


"""simple docstring"""







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 TYPE_CHECKING, Any, List, Mapping, Optional, Union

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


if TYPE_CHECKING:
 from ...onnx.config import PatchingSpec
 from ...tokenization_utils_base import PreTrainedTokenizerBase


__lowercase    =				logging.get_logger(__name__)

__lowercase    =				{
    """allenai/longformer-base-4096""": """https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096""": """https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json""",
    """allenai/longformer-large-4096-finetuned-triviaqa""": (
        """https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json"""
    ),
    """allenai/longformer-base-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
    """allenai/longformer-large-4096-extra.pos.embd.only""": (
        """https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json"""
    ),
}






class 						_A  (  _a  ):







    """simple docstring"""




    UpperCAmelCase	:							Optional[Any]     =	"""longformer"""






    def __init__( self    :   List[Any]    ,			__UpperCAmelCase    :   Union[List[int], int] = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 1    ,			__UpperCAmelCase    :   int = 0    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   int = 30522    ,			__UpperCAmelCase    :   int = 768    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 12    ,			__UpperCAmelCase    :   int = 3072    ,			__UpperCAmelCase    :   str = "gelu"    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   float = 0.1    ,			__UpperCAmelCase    :   int = 512    ,			__UpperCAmelCase    :   int = 2    ,			__UpperCAmelCase    :   float = 0.02    ,			__UpperCAmelCase    :   float = 1e-12    ,			__UpperCAmelCase    :   bool = False    ,			**__UpperCAmelCase    :   int    ,			):
      super().__init__(pad_token_id=__UpperCAmelCase    ,			**__UpperCAmelCase)

      a						:		int											=      attention_window
      a						:		List[str]											=      sep_token_id
      a						:		List[Any]											=      bos_token_id
      a						:		int											=      eos_token_id
      a						:		Union[str, Any]											=      vocab_size
      a						:		Dict											=      hidden_size
      a						:		int											=      num_hidden_layers
      a						:		int											=      num_attention_heads
      a						:		Optional[int]											=      hidden_act
      a						:		Union[str, Any]											=      intermediate_size
      a						:		Tuple											=      hidden_dropout_prob
      a						:		Union[str, Any]											=      attention_probs_dropout_prob
      a						:		str											=      max_position_embeddings
      a						:		Dict											=      type_vocab_size
      a						:		Optional[Any]											=      initializer_range
      a						:		List[Any]											=      layer_norm_eps
      a						:		List[str]											=      onnx_export






class 						_A  (  _a  ):







    """simple docstring"""




    def __init__( self    :   Tuple    ,			__UpperCAmelCase    :   "PretrainedConfig"    ,			__UpperCAmelCase    :   str = "default"    ,			__UpperCAmelCase    :   "List[PatchingSpec]" = None):
      super().__init__(__UpperCAmelCase    ,			__UpperCAmelCase    ,			__UpperCAmelCase)
      a						:		Optional[Any]											=      True






    @property
    def      __snake_case							( self    :   int):
      if self.task == "multiple-choice":
        a						:		List[Any]											=      {0: "batch", 1: "choice", 2: "sequence"}
      else:
        a						:		int											=      {0: "batch", 1: "sequence"}
      return OrderedDict(
          [
              ("input_ids", dynamic_axis),
              ("attention_mask", dynamic_axis),
              ("global_attention_mask", dynamic_axis),
          ])






    @property
    def      __snake_case							( self    :   Union[str, Any]):
      a						:		str											=      super().outputs
      if self.task == "default":
        a						:		Tuple											=      {0: "batch"}
      return outputs






    @property
    def      __snake_case							( self    :   Optional[int]):
      return 1e-4






    @property
    def      __snake_case							( self    :   str):
      # needs to be >= 14 to support tril operator
      return max(super().default_onnx_opset    ,			14)






    def      __snake_case							( self    :   List[str]    ,			__UpperCAmelCase    :   "PreTrainedTokenizerBase"    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   int = -1    ,			__UpperCAmelCase    :   bool = False    ,			__UpperCAmelCase    :   Optional[TensorType] = None    ,			):
      a						:		str											=      super().generate_dummy_inputs(
          preprocessor=__UpperCAmelCase    ,			batch_size=__UpperCAmelCase    ,			seq_length=__UpperCAmelCase    ,			is_pair=__UpperCAmelCase    ,			framework=__UpperCAmelCase)
      import torch

      # for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
      # makes the export fail randomly
      a						:		Union[str, Any]											=      torch.zeros_like(inputs["input_ids"])
      # make every second token global
      a						:		Dict											=      1

      return inputs






