
import math
import os
from copy import deepcopy

import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed


lowerCamelCase      ="true"


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=8_2 ,		UpperCamelCase__=1_6		):
      set_seed(4_2		)
      UpperCamelCase__    :   Optional[Any]					=				RegressionModel()
      UpperCamelCase__    :   Optional[Any]					=				deepcopy(UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				RegressionDataset(length=UpperCamelCase__		)
      UpperCamelCase__    :   Union[str, Any]					=				DataLoader(UpperCamelCase__ ,		batch_size=UpperCamelCase__		)
      model.to(accelerator.device		)
      UpperCamelCase__				,UpperCamelCase__    :   str					=				accelerator.prepare(UpperCamelCase__ ,		UpperCamelCase__		)
      return model, ddp_model, dataloader


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=False		):
      UpperCamelCase__    :   List[str]					=				AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased'''		)
      UpperCamelCase__    :   List[Any]					=				load_dataset('''glue''' ,		'''mrpc''' ,		split='''validation'''		)

      def tokenize_function(UpperCamelCase__		):
            UpperCamelCase__    :   int					=				tokenizer(examples['''sentence1'''] ,		examples['''sentence2'''] ,		truncation=UpperCamelCase__ ,		max_length=UpperCamelCase__		)
            return outputs

      with accelerator.main_process_first():
            UpperCamelCase__    :   Dict					=				dataset.map(
                UpperCamelCase__ ,		batched=UpperCamelCase__ ,		remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,		)

      UpperCamelCase__    :   Optional[int]					=				tokenized_datasets.rename_column('''label''' ,		'''labels'''		)

      def collate_fn(UpperCamelCase__		):
            if use_longest:
                  return tokenizer.pad(UpperCamelCase__ ,		padding='''longest''' ,		return_tensors='''pt'''		)
            return tokenizer.pad(UpperCamelCase__ ,		padding='''max_length''' ,		max_length=1_2_8 ,		return_tensors='''pt'''		)

      return DataLoader(UpperCamelCase__ ,		shuffle=UpperCamelCase__ ,		collate_fn=UpperCamelCase__ ,		batch_size=1_6		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Tuple					=				Accelerator(dispatch_batches=UpperCamelCase__ ,		split_batches=UpperCamelCase__		)
      UpperCamelCase__    :   str					=				get_dataloader(UpperCamelCase__ ,		not dispatch_batches		)
      UpperCamelCase__    :   Optional[Any]					=				AutoModelForSequenceClassification.from_pretrained(
          '''hf-internal-testing/mrpc-bert-base-cased''' ,		return_dict=UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Dict					=				accelerator.prepare(UpperCamelCase__ ,		UpperCamelCase__		)
      return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   List[str]					=				[]
      for batch in dataloader:
            UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				batch.values()
            with torch.no_grad():
                  UpperCamelCase__    :   str					=				model(UpperCamelCase__		)
                  UpperCamelCase__				,UpperCamelCase__    :   Any					=				accelerator.gather_for_metrics((logit, target)		)
                  logits_and_targets.append((logit, target)		)
      UpperCamelCase__				,UpperCamelCase__    :   Union[str, Any]					=				[], []
      for logit, targ in logits_and_targets:
            logits.append(UpperCamelCase__		)
            targs.append(UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Optional[Any]					=				torch.cat(UpperCamelCase__		), torch.cat(UpperCamelCase__		)
      return logits, targs


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__=8_2 ,		UpperCamelCase__=False ,		UpperCamelCase__=False ,		UpperCamelCase__=1_6		):
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   str					=				get_basic_setup(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
      UpperCamelCase__				,UpperCamelCase__    :   Tuple					=				generate_predictions(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
      assert (
          len(UpperCamelCase__		) == num_samples
      ), f'''Unexpected number of inputs:\n    Expected: {num_samples}\n    Actual: {len(UpperCamelCase__		)}'''


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ = False ,		UpperCamelCase__ = False		):
      UpperCamelCase__    :   Optional[Any]					=				evaluate.load('''glue''' ,		'''mrpc'''		)
      UpperCamelCase__				,UpperCamelCase__    :   List[Any]					=				get_mrpc_setup(UpperCamelCase__ ,		UpperCamelCase__		)
      # First do baseline
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Optional[int]					=				setup['''no''']
      model.to(UpperCamelCase__		)
      model.eval()
      for batch in dataloader:
            batch.to(UpperCamelCase__		)
            with torch.inference_mode():
                  UpperCamelCase__    :   Dict					=				model(**UpperCamelCase__		)
            UpperCamelCase__    :   List[Any]					=				outputs.logits.argmax(dim=-1		)
            metric.add_batch(predictions=UpperCamelCase__ ,		references=batch['''labels''']		)
      UpperCamelCase__    :   Union[str, Any]					=				metric.compute()

      # Then do distributed
      UpperCamelCase__				,UpperCamelCase__				,UpperCamelCase__    :   Any					=				setup['''ddp''']
      model.eval()
      for batch in dataloader:
            with torch.inference_mode():
                  UpperCamelCase__    :   Tuple					=				model(**UpperCamelCase__		)
            UpperCamelCase__    :   Optional[int]					=				outputs.logits.argmax(dim=-1		)
            UpperCamelCase__    :   int					=				batch['''labels''']
            UpperCamelCase__				,UpperCamelCase__    :   str					=				accelerator.gather_for_metrics((preds, references)		)
            metric.add_batch(predictions=UpperCamelCase__ ,		references=UpperCamelCase__		)
      UpperCamelCase__    :   int					=				metric.compute()

      for key in "accuracy f1".split():
            assert math.isclose(
                baseline[key] ,		distributed[key]		), f'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''


def 							SCREAMING_SNAKE_CASE_      (			):
      UpperCamelCase__    :   Dict					=				Accelerator(split_batches=UpperCamelCase__ ,		dispatch_batches=UpperCamelCase__		)
      if accelerator.is_local_main_process:
            datasets.utils.logging.set_verbosity_warning()
            transformers.utils.logging.set_verbosity_warning()
      else:
            datasets.utils.logging.set_verbosity_error()
            transformers.utils.logging.set_verbosity_error()
      # These are a bit slower so they should only be ran on the GPU or TPU
      if torch.cuda.is_available() or is_tpu_available():
            if accelerator.is_local_main_process:
                  print('''**Testing gather_for_metrics**'''		)
            for split_batches in [True, False]:
                  for dispatch_batches in [True, False]:
                        if accelerator.is_local_main_process:
                              print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`'''		)
                        test_mrpc(UpperCamelCase__ ,		UpperCamelCase__		)
                        accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('''**Test torch metrics**'''		)
      for split_batches in [True, False]:
            for dispatch_batches in [True, False]:
                  UpperCamelCase__    :   Optional[Any]					=				Accelerator(split_batches=UpperCamelCase__ ,		dispatch_batches=UpperCamelCase__		)
                  if accelerator.is_local_main_process:
                        print(f'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99'''		)
                  test_torch_metrics(UpperCamelCase__ ,		9_9		)
                  accelerator.state._reset_state()
      if accelerator.is_local_main_process:
            print('''**Test last batch is not dropped when perfectly divisible**'''		)
      UpperCamelCase__    :   Tuple					=				Accelerator()
      test_torch_metrics(UpperCamelCase__ ,		5_1_2		)
      accelerator.state._reset_state()


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__		):
      # For xla_spawn (TPUs)
      main()


if __name__ == "__main__":
    main()




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCamelCase      =False
lowerCamelCase      =True
lowerCamelCase      =False


if __name__ == "__main__":
    lowerCamelCase      =argparse.ArgumentParser()

    parser.add_argument(
        "--repo_path",
        default=None,
        type=str,
        required=True,
        help="The config json file corresponding to the architecture.",
    )

    parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")

    lowerCamelCase      =parser.parse_args()

    lowerCamelCase      ={
        "image_size": "sample_size",
        "num_res_blocks": "layers_per_block",
        "block_channels": "block_out_channels",
        "down_blocks": "down_block_types",
        "up_blocks": "up_block_types",
        "downscale_freq_shift": "freq_shift",
        "resnet_num_groups": "norm_num_groups",
        "resnet_act_fn": "act_fn",
        "resnet_eps": "norm_eps",
        "num_head_channels": "attention_head_dim",
    }

    lowerCamelCase      ={
        "time_steps": "time_proj",
        "mid": "mid_block",
        "downsample_blocks": "down_blocks",
        "upsample_blocks": "up_blocks",
    }

    lowerCamelCase      ="" if has_file(args.repo_path, "config.json") else "unet"

    with open(os.path.join(args.repo_path, subfolder, "config.json"), "r", encoding="utf-8") as reader:
        lowerCamelCase      =reader.read()
        lowerCamelCase      =json.loads(text)

    if do_only_config:
        for key in config_parameters_to_change.keys():
            config.pop(key, None)

    if has_file(args.repo_path, "config.json"):
        lowerCamelCase      =UNetaDModel(**config)
    else:
        lowerCamelCase      =UNetaDConditionModel if "ldm-text2im-large-256" in args.repo_path else UNetaDModel
        lowerCamelCase      =class_name(**config)

    if do_only_config:
        model.save_config(os.path.join(args.repo_path, subfolder))

    lowerCamelCase      =dict(model.config)

    if do_only_renaming:
        for key, value in config_parameters_to_change.items():
            if key in config:
                lowerCamelCase      =config[key]
                del config[key]

        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["down_block_types"]]
        lowerCamelCase      =[k.replace("UNetRes", "") for k in config["up_block_types"]]

    if do_only_weights:
        lowerCamelCase      =torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

        lowerCamelCase      ={}
        for param_key, param_value in state_dict.items():
            if param_key.endswith(".op.bias") or param_key.endswith(".op.weight"):
                continue
            lowerCamelCase      =False
            for key, new_key in key_parameters_to_change.items():
                if not has_changed and param_key.split(".")[0] == key:
                    lowerCamelCase      =param_value
                    lowerCamelCase      =True
            if not has_changed:
                lowerCamelCase      =param_value

        model.load_state_dict(new_state_dict)
        model.save_pretrained(os.path.join(args.repo_path, subfolder))


