


'''simple docstring'''







from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase    :     Optional[Any]					=						logging.get_logger(__name__)

lowerCAmelCase    :     Any					=						{
    """google/canine-s""": """https://huggingface.co/google/canine-s/resolve/main/config.json""",
    # See all CANINE models at https://huggingface.co/models?filter=canine
}
class 	UpperCamelCase__					( __lowerCamelCase	):



     """simple docstring"""






     __magic_name__    			=    '''canine'''







     def __init__( self      ,	snake_case__=768      ,	snake_case__=12      ,	snake_case__=12      ,	snake_case__=3072      ,	snake_case__="gelu"      ,	snake_case__=0.1      ,	snake_case__=0.1      ,	snake_case__=1_6384      ,	snake_case__=16      ,	snake_case__=0.02      ,	snake_case__=1E-12      ,	snake_case__=0      ,	snake_case__=0xE0_00      ,	snake_case__=0xE0_01      ,	snake_case__=4      ,	snake_case__=4      ,	snake_case__=8      ,	snake_case__=1_6384      ,	snake_case__=128      ,	**snake_case__      ,	):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Dict   			=       max_position_embeddings
            _lowerCAmelCase    :							List[Any]   			=       hidden_size
            _lowerCAmelCase    :							str   			=       num_hidden_layers
            _lowerCAmelCase    :							Optional[int]   			=       num_attention_heads
            _lowerCAmelCase    :							List[str]   			=       intermediate_size
            _lowerCAmelCase    :							Tuple   			=       hidden_act
            _lowerCAmelCase    :							Any   			=       hidden_dropout_prob
            _lowerCAmelCase    :							Any   			=       attention_probs_dropout_prob
            _lowerCAmelCase    :							List[Any]   			=       initializer_range
            _lowerCAmelCase    :							Union[str, Any]   			=       type_vocab_size
            _lowerCAmelCase    :							List[str]   			=       layer_norm_eps

            # Character config:
            _lowerCAmelCase    :							List[Any]   			=       downsampling_rate
            _lowerCAmelCase    :							Union[str, Any]   			=       upsampling_kernel_size
            _lowerCAmelCase    :							Optional[Any]   			=       num_hash_functions
            _lowerCAmelCase    :							str   			=       num_hash_buckets
            _lowerCAmelCase    :							int   			=       local_transformer_stride





'''simple docstring'''







import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


lowerCAmelCase    :     Tuple					=						False
lowerCAmelCase    :     str					=						True
lowerCAmelCase    :     List[Any]					=						False


if __name__ == "__main__":
       lowerCAmelCase    :     Any					=						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    :     Optional[int]					=						parser.parse_args()

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

       lowerCAmelCase    :     Optional[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:
              lowerCAmelCase    :     int					=						reader.read()
              lowerCAmelCase    :     List[str]					=						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    :     str					=						UNetaDModel(**config)
       else:
              lowerCAmelCase    :     Union[str, Any]					=						UNetaDConditionModel if """ldm-text2im-large-256""" in args.repo_path else UNetaDModel
              lowerCAmelCase    :     Dict					=						class_name(**config)

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

       lowerCAmelCase    :     Union[str, Any]					=						dict(model.config)

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

              lowerCAmelCase    :     Optional[int]					=						[k.replace("""UNetRes""", """""") for k in config["""down_block_types"""]]
              lowerCAmelCase    :     Dict					=						[k.replace("""UNetRes""", """""") for k in config["""up_block_types"""]]

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

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

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

