
"""simple docstring"""

import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


UpperCamelCase			:       Any									=				False
UpperCamelCase			:       Union[str, Any]									=				True
UpperCamelCase			:       int									=				False


if __name__ == "__main__":
 UpperCamelCase			:       int									=				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.")

 UpperCamelCase			:       str									=				parser.parse_args()

 UpperCamelCase			:       str									=				{
     "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",
 }

 UpperCamelCase			:       Union[str, Any]									=				{
     "time_steps": "time_proj",
     "mid": "mid_block",
     "downsample_blocks": "down_blocks",
     "upsample_blocks": "up_blocks",
 }

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

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

 UpperCamelCase			:       List[Any]									=				dict(model.config)

 if do_only_renaming:
  for key, value in config_parameters_to_change.items():
   if key in config:
    UpperCamelCase			:       List[Any]									=				config[key]
    del config[key]

  UpperCamelCase			:       int									=				[k.replace("UNetRes", "") for k in config["down_block_types"]]
  UpperCamelCase			:       str									=				[k.replace("UNetRes", "") for k in config["up_block_types"]]

 if do_only_weights:
  UpperCamelCase			:       Union[str, Any]									=				torch.load(os.path.join(args.repo_path, subfolder, "diffusion_pytorch_model.bin"))

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

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


UpperCamelCase			:       Optional[int]									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"gptj"
     lowercase		     =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(			self			,  __UpperCAmelCase=5_0400			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=4096			,  __UpperCAmelCase=28			,  __UpperCAmelCase=16			,  __UpperCAmelCase=64			,  __UpperCAmelCase=None			,  __UpperCAmelCase="gelu_new"			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=True			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= vocab_size
      __UpperCamelCase       						= n_positions
      __UpperCamelCase       						= n_embd
      __UpperCamelCase       						= n_layer
      __UpperCamelCase       						= n_head
      __UpperCamelCase       						= n_inner
      __UpperCamelCase       						= rotary_dim
      __UpperCamelCase       						= activation_function
      __UpperCamelCase       						= resid_pdrop
      __UpperCamelCase       						= embd_pdrop
      __UpperCamelCase       						= attn_pdrop
      __UpperCamelCase       						= layer_norm_epsilon
      __UpperCamelCase       						= initializer_range
      __UpperCamelCase       						= use_cache

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id

      super().__init__(
          bos_token_id=__UpperCAmelCase			,  eos_token_id=__UpperCAmelCase			,  tie_word_embeddings=__UpperCAmelCase			,  **__UpperCAmelCase   )


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "default"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




      super().__init__(__UpperCAmelCase			,  task=__UpperCAmelCase			,  patching_specs=__UpperCAmelCase			,  use_past=__UpperCAmelCase   )
      if not getattr(self._config			,  'pad_token_id'			,  __UpperCAmelCase   ):
       # TODO: how to do that better?
       __UpperCamelCase       						= 0





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      __UpperCamelCase       						= OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}   )
      if self.use_past:
       self.fill_with_past_key_values_(__UpperCAmelCase			,  direction='inputs'   )
       __UpperCamelCase       						= {0: 'batch', 1: 'past_sequence + sequence'}
      else:
       __UpperCamelCase       						= {0: 'batch', 1: 'sequence'}

      return common_inputs





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_head





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = False			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= super(__UpperCAmelCase			,  self   ).generate_dummy_inputs(
          __UpperCAmelCase			,  batch_size=__UpperCAmelCase			,  seq_length=__UpperCAmelCase			,  is_pair=__UpperCAmelCase			,  framework=__UpperCAmelCase   )

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase       						= OrderedDict({'input_ids': common_inputs['input_ids']}   )

      # Need to add the past_keys
      if self.use_past:
       if not is_torch_available():
        raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'   )
       else:
        import torch

        __UpperCamelCase						,			__UpperCamelCase       						= common_inputs['input_ids'].shape
        # Not using the same length for past_key_values
        __UpperCamelCase       						= seqlen + 2
        __UpperCamelCase       						= (
            batch,
            self.num_attention_heads,
            past_key_values_length,
            self._config.hidden_size // self.num_attention_heads,
        )
        __UpperCamelCase       						= [
            (torch.zeros(__UpperCAmelCase   ), torch.zeros(__UpperCAmelCase   )) for _ in range(self.num_layers   )
        ]

      __UpperCamelCase       						= common_inputs['attention_mask']
      if self.use_past:
       __UpperCamelCase       						= ordered_inputs['attention_mask'].dtype
       __UpperCamelCase       						= torch.cat(
           [ordered_inputs['attention_mask'], torch.ones(__UpperCAmelCase			,  __UpperCAmelCase			,  dtype=__UpperCAmelCase   )]			,  dim=1   )

      return ordered_inputs







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 13



