e.g. layernorm) to fp32 for stability
                       SCREAMING_SNAKE_CASE	=		param.data.to(torch.floataa )

        # Step 2: add adapters
           for _, module in model.named_modules():
                 if "OPTAttention" in repr(type(lowerCamelCase__ ) ):
                       SCREAMING_SNAKE_CASE	=		LoRALayer(module.q_proj		,rank=16 )
                       SCREAMING_SNAKE_CASE	=		LoRALayer(module.k_proj		,rank=16 )
                       SCREAMING_SNAKE_CASE	=		LoRALayer(module.v_proj		,rank=16 )

        # Step 3: dummy batch
           SCREAMING_SNAKE_CASE	=		self.tokenizer("""Test batch """		,return_tensors="""pt""" ).to(0 )

           # Step 4: Check if the gradient is not None
           with torch.cuda.amp.autocast():
                 SCREAMING_SNAKE_CASE	=		model.forward(**lowerCamelCase__ )
                 out.logits.norm().backward()

           for module in model.modules():
                 if isinstance(lowerCamelCase__		,lowerCamelCase__ ):
                       self.assertTrue(module.adapter[1].weight.grad is not None )
                       self.assertTrue(module.adapter[1].weight.grad.norm().item() > 0 )
                 elif isinstance(lowerCamelCase__		,nn.Embedding ):
                       self.assertTrue(module.weight.grad is None )

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			List[str]				=    "gpt2-xl"
     __snake_case					:			List[Any]				=    3.3191854854152187









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
SCREAMING_SNAKE_CASE_		     = 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__				:



     '''simple docstring'''

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






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict )  ->	int:




           '''simple docstring'''


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

@dataclass
class 	UpperCamelCase__				:



     '''simple docstring'''

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

@dataclass
class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			float				=    field(
         default=1E-3							,   metadata={"help": "Base learning rate: absolute_lr = base_lr * total_batch_size / 256."}     )



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     Dict:


      '''simple docstring'''


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



def      __lowercase     (   )				->     List[Any]:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE	=		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.
            SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]			)			)
      else:
            SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		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"""				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)

      # 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()

      SCREAMING_SNAKE_CASE	=		training_args.get_process_log_level()
      logger.setLevel(_SCREAMING_SNAKE_CASE			)
      transformers.utils.logging.set_verbosity(_SCREAMING_SNAKE_CASE			)
      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.
      SCREAMING_SNAKE_CASE	=		None
      if os.path.isdir(training_args.output_dir			) and training_args.do_train and not training_args.overwrite_output_dir:
            SCREAMING_SNAKE_CASE	=		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.
      SCREAMING_SNAKE_CASE	=		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.
      SCREAMING_SNAKE_CASE	=		None if """validation""" in ds.keys() else data_args.train_val_split
      if isinstance(data_args.train_val_split				,		_SCREAMING_SNAKE_CASE			) and data_args.train_val_split > 0.0:
            SCREAMING_SNAKE_CASE	=		ds["""train"""].train_test_split(data_args.train_val_split			)
            SCREAMING_SNAKE_CASE	=		split["""train"""]
            SCREAMING_SNAKE_CASE	=		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.
      SCREAMING_SNAKE_CASE	=		{
          """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:
            SCREAMING_SNAKE_CASE	=		ViTMAEConfig.from_pretrained(model_args.config_name				,		**_SCREAMING_SNAKE_CASE			)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE	=		ViTMAEConfig.from_pretrained(model_args.model_name_or_path				,		**_SCREAMING_SNAKE_CASE			)
      else:
            SCREAMING_SNAKE_CASE	=		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:
            SCREAMING_SNAKE_CASE	=		ViTImageProcessor.from_pretrained(model_args.image_processor_name				,		**_SCREAMING_SNAKE_CASE			)
      elif model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE	=		ViTImageProcessor.from_pretrained(model_args.model_name_or_path				,		**_SCREAMING_SNAKE_CASE			)
      else:
            SCREAMING_SNAKE_CASE	=		ViTImageProcessor()

      # create model
      if model_args.model_name_or_path:
            SCREAMING_SNAKE_CASE	=		ViTMAEForPreTraining.from_pretrained(
                model_args.model_name_or_path				,		from_tf=bool(""".ckpt""" in model_args.model_name_or_path			)				,		config=_SCREAMING_SNAKE_CASE				,		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"""			)
            SCREAMING_SNAKE_CASE	=		ViTMAEForPreTraining(_SCREAMING_SNAKE_CASE			)

      if training_args.do_train:
            SCREAMING_SNAKE_CASE	=		ds["""train"""].column_names
      else:
            SCREAMING_SNAKE_CASE	=		ds["""validation"""].column_names

      if data_args.image_column_name is not None:
            SCREAMING_SNAKE_CASE	=		data_args.image_column_name
      elif "image" in column_names:
            SCREAMING_SNAKE_CASE	=		"""image"""
      elif "img" in column_names:
            SCREAMING_SNAKE_CASE	=		"""img"""
      else:
            SCREAMING_SNAKE_CASE	=		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:
            SCREAMING_SNAKE_CASE	=		image_processor.size["""shortest_edge"""]
      else:
            SCREAMING_SNAKE_CASE	=		(image_processor.size["""height"""], image_processor.size["""width"""])
      SCREAMING_SNAKE_CASE	=		Compose(
          [
              Lambda(lambda _SCREAMING_SNAKE_CASE			: img.convert("""RGB"""			) if img.mode != "RGB" else img			),
              RandomResizedCrop(_SCREAMING_SNAKE_CASE				,		scale=(0.2, 1.0)				,		interpolation=InterpolationMode.BICUBIC			),
              RandomHorizontalFlip(),
              ToTensor(),
              Normalize(mean=image_processor.image_mean				,		std=image_processor.image_std			),
          ]			)

      def preprocess_images(_SCREAMING_SNAKE_CASE			):
            SCREAMING_SNAKE_CASE	=		[transforms(_SCREAMING_SNAKE_CASE			) 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:
                  SCREAMING_SNAKE_CASE	=		ds["""train"""].shuffle(seed=training_args.seed			).select(range(data_args.max_train_samples			)			)
            # Set the training transforms
            ds["train"].set_transform(_SCREAMING_SNAKE_CASE			)

      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:
                  SCREAMING_SNAKE_CASE	=		(
                      ds["""validation"""].shuffle(seed=training_args.seed			).select(range(data_args.max_eval_samples			)			)
                  )
            # Set the validation transforms
            ds["validation"].set_transform(_SCREAMING_SNAKE_CASE			)

      # Compute absolute learning rate
      SCREAMING_SNAKE_CASE	=		(
          training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
      )
      if training_args.base_learning_rate is not None:
            SCREAMING_SNAKE_CASE	=		training_args.base_learning_rate * total_train_batch_size / 2_56

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

      # Training
      if training_args.do_train:
            SCREAMING_SNAKE_CASE	=		None
            if training_args.resume_from_checkpoint is not None:
                  SCREAMING_SNAKE_CASE	=		training_args.resume_from_checkpoint
            elif last_checkpoint is not None:
                  SCREAMING_SNAKE_CASE	=		last_checkpoint
            SCREAMING_SNAKE_CASE	=		trainer.train(resume_from_checkpoint=_SCREAMING_SNAKE_CASE			)
            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:
            SCREAMING_SNAKE_CASE	=		trainer.evaluate()
            trainer.log_metrics("""eval"""				,		_SCREAMING_SNAKE_CASE			)
            trainer.save_metrics("""eval"""				,		_SCREAMING_SNAKE_CASE			)

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



def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     str:


      '''simple docstring'''


      main()


if __name__ == "__main__":
      main()


