

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_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    List[str]="attention"			):



     '''simple docstring'''





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

def 	snake_case__			(    SCREAMING_SNAKE_CASE_		:    str	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    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__    :   str      =       params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
          lowercase__    :   Optional[int]      =       (wi_a, wi_a)
     else:
          lowercase__    :   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_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Dict	,     SCREAMING_SNAKE_CASE_		:    Tuple			):



     '''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__    :   Dict      =       traverse_util.flatten_dict(variables['target']			)
     lowercase__    :   Dict      =       {'/'.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__    :   Union[str, Any]      =       'encoder/layers_0/mlp/wi_0/kernel' in old
     print('Split MLP:'	,     SCREAMING_SNAKE_CASE_			)

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

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

     # Encoder.
     for i in range(SCREAMING_SNAKE_CASE_			):
          # Block i, layer 0 (Self Attention).
          lowercase__    :   Any      =       tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     'encoder'	,     'pre_attention_layer_norm'			)
          lowercase__      , lowercase__      , lowercase__      , lowercase__    :   str      =       tax_attention_lookup(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     'encoder'	,     'attention'			)
          lowercase__    :   List[str]      =       layer_norm
          lowercase__    :   Optional[Any]      =       k.T
          lowercase__    :   Optional[int]      =       o.T
          lowercase__    :   str      =       q.T
          lowercase__    :   List[Any]      =       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__      , lowercase__    :   int      =       tax_mlp_lookup(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     'encoder'	,     SCREAMING_SNAKE_CASE_			)
          lowercase__    :   Optional[Any]      =       layer_norm
          if split_mlp_wi:
               lowercase__    :   int      =       wi[0].T
               lowercase__    :   List[Any]      =       wi[1].T
          else:
               lowercase__    :   Optional[Any]      =       wi.T
          lowercase__    :   List[Any]      =       wo.T

     lowercase__    :   str      =       old[
         'encoder/relpos_bias/rel_embedding'
     ].T
     lowercase__    :   Optional[Any]      =       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__      , lowercase__      , lowercase__      , lowercase__    :   Union[str, Any]      =       tax_attention_lookup(SCREAMING_SNAKE_CASE_	,     SCREAMING_SNAKE_CASE_	,     'decoder'	,     'self_attention'			)
               lowercase__    :   Tuple      =       layer_norm
               lowercase__    :   Union[str, Any]      =       k.T
               lowercase__    :   str      =       o.T
               lowercase__    :   Tuple      =       q.T
               lowercase__    :   List[str]      =       v.T

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

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

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

     return new

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



     '''simple docstring'''





     lowercase__    :   int      =       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__    :   int      =       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__    :   Union[str, Any]      =       state_dict['shared.weight']

     return state_dict

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



     '''simple docstring'''





     lowercase__    :   Any      =       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__    :   Dict      =       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_		:    Union[str, Any]	,     SCREAMING_SNAKE_CASE_		:    Optional[Any]	,     SCREAMING_SNAKE_CASE_		:    Tuple	,     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__    :   str      =       TaEncoderModel(SCREAMING_SNAKE_CASE_			)
     else:
          lowercase__    :   Optional[int]      =       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 warnings

from ...utils import logging
from .image_processing_dpt import DPTImageProcessor


snake_case_			  = logging.get_logger(__name__)


class       SCREAMING_SNAKE_CASE__					(__snake_case     ):





    def __init__(      self						,   *a						,   **a):
         warnings.warn(
             'The class DPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
             ' use DPTImageProcessor instead.'						,   a						,   )
         super().__init__(*a						,   **a)

