


import argparse

import requests
import torch

# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image

from transformers import (
    AutoTokenizer,
    BlipaConfig,
    BlipaForConditionalGeneration,
    BlipaProcessor,
    BlipaVisionConfig,
    BlipImageProcessor,
    OPTConfig,
    TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD


def 			UpperCAmelCase_				(      ):
					lowercase_  							=			"""https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
					lowercase_  							=			Image.open(requests.get(UpperCAmelCase__						,							stream=UpperCAmelCase__  ).raw  ).convert("""RGB"""  )

					return image


def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			[]
					# fmt: off

					# vision encoder
					rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""")  )
					rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""")  )
					rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""")  )
					rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""")  )
					rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""")  )
					rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""")  )

					for i in range(config.vision_config.num_hidden_layers  ):
										rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.weight''', F'''vision_model.encoder.layers.{i}.layer_norm1.weight''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.norm1.bias''', F'''vision_model.encoder.layers.{i}.layer_norm1.bias''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.weight''', F'''vision_model.encoder.layers.{i}.layer_norm2.weight''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.norm2.bias''', F'''vision_model.encoder.layers.{i}.layer_norm2.bias''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.attn.qkv.weight''', F'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.weight''', F'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',)  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.attn.proj.bias''', F'''vision_model.encoder.layers.{i}.self_attn.projection.bias''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc1.weight''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc1.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc1.bias''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.weight''', F'''vision_model.encoder.layers.{i}.mlp.fc2.weight''')  )
										rename_keys.append((F'''visual_encoder.blocks.{i}.mlp.fc2.bias''', F'''vision_model.encoder.layers.{i}.mlp.fc2.bias''')  )

					# QFormer
					rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.layernorm.weight""")  )
					rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""")  )

					# fmt: on
					return rename_keys


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			dct.pop(UpperCAmelCase__  )
					lowercase_  							=			val


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					for i in range(config.vision_config.num_hidden_layers  ):
										# read in original q and v biases
										lowercase_  							=			state_dict.pop(F'''visual_encoder.blocks.{i}.attn.q_bias'''  )
										lowercase_  							=			state_dict.pop(F'''visual_encoder.blocks.{i}.attn.v_bias'''  )

										# next, set bias in the state dict
										lowercase_  							=			torch.cat((q_bias, torch.zeros_like(UpperCAmelCase__						,							requires_grad=UpperCAmelCase__  ), v_bias)  )
										lowercase_  							=			qkv_bias


def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__  ):
					lowercase_  							=			3_6_4 if """coco""" in model_name else 2_2_4
					lowercase_  							=			BlipaVisionConfig(image_size=UpperCAmelCase__  ).to_dict()

					# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
					# seems like flan-T5 models don't have bos_token_id properly set?
					if "opt-2.7b" in model_name:
										lowercase_  							=			OPTConfig.from_pretrained("""facebook/opt-2.7b"""						,							eos_token_id=UpperCAmelCase__  ).to_dict()
					elif "opt-6.7b" in model_name:
										lowercase_  							=			OPTConfig.from_pretrained("""facebook/opt-6.7b"""						,							eos_token_id=UpperCAmelCase__  ).to_dict()
					elif "t5-xl" in model_name:
										lowercase_  							=			TaConfig.from_pretrained("""google/flan-t5-xl"""						,							dense_act_fn="""gelu"""						,							bos_token_id=1  ).to_dict()
					elif "t5-xxl" in model_name:
										lowercase_  							=			TaConfig.from_pretrained("""google/flan-t5-xxl"""						,							dense_act_fn="""gelu"""						,							bos_token_id=1  ).to_dict()

					lowercase_  							=			BlipaConfig(vision_config=UpperCAmelCase__						,							text_config=UpperCAmelCase__  )

					return config, image_size


@torch.no_grad()
def 			UpperCAmelCase_				(      UpperCAmelCase__						,							UpperCAmelCase__=None						,							UpperCAmelCase__=False  ):
					lowercase_  							=			(
					    AutoTokenizer.from_pretrained("""facebook/opt-2.7b"""  )
					    if """opt""" in model_name
					    else AutoTokenizer.from_pretrained("""google/flan-t5-xl"""  )
					)
					lowercase_  							=			tokenizer("""\n"""						,							add_special_tokens=UpperCAmelCase__  ).input_ids[0]
					lowercase_							,					lowercase_  							=			get_blipa_config(UpperCAmelCase__						,							eos_token_id=UpperCAmelCase__  )

					lowercase_  							=			BlipaForConditionalGeneration(UpperCAmelCase__  ).eval()

					lowercase_  							=			{
					    """blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
					    """blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
					    """blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
					    """blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
					    """blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
					    """blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
					    """blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
					}

					lowercase_							,					lowercase_  							=			model_name_to_original[model_name]

					# load original model
					print("""Loading original model..."""  )
					lowercase_  							=			"""cuda""" if torch.cuda.is_available() else """cpu"""
					lowercase_							,					lowercase_							,					lowercase_  							=			load_model_and_preprocess(
					    name=UpperCAmelCase__						,							model_type=UpperCAmelCase__						,							is_eval=UpperCAmelCase__						,							device=UpperCAmelCase__  )
					original_model.eval()
					print("""Done!"""  )

					# update state dict keys
					lowercase_  							=			original_model.state_dict()
					lowercase_  							=			create_rename_keys(UpperCAmelCase__  )
					for src, dest in rename_keys:
										rename_key(UpperCAmelCase__						,							UpperCAmelCase__						,							UpperCAmelCase__  )

					# some keys can be renamed efficiently
					for key, val in state_dict.copy().items():
										lowercase_  							=			state_dict.pop(UpperCAmelCase__  )
										if key.startswith("""Qformer.bert"""  ):
															lowercase_  							=			key.replace("""Qformer.bert"""						,							"""qformer"""  )
										if "attention.self" in key:
															lowercase_  							=			key.replace("""self"""						,							"""attention"""  )
										if "opt_proj" in key:
															lowercase_  							=			key.replace("""opt_proj"""						,							"""language_projection"""  )
										if "t5_proj" in key:
															lowercase_  							=			key.replace("""t5_proj"""						,							"""language_projection"""  )
										if key.startswith("""opt"""  ):
															lowercase_  							=			key.replace("""opt"""						,							"""language"""  )
										if key.startswith("""t5"""  ):
															lowercase_  							=			key.replace("""t5"""						,							"""language"""  )
										lowercase_  							=			val

					# read in qv biases
					read_in_q_v_bias(UpperCAmelCase__						,							UpperCAmelCase__  )

					lowercase_							,					lowercase_  							=			hf_model.load_state_dict(UpperCAmelCase__						,							strict=UpperCAmelCase__  )
					assert len(UpperCAmelCase__  ) == 0
					assert unexpected_keys == ["qformer.embeddings.position_ids"]

					lowercase_  							=			load_demo_image()
					lowercase_  							=			vis_processors["""eval"""](UpperCAmelCase__  ).unsqueeze(0  ).to(UpperCAmelCase__  )
					lowercase_  							=			tokenizer(["""\n"""]						,							return_tensors="""pt"""  ).input_ids.to(UpperCAmelCase__  )

					# create processor
					lowercase_  							=			BlipImageProcessor(
					    size={"""height""": image_size, """width""": image_size}						,							image_mean=UpperCAmelCase__						,							image_std=UpperCAmelCase__  )
					lowercase_  							=			BlipaProcessor(image_processor=UpperCAmelCase__						,							tokenizer=UpperCAmelCase__  )
					lowercase_  							=			processor(images=UpperCAmelCase__						,							return_tensors="""pt"""  ).pixel_values.to(UpperCAmelCase__  )

					# make sure processor creates exact same pixel values
					assert torch.allclose(UpperCAmelCase__						,							UpperCAmelCase__  )

					original_model.to(UpperCAmelCase__  )
					hf_model.to(UpperCAmelCase__  )
					with torch.no_grad():
										if "opt" in model_name:
															lowercase_  							=			original_model({"""image""": original_pixel_values, """text_input""": [""""""]}  ).logits
															lowercase_  							=			hf_model(UpperCAmelCase__						,							UpperCAmelCase__  ).logits
										else:
															lowercase_  							=			original_model(
															    {"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]}  ).logits
															lowercase_  							=			input_ids.masked_fill(input_ids == tokenizer.pad_token_id						,							-1_0_0  )
															lowercase_  							=			hf_model(UpperCAmelCase__						,							UpperCAmelCase__						,							labels=UpperCAmelCase__  ).logits

					assert original_logits.shape == logits.shape
					print("""First values of original logits:"""						,							original_logits[0, :3, :3]  )
					print("""First values of HF logits:"""						,							logits[0, :3, :3]  )

					# assert values
					if model_name == "blip2-flan-t5-xl":
										lowercase_  							=			torch.tensor(
										    [[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]]						,							device=UpperCAmelCase__  )
										assert torch.allclose(logits[0, :3, :3]						,							UpperCAmelCase__						,							atol=1e-4  )
					elif model_name == "blip2-flan-t5-xl-coco":
										lowercase_  							=			torch.tensor(
										    [[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]]						,							device=UpperCAmelCase__  )
					else:
										# cast to same type
										lowercase_  							=			logits.dtype
										assert torch.allclose(original_logits.to(UpperCAmelCase__  )						,							UpperCAmelCase__						,							atol=1e-2  )
					print("""Looks ok!"""  )

					print("""Generating a caption..."""  )
					lowercase_  							=			""""""
					lowercase_  							=			tokenizer(UpperCAmelCase__						,							return_tensors="""pt"""  ).input_ids.to(UpperCAmelCase__  )

					lowercase_  							=			original_model.generate({"""image""": original_pixel_values}  )
					lowercase_  							=			hf_model.generate(
					    UpperCAmelCase__						,							UpperCAmelCase__						,							do_sample=UpperCAmelCase__						,							num_beams=5						,							max_length=3_0						,							min_length=1						,							top_p=0.9						,							repetition_penalty=1.0						,							length_penalty=1.0						,							temperature=1						,							)
					print("""Original generation:"""						,							UpperCAmelCase__  )
					lowercase_  							=			input_ids.shape[1]
					lowercase_  							=			processor.batch_decode(outputs[:, prompt_length:]						,							skip_special_tokens=UpperCAmelCase__  )
					lowercase_  							=			[text.strip() for text in output_text]
					print("""HF generation:"""						,							UpperCAmelCase__  )

					if pytorch_dump_folder_path is not None:
										processor.save_pretrained(UpperCAmelCase__  )
										hf_model.save_pretrained(UpperCAmelCase__  )

					if push_to_hub:
										processor.push_to_hub(F'''nielsr/{model_name}'''  )
										hf_model.push_to_hub(F'''nielsr/{model_name}'''  )


if __name__ == "__main__":
							a					  =     argparse.ArgumentParser()
							a					  =     [
							    'blip2-opt-2.7b',
							    'blip2-opt-6.7b',
							    'blip2-opt-2.7b-coco',
							    'blip2-opt-6.7b-coco',
							    'blip2-flan-t5-xl',
							    'blip2-flan-t5-xl-coco',
							    'blip2-flan-t5-xxl',
							]
							parser.add_argument(
							    '--model_name',
							    default='blip2-opt-2.7b',
							    choices=choices,
							    type=str,
							    help='Path to hf config.json of model to convert',
							)
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							parser.add_argument(
							    '--push_to_hub',
							    action='store_true',
							    help='Whether to push the model and processor to the hub after converting',
							)

							a					  =     parser.parse_args()

							convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






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

import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor

import transformers
from transformers import (
    CONFIG_MAPPING,
    IMAGE_PROCESSOR_MAPPING,
    MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForMaskedImageModeling,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
)
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					  =     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')

a					  =     list(MODEL_FOR_MASKED_IMAGE_MODELING_MAPPING.keys())
a					  =     tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default='cifar10'							,				metadata={'help': 'Name of a dataset from the datasets package'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'The column name of the images in the files. If not set, will try to use \'image\' or \'img\'.'}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(default=__magic_name__							,				metadata={'help': 'A folder containing the training data.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(default=__magic_name__							,				metadata={'help': 'A folder containing the validation data.'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[float]						=   field(
					    default=0.1_5							,				metadata={'help': 'Percent to split off of train for validation.'}   )
					__SCREAMING_SNAKE_CASE       :  int						=   field(default=32							,				metadata={'help': 'The size of the square patches to use for masking.'}   )
					__SCREAMING_SNAKE_CASE       :  float						=   field(
					    default=0.6							,				metadata={'help': 'Percentage of patches to mask.'}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of training examples to this '
					            'value if set.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
					            'value if set.'
					        )
					    }							,				)

					def   UpperCAmelCase__       (     self       :   Tuple       ):






										'''simple docstring'''






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







@dataclass
class        UpperCamelCase__   :
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The model checkpoint for weights initialization. Can be a local path to a pytorch_model.bin or a '
					            'checkpoint identifier on the hub. '
					            'Don\'t set if you want to train a model from scratch.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__magic_name__   )}							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Pretrained config name or path if not the same as model_name'}   )
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				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'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[str]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Where do you want to store (cache) the pretrained models/datasets downloaded from the hub'}							,				)
					__SCREAMING_SNAKE_CASE       :  str						=   field(
					    default='main'							,				metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}							,				)
					__SCREAMING_SNAKE_CASE       :  str						=   field(default=__magic_name__							,				metadata={'help': 'Name or path of preprocessor config.'}   )
					__SCREAMING_SNAKE_CASE       :  bool						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
					            'with private models).'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The size (resolution) of each image. If not specified, will use `image_size` of the configuration.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={
					        'help': (
					            'The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration.'
					        )
					    }							,				)
					__SCREAMING_SNAKE_CASE       :  Optional[int]						=   field(
					    default=__magic_name__							,				metadata={'help': 'Stride to use for the encoder.'}							,				)







class        UpperCamelCase__   :

					def __init__(     self       :   Dict							,					UpperCamelCase__       :   List[Any]=192							,					UpperCamelCase__       :   Tuple=32							,					UpperCamelCase__       :   Dict=4							,					UpperCamelCase__       :   str=0.6       ):






										'''simple docstring'''






										lowercase_  							=			input_size
										lowercase_  							=			mask_patch_size
										lowercase_  							=			model_patch_size
										lowercase_  							=			mask_ratio

										if self.input_size % self.mask_patch_size != 0:
															raise ValueError("""Input size must be divisible by mask patch size"""       )
										if self.mask_patch_size % self.model_patch_size != 0:
															raise ValueError("""Mask patch size must be divisible by model patch size"""       )

										lowercase_  							=			self.input_size // self.mask_patch_size
										lowercase_  							=			self.mask_patch_size // self.model_patch_size

										lowercase_  							=			self.rand_size**2
										lowercase_  							=			int(np.ceil(self.token_count * self.mask_ratio       )       )







					def __call__(     self       :   int       ):






										'''simple docstring'''






										lowercase_  							=			np.random.permutation(self.token_count       )[: self.mask_count]
										lowercase_  							=			np.zeros(self.token_count							,					dtype=UpperCamelCase__       )
										lowercase_  							=			1

										lowercase_  							=			mask.reshape((self.rand_size, self.rand_size)       )
										lowercase_  							=			mask.repeat(self.scale							,					axis=0       ).repeat(self.scale							,					axis=1       )

										return torch.tensor(mask.flatten()       )



def 			UpperCAmelCase_				(      UpperCAmelCase__  ):
					lowercase_  							=			torch.stack([example["""pixel_values"""] for example in examples]  )
					lowercase_  							=			torch.stack([example["""mask"""] for example in examples]  )
					return {"pixel_values": pixel_values, "bool_masked_pos": mask}


def 			UpperCAmelCase_				(      ):
					# See all possible arguments in src/transformers/training_args.py
					# or by passing the --help flag to this script.
					# We now keep distinct sets of args, for a cleaner separation of concerns.

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

					lowercase_  							=			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.
					lowercase_  							=			None
					if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
										lowercase_  							=			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.
					lowercase_  							=			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.
					lowercase_  							=			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:
										lowercase_  							=			ds["""train"""].train_test_split(data_args.train_val_split  )
										lowercase_  							=			split["""train"""]
										lowercase_  							=			split["""test"""]

					# Create config
					# Distributed training:
					# The .from_pretrained methods guarantee that only one local process can concurrently
					# download model & vocab.
					lowercase_  							=			{
					    """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_or_path:
										lowercase_  							=			AutoConfig.from_pretrained(model_args.config_name_or_path						,							**UpperCAmelCase__  )
					elif model_args.model_name_or_path:
										lowercase_  							=			AutoConfig.from_pretrained(model_args.model_name_or_path						,							**UpperCAmelCase__  )
					else:
										lowercase_  							=			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}'''  )

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
					if hasattr(UpperCAmelCase__						,							"""decoder_type"""  ):
										lowercase_  							=			"""simmim"""

					# adapt config
					lowercase_  							=			model_args.image_size if model_args.image_size is not None else config.image_size
					lowercase_  							=			model_args.patch_size if model_args.patch_size is not None else config.patch_size
					lowercase_  							=			(
					    model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
					)

					config.update(
					    {
					        """image_size""": model_args.image_size,
					        """patch_size""": model_args.patch_size,
					        """encoder_stride""": model_args.encoder_stride,
					    }  )

					# create image processor
					if model_args.image_processor_name:
										lowercase_  							=			AutoImageProcessor.from_pretrained(model_args.image_processor_name						,							**UpperCAmelCase__  )
					elif model_args.model_name_or_path:
										lowercase_  							=			AutoImageProcessor.from_pretrained(model_args.model_name_or_path						,							**UpperCAmelCase__  )
					else:
										lowercase_  							=			{
										    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
										}
										lowercase_  							=			IMAGE_PROCESSOR_TYPES[model_args.model_type]()

					# create model
					if model_args.model_name_or_path:
										lowercase_  							=			AutoModelForMaskedImageModeling.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"""  )
										lowercase_  							=			AutoModelForMaskedImageModeling.from_config(UpperCAmelCase__  )

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

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

					# transformations as done in original SimMIM paper
					# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
					lowercase_  							=			Compose(
					    [
					        Lambda(lambda UpperCAmelCase__  : img.convert("""RGB"""  ) if img.mode != "RGB" else img  ),
					        RandomResizedCrop(model_args.image_size						,							scale=(0.67, 1.0)						,							ratio=(3.0 / 4.0, 4.0 / 3.0)  ),
					        RandomHorizontalFlip(),
					        ToTensor(),
					        Normalize(mean=image_processor.image_mean						,							std=image_processor.image_std  ),
					    ]  )

					# create mask generator
					lowercase_  							=			MaskGenerator(
					    input_size=model_args.image_size						,							mask_patch_size=data_args.mask_patch_size						,							model_patch_size=model_args.patch_size						,							mask_ratio=data_args.mask_ratio						,							)

					def preprocess_images(UpperCAmelCase__  ):
										lowercase_  							=			[transforms(UpperCAmelCase__  ) for image in examples[image_column_name]]
										lowercase_  							=			[mask_generator() for i in range(len(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:
															lowercase_  							=			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:
															lowercase_  							=			(
															    ds["""validation"""].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
															)
										# Set the validation transforms
										ds["validation"].set_transform(UpperCAmelCase__  )

					# Initialize our trainer
					lowercase_  							=			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:
										lowercase_  							=			None
										if training_args.resume_from_checkpoint is not None:
															lowercase_  							=			training_args.resume_from_checkpoint
										elif last_checkpoint is not None:
															lowercase_  							=			last_checkpoint
										lowercase_  							=			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:
										lowercase_  							=			trainer.evaluate()
										trainer.log_metrics("""eval"""						,							UpperCAmelCase__  )
										trainer.save_metrics("""eval"""						,							UpperCAmelCase__  )

					# Write model card and (optionally) push to hub
					lowercase_  							=			{
					    """finetuned_from""": model_args.model_name_or_path,
					    """tasks""": """masked-image-modeling""",
					    """dataset""": data_args.dataset_name,
					    """tags""": ["""masked-image-modeling"""],
					}
					if training_args.push_to_hub:
										trainer.push_to_hub(**UpperCAmelCase__  )
					else:
										trainer.create_model_card(**UpperCAmelCase__  )


if __name__ == "__main__":
							main()


