

import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType





class 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase=2				,  lowerCAmelCase=3				,  lowerCAmelCase=64				,  lowerCAmelCase=None   )    ->					Any:
        '''simple docstring'''






        _lowercase      			=np.random.default_rng(lowerCAmelCase   )
        _lowercase      			=length
        _lowercase      			=rng.normal(size=(length,)   ).astype(np.floataa   )
        _lowercase      			=a * self.x + b + rng.normal(scale=0.1				,  size=(length,)   ).astype(np.floataa   )




  def __len__(							self   )    ->					str:
        '''simple docstring'''






        return self.length




  def __getitem__(							self				,  lowerCAmelCase   )    ->					Dict:
        '''simple docstring'''






        return {"x": self.x[i], "y": self.y[i]}





class 					__lowerCAmelCase	(   torch.nn.Module				):




  def __init__(							self				,  lowerCAmelCase=0				,  lowerCAmelCase=0				,  lowerCAmelCase=False   )    ->					str:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
        _lowercase      			=torch.nn.Parameter(torch.tensor([2, 3]   ).float()   )
        _lowercase      			=True




  def     A__	(							self				,  lowerCAmelCase=None   )    ->					Tuple:
        '''simple docstring'''






        if self.first_batch:
              print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
              _lowercase      			=False
        return x * self.a[0] + self.b[0]





class 					__lowerCAmelCase	(   torch.nn.Module				):




  def __init__(							self				,  lowerCAmelCase=0				,  lowerCAmelCase=0				,  lowerCAmelCase=False   )    ->					int:
        '''simple docstring'''






        super().__init__()
        _lowercase      			=torch.nn.Parameter(torch.tensor(lowerCAmelCase   ).float()   )
        _lowercase      			=torch.nn.Parameter(torch.tensor(lowerCAmelCase   ).float()   )
        _lowercase      			=True




  def     A__	(							self				,  lowerCAmelCase=None   )    ->					Dict:
        '''simple docstring'''






        if self.first_batch:
              print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''   )
              _lowercase      			=False
        return x * self.a + self.b
def 	a (     A__      :  str ,  A__      :  int = 16	)       -> Tuple:





      """simple docstring"""



      from datasets import load_dataset
      from transformers import AutoTokenizer

      _lowercase      			=AutoTokenizer.from_pretrained('bert-base-cased'	)
      _lowercase      			={'train': 'tests/test_samples/MRPC/train.csv', 'validation': 'tests/test_samples/MRPC/dev.csv'}
      _lowercase      			=load_dataset('csv' ,  data_files=A__	)
      _lowercase      			=datasets['train'].unique('label'	)

      _lowercase      			={v: i for i, v in enumerate(A__	)}

      def tokenize_function(A__      :  Optional[int]	):
            # max_length=None => use the model max length (it's actually the default)
            _lowercase      			=tokenizer(
                examples['sentence1'] ,  examples['sentence2'] ,  truncation=A__ ,  max_length=A__ ,  padding='max_length'	)
            if "label" in examples:
                  _lowercase      			=[label_to_id[l] for l in examples['label']]
            return outputs

      # Apply the method we just defined to all the examples in all the splits of the dataset
      _lowercase      			=datasets.map(
          A__ ,  batched=A__ ,  remove_columns=['sentence1', 'sentence2', 'label'] ,  )

      def collate_fn(A__      :  str	):
            # On TPU it's best to pad everything to the same length or training will be very slow.
            if accelerator.distributed_type == DistributedType.TPU:
                  return tokenizer.pad(A__ ,  padding='max_length' ,  max_length=128 ,  return_tensors='pt'	)
            return tokenizer.pad(A__ ,  padding='longest' ,  return_tensors='pt'	)

      # Instantiate dataloaders.
      _lowercase      			=DataLoader(tokenized_datasets['train'] ,  shuffle=A__ ,  collate_fn=A__ ,  batch_size=2	)
      _lowercase      			=DataLoader(tokenized_datasets['validation'] ,  shuffle=A__ ,  collate_fn=A__ ,  batch_size=1	)

      return train_dataloader, eval_dataloader





import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowercase_   							=  False
lowercase_   							=  True
lowercase_   							=  False


if __name__ == "__main__":
  lowercase_   							=  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.')

  lowercase_   							=  parser.parse_args()

  lowercase_   							=  {
      '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',
  }

  lowercase_   							=  {
      'time_steps': 'time_proj',
      'mid': 'mid_block',
      'downsample_blocks': 'down_blocks',
      'upsample_blocks': 'up_blocks',
  }

  lowercase_   							=  '' 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:
    lowercase_   							=  reader.read()
    lowercase_   							=  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'):
    lowercase_   							=  UNetaDModel(**config)
  else:
    lowercase_   							=  UNetaDConditionModel if 'ldm-text2im-large-256' in args.repo_path else UNetaDModel
    lowercase_   							=  class_name(**config)

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

  lowercase_   							=  dict(model.config)

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

    lowercase_   							=  [k.replace('UNetRes', '') for k in config['down_block_types']]
    lowercase_   							=  [k.replace('UNetRes', '') for k in config['up_block_types']]

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

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

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


