


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					Any			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					Any="attention"				):
     __UpperCamelCase    							=__UpperCamelCase    							=np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]				)
     __UpperCamelCase    							=k_tmp.reshape(k_tmp.shape[0]			,     k_tmp.shape[1] * k_tmp.shape[2]				)
     __UpperCamelCase    							=np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]				)
     __UpperCamelCase    							=o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]			,     o_tmp.shape[2]				)
     __UpperCamelCase    							=np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]				)
     __UpperCamelCase    							=q_tmp.reshape(q_tmp.shape[0]			,     q_tmp.shape[1] * q_tmp.shape[2]				)
     __UpperCamelCase    							=np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]				)
     __UpperCamelCase    							=v_tmp.reshape(v_tmp.shape[0]			,     v_tmp.shape[1] * v_tmp.shape[2]				)
     return k, o, q, v

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]=False				):
     if split_mlp_wi:
          __UpperCamelCase    							=params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
          __UpperCamelCase    							=params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
          __UpperCamelCase    							=(wi_a, wi_a)
     else:
          __UpperCamelCase    							=params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]				):
     return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict			,     *, SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					bool			,     SCREAMING_SNAKE_CASE__			:					bool = False				):
     __UpperCamelCase    							=traverse_util.flatten_dict(variables['target']				)
     __UpperCamelCase    							={'/'.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
     __UpperCamelCase    							='encoder/encoder/mlp/wi_0/kernel' in old
     print('Split MLP:'			,     SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=collections.OrderedDict()

     # Shared embeddings.
     __UpperCamelCase    							=old['token_embedder/embedding']

     # Encoder.
     for i in range(SCREAMING_SNAKE_CASE__				):
          # Block i, layer 0 (Self Attention).
          __UpperCamelCase    							=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'			,     'pre_attention_layer_norm'				)
          __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=tax_attention_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'			,     'attention'				)
          __UpperCamelCase    							=layer_norm
          __UpperCamelCase    							=k.T
          __UpperCamelCase    							=o.T
          __UpperCamelCase    							=q.T
          __UpperCamelCase    							=v.T

          # Block i, layer 1 (MLP).
          __UpperCamelCase    							=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'			,     'pre_mlp_layer_norm'				)
          __UpperCamelCase							,      __UpperCamelCase    							=tax_mlp_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'			,     SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=layer_norm
          if split_mlp_wi:
               __UpperCamelCase    							=wi[0].T
               __UpperCamelCase    							=wi[1].T
          else:
               __UpperCamelCase    							=wi.T
          __UpperCamelCase    							=wo.T
          if scalable_attention:
               # convert the rel_embedding of each layer
               __UpperCamelCase    							=tax_relpos_bias_lookup(
                   SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'				).T

     __UpperCamelCase    							=old['encoder/encoder_norm/scale']

     if not scalable_attention:
          __UpperCamelCase    							=tax_relpos_bias_lookup(
              SCREAMING_SNAKE_CASE__			,     0			,     'encoder'				).T
          __UpperCamelCase    							=tax_relpos_bias_lookup(
              SCREAMING_SNAKE_CASE__			,     0			,     'decoder'				).T

     if not is_encoder_only:
          # Decoder.
          for i in range(SCREAMING_SNAKE_CASE__				):
               # Block i, layer 0 (Self Attention).
               __UpperCamelCase    							=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'pre_self_attention_layer_norm'				)
               __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=tax_attention_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'self_attention'				)
               __UpperCamelCase    							=layer_norm
               __UpperCamelCase    							=k.T
               __UpperCamelCase    							=o.T
               __UpperCamelCase    							=q.T
               __UpperCamelCase    							=v.T

               # Block i, layer 1 (Cross Attention).
               __UpperCamelCase    							=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'pre_cross_attention_layer_norm'				)
               __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=tax_attention_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'encoder_decoder_attention'				)
               __UpperCamelCase    							=layer_norm
               __UpperCamelCase    							=k.T
               __UpperCamelCase    							=o.T
               __UpperCamelCase    							=q.T
               __UpperCamelCase    							=v.T

               # Block i, layer 2 (MLP).
               __UpperCamelCase    							=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'pre_mlp_layer_norm'				)
               __UpperCamelCase							,      __UpperCamelCase    							=tax_mlp_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     SCREAMING_SNAKE_CASE__				)
               __UpperCamelCase    							=layer_norm
               if split_mlp_wi:
                    __UpperCamelCase    							=wi[0].T
                    __UpperCamelCase    							=wi[1].T
               else:
                    __UpperCamelCase    							=wi.T
               __UpperCamelCase    							=wo.T

               if scalable_attention:
                    # convert the rel_embedding of each layer
                    __UpperCamelCase    							=tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'				).T

          __UpperCamelCase    							=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    							=old['decoder/logits_dense/kernel'].T

     return new

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					bool				):
     __UpperCamelCase    							=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    							=state_dict['shared.weight']

     if not is_encoder_only:
          if "decoder.embed_tokens.weight" not in state_dict:
               __UpperCamelCase    							=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    							=state_dict['shared.weight']

     return state_dict

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     __UpperCamelCase    							=checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=convert_tax_to_pytorch(
         SCREAMING_SNAKE_CASE__			,     num_layers=config.num_layers			,     is_encoder_only=SCREAMING_SNAKE_CASE__			,     scalable_attention=SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=make_state_dict(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     model.load_state_dict(SCREAMING_SNAKE_CASE__			,     strict=SCREAMING_SNAKE_CASE__				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					bool = False			,     SCREAMING_SNAKE_CASE__			:					bool = False			,     ):
     __UpperCamelCase    							=MTaConfig.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:
          __UpperCamelCase    							=UMTaEncoderModel(SCREAMING_SNAKE_CASE__				)
     else:
          __UpperCamelCase    							=UMTaForConditionalGeneration(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__			,     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__":
   _A		       =						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,
   )
   _A		       =						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,
   )









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'SEW_PRETRAINED_MODEL_ARCHIVE_LIST',
       'SEWForCTC',
       'SEWForSequenceClassification',
       'SEWModel',
       'SEWPreTrainedModel',
   ]

if TYPE_CHECKING:
   from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_sew import (
          SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
          SEWForCTC,
          SEWForSequenceClassification,
          SEWModel,
          SEWPreTrainedModel,
      )


else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





