





import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Optional[Any]   )   ->			Union[str, Any]:
    return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    List[Any]	,     __UpperCAmelCase:    List[str]="attention"   )   ->			Optional[Any]:
    UpperCamelCase__							:       Tuple      =   np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]   )
    UpperCamelCase__							:       Any      =   k_tmp.reshape(k_tmp.shape[0]	,     k_tmp.shape[1] * k_tmp.shape[2]   )
    UpperCamelCase__							:       List[Any]      =   np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]   )
    UpperCamelCase__							:       str      =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]	,     o_tmp.shape[2]   )
    UpperCamelCase__							:       Dict      =   np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]   )
    UpperCamelCase__							:       Dict      =   q_tmp.reshape(q_tmp.shape[0]	,     q_tmp.shape[1] * q_tmp.shape[2]   )
    UpperCamelCase__							:       List[Any]      =   np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]   )
    UpperCamelCase__							:       Union[str, Any]      =   v_tmp.reshape(v_tmp.shape[0]	,     v_tmp.shape[1] * v_tmp.shape[2]   )
    return k, o, q, v




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Optional[int]	,     __UpperCAmelCase:    Any	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    Optional[Any]=False   )   ->			List[Any]:
    if split_mlp_wi:
        UpperCamelCase__							:       Optional[Any]      =   params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
        UpperCamelCase__							:       List[Any]      =   params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
        UpperCamelCase__							:       Dict      =   (wi_a, wi_a)
    else:
        UpperCamelCase__							:       Dict      =   params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

    UpperCamelCase__							:       List[str]      =   params[f"{prefix}/{prefix}/mlp/wo/kernel"][:, i, :]
    return wi, wo




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Any	,     __UpperCAmelCase:    Tuple   )   ->			List[str]:
    return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict	,     *, __UpperCAmelCase:    int	,     __UpperCAmelCase:    bool	,     __UpperCAmelCase:    bool = False   )   ->			Optional[Any]:
    UpperCamelCase__							:       List[Any]      =   traverse_util.flatten_dict(variables['''target''']   )
    UpperCamelCase__							:       Optional[int]      =   {'''/'''.join(__UpperCAmelCase   ): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    UpperCamelCase__							:       List[str]      =   '''encoder/encoder/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''	,     __UpperCAmelCase   )

    UpperCamelCase__							:       Union[str, Any]      =   collections.OrderedDict()

    # Shared embeddings.
    UpperCamelCase__							:       str      =   old['''token_embedder/embedding''']

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

        # Block i, layer 1 (MLP).
        UpperCamelCase__							:       Dict      =   tax_layer_norm_lookup(__UpperCAmelCase	,     __UpperCAmelCase	,     '''encoder'''	,     '''pre_mlp_layer_norm'''   )
        UpperCamelCase__     ,UpperCamelCase__							:       Tuple      =   tax_mlp_lookup(__UpperCAmelCase	,     __UpperCAmelCase	,     '''encoder'''	,     __UpperCAmelCase   )
        UpperCamelCase__							:       str      =   layer_norm
        if split_mlp_wi:
            UpperCamelCase__							:       Dict      =   wi[0].T
            UpperCamelCase__							:       List[Any]      =   wi[1].T
        else:
            UpperCamelCase__							:       str      =   wi.T
        UpperCamelCase__							:       List[Any]      =   wo.T
        if scalable_attention:
            # convert the rel_embedding of each layer
            UpperCamelCase__							:       str      =   tax_relpos_bias_lookup(
                __UpperCAmelCase	,     __UpperCAmelCase	,     '''encoder'''   ).T

    UpperCamelCase__							:       List[str]      =   old['''encoder/encoder_norm/scale''']

    if not scalable_attention:
        UpperCamelCase__							:       Tuple      =   tax_relpos_bias_lookup(
            __UpperCAmelCase	,     0	,     '''encoder'''   ).T
        UpperCamelCase__							:       Tuple      =   tax_relpos_bias_lookup(
            __UpperCAmelCase	,     0	,     '''decoder'''   ).T

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

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

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

            if scalable_attention:
                # convert the rel_embedding of each layer
                UpperCamelCase__							:       List[Any]      =   tax_relpos_bias_lookup(__UpperCAmelCase	,     __UpperCAmelCase	,     '''decoder'''   ).T

        UpperCamelCase__							:       Dict      =   old['''decoder/decoder_norm/scale''']

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            UpperCamelCase__							:       Any      =   old['''decoder/logits_dense/kernel'''].T

    return new




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Any	,     __UpperCAmelCase:    bool   )   ->			Dict:
    UpperCamelCase__							:       Any      =   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:
        UpperCamelCase__							:       List[Any]      =   state_dict['''shared.weight''']

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

    return state_dict




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    int	,     __UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Any   )   ->			Optional[Any]:
    UpperCamelCase__							:       Dict      =   checkpoints.load_tax_checkpoint(__UpperCAmelCase   )
    UpperCamelCase__							:       str      =   convert_tax_to_pytorch(
        __UpperCAmelCase	,     num_layers=config.num_layers	,     is_encoder_only=__UpperCAmelCase	,     scalable_attention=__UpperCAmelCase   )
    UpperCamelCase__							:       List[Any]      =   make_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase	,     strict=__UpperCAmelCase   )




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Union[str, Any]	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    Tuple	,     __UpperCAmelCase:    bool = False	,     __UpperCAmelCase:    bool = False	,     )   ->			int:
    UpperCamelCase__							:       Optional[Any]      =   MTaConfig.from_json_file(__UpperCAmelCase   )
    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:
        UpperCamelCase__							:       str      =   UMTaEncoderModel(__UpperCAmelCase   )
    else:
        UpperCamelCase__							:       Union[str, Any]      =   UMTaForConditionalGeneration(__UpperCAmelCase   )

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

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

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


if __name__ == "__main__":
  UpperCAmelCase_  							=				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
  )
  parser.add_argument(
      '--scalable_attention',
      action='store_true',
      help='Whether the model uses scaled attention (umt5 model)',
      default=False,
  )
  UpperCAmelCase_  							=				parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path,
      args.config_file,
      args.pytorch_dump_path,
      args.is_encoder_only,
      args.scalable_attention,
  )








import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


UpperCAmelCase_  							=				logging.get_logger(__name__)






class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							Any     	=	"upernet"




   def __init__(					self,	__magic_name__=None,	__magic_name__=512,	__magic_name__=0.02,	__magic_name__=[1, 2, 3, 6],	__magic_name__=True,	__magic_name__=0.4,	__magic_name__=384,	__magic_name__=256,	__magic_name__=1,	__magic_name__=False,	__magic_name__=255,	**__magic_name__,	)				->   Dict:






       """simple docstring"""
       super().__init__(**__magic_name__							)

       if backbone_config is None:
           logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''							)
           UpperCamelCase__							:       str      =   CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']							)
       elif isinstance(__magic_name__,	__magic_name__							):
           UpperCamelCase__							:       Tuple      =   backbone_config.get('''model_type'''							)
           UpperCamelCase__							:       List[str]      =   CONFIG_MAPPING[backbone_model_type]
           UpperCamelCase__							:       Optional[int]      =   config_class.from_dict(__magic_name__							)

       UpperCamelCase__							:       Dict      =   backbone_config
       UpperCamelCase__							:       int      =   hidden_size
       UpperCamelCase__							:       int      =   initializer_range
       UpperCamelCase__							:       Dict      =   pool_scales
       UpperCamelCase__							:       Tuple      =   use_auxiliary_head
       UpperCamelCase__							:       Tuple      =   auxiliary_loss_weight
       UpperCamelCase__							:       Optional[int]      =   auxiliary_in_channels
       UpperCamelCase__							:       Optional[Any]      =   auxiliary_channels
       UpperCamelCase__							:       Any      =   auxiliary_num_convs
       UpperCamelCase__							:       Union[str, Any]      =   auxiliary_concat_input
       UpperCamelCase__							:       List[str]      =   loss_ignore_index




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   copy.deepcopy(self.__dict__							)
       UpperCamelCase__							:       int      =   self.backbone_config.to_dict()
       UpperCamelCase__							:       Any      =   self.__class__.model_type
       return output

