


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
UpperCamelCase__     =	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")

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

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






							def 			_lowerCamelCase						(  self					):
														UpperCamelCase__			       =     {}
														if self.train_dir is not None:
																					UpperCamelCase__			       =     self.train_dir
														if self.validation_dir is not None:
																					UpperCamelCase__			       =     self.validation_dir
														UpperCamelCase__			       =     data_files if data_files else None






@dataclass
class     __SCREAMING_SNAKE_CASE   :
							snake_case       : str         =    field(
							    default=a__				,						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."""
							        )
							    }				,						)
							snake_case       : Optional[str]         =    field(
							    default=a__				,						metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a__    )}				,						)
							snake_case       : Optional[str]         =    field(
							    default=a__				,						metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}    )
							snake_case       : Optional[str]         =    field(
							    default=a__				,						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=a__				,						metadata={"""help""": """Where do you want to store (cache) the pretrained models/datasets downloaded from the hub"""}				,						)
							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=a__				,						metadata={"""help""": """Name or path of preprocessor config."""}    )
							snake_case       : bool         =    field(
							    default=a__				,						metadata={
							        """help""": (
							            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
							            """with private models)."""
							        )
							    }				,						)
							snake_case       : Optional[int]         =    field(
							    default=a__				,						metadata={
							        """help""": (
							            """The size (resolution) of each image. If not specified, will use `image_size` of the configuration."""
							        )
							    }				,						)
							snake_case       : Optional[int]         =    field(
							    default=a__				,						metadata={
							        """help""": (
							            """The size (resolution) of each patch. If not specified, will use `patch_size` of the configuration."""
							        )
							    }				,						)
							snake_case       : Optional[int]         =    field(
							    default=a__				,						metadata={"""help""": """Stride to use for the encoder."""}				,						)






class     __SCREAMING_SNAKE_CASE   :






							def __init__(  self					,	__lowerCAmelCase=192					,	__lowerCAmelCase=32					,	__lowerCAmelCase=4					,	__lowerCAmelCase=0.6					):
														UpperCamelCase__			       =     input_size
														UpperCamelCase__			       =     mask_patch_size
														UpperCamelCase__			       =     model_patch_size
														UpperCamelCase__			       =     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"""					)

														UpperCamelCase__			       =     self.input_size // self.mask_patch_size
														UpperCamelCase__			       =     self.mask_patch_size // self.model_patch_size

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







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

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

														return torch.tensor(mask.flatten()					)






def       _UpperCamelCase       (a__				:Optional[int]    ):






							"""simple docstring"""





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





def       _UpperCamelCase       ():






							"""simple docstring"""





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

							# 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()

							UpperCamelCase__			       =     training_args.get_process_log_level()
							logger.setLevel(_lowercase    )
							transformers.utils.logging.set_verbosity(_lowercase    )
							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.
							UpperCamelCase__			       =     None
							if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
														UpperCamelCase__			       =     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.
							UpperCamelCase__			       =     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.
							UpperCamelCase__			       =     None if "validation" in ds.keys() else data_args.train_val_split
							if isinstance(data_args.train_val_split				,     _lowercase    ) and data_args.train_val_split > 0.0:
														UpperCamelCase__			       =     ds["train"].train_test_split(data_args.train_val_split    )
														UpperCamelCase__			       =     split["train"]
														UpperCamelCase__			       =     split["test"]

							# Create config
							# Distributed training:
							# The .from_pretrained methods guarantee that only one local process can concurrently
							# download model & vocab.
							UpperCamelCase__			       =     {
							    "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:
														UpperCamelCase__			       =     AutoConfig.from_pretrained(model_args.config_name_or_path				,     **_lowercase    )
							elif model_args.model_name_or_path:
														UpperCamelCase__			       =     AutoConfig.from_pretrained(model_args.model_name_or_path				,     **_lowercase    )
							else:
														UpperCamelCase__			       =     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(_lowercase				,     """decoder_type"""    ):
														UpperCamelCase__			       =     "simmim"

							# adapt config
							UpperCamelCase__			       =     model_args.image_size if model_args.image_size is not None else config.image_size
							UpperCamelCase__			       =     model_args.patch_size if model_args.patch_size is not None else config.patch_size
							UpperCamelCase__			       =     (
							    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:
														UpperCamelCase__			       =     AutoImageProcessor.from_pretrained(model_args.image_processor_name				,     **_lowercase    )
							elif model_args.model_name_or_path:
														UpperCamelCase__			       =     AutoImageProcessor.from_pretrained(model_args.model_name_or_path				,     **_lowercase    )
							else:
														UpperCamelCase__			       =     {
														    conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
														}
														UpperCamelCase__			       =     IMAGE_PROCESSOR_TYPES[model_args.model_type]()

							# create model
							if model_args.model_name_or_path:
														UpperCamelCase__			       =     AutoModelForMaskedImageModeling.from_pretrained(
														    model_args.model_name_or_path				,     from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )				,     config=_lowercase				,     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"""    )
														UpperCamelCase__			       =     AutoModelForMaskedImageModeling.from_config(_lowercase    )

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

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

							# transformations as done in original SimMIM paper
							# source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
							UpperCamelCase__			       =     Compose(
							    [
							        Lambda(lambda a__    : 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
							UpperCamelCase__			       =     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(a__				:List[str]    ):
														UpperCamelCase__			       =     [transforms(_lowercase    ) for image in examples[image_column_name]]
														UpperCamelCase__			       =     [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:
																					UpperCamelCase__			       =     ds["train"].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
														# Set the training transforms
														ds["train"].set_transform(_lowercase    )

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

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

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

							# Write model card and (optionally) push to hub
							UpperCamelCase__			       =     {
							    "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(**_lowercase    )
							else:
														trainer.create_model_card(**_lowercase    )


if __name__ == "__main__":
					main()











import json
import os

import torch

from diffusers import UNetaDModel


os.makedirs("hub/hopper-medium-v2/unet/hor32", exist_ok=True)
os.makedirs("hub/hopper-medium-v2/unet/hor128", exist_ok=True)

os.makedirs("hub/hopper-medium-v2/value_function", exist_ok=True)





def       _UpperCamelCase       (a__				:int    ):






							"""simple docstring"""





							if hor == 128:
														UpperCamelCase__			       =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														UpperCamelCase__			       =     (32, 128, 256)
														UpperCamelCase__			       =     ("""UpResnetBlock1D""", """UpResnetBlock1D""")

							elif hor == 32:
														UpperCamelCase__			       =     ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""")
														UpperCamelCase__			       =     (32, 64, 128, 256)
														UpperCamelCase__			       =     ("""UpResnetBlock1D""", """UpResnetBlock1D""", """UpResnetBlock1D""")
							UpperCamelCase__			       =     torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch"""    )
							UpperCamelCase__			       =     model.state_dict()
							UpperCamelCase__			       =     {
							    """down_block_types""": down_block_types,
							    """block_out_channels""": block_out_channels,
							    """up_block_types""": up_block_types,
							    """layers_per_block""": 1,
							    """use_timestep_embedding""": True,
							    """out_block_type""": """OutConv1DBlock""",
							    """norm_num_groups""": 8,
							    """downsample_each_block""": False,
							    """in_channels""": 14,
							    """out_channels""": 14,
							    """extra_in_channels""": 0,
							    """time_embedding_type""": """positional""",
							    """flip_sin_to_cos""": False,
							    """freq_shift""": 1,
							    """sample_size""": 6_5536,
							    """mid_block_type""": """MidResTemporalBlock1D""",
							    """act_fn""": """mish""",
							}
							UpperCamelCase__			       =     UNetaDModel(**a__    )
							print(f"""length of state dict: {len(state_dict.keys()    )}"""    )
							print(f"""length of value function dict: {len(hf_value_function.state_dict().keys()    )}"""    )
							UpperCamelCase__			       =     dict(zip(model.state_dict().keys()				,     hf_value_function.state_dict().keys()    )    )
							for k, v in mapping.items():
														UpperCamelCase__			       =     state_dict.pop(a__    )
							hf_value_function.load_state_dict(a__    )

							torch.save(hf_value_function.state_dict()				,     f"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin"""    )
							with open(f"""hub/hopper-medium-v2/unet/hor{hor}/config.json"""				,     """w"""    ) as f:
														json.dump(a__				,     a__    )





def       _UpperCamelCase       ():






							"""simple docstring"""





							UpperCamelCase__			       =     {
							    """in_channels""": 14,
							    """down_block_types""": ("""DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D""", """DownResnetBlock1D"""),
							    """up_block_types""": (),
							    """out_block_type""": """ValueFunction""",
							    """mid_block_type""": """ValueFunctionMidBlock1D""",
							    """block_out_channels""": (32, 64, 128, 256),
							    """layers_per_block""": 1,
							    """downsample_each_block""": True,
							    """sample_size""": 6_5536,
							    """out_channels""": 14,
							    """extra_in_channels""": 0,
							    """time_embedding_type""": """positional""",
							    """use_timestep_embedding""": True,
							    """flip_sin_to_cos""": False,
							    """freq_shift""": 1,
							    """norm_num_groups""": 8,
							    """act_fn""": """mish""",
							}

							UpperCamelCase__			       =     torch.load("""/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch"""    )
							UpperCamelCase__			       =     model
							UpperCamelCase__			       =     UNetaDModel(**a__    )
							print(f"""length of state dict: {len(state_dict.keys()    )}"""    )
							print(f"""length of value function dict: {len(hf_value_function.state_dict().keys()    )}"""    )

							UpperCamelCase__			       =     dict(zip(state_dict.keys()				,     hf_value_function.state_dict().keys()    )    )
							for k, v in mapping.items():
														UpperCamelCase__			       =     state_dict.pop(a__    )

							hf_value_function.load_state_dict(a__    )

							torch.save(hf_value_function.state_dict()				,     """hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin"""    )
							with open("""hub/hopper-medium-v2/value_function/config.json"""				,     """w"""    ) as f:
														json.dump(a__				,     a__    )


if __name__ == "__main__":
					unet(32)
					# unet(128)
					value_function()




