


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






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






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase=False   ):

		if split_mlp_wi:
				_UpperCAmelCase		:       Dict								=      params[F"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
				_UpperCAmelCase		:       Optional[Any]								=      params[F"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
				_UpperCAmelCase		:       Union[str, Any]								=      (wi_a, wi_a)
		else:
				_UpperCAmelCase		:       Optional[Any]								=      params[F"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

		_UpperCAmelCase		:       int								=      params[F"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
		return wi, wo






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase   ):
		return params[F"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]






def  __lowerCAmelCase (__lowerCAmelCase      , *, __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = False   ):
		_UpperCAmelCase		:       int								=      traverse_util.flatten_dict(variables["target"]   )
		_UpperCAmelCase		:       str								=      {"/".join(lowercase_   ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		_UpperCAmelCase		:       Union[str, Any]								=      "encoder/encoder/mlp/wi_0/kernel" in old
		print("Split MLP:"      , lowercase_   )

		_UpperCAmelCase		:       List[Any]								=      collections.OrderedDict()

		# Shared embeddings.
		_UpperCAmelCase		:       str								=      old["token_embedder/embedding"]

		# Encoder.
		for i in range(lowercase_   ):
				# Block i, layer 0 (Self Attention).
				_UpperCAmelCase		:       int								=      tax_layer_norm_lookup(lowercase_      , lowercase_      , "encoder"      , "pre_attention_layer_norm"   )
				_UpperCAmelCase		:       str								=      tax_attention_lookup(lowercase_      , lowercase_      , "encoder"      , "attention"   )
				_UpperCAmelCase		:       List[Any]								=      layer_norm
				_UpperCAmelCase		:       str								=      k.T
				_UpperCAmelCase		:       Optional[Any]								=      o.T
				_UpperCAmelCase		:       List[str]								=      q.T
				_UpperCAmelCase		:       int								=      v.T

				# Block i, layer 1 (MLP).
				_UpperCAmelCase		:       Dict								=      tax_layer_norm_lookup(lowercase_      , lowercase_      , "encoder"      , "pre_mlp_layer_norm"   )
				_UpperCAmelCase		:       int								=      tax_mlp_lookup(lowercase_      , lowercase_      , "encoder"      , lowercase_   )
				_UpperCAmelCase		:       int								=      layer_norm
				if split_mlp_wi:
						_UpperCAmelCase		:       Optional[int]								=      wi[0].T
						_UpperCAmelCase		:       Optional[Any]								=      wi[1].T
				else:
						_UpperCAmelCase		:       Any								=      wi.T
				_UpperCAmelCase		:       Optional[Any]								=      wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						_UpperCAmelCase		:       Optional[int]								=      tax_relpos_bias_lookup(
						    lowercase_      , lowercase_      , "encoder"   ).T

		_UpperCAmelCase		:       str								=      old["encoder/encoder_norm/scale"]

		if not scalable_attention:
				_UpperCAmelCase		:       Any								=      tax_relpos_bias_lookup(
				    lowercase_      , 0      , "encoder"   ).T
				_UpperCAmelCase		:       str								=      tax_relpos_bias_lookup(
				    lowercase_      , 0      , "decoder"   ).T

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

						# Block i, layer 1 (Cross Attention).
						_UpperCAmelCase		:       Dict								=      tax_layer_norm_lookup(lowercase_      , lowercase_      , "decoder"      , "pre_cross_attention_layer_norm"   )
						_UpperCAmelCase		:       int								=      tax_attention_lookup(lowercase_      , lowercase_      , "decoder"      , "encoder_decoder_attention"   )
						_UpperCAmelCase		:       List[str]								=      layer_norm
						_UpperCAmelCase		:       str								=      k.T
						_UpperCAmelCase		:       List[Any]								=      o.T
						_UpperCAmelCase		:       int								=      q.T
						_UpperCAmelCase		:       List[Any]								=      v.T

						# Block i, layer 2 (MLP).
						_UpperCAmelCase		:       Tuple								=      tax_layer_norm_lookup(lowercase_      , lowercase_      , "decoder"      , "pre_mlp_layer_norm"   )
						_UpperCAmelCase		:       Tuple								=      tax_mlp_lookup(lowercase_      , lowercase_      , "decoder"      , lowercase_   )
						_UpperCAmelCase		:       Optional[int]								=      layer_norm
						if split_mlp_wi:
								_UpperCAmelCase		:       List[Any]								=      wi[0].T
								_UpperCAmelCase		:       Optional[int]								=      wi[1].T
						else:
								_UpperCAmelCase		:       str								=      wi.T
						_UpperCAmelCase		:       Union[str, Any]								=      wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								_UpperCAmelCase		:       Optional[int]								=      tax_relpos_bias_lookup(lowercase_      , lowercase_      , "decoder"   ).T

				_UpperCAmelCase		:       Tuple								=      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		:       Any								=      old["decoder/logits_dense/kernel"].T

		return new






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase   ):
		_UpperCAmelCase		:       Dict								=      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		:       Optional[int]								=      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		:       int								=      state_dict["shared.weight"]

		return state_dict






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






def  __lowerCAmelCase (__lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase      , __lowerCAmelCase = False      , __lowerCAmelCase = False      , ):
		_UpperCAmelCase		:       int								=      MTaConfig.from_json_file(lowercase_   )
		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[int]								=      UMTaEncoderModel(lowercase_   )
		else:
				_UpperCAmelCase		:       Optional[int]								=      UMTaForConditionalGeneration(lowercase_   )

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

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

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowercase_   )
		print("Done"   )


if __name__ == "__main__":
						lowerCamelCase__    	=     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__    	=     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

import numpy as np






def  __lowerCAmelCase (__lowerCAmelCase   ):
		return np.maximum(0      , __lowerCAmelCase   )


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]



