
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))





"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A_						:    Any				=      logging.get_logger(__name__)

A_						:    Optional[int]				=      {
    'facebook/xmod-base': 'https://huggingface.co/facebook/xmod-base/resolve/main/config.json',
    'facebook/xmod-large-prenorm': 'https://huggingface.co/facebook/xmod-large-prenorm/resolve/main/config.json',
    'facebook/xmod-base-13-125k': 'https://huggingface.co/facebook/xmod-base-13-125k/resolve/main/config.json',
    'facebook/xmod-base-30-125k': 'https://huggingface.co/facebook/xmod-base-30-125k/resolve/main/config.json',
    'facebook/xmod-base-30-195k': 'https://huggingface.co/facebook/xmod-base-30-195k/resolve/main/config.json',
    'facebook/xmod-base-60-125k': 'https://huggingface.co/facebook/xmod-base-60-125k/resolve/main/config.json',
    'facebook/xmod-base-60-265k': 'https://huggingface.co/facebook/xmod-base-60-265k/resolve/main/config.json',
    'facebook/xmod-base-75-125k': 'https://huggingface.co/facebook/xmod-base-75-125k/resolve/main/config.json',
    'facebook/xmod-base-75-269k': 'https://huggingface.co/facebook/xmod-base-75-269k/resolve/main/config.json',
}
class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE	:		int  						= '''xmod'''





	def __init__(      self				:       Union[str, Any]     ,       _SCREAMING_SNAKE_CASE				:       Tuple=30_522     ,       _SCREAMING_SNAKE_CASE				:       Tuple=768     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=12     ,       _SCREAMING_SNAKE_CASE				:       List[str]=12     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=3_072     ,       _SCREAMING_SNAKE_CASE				:       Any="gelu"     ,       _SCREAMING_SNAKE_CASE				:       List[Any]=0.1     ,       _SCREAMING_SNAKE_CASE				:       Dict=0.1     ,       _SCREAMING_SNAKE_CASE				:       str=512     ,       _SCREAMING_SNAKE_CASE				:       Tuple=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=0.0_2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=1E-1_2     ,       _SCREAMING_SNAKE_CASE				:       List[str]=1     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=0     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=2     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]="absolute"     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Optional[Any]=None     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       Dict=2     ,       _SCREAMING_SNAKE_CASE				:       str=False     ,       _SCREAMING_SNAKE_CASE				:       int=True     ,       _SCREAMING_SNAKE_CASE				:       Union[str, Any]=True     ,       _SCREAMING_SNAKE_CASE				:       Tuple=("en_XX",)     ,       _SCREAMING_SNAKE_CASE				:       Optional[int]=None     ,       **_SCREAMING_SNAKE_CASE				:       List[Any]     ,       )						->	List[str]:





					"""simple docstring"""


					super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE     ,       bos_token_id=_SCREAMING_SNAKE_CASE     ,       eos_token_id=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE    )

					SCREAMING_SNAKE_CASE						:							Any							=     vocab_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_size
					SCREAMING_SNAKE_CASE						:							Optional[Any]							=     num_hidden_layers
					SCREAMING_SNAKE_CASE						:							str							=     num_attention_heads
					SCREAMING_SNAKE_CASE						:							List[Any]							=     hidden_act
					SCREAMING_SNAKE_CASE						:							Any							=     intermediate_size
					SCREAMING_SNAKE_CASE						:							Any							=     hidden_dropout_prob
					SCREAMING_SNAKE_CASE						:							str							=     attention_probs_dropout_prob
					SCREAMING_SNAKE_CASE						:							Union[str, Any]							=     max_position_embeddings
					SCREAMING_SNAKE_CASE						:							str							=     type_vocab_size
					SCREAMING_SNAKE_CASE						:							List[str]							=     initializer_range
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     layer_norm_eps
					SCREAMING_SNAKE_CASE						:							Tuple							=     position_embedding_type
					SCREAMING_SNAKE_CASE						:							int							=     use_cache
					SCREAMING_SNAKE_CASE						:							Optional[int]							=     classifier_dropout
					SCREAMING_SNAKE_CASE						:							List[Any]							=     pre_norm
					SCREAMING_SNAKE_CASE						:							int							=     adapter_reduction_factor
					SCREAMING_SNAKE_CASE						:							List[Any]							=     adapter_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     adapter_reuse_layer_norm
					SCREAMING_SNAKE_CASE						:							Any							=     ln_before_adapter
					SCREAMING_SNAKE_CASE						:							Tuple							=     list(_SCREAMING_SNAKE_CASE    )
					SCREAMING_SNAKE_CASE						:							List[Any]							=     default_language




class     lowerCAmelCase__						(	_lowerCamelCase ):


	'''simple docstring'''







	@property
	def 			_lowerCAmelCase     (      self				:       Union[str, Any]    )						->	Mapping[str, Mapping[int, str]]:





					"""simple docstring"""


					if self.task == "multiple-choice":
									SCREAMING_SNAKE_CASE						:							Optional[int]							=     {0: 'batch', 1: 'choice', 2: 'sequence'}
					else:
									SCREAMING_SNAKE_CASE						:							List[str]							=     {0: 'batch', 1: 'sequence'}
					return OrderedDict(
					    [
					        ('input_ids', dynamic_axis),
					        ('attention_mask', dynamic_axis),
					    ]    )





