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    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     int	):
 return params[F"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]





def    __lowercase     (						lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Any  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Any="attention"	):
 UpperCamelCase_					:						List[Any] =					np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]	)
 UpperCamelCase_					:						Union[str, Any] =					k_tmp.reshape(k_tmp.shape[0]  ,  k_tmp.shape[1] * k_tmp.shape[2]	)
 UpperCamelCase_					:						Union[str, Any] =					np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]	)
 UpperCamelCase_					:						Dict =					o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]  ,  o_tmp.shape[2]	)
 UpperCamelCase_					:						int =					np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]	)
 UpperCamelCase_					:						str =					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_					:						Any =					v_tmp.reshape(v_tmp.shape[0]  ,  v_tmp.shape[1] * v_tmp.shape[2]	)
 return k, o, q, v





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     List[Any]  ,  lowerCamelCase					:     int  ,  lowerCamelCase					:     Union[str, Any]=False	):
 if split_mlp_wi:
  UpperCamelCase_					:						Dict =					params[F"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
  UpperCamelCase_					:						List[Any] =					params[F"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
  UpperCamelCase_					:						List[str] =					(wi_a, wi_a)
 else:
  UpperCamelCase_					:						int =					params[F"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

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





def    __lowercase     (						lowerCamelCase					:     str  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     List[Any]	):
 return params[F"{prefix}/{prefix}/{layer_name}/scale"][:, i]





def    __lowercase     (						lowerCamelCase					:     dict  ,  *, lowerCamelCase					:     int  ,  lowerCamelCase					:     bool  ,  lowerCamelCase					:     bool = False	):
 UpperCamelCase_					:						Tuple =					traverse_util.flatten_dict(variables['target']	)
 UpperCamelCase_					:						List[str] =					{'/'.join(lowerCamelCase	): v for k, v in old.items()}

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

 UpperCamelCase_					:						str =					collections.OrderedDict()

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

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

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

 UpperCamelCase_					:						int =					old['encoder/encoder_norm/scale']

 if not scalable_attention:
  UpperCamelCase_					:						Any =					tax_relpos_bias_lookup(
      lowerCamelCase  ,  0  ,  'encoder'	).T
  UpperCamelCase_					:						str =					tax_relpos_bias_lookup(
      lowerCamelCase  ,  0  ,  'decoder'	).T

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

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

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

   if scalable_attention:
    # convert the rel_embedding of each layer
    UpperCamelCase_					:						Tuple =					tax_relpos_bias_lookup(lowerCamelCase  ,  lowerCamelCase  ,  'decoder'	).T

  UpperCamelCase_					:						Optional[Any] =					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_					:						int =					old['decoder/logits_dense/kernel'].T

 return new





def    __lowercase     (						lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     bool	):
 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_					:						str =					state_dict['shared.weight']

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   UpperCamelCase_					:						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.'	)
   UpperCamelCase_					:						Union[str, Any] =					state_dict['shared.weight']

 return state_dict





def    __lowercase     (						lowerCamelCase					:     int  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     List[str]  ,  lowerCamelCase					:     Tuple  ,  lowerCamelCase					:     int	):
 UpperCamelCase_					:						Optional[Any] =					checkpoints.load_tax_checkpoint(lowerCamelCase	)
 UpperCamelCase_					:						List[Any] =					convert_tax_to_pytorch(
     lowerCamelCase  ,  num_layers=config.num_layers  ,  is_encoder_only=lowerCamelCase  ,  scalable_attention=lowerCamelCase	)
 UpperCamelCase_					:						str =					make_state_dict(lowerCamelCase  ,  lowerCamelCase	)
 model.load_state_dict(lowerCamelCase  ,  strict=lowerCamelCase	)





def    __lowercase     (						lowerCamelCase					:     Any  ,  lowerCamelCase					:     Optional[int]  ,  lowerCamelCase					:     str  ,  lowerCamelCase					:     bool = False  ,  lowerCamelCase					:     bool = False  ,  ):
 UpperCamelCase_					:						List[Any] =					MTaConfig.from_json_file(lowerCamelCase	)
 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_					:						Optional[Any] =					UMTaEncoderModel(lowerCamelCase	)
 else:
  UpperCamelCase_					:						List[Any] =					UMTaForConditionalGeneration(lowerCamelCase	)

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

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

 # Verify that we can load the checkpoint.
 model.from_pretrained(lowerCamelCase	)
 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 __future__ import annotations





def    __lowercase     (						lowerCamelCase					:     Optional[Any]  ,  lowerCamelCase					:     Dict  ,  lowerCamelCase					:     Union[str, Any]  ,  lowerCamelCase					:     List[str]	):  # noqa: E741
 while r - l > 1:
  UpperCamelCase_					:						Union[str, Any] =					(l + r) // 2
  if v[m] >= key:
   UpperCamelCase_					:						str =					m
  else:
   UpperCamelCase_					:						List[Any] =					m  # noqa: E741
 return r





def    __lowercase     (						lowerCamelCase					:     list[int]	):
 if len(lowerCamelCase	) == 0:
  return 0

 UpperCamelCase_					:						Tuple =					[0] * len(lowerCamelCase	)
 UpperCamelCase_					:						int =					1

 UpperCamelCase_					:						Dict =					v[0]

 for i in range(1  ,  len(lowerCamelCase	)	):
  if v[i] < tail[0]:
   UpperCamelCase_					:						Any =					v[i]
  elif v[i] > tail[length - 1]:
   UpperCamelCase_					:						Dict =					v[i]
   length += 1
  else:
   UpperCamelCase_					:						List[str] =					v[i]

 return length


if __name__ == "__main__":
   import doctest

   doctest.testmod()



