

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      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[Any]     ,					SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]="attention"      ):



     '''simple docstring'''





     lowercase__   :       str				=	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
     lowercase__   :       Dict				=	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
     lowercase__   :       Any				=	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
     lowercase__   :       Tuple				=	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
     return k, o, q, v

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]=False      ):



     '''simple docstring'''





     if split_mlp_wi:
          lowercase__   :       Optional[int]				=	params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
          lowercase__   :       Optional[int]				=	params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
          lowercase__   :       str				=	(wi_a, wi_a)
     else:
          lowercase__   :       Union[str, Any]				=	params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

     lowercase__   :       Union[str, Any]				=	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
     return wi, wo

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Optional[Any]     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]      ):



     '''simple docstring'''





     return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   dict     ,					*, SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   bool      ):



     '''simple docstring'''





     lowercase__   :       str				=	traverse_util.flatten_dict(variables['target']      )
     lowercase__   :       List[str]				=	{'/'.join(SCREAMING_SNAKE_CASE_      ): v for k, v in old.items()}

     # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
     lowercase__   :       Optional[Any]				=	'encoder/layers_0/mlp/wi_0/kernel' in old
     print('Split MLP:'     ,					SCREAMING_SNAKE_CASE_      )

     lowercase__   :       Optional[Any]				=	collections.OrderedDict()

     # Shared embeddings.
     lowercase__   :       Dict				=	old['token_embedder/embedding']

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

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

     lowercase__   :       Tuple				=	old[
         'encoder/relpos_bias/rel_embedding'
     ].T
     lowercase__   :       List[str]				=	old['encoder/encoder_norm/scale']

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

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

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

          lowercase__   :       str				=	old['decoder/decoder_norm/scale']
          lowercase__   :       List[str]				=	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:
               lowercase__   :       Any				=	old['decoder/logits_dense/kernel'].T

     return new

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Dict     ,					SCREAMING_SNAKE_CASE_		:   bool      ):



     '''simple docstring'''





     lowercase__   :       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:
          lowercase__   :       Any				=	state_dict['shared.weight']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               lowercase__   :       Tuple				=	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.'      )
               lowercase__   :       List[str]				=	state_dict['shared.weight']

     return state_dict

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   List[str]     ,					SCREAMING_SNAKE_CASE_		:   Tuple     ,					SCREAMING_SNAKE_CASE_		:   Any     ,					SCREAMING_SNAKE_CASE_		:   Union[str, Any]      ):



     '''simple docstring'''





     lowercase__   :       int				=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_      )
     lowercase__   :       Optional[Any]				=	convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_     ,					num_layers=config.num_layers     ,					is_encoder_only=SCREAMING_SNAKE_CASE_      )
     lowercase__   :       List[str]				=	make_state_dict(SCREAMING_SNAKE_CASE_     ,					SCREAMING_SNAKE_CASE_      )
     model.load_state_dict(SCREAMING_SNAKE_CASE_     ,					strict=SCREAMING_SNAKE_CASE_      )

def      snake_case__      (   SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   int     ,					SCREAMING_SNAKE_CASE_		:   Optional[int]     ,					SCREAMING_SNAKE_CASE_		:   bool = False      ):



     '''simple docstring'''





     lowercase__   :       Any				=	TaConfig.from_json_file(SCREAMING_SNAKE_CASE_      )
     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:
          lowercase__   :       Any				=	TaEncoderModel(SCREAMING_SNAKE_CASE_      )
     else:
          lowercase__   :       Dict				=	TaForConditionalGeneration(SCREAMING_SNAKE_CASE_      )

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

     # Save pytorch-model
     print(f"""Save PyTorch model to {pytorch_dump_path}"""      )
     model.save_pretrained(SCREAMING_SNAKE_CASE_      )

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


if __name__ == "__main__":
       snake_case_        =			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
       )
       snake_case_        =			parser.parse_args()
       convert_tax_checkpoint_to_pytorch(
           args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
       )




import argparse
import json
import os

import torch
from transformers.file_utils import has_file

from diffusers import UNetaDConditionModel, UNetaDModel


snake_case_        =			False
snake_case_        =			True
snake_case_        =			False


if __name__ == "__main__":
       snake_case_        =			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.''')

       snake_case_        =			parser.parse_args()

       snake_case_        =			{
           '''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''',
       }

       snake_case_        =			{
           '''time_steps''': '''time_proj''',
           '''mid''': '''mid_block''',
           '''downsample_blocks''': '''down_blocks''',
           '''upsample_blocks''': '''up_blocks''',
       }

       snake_case_        =			'''''' 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:
              snake_case_        =			reader.read()
              snake_case_        =			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'''):
              snake_case_        =			UNetaDModel(**config)
       else:
              snake_case_        =			UNetaDConditionModel if '''ldm-text2im-large-256''' in args.repo_path else UNetaDModel
              snake_case_        =			class_name(**config)

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

       snake_case_        =			dict(model.config)

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

              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''down_block_types''']]
              snake_case_        =			[k.replace('''UNetRes''', '''''') for k in config['''up_block_types''']]

       if do_only_weights:
              snake_case_        =			torch.load(os.path.join(args.repo_path, subfolder, '''diffusion_pytorch_model.bin'''))

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

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

