





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def A_      (						A__						, A__						, A__						, A__="attention"							)    ->						str:
       a__		:      str					=							params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
       a__		:      List[Any]					=							params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
       a__		:      Tuple					=							params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
       a__		:      Any					=							params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
       return k, o, q, v



def A_      (						A__						, A__						, A__						, A__=False							)    ->						Tuple:

       if split_mlp_wi:
              a__		:      int					=							params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
              a__		:      Optional[int]					=							params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
              a__		:      int					=							(wi_a, wi_a)
       else:
              a__		:      int					=							params[F'{prefix}/layers_{i}/mlp/wi/kernel']

       a__		:      Optional[Any]					=							params[F'{prefix}/layers_{i}/mlp/wo/kernel']
       return wi, wo



def A_      (						A__						, A__						, A__						, A__							)    ->						Union[str, Any]:
       return params[F'{prefix}/layers_{i}/{layer_name}/scale']



def A_      (						A__						, *, A__						, A__							)    ->						int:
       a__		:      Optional[Any]					=							traverse_util.flatten_dict(variables['target']							)
       a__		:      str					=							{"/".join(lowercase_							): v for k, v in old.items()}

       # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
       a__		:      int					=							"encoder/layers_0/mlp/wi_0/kernel" in old
       print('Split MLP:'						, lowercase_							)

       a__		:      Optional[int]					=							collections.OrderedDict()

       # Shared embeddings.
       a__		:      Any					=							old["token_embedder/embedding"]

       # Encoder.
       for i in range(lowercase_							):
              # Block i, layer 0 (Self Attention).
              a__		:      str					=							tax_layer_norm_lookup(lowercase_						, lowercase_						, 'encoder'						, 'pre_attention_layer_norm'							)
              a__		:      List[str]					=							tax_attention_lookup(lowercase_						, lowercase_						, 'encoder'						, 'attention'							)
              a__		:      Union[str, Any]					=							layer_norm
              a__		:      Tuple					=							k.T
              a__		:      Any					=							o.T
              a__		:      Dict					=							q.T
              a__		:      List[Any]					=							v.T

              # Block i, layer 1 (MLP).
              a__		:      Any					=							tax_layer_norm_lookup(lowercase_						, lowercase_						, 'encoder'						, 'pre_mlp_layer_norm'							)
              a__		:      str					=							tax_mlp_lookup(lowercase_						, lowercase_						, 'encoder'						, lowercase_							)
              a__		:      List[Any]					=							layer_norm
              if split_mlp_wi:
                     a__		:      Optional[Any]					=							wi[0].T
                     a__		:      str					=							wi[1].T
              else:
                     a__		:      Optional[int]					=							wi.T
              a__		:      Tuple					=							wo.T

       a__		:      Tuple					=							old[
           "encoder/relpos_bias/rel_embedding"
       ].T
       a__		:      Tuple					=							old["encoder/encoder_norm/scale"]

       if not is_encoder_only:
              # Decoder.
              for i in range(lowercase_							):
                     # Block i, layer 0 (Self Attention).
                     a__		:      Dict					=							tax_layer_norm_lookup(lowercase_						, lowercase_						, 'decoder'						, 'pre_self_attention_layer_norm'							)
                     a__		:      Tuple					=							tax_attention_lookup(lowercase_						, lowercase_						, 'decoder'						, 'self_attention'							)
                     a__		:      List[Any]					=							layer_norm
                     a__		:      str					=							k.T
                     a__		:      Tuple					=							o.T
                     a__		:      Optional[Any]					=							q.T
                     a__		:      List[str]					=							v.T

                     # Block i, layer 1 (Cross Attention).
                     a__		:      List[str]					=							tax_layer_norm_lookup(lowercase_						, lowercase_						, 'decoder'						, 'pre_cross_attention_layer_norm'							)
                     a__		:      Tuple					=							tax_attention_lookup(lowercase_						, lowercase_						, 'decoder'						, 'encoder_decoder_attention'							)
                     a__		:      str					=							layer_norm
                     a__		:      Any					=							k.T
                     a__		:      str					=							o.T
                     a__		:      List[str]					=							q.T
                     a__		:      Union[str, Any]					=							v.T

                     # Block i, layer 2 (MLP).
                     a__		:      str					=							tax_layer_norm_lookup(lowercase_						, lowercase_						, 'decoder'						, 'pre_mlp_layer_norm'							)
                     a__		:      int					=							tax_mlp_lookup(lowercase_						, lowercase_						, 'decoder'						, lowercase_							)
                     a__		:      Dict					=							layer_norm
                     if split_mlp_wi:
                            a__		:      Tuple					=							wi[0].T
                            a__		:      List[Any]					=							wi[1].T
                     else:
                            a__		:      int					=							wi.T
                     a__		:      str					=							wo.T

              a__		:      Optional[int]					=							old["decoder/decoder_norm/scale"]
              a__		:      Tuple					=							old[
                  "decoder/relpos_bias/rel_embedding"
              ].T

              # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
              if "decoder/logits_dense/kernel" in old:
                     a__		:      str					=							old["decoder/logits_dense/kernel"].T

       return new



def A_      (						A__						, A__							)    ->						Union[str, Any]:
       a__		:      List[str]					=							collections.OrderedDict([(k, torch.from_numpy(v.copy()							)) for (k, v) in converted_params.items()]							)

       # Add what is missing.
       if "encoder.embed_tokens.weight" not in state_dict:
              a__		:      Union[str, Any]					=							state_dict["shared.weight"]

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     a__		:      Optional[int]					=							state_dict["shared.weight"]

              if "lm_head.weight" not in state_dict:  # For old 1.0 models.
                     print('Using shared word embeddings as lm_head.'							)
                     a__		:      List[str]					=							state_dict["shared.weight"]

       return state_dict



def A_      (						A__						, A__						, A__						, A__							)    ->						List[str]:
       a__		:      Dict					=							checkpoints.load_tax_checkpoint(lowercase_							)
       a__		:      List[str]					=							convert_tax_to_pytorch(lowercase_						, num_layers=config.num_layers						, is_encoder_only=lowercase_							)
       a__		:      Optional[int]					=							make_state_dict(lowercase_						, lowercase_							)
       model.load_state_dict(lowercase_						, strict=lowercase_							)



def A_      (						A__						, A__						, A__						, A__ = False							)    ->						List[str]:
       a__		:      str					=							TaConfig.from_json_file(lowercase_							)
       print(F'Building PyTorch model from configuration: {config}'							)
       # Non-v1.1 checkpoints could also use T5Model, but this works for all.
       # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
       if is_encoder_only:
              a__		:      Optional[int]					=							TaEncoderModel(lowercase_							)
       else:
              a__		:      List[Any]					=							TaForConditionalGeneration(lowercase_							)

       # Load weights from tf checkpoint
       load_tax_weights_in_ta(lowercase_						, lowercase_						, lowercase_						, lowercase_							)

       # Save pytorch-model
       print(F'Save PyTorch model to {pytorch_dump_path}'							)
       model.save_pretrained(lowercase_							)

       # Verify that we can load the checkpoint.
       model.from_pretrained(lowercase_							)
       print('Done'							)


if __name__ == "__main__":
    lowercase :			Tuple     		=    argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
    # Required parameters
    parser.add_argument(
        """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
    )
    parser.add_argument(
        """--config_file""",
        default=None,
        type=str,
        required=True,
        help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
    )
    parser.add_argument(
        """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
    )
    lowercase :			Tuple     		=    parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )









"""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))
