





'''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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			Optional[int] )							->     Any:

       """simple docstring"""


       return params[f"{prefix}/{prefix}/relpos_bias/rel_embedding"][:, i, :]
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			List[Any]="attention" )							->     Dict:

       """simple docstring"""


       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Any			,	SCREAMING_SNAKE_CASE_ :			str			,	SCREAMING_SNAKE_CASE_ :			Tuple			,	SCREAMING_SNAKE_CASE_ :			List[str]=False )							->     List[str]:

       """simple docstring"""


       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			Any			,	SCREAMING_SNAKE_CASE_ :			List[Any]			,	SCREAMING_SNAKE_CASE_ :			Any )							->     Any:

       """simple docstring"""


       return params[f"{prefix}/{prefix}/{layer_name}/scale"][:, i]
def       __snake_case		( SCREAMING_SNAKE_CASE_ :			dict			,	*, SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			bool			,	SCREAMING_SNAKE_CASE_ :			bool = False )							->     List[str]:

       """simple docstring"""


       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[int]			,	SCREAMING_SNAKE_CASE_ :			bool )							->     Any:

       """simple docstring"""


       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			Optional[Any]			,	SCREAMING_SNAKE_CASE_ :			List[Any]			,	SCREAMING_SNAKE_CASE_ :			Optional[int] )							->     Any:

       """simple docstring"""


       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       __snake_case		( SCREAMING_SNAKE_CASE_ :			List[Any]			,	SCREAMING_SNAKE_CASE_ :			Union[str, Any]			,	SCREAMING_SNAKE_CASE_ :			int			,	SCREAMING_SNAKE_CASE_ :			bool = False			,	SCREAMING_SNAKE_CASE_ :			bool = False			,	)							->     Dict:

       """simple docstring"""


       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__			:					List[str]       						=						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__			:					List[str]       						=						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

from dataclasses import dataclass

@dataclass
class 							UpperCAmelCase_						:
							"""simple docstring"""







							UpperCAmelCase__			:		float
							UpperCAmelCase__			:		TreeNode | None             =       None
							UpperCAmelCase__			:		TreeNode | None             =       None



def   __UpperCAmelCase					(      __a :     TreeNode | None      )     ->	bool:

					"""simple docstring"""

					def is_valid_tree(__a :     TreeNode | None      ) -> bool:
										if node is None:
															return True

										if not isinstance(__a						,__a      ):
															return False

										try:
															float(node.data      )
										except (TypeError, ValueError):
															return False

										return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

					if not is_valid_tree(__a      ):
										raise ValueError(
										    '''Each node should be type of TreeNode and data should be float.'''      )

					def is_binary_search_tree_recursive_check(
					    __a :     TreeNode | None						,__a :     float						,__a :     float      ) -> bool:

										if node is None:
															return True

										return (
										    left_bound < node.data < right_bound
										    and is_binary_search_tree_recursive_check(node.left						,__a						,node.data      )
										    and is_binary_search_tree_recursive_check(
										        node.right						,node.data						,__a      )
										)

					return is_binary_search_tree_recursive_check(__a						,-float('''inf'''      )						,float('''inf'''      )      )


if __name__ == "__main__":
			import doctest

			doctest.testmod()



