
'''simple docstring'''







import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A="attention"	)       ->	List[Any]:
	_snake_case   						=		params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
	_snake_case   						=		params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
	_snake_case   						=		params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
	_snake_case   						=		params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
	return k, o, q, v







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A=False	)       ->	Dict:







	if split_mlp_wi:
		_snake_case   						=		params[F'{prefix}/layers_{i}/mlp/wi_0/kernel']
		_snake_case   						=		params[F'{prefix}/layers_{i}/mlp/wi_1/kernel']
		_snake_case   						=		(wi_a, wi_a)
	else:
		_snake_case   						=		params[F'{prefix}/layers_{i}/mlp/wi/kernel']

	_snake_case   						=		params[F'{prefix}/layers_{i}/mlp/wo/kernel']
	return wi, wo







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A	)       ->	List[Any]:
	return params[F'{prefix}/layers_{i}/{layer_name}/scale']







def 				SCREAMING_SNAKE_CASE__	(   __A			,				*, __A			,				__A	)       ->	Dict:
	_snake_case   						=		traverse_util.flatten_dict(variables['target']	)
	_snake_case   						=		{'/'.join(__UpperCamelCase	): v for k, v in old.items()}

	# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
	_snake_case   						=		'encoder/layers_0/mlp/wi_0/kernel' in old
	print('Split MLP:'			,				__UpperCamelCase	)

	_snake_case   						=		collections.OrderedDict()

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

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

		# Block i, layer 1 (MLP).
		_snake_case   						=		tax_layer_norm_lookup(__UpperCamelCase			,				__UpperCamelCase			,				'encoder'			,				'pre_mlp_layer_norm'	)
		_snake_case						, _snake_case   						=		tax_mlp_lookup(__UpperCamelCase			,				__UpperCamelCase			,				'encoder'			,				__UpperCamelCase	)
		_snake_case   						=		layer_norm
		if split_mlp_wi:
			_snake_case   						=		wi[0].T
			_snake_case   						=		wi[1].T
		else:
			_snake_case   						=		wi.T
		_snake_case   						=		wo.T

	_snake_case   						=		old[
	    'encoder/relpos_bias/rel_embedding'
	].T
	_snake_case   						=		old['encoder/encoder_norm/scale']

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

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

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

		_snake_case   						=		old['decoder/decoder_norm/scale']
		_snake_case   						=		old[
		    'decoder/relpos_bias/rel_embedding'
		].T

		# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
		if "decoder/logits_dense/kernel" in old:
			_snake_case   						=		old['decoder/logits_dense/kernel'].T

	return new







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	List[Any]:
	_snake_case   						=		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:
		_snake_case   						=		state_dict['shared.weight']

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

	return state_dict







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A	)       ->	Union[str, Any]:
	_snake_case   						=		checkpoints.load_tax_checkpoint(__UpperCamelCase	)
	_snake_case   						=		convert_tax_to_pytorch(__UpperCamelCase			,				num_layers=config.num_layers			,				is_encoder_only=__UpperCamelCase	)
	_snake_case   						=		make_state_dict(__UpperCamelCase			,				__UpperCamelCase	)
	model.load_state_dict(__UpperCamelCase			,				strict=__UpperCamelCase	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A = False	)       ->	Tuple:
	_snake_case   						=		TaConfig.from_json_file(__UpperCamelCase	)
	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:
		_snake_case   						=		TaEncoderModel(__UpperCamelCase	)
	else:
		_snake_case   						=		TaForConditionalGeneration(__UpperCamelCase	)

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

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

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


if __name__ == "__main__":
					lowercase    :							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
					)
					lowercase    :							List[Any]      =					parser.parse_args()
					convert_tax_checkpoint_to_pytorch(
					    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
					)









"""simple docstring"""


class 					snake_case		:


   '''simple docstring'''

   def __init__(	self :     List[str],       _lowerCamelCase :     list[int]  ):

     '''simple docstring'''
     __A									= len(_lowerCamelCase  )
     __A									= [0] * len_array

     if len_array > 0:
       __A									= array[0]

     for i in range(1,       _lowerCamelCase  ):
       __A									= self.prefix_sum[i - 1] + array[i]


   def 				_SCREAMING_SNAKE_CASE				(	self :     List[Any],       _lowerCamelCase :     int,       _lowerCamelCase :     int  ):

     '''simple docstring'''
     if start == 0:
       return self.prefix_sum[end]

     return self.prefix_sum[end] - self.prefix_sum[start - 1]






   def 				_SCREAMING_SNAKE_CASE				(	self :     List[str],       _lowerCamelCase :     int  ):

     '''simple docstring'''
     __A									= {0}
     for sum_item in self.prefix_sum:
       if sum_item - target_sum in sums:
         return True

       sums.add(_lowerCamelCase  )

     return False


if __name__ == "__main__":
       import doctest

       doctest.testmod()




