



import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


__snake_case						:Tuple						=					False
__snake_case						:Optional[int]						=					True
__snake_case						:List[Any]						=					False


if __name__ == "__main__":
 __snake_case						:Dict						=					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.''')

 __snake_case						:Optional[int]						=					parser.parse_args()

 __snake_case						:Union[str, Any]						=					{
     '''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''',
 }

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

 __snake_case						:Any						=					'''''' 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:
  __snake_case						:int						=					reader.read()
  __snake_case						:Dict						=					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'''):
  __snake_case						:Any						=					UNetaDModel(**config)
 else:
  __snake_case						:str						=					UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
  __snake_case						:Any						=					class_name(**config)

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

 __snake_case						:Optional[Any]						=					dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    __snake_case						:Union[str, Any]						=					config[key]
    del config[key]

  __snake_case						:Any						=					[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
  __snake_case						:str						=					[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

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

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

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










import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader

class    _A	(     __UpperCAmelCase   ):



       def __init__(		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					NestedDataStructureLike[PathLike]					,		__SCREAMING_SNAKE_CASE							:					Optional[NamedSplit] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[Features] = None					,		__SCREAMING_SNAKE_CASE							:					str = None					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					bool = False					,		__SCREAMING_SNAKE_CASE							:					Optional[str] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					Any					,		):


           '''simple docstring'''

           super().__init__(
               __SCREAMING_SNAKE_CASE					,		split=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		cache_dir=__SCREAMING_SNAKE_CASE					,		keep_in_memory=__SCREAMING_SNAKE_CASE					,		streaming=__SCREAMING_SNAKE_CASE					,		num_proc=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)
           __a							=    field
           __a							=    path_or_paths if isinstance(__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE) else {self.split: path_or_paths}
           __a							=    Json(
               cache_dir=__SCREAMING_SNAKE_CASE					,		data_files=__SCREAMING_SNAKE_CASE					,		features=__SCREAMING_SNAKE_CASE					,		field=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE					,		)





       def 		_lowerCamelCase      (		self							:					List[Any]):


           '''simple docstring'''

           if self.streaming:
               __a							=    self.builder.as_streaming_dataset(split=self.split)
           # Build regular (map-style) dataset
           else:
               __a							=    None
               __a							=    None
               __a							=    None
               __a							=    None

               self.builder.download_and_prepare(
                   download_config=__SCREAMING_SNAKE_CASE					,		download_mode=__SCREAMING_SNAKE_CASE					,		verification_mode=__SCREAMING_SNAKE_CASE					,		base_path=__SCREAMING_SNAKE_CASE					,		num_proc=self.num_proc					,		)
               __a							=    self.builder.as_dataset(
                   split=self.split					,		verification_mode=__SCREAMING_SNAKE_CASE					,		in_memory=self.keep_in_memory)
           return dataset


class    _A	:



       def __init__(		self							:					Any					,		__SCREAMING_SNAKE_CASE							:					Dataset					,		__SCREAMING_SNAKE_CASE							:					Union[PathLike, BinaryIO]					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		__SCREAMING_SNAKE_CASE							:					Optional[int] = None					,		**__SCREAMING_SNAKE_CASE							:					List[str]					,		):


           '''simple docstring'''

           if num_proc is not None and num_proc <= 0:
               raise ValueError(F'num_proc {num_proc} must be an integer > 0.')

           __a							=    dataset
           __a							=    path_or_buf
           __a							=    batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
           __a							=    num_proc
           __a							=    '''utf-8'''
           __a							=    to_json_kwargs



       def 		_lowerCamelCase      (		self							:					int):


           '''simple docstring'''

           __a							=    self.to_json_kwargs.pop('''path_or_buf'''					,		__SCREAMING_SNAKE_CASE)
           __a							=    self.to_json_kwargs.pop('''orient'''					,		'''records''')
           __a							=    self.to_json_kwargs.pop('''lines'''					,		True if orient == '''records''' else False)
           __a							=    self.to_json_kwargs.pop('''index'''					,		False if orient in ['''split''', '''table'''] else True)
           __a							=    self.to_json_kwargs.pop('''compression'''					,		__SCREAMING_SNAKE_CASE)

           if compression not in [None, "infer", "gzip", "bz2", "xz"]:
               raise NotImplementedError(F'`datasets` currently does not support {compression} compression')

           if isinstance(self.path_or_buf					,		(str, bytes, os.PathLike)):
               with fsspec.open(self.path_or_buf					,		'''wb'''					,		compression=__SCREAMING_SNAKE_CASE) as buffer:
                   __a							=    self._write(file_obj=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           else:
               if compression:
                   raise NotImplementedError(
                       F'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                       ''' was passed. Please provide a local path instead.''')
               __a							=    self._write(
                   file_obj=self.path_or_buf					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**self.to_json_kwargs)
           return written



       def 		_lowerCamelCase      (		self							:					Dict					,		__SCREAMING_SNAKE_CASE							:					str):


           '''simple docstring'''

           __a	,       __a	,       __a	,       __a	,       __a							=    args

           __a							=    query_table(
               table=self.dataset.data					,		key=slice(__SCREAMING_SNAKE_CASE					,		offset + self.batch_size)					,		indices=self.dataset._indices					,		)
           __a							=    batch.to_pandas().to_json(
               path_or_buf=__SCREAMING_SNAKE_CASE					,		orient=__SCREAMING_SNAKE_CASE					,		lines=__SCREAMING_SNAKE_CASE					,		index=__SCREAMING_SNAKE_CASE					,		**__SCREAMING_SNAKE_CASE)
           if not json_str.endswith('''\n'''):
               json_str += "\n"
           return json_str.encode(self.encoding)





       def 		_lowerCamelCase      (		self							:					Tuple					,		__SCREAMING_SNAKE_CASE							:					BinaryIO					,		__SCREAMING_SNAKE_CASE							:					Optional[int]					,		__SCREAMING_SNAKE_CASE							:					Union[str, Any]					,		__SCREAMING_SNAKE_CASE							:					List[Any]					,		**__SCREAMING_SNAKE_CASE							:					Optional[Any]					,		):


           '''simple docstring'''

           __a							=    0

           if self.num_proc is None or self.num_proc == 1:
               for offset in logging.tqdm(
                   range(0					,		len(self.dataset)					,		self.batch_size)					,		unit='''ba'''					,		disable=not logging.is_progress_bar_enabled()					,		desc='''Creating json from Arrow format'''					,		):
                   __a							=    self._batch_json((offset, orient, lines, index, to_json_kwargs))
                   written += file_obj.write(__SCREAMING_SNAKE_CASE)
           else:
               __a	,       __a							=    len(self.dataset), self.batch_size
               with multiprocessing.Pool(self.num_proc) as pool:
                   for json_str in logging.tqdm(
                       pool.imap(
                           self._batch_json					,		[(offset, orient, lines, index, to_json_kwargs) for offset in range(0					,		__SCREAMING_SNAKE_CASE					,		__SCREAMING_SNAKE_CASE)]					,		)					,		total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size					,		unit='''ba'''					,		disable=not logging.is_progress_bar_enabled()					,		desc='''Creating json from Arrow format'''					,		):
                       written += file_obj.write(__SCREAMING_SNAKE_CASE)

           return written





