
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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[Any]:

		"""simple docstring"""






		return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase="attention" )		->				Tuple:

		"""simple docstring"""






		_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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase=False )		->				List[Any]:

		"""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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]


def 							__A(lowerCAmelCase						,  *, lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = False )		->				Dict:

		"""simple docstring"""






		_UpperCamelCase						    =	traverse_util.flatten_dict(variables["""target"""] )
		_UpperCamelCase						    =	{"/".join(__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:"""						,  __snake_case )

		_UpperCamelCase						    =	collections.OrderedDict()

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

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

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

		if not scalable_attention:
				_UpperCamelCase						    =	tax_relpos_bias_lookup(
				    __snake_case						,  0						,  """encoder""" ).T
				_UpperCamelCase						    =	tax_relpos_bias_lookup(
				    __snake_case						,  0						,  """decoder""" ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(__snake_case ):
						# Block i, layer 0 (Self Attention).
						_UpperCamelCase						    =	tax_layer_norm_lookup(__snake_case						,  __snake_case						,  """decoder"""						,  """pre_self_attention_layer_norm""" )
						_UpperCamelCase						    =	tax_attention_lookup(__snake_case						,  __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(__snake_case						,  __snake_case						,  """decoder"""						,  """pre_cross_attention_layer_norm""" )
						_UpperCamelCase						    =	tax_attention_lookup(__snake_case						,  __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(__snake_case						,  __snake_case						,  """decoder"""						,  """pre_mlp_layer_norm""" )
						_UpperCamelCase						    =	tax_mlp_lookup(__snake_case						,  __snake_case						,  """decoder"""						,  __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(__snake_case						,  __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 							__A(lowerCAmelCase						,  lowerCAmelCase )		->				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 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






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


def 							__A(lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase						,  lowerCAmelCase = False						,  lowerCAmelCase = False						,  )		->				List[Any]:

		"""simple docstring"""






		_UpperCamelCase						    =	MTaConfig.from_json_file(__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(__snake_case )
		else:
				_UpperCamelCase						    =	UMTaForConditionalGeneration(__snake_case )

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

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

		# Verify that we can load the checkpoint.
		model.from_pretrained(__snake_case )
		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,
						)





def 							__A(lowerCAmelCase )		->				list[list[int]]:

		"""simple docstring"""






		_UpperCamelCase						    =	[]
		if len(lowerCAmelCase ) == 1:
				return [nums.copy()]
		for _ in range(len(lowerCAmelCase ) ):
				_UpperCamelCase						    =	nums.pop(0 )
				_UpperCamelCase						    =	permute(lowerCAmelCase )
				for perm in permutations:
						perm.append(lowerCAmelCase )
				result.extend(lowerCAmelCase )
				nums.append(lowerCAmelCase )
		return result


def 							__A(lowerCAmelCase )		->				Optional[int]:

		"""simple docstring"""






		def backtrack(lowerCAmelCase ):
				if start == len(lowerCAmelCase ) - 1:
						output.append(nums[:] )
				else:
						for i in range(lowerCAmelCase						,  len(lowerCAmelCase ) ):
								_UpperCamelCase    ,	_UpperCamelCase						    =	nums[i], nums[start]
								backtrack(start + 1 )
								_UpperCamelCase    ,	_UpperCamelCase						    =	nums[i], nums[start]  # backtrack

		_UpperCamelCase						    =	[]
		backtrack(0 )
		return output


if __name__ == "__main__":
		import doctest

		# use res to print the data in permute2 function
		lowerCamelCase__	=							permutea([1, 2, 3])
		print(res)
		doctest.testmod()



