






import functools
import operator

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


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__				     =			{
    '''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
    # See all SEW-D models at https://huggingface.co/models?filter=sew-d
}






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       Optional[Any]							=							'''sew-d'''

		def __init__(					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    Dict=3_2    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=7_6_8    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=1_2    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=1_2    ,       SCREAMING_SNAKE_CASE__		:    str=3_0_7_2    ,       SCREAMING_SNAKE_CASE__		:    str=2    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=5_1_2    ,       SCREAMING_SNAKE_CASE__		:    List[str]=2_5_6    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]=True    ,       SCREAMING_SNAKE_CASE__		:    Tuple=True    ,       SCREAMING_SNAKE_CASE__		:    List[str]=("p2c", "c2p")    ,       SCREAMING_SNAKE_CASE__		:    str="layer_norm"    ,       SCREAMING_SNAKE_CASE__		:    Tuple="gelu_python"    ,       SCREAMING_SNAKE_CASE__		:    Tuple=0.1    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=0.1    ,       SCREAMING_SNAKE_CASE__		:    Any=0.1    ,       SCREAMING_SNAKE_CASE__		:    Any=0.0    ,       SCREAMING_SNAKE_CASE__		:    str=0.1    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=0.02    ,       SCREAMING_SNAKE_CASE__		:    int=1e-7    ,       SCREAMING_SNAKE_CASE__		:    Any=1e-5    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]="group"    ,       SCREAMING_SNAKE_CASE__		:    Optional[Any]="gelu"    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=(6_4, 1_2_8, 1_2_8, 1_2_8, 1_2_8, 2_5_6, 2_5_6, 2_5_6, 2_5_6, 5_1_2, 5_1_2, 5_1_2, 5_1_2)    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1)    ,       SCREAMING_SNAKE_CASE__		:    str=(1_0, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1)    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=False    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=1_2_8    ,       SCREAMING_SNAKE_CASE__		:    Tuple=1_6    ,       SCREAMING_SNAKE_CASE__		:    Any=True    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=0.05    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=1_0    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=2    ,       SCREAMING_SNAKE_CASE__		:    int=0.0    ,       SCREAMING_SNAKE_CASE__		:    Union[str, Any]=1_0    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]=0    ,       SCREAMING_SNAKE_CASE__		:    Optional[int]="mean"    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=False    ,       SCREAMING_SNAKE_CASE__		:    List[str]=False    ,       SCREAMING_SNAKE_CASE__		:    str=2_5_6    ,       SCREAMING_SNAKE_CASE__		:    str=0    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=1    ,       SCREAMING_SNAKE_CASE__		:    List[Any]=2    ,       **SCREAMING_SNAKE_CASE__		:    Any    ,       ):



					'''simple docstring'''



					super().__init__(**SCREAMING_SNAKE_CASE__    ,       pad_token_id=SCREAMING_SNAKE_CASE__    ,       bos_token_id=SCREAMING_SNAKE_CASE__    ,       eos_token_id=SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     hidden_size
					__a						:  Optional[Any]									=     feat_extract_norm
					__a						:  List[str]									=     feat_extract_activation
					__a						:  Dict									=     list(SCREAMING_SNAKE_CASE__    )
					__a						:  Union[str, Any]									=     list(SCREAMING_SNAKE_CASE__    )
					__a						:  List[str]									=     list(SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     conv_bias
					__a						:  Tuple									=     num_conv_pos_embeddings
					__a						:  List[str]									=     num_conv_pos_embedding_groups
					__a						:  Optional[Any]									=     len(self.conv_dim    )
					__a						:  Union[str, Any]									=     num_hidden_layers
					__a						:  Optional[Any]									=     intermediate_size
					__a						:  Union[str, Any]									=     squeeze_factor
					__a						:  List[Any]									=     max_position_embeddings
					__a						:  Tuple									=     position_buckets
					__a						:  Optional[int]									=     share_att_key
					__a						:  List[str]									=     relative_attention
					__a						:  Any									=     norm_rel_ebd
					__a						:  Any									=     list(SCREAMING_SNAKE_CASE__    )
					__a						:  Union[str, Any]									=     hidden_act
					__a						:  str									=     num_attention_heads
					__a						:  Union[str, Any]									=     hidden_dropout
					__a						:  Optional[int]									=     attention_dropout
					__a						:  List[str]									=     activation_dropout
					__a						:  int									=     feat_proj_dropout
					__a						:  int									=     final_dropout
					__a						:  Dict									=     layer_norm_eps
					__a						:  Tuple									=     feature_layer_norm_eps
					__a						:  str									=     initializer_range
					__a						:  Tuple									=     vocab_size

					if (
					    (len(self.conv_stride    ) != self.num_feat_extract_layers)
					    or (len(self.conv_kernel    ) != self.num_feat_extract_layers)
					    or (len(self.conv_dim    ) != self.num_feat_extract_layers)
					):
								raise ValueError(
								    'Configuration for convolutional layers is incorrect.'
								    'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
								    f'''but is `len(config.conv_dim) = {len(self.conv_dim    )}`, `len(config.conv_stride)'''
								    f'''= {len(self.conv_stride    )}`, `len(config.conv_kernel) = {len(self.conv_kernel    )}`.'''    )

					# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
					__a						:  Tuple									=     apply_spec_augment
					__a						:  Optional[Any]									=     mask_time_prob
					__a						:  Any									=     mask_time_length
					__a						:  List[str]									=     mask_time_min_masks
					__a						:  List[str]									=     mask_feature_prob
					__a						:  Tuple									=     mask_feature_length
					__a						:  Any									=     mask_feature_min_masks

					# ctc loss
					__a						:  Optional[int]									=     ctc_loss_reduction
					__a						:  List[Any]									=     ctc_zero_infinity

					# sequence classification
					__a						:  Dict									=     use_weighted_layer_sum
					__a						:  Optional[Any]									=     classifier_proj_size



		@property
		def    __lowerCAmelCase  (					self		:    Optional[Any]    ):



					'''simple docstring'''



					return functools.reduce(operator.mul    ,       self.conv_stride    ,       1    )












"""simple docstring"""


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))
