


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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__="attention"						):
	__lowerCamelCase		:							Union[str, Any]		 =						params[f'{prefix}/layers_{i}/{layer_name}/key/kernel']
	__lowerCamelCase		:							Any		 =						params[f'{prefix}/layers_{i}/{layer_name}/out/kernel']
	__lowerCamelCase		:							str		 =						params[f'{prefix}/layers_{i}/{layer_name}/query/kernel']
	__lowerCamelCase		:							Any		 =						params[f'{prefix}/layers_{i}/{layer_name}/value/kernel']
	return k, o, q, v
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=False						):
	if split_mlp_wi:
		__lowerCamelCase		:							List[Any]		 =						params[f'{prefix}/layers_{i}/mlp/wi_0/kernel']
		__lowerCamelCase		:							Optional[int]		 =						params[f'{prefix}/layers_{i}/mlp/wi_1/kernel']
		__lowerCamelCase		:							Any		 =						(wi_a, wi_a)
	else:
		__lowerCamelCase		:							Optional[int]		 =						params[f'{prefix}/layers_{i}/mlp/wi/kernel']

	__lowerCamelCase		:							Tuple		 =						params[f'{prefix}/layers_{i}/mlp/wo/kernel']
	return wi, wo
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
	return params[f'{prefix}/layers_{i}/{layer_name}/scale']
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	*, SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
	__lowerCamelCase		:							Union[str, Any]		 =						traverse_util.flatten_dict(variables['target']						)
	__lowerCamelCase		:							Optional[int]		 =						{'/'.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
	__lowerCamelCase		:							Union[str, Any]		 =						'encoder/layers_0/mlp/wi_0/kernel' in old
	print('Split MLP:'      ,	SCREAMING_SNAKE_CASE__						)

	__lowerCamelCase		:							Union[str, Any]		 =						collections.OrderedDict()

	# Shared embeddings.
	__lowerCamelCase		:							Any		 =						old['token_embedder/embedding']

	# Encoder.
	for i in range(SCREAMING_SNAKE_CASE__						):
		# Block i, layer 0 (Self Attention).
		__lowerCamelCase		:							List[str]		 =						tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'encoder'      ,	'pre_attention_layer_norm'						)
		__lowerCamelCase		:							List[str]		 =						tax_attention_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'encoder'      ,	'attention'						)
		__lowerCamelCase		:							str		 =						layer_norm
		__lowerCamelCase		:							Tuple		 =						k.T
		__lowerCamelCase		:							int		 =						o.T
		__lowerCamelCase		:							Any		 =						q.T
		__lowerCamelCase		:							Dict		 =						v.T

		# Block i, layer 1 (MLP).
		__lowerCamelCase		:							str		 =						tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'encoder'      ,	'pre_mlp_layer_norm'						)
		__lowerCamelCase		:							Dict		 =						tax_mlp_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'encoder'      ,	SCREAMING_SNAKE_CASE__						)
		__lowerCamelCase		:							str		 =						layer_norm
		if split_mlp_wi:
			__lowerCamelCase		:							Optional[int]		 =						wi[0].T
			__lowerCamelCase		:							int		 =						wi[1].T
		else:
			__lowerCamelCase		:							Tuple		 =						wi.T
		__lowerCamelCase		:							List[str]		 =						wo.T

	__lowerCamelCase		:							str		 =						old[
	    'encoder/relpos_bias/rel_embedding'
	].T
	__lowerCamelCase		:							List[Any]		 =						old['encoder/encoder_norm/scale']

	if not is_encoder_only:
		# Decoder.
		for i in range(SCREAMING_SNAKE_CASE__						):
			# Block i, layer 0 (Self Attention).
			__lowerCamelCase		:							Dict		 =						tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'decoder'      ,	'pre_self_attention_layer_norm'						)
			__lowerCamelCase		:							int		 =						tax_attention_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'decoder'      ,	'self_attention'						)
			__lowerCamelCase		:							Tuple		 =						layer_norm
			__lowerCamelCase		:							Optional[int]		 =						k.T
			__lowerCamelCase		:							Optional[int]		 =						o.T
			__lowerCamelCase		:							Optional[Any]		 =						q.T
			__lowerCamelCase		:							Dict		 =						v.T

			# Block i, layer 1 (Cross Attention).
			__lowerCamelCase		:							str		 =						tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'decoder'      ,	'pre_cross_attention_layer_norm'						)
			__lowerCamelCase		:							Tuple		 =						tax_attention_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'decoder'      ,	'encoder_decoder_attention'						)
			__lowerCamelCase		:							str		 =						layer_norm
			__lowerCamelCase		:							Union[str, Any]		 =						k.T
			__lowerCamelCase		:							Tuple		 =						o.T
			__lowerCamelCase		:							List[str]		 =						q.T
			__lowerCamelCase		:							Dict		 =						v.T

			# Block i, layer 2 (MLP).
			__lowerCamelCase		:							int		 =						tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'decoder'      ,	'pre_mlp_layer_norm'						)
			__lowerCamelCase		:							int		 =						tax_mlp_lookup(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	'decoder'      ,	SCREAMING_SNAKE_CASE__						)
			__lowerCamelCase		:							List[str]		 =						layer_norm
			if split_mlp_wi:
				__lowerCamelCase		:							Tuple		 =						wi[0].T
				__lowerCamelCase		:							str		 =						wi[1].T
			else:
				__lowerCamelCase		:							Optional[int]		 =						wi.T
			__lowerCamelCase		:							List[str]		 =						wo.T

		__lowerCamelCase		:							Dict		 =						old['decoder/decoder_norm/scale']
		__lowerCamelCase		:							List[str]		 =						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:
			__lowerCamelCase		:							Any		 =						old['decoder/logits_dense/kernel'].T

	return new
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
	__lowerCamelCase		:							List[str]		 =						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:
		__lowerCamelCase		:							Any		 =						state_dict['shared.weight']

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

	return state_dict
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
	__lowerCamelCase		:							Any		 =						checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__						)
	__lowerCamelCase		:							Optional[int]		 =						convert_tax_to_pytorch(SCREAMING_SNAKE_CASE__      ,	num_layers=config.num_layers      ,	is_encoder_only=SCREAMING_SNAKE_CASE__						)
	__lowerCamelCase		:							Optional[int]		 =						make_state_dict(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
	model.load_state_dict(SCREAMING_SNAKE_CASE__      ,	strict=SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__ = False						):
	__lowerCamelCase		:							Optional[int]		 =						TaConfig.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:
		__lowerCamelCase		:							Tuple		 =						TaEncoderModel(SCREAMING_SNAKE_CASE__						)
	else:
		__lowerCamelCase		:							int		 =						TaForConditionalGeneration(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__						)

	# 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
							)
							lowercase_	   =     parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
							)





def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Union[str, Any]		 =						len(SCREAMING_SNAKE_CASE__						)
 __lowerCamelCase		:							Any		 =						[[False] * (required_sum + 1) for _ in range(arr_len + 1						)]

 # for each arr value, a sum of zero(0) can be formed by not taking any element
 # hence True/1
 for i in range(arr_len + 1						):
  __lowerCamelCase		:							Dict		 =						True

 # sum is not zero and set is empty then false
 for i in range(1      ,	required_sum + 1						):
  __lowerCamelCase		:							Union[str, Any]		 =						False

 for i in range(1      ,	arr_len + 1						):
  for j in range(1      ,	required_sum + 1						):
   if arr[i - 1] > j:
    __lowerCamelCase		:							Tuple		 =						subset[i - 1][j]
   if arr[i - 1] <= j:
    __lowerCamelCase		:							List[Any]		 =						subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]

 return subset[arr_len][required_sum]


if __name__ == "__main__":
       import doctest

       doctest.testmod()

