


'''simple docstring'''

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 _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			List[str]:






       """simple docstring"""







       return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A="attention"			)			->			Tuple:






       """simple docstring"""







       lowercase__      =					lowercase__      =					np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :]			)
       lowercase__      =					k_tmp.reshape(k_tmp.shape[0]    ,					k_tmp.shape[1] * k_tmp.shape[2]			)
       lowercase__      =					np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :]			)
       lowercase__      =					o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]    ,					o_tmp.shape[2]			)
       lowercase__      =					np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :]			)
       lowercase__      =					q_tmp.reshape(q_tmp.shape[0]    ,					q_tmp.shape[1] * q_tmp.shape[2]			)
       lowercase__      =					np.ascontiguousarray(params[f"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :]			)
       lowercase__      =					v_tmp.reshape(v_tmp.shape[0]    ,					v_tmp.shape[1] * v_tmp.shape[2]			)
       return k, o, q, v

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A=False			)			->			Tuple:






       """simple docstring"""







       if split_mlp_wi:
              lowercase__      =					params[f"{prefix}/{prefix}/mlp/wi_0/kernel"][:, i, :]
              lowercase__      =					params[f"{prefix}/{prefix}/mlp/wi_1/kernel"][:, i, :]
              lowercase__      =					(wi_a, wi_a)
       else:
              lowercase__      =					params[f"{prefix}/{prefix}/mlp/wi/kernel"][:, i, :]

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

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A			)			->			Tuple:






       """simple docstring"""







       return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]

def _SCREAMING_SNAKE_CASE (A    ,					*, A    ,					A    ,					A = False			)			->			int:






       """simple docstring"""







       lowercase__      =					traverse_util.flatten_dict(variables['''target''']			)
       lowercase__      =					{'''/'''.join(A			): v for k, v in old.items()}

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

       lowercase__      =					collections.OrderedDict()

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

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

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

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

       if not scalable_attention:
              lowercase__      =					tax_relpos_bias_lookup(
                  A    ,					0    ,					'''encoder'''			).T
              lowercase__      =					tax_relpos_bias_lookup(
                  A    ,					0    ,					'''decoder'''			).T

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

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

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

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

              lowercase__      =					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:
                     lowercase__      =					old['''decoder/logits_dense/kernel'''].T

       return new

def _SCREAMING_SNAKE_CASE (A    ,					A			)			->			Union[str, Any]:






       """simple docstring"""







       lowercase__      =					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__      =					state_dict['''shared.weight''']

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

       return state_dict

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A    ,					A			)			->			str:






       """simple docstring"""







       lowercase__      =					checkpoints.load_tax_checkpoint(A			)
       lowercase__      =					convert_tax_to_pytorch(
           A    ,					num_layers=config.num_layers    ,					is_encoder_only=A    ,					scalable_attention=A			)
       lowercase__      =					make_state_dict(A    ,					A			)
       model.load_state_dict(A    ,					strict=A			)

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A    ,					A = False    ,					A = False    ,					)			->			Optional[Any]:






       """simple docstring"""







       lowercase__      =					MTaConfig.from_json_file(A			)
       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__      =					UMTaEncoderModel(A			)
       else:
              lowercase__      =					UMTaForConditionalGeneration(A			)

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

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

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


if __name__ == "__main__":
 lowerCamelCase				:  int    		=      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,
 )
 lowerCamelCase				:  Tuple    		=      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,
 )








'''simple docstring'''

from __future__ import annotations

from typing import Any
class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Optional[Any]  ,		UpperCamelCase       :       int = 6					):

             '''simple docstring'''







             lowercase__      =					None
             lowercase__      =					None
             self.create_linked_list(UpperCamelCase					)



      def  UpperCamelCase__						(self       :       List[str]  ,		UpperCamelCase       :       int					):

             '''simple docstring'''







             lowercase__      =					Node()
             lowercase__      =					current_node
             lowercase__      =					current_node
             lowercase__      =					current_node
             for _ in range(1  ,		UpperCamelCase					):
                    lowercase__      =					Node()
                    lowercase__      =					current_node
                    lowercase__      =					previous_node
                    lowercase__      =					current_node
             lowercase__      =					self.front
             lowercase__      =					previous_node



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             return (
                 self.front == self.rear
                 and self.front is not None
                 and self.front.data is None
             )



      def  UpperCamelCase__						(self       :       Dict					):

             '''simple docstring'''







             self.check_can_perform_operation()
             return self.front.data if self.front else None



      def  UpperCamelCase__						(self       :       Any  ,		UpperCamelCase       :       Any					):

             '''simple docstring'''







             if self.rear is None:
                    return

             self.check_is_full()
             if not self.is_empty():
                    lowercase__      =					self.rear.next
             if self.rear:
                    lowercase__      =					data



      def  UpperCamelCase__						(self       :       List[Any]					):

             '''simple docstring'''







             self.check_can_perform_operation()
             if self.rear is None or self.front is None:
                    return None
             if self.front == self.rear:
                    lowercase__      =					self.front.data
                    lowercase__      =					None
                    return data

             lowercase__      =					self.front
             lowercase__      =					old_front.next
             lowercase__      =					old_front.data
             lowercase__      =					None
             return data



      def  UpperCamelCase__						(self       :       Union[str, Any]					):

             '''simple docstring'''







             if self.is_empty():
                    raise Exception('''Empty Queue'''					)






      def  UpperCamelCase__						(self       :       int					):

             '''simple docstring'''







             if self.rear and self.rear.next == self.front:
                    raise Exception('''Full Queue'''					)






class     __lowerCAmelCase		:





      '''simple docstring'''






      def __init__(self       :       Any					):

             '''simple docstring'''







             lowercase__      =					None
             lowercase__      =					None
             lowercase__      =					None


if __name__ == "__main__":
 import doctest

 doctest.testmod()




