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__  (			lowercase:     Any,		lowercase:     int,		lowercase:     List[str],		lowercase:     List[Any]="attention"					)				->		Dict:
       A						:     Any         =params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
       A						:     List[str]         =params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
       A						:     str         =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__  (			lowercase:     Dict,		lowercase:     Dict,		lowercase:     Any,		lowercase:     Union[str, Any]=False					)				->		Tuple:
       if split_mlp_wi:
              A						:     Tuple         =params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
              A						:     Union[str, Any]         =params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
              A						:     Optional[int]         =(wi_a, wi_a)
       else:
              A						:     Optional[int]         =params[F'{prefix}/layers_{i}/mlp/wi/kernel']

       A						:     Tuple         =params[F'{prefix}/layers_{i}/mlp/wo/kernel']
       return wi, wo



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



def       A__  (			lowercase:     dict,		*, lowercase:     int,		lowercase:     bool					)				->		List[Any]:
       A						:     Optional[int]         =traverse_util.flatten_dict(variables['target']					)
       A						:     List[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						:     List[str]         ='encoder/layers_0/mlp/wi_0/kernel' in old
       print('Split MLP:',		lowercase					)

       A						:     List[str]         =collections.OrderedDict()

       # Shared embeddings.
       A						:     Union[str, 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						:     Optional[Any]         =tax_attention_lookup(lowercase,		lowercase,		'encoder',		'attention'					)
              A						:     Any         =layer_norm
              A						:     Tuple         =k.T
              A						:     Tuple         =o.T
              A						:     int         =q.T
              A						:     Any         =v.T

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

       A						:     List[str]         =old[
           'encoder/relpos_bias/rel_embedding'
       ].T
       A						:     Optional[int]         =old['encoder/encoder_norm/scale']

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

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

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

              A						:     Optional[int]         =old['decoder/decoder_norm/scale']
              A						:     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:
                     A						:     Optional[Any]         =old['decoder/logits_dense/kernel'].T

       return new



def       A__  (			lowercase:     Union[str, Any],		lowercase:     bool					)				->		int:
       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						:     List[Any]         =state_dict['shared.weight']

       if not is_encoder_only:
              if "decoder.embed_tokens.weight" not in state_dict:
                     A						:     Dict         =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__  (			lowercase:     Any,		lowercase:     Optional[int],		lowercase:     List[Any],		lowercase:     Tuple					)				->		Optional[int]:
       A						:     str         =checkpoints.load_tax_checkpoint(lowercase					)
       A						:     Union[str, Any]         =convert_tax_to_pytorch(lowercase,		num_layers=config.num_layers,		is_encoder_only=lowercase					)
       A						:     List[Any]         =make_state_dict(lowercase,		lowercase					)
       model.load_state_dict(lowercase,		strict=lowercase					)



def       A__  (			lowercase:     Union[str, Any],		lowercase:     Optional[int],		lowercase:     List[Any],		lowercase:     bool = False					)				->		List[Any]:
       A						:     List[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[Any]         =TaEncoderModel(lowercase					)
       else:
              A						:     Optional[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							:      Any      							=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							:      Dict      							=parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )







import inspect
import warnings
from typing import Any, Dict, Optional, Union

from packaging import version



def A__  (			*lowercase:     Tuple,		lowercase:     Optional[Union[Dict, Any]] = None,		lowercase:     Dict=True,		lowercase:     Any=2					)				->		List[Any]:
       from .. import __version__

       A						:     Optional[Any]         =take_from
       A						:     Union[str, Any]         =()
       if not isinstance(args[0],		lowercase					):
              A						:     List[str]         =(args,)

       for attribute, version_name, message in args:
              if version.parse(version.parse(lowercase					).base_version					) >= version.parse(lowercase					):
                     raise ValueError(
                         F'The deprecation tuple {(attribute, version_name, message)} should be removed since diffusers\''
                         F' version {__version__} is >= {version_name}'					)

              A						:     Tuple         =None
              if isinstance(lowercase,		lowercase					) and attribute in deprecated_kwargs:
                     values += (deprecated_kwargs.pop(lowercase					),)
                     A						:     Union[str, Any]         =F'The `{attribute}` argument is deprecated and will be removed in version {version_name}.'
              elif hasattr(lowercase,		lowercase					):
                     values += (getattr(lowercase,		lowercase					),)
                     A						:     Optional[Any]         =F'The `{attribute}` attribute is deprecated and will be removed in version {version_name}.'
              elif deprecated_kwargs is None:
                     A						:     List[Any]         =F'`{attribute}` is deprecated and will be removed in version {version_name}.'

              if warning is not None:
                     A						:     List[Any]         =warning + ' ' if standard_warn else ''
                     warnings.warn(warning + message,		lowercase,		stacklevel=lowercase					)

       if isinstance(lowercase,		lowercase					) and len(lowercase					) > 0:
              A						:     Any         =inspect.getouterframes(inspect.currentframe()					)[1]
              A						:     int         =call_frame.filename
              A						:     int         =call_frame.lineno
              A						:     Optional[int]         =call_frame.function
              A  ,  A						:     int         =next(iter(deprecated_kwargs.items()					)					)
              raise TypeError(F'{function} in {filename} line {line_number-1} got an unexpected keyword argument `{key}`'					)

       if len(lowercase					) == 0:
              return
       elif len(lowercase					) == 1:
              return values[0]
       return values






