



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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_="attention"					):
   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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_=False					):
   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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					*, SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = False					):
   lowercase__   					=			traverse_util.flatten_dict(variables["target"]					)
   lowercase__   					=			{"/".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__   					=			"encoder/encoder/mlp/wi_0/kernel" in old
   print("Split MLP:"  ,					SCREAMING_SNAKE_CASE_					)

   lowercase__   					=			collections.OrderedDict()

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

   # Encoder.
   for i in range(SCREAMING_SNAKE_CASE_					):
      # Block i, layer 0 (Self Attention).
      lowercase__   					=			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"encoder"  ,					"pre_attention_layer_norm"					)
      lowercase__					,    lowercase__					,    lowercase__					,    lowercase__   					=			tax_attention_lookup(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"encoder"  ,					"pre_mlp_layer_norm"					)
      lowercase__					,    lowercase__   					=			tax_mlp_lookup(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"encoder"  ,					SCREAMING_SNAKE_CASE_					)
      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(
             SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"encoder"					).T

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

   if not scalable_attention:
      lowercase__   					=			tax_relpos_bias_lookup(
          SCREAMING_SNAKE_CASE_  ,					0  ,					"encoder"					).T
      lowercase__   					=			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).
         lowercase__   					=			tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"decoder"  ,					"pre_self_attention_layer_norm"					)
         lowercase__					,    lowercase__					,    lowercase__					,    lowercase__   					=			tax_attention_lookup(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"decoder"  ,					"pre_cross_attention_layer_norm"					)
         lowercase__					,    lowercase__					,    lowercase__					,    lowercase__   					=			tax_attention_lookup(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"decoder"  ,					"pre_mlp_layer_norm"					)
         lowercase__					,    lowercase__   					=			tax_mlp_lookup(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"decoder"  ,					SCREAMING_SNAKE_CASE_					)
         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(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					"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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   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 __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					):
   lowercase__   					=			checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_					)
   lowercase__   					=			convert_tax_to_pytorch(
       SCREAMING_SNAKE_CASE_  ,					num_layers=config.num_layers  ,					is_encoder_only=SCREAMING_SNAKE_CASE_  ,					scalable_attention=SCREAMING_SNAKE_CASE_					)
   lowercase__   					=			make_state_dict(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)
   model.load_state_dict(SCREAMING_SNAKE_CASE_  ,					strict=SCREAMING_SNAKE_CASE_					)



def __lowerCAmelCase	(       SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_ = False  ,					SCREAMING_SNAKE_CASE_ = False  ,					):
   lowercase__   					=			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:
      lowercase__   					=			UMTaEncoderModel(SCREAMING_SNAKE_CASE_					)
   else:
      lowercase__   					=			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__":
       lowercase_						=       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,
       )
       lowercase_						=       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 graphs.minimum_spanning_tree_kruskal import kruskal



def __lowerCAmelCase	(       ):
			lowercase__   					=			9
			lowercase__   					=			[
			    [0, 1, 4],
			    [0, 7, 8],
			    [1, 2, 8],
			    [7, 8, 7],
			    [7, 6, 1],
			    [2, 8, 2],
			    [8, 6, 6],
			    [2, 3, 7],
			    [2, 5, 4],
			    [6, 5, 2],
			    [3, 5, 14],
			    [3, 4, 9],
			    [5, 4, 10],
			    [1, 7, 11],
			]

			lowercase__   					=			kruskal(SCREAMING_SNAKE_CASE_  ,					SCREAMING_SNAKE_CASE_					)

			lowercase__   					=			[
			    [7, 6, 1],
			    [2, 8, 2],
			    [6, 5, 2],
			    [0, 1, 4],
			    [2, 5, 4],
			    [2, 3, 7],
			    [0, 7, 8],
			    [3, 4, 9],
			]

			assert sorted(SCREAMING_SNAKE_CASE_					) == sorted(SCREAMING_SNAKE_CASE_					)




