
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       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A="attention"    ):
	UpperCAmelCase_       				=			params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
	UpperCAmelCase_       				=			params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
	UpperCAmelCase_       				=			params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
	UpperCAmelCase_       				=			params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
	return k, o, q, v



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


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

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



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



def       __lowerCAmelCase      (						A       ,     *, A       ,     A    ):
	UpperCAmelCase_       				=			traverse_util.flatten_dict(variables["target"]    )
	UpperCAmelCase_       				=			{"/".join(lowerCamelCase__    ): 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/layers_0/mlp/wi_0/kernel" in old
	print("Split MLP:"       ,     lowerCamelCase__    )

	UpperCAmelCase_       				=			collections.OrderedDict()

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

	# Encoder.
	for i in range(lowerCamelCase__    ):
		# Block i, layer 0 (Self Attention).
		UpperCAmelCase_       				=			tax_layer_norm_lookup(lowerCamelCase__       ,     lowerCamelCase__       ,     "encoder"       ,     "pre_attention_layer_norm"    )
		UpperCAmelCase_       				=			tax_attention_lookup(lowerCamelCase__       ,     lowerCamelCase__       ,     "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(lowerCamelCase__       ,     lowerCamelCase__       ,     "encoder"       ,     "pre_mlp_layer_norm"    )
		UpperCAmelCase_       				=			tax_mlp_lookup(lowerCamelCase__       ,     lowerCamelCase__       ,     "encoder"       ,     lowerCamelCase__    )
		UpperCAmelCase_       				=			layer_norm
		if split_mlp_wi:
			UpperCAmelCase_       				=			wi[0].T
			UpperCAmelCase_       				=			wi[1].T
		else:
			UpperCAmelCase_       				=			wi.T
		UpperCAmelCase_       				=			wo.T

	UpperCAmelCase_       				=			old[
	    "encoder/relpos_bias/rel_embedding"
	].T
	UpperCAmelCase_       				=			old["encoder/encoder_norm/scale"]

	if not is_encoder_only:
		# Decoder.
		for i in range(lowerCamelCase__    ):
			# Block i, layer 0 (Self Attention).
			UpperCAmelCase_       				=			tax_layer_norm_lookup(lowerCamelCase__       ,     lowerCamelCase__       ,     "decoder"       ,     "pre_self_attention_layer_norm"    )
			UpperCAmelCase_       				=			tax_attention_lookup(lowerCamelCase__       ,     lowerCamelCase__       ,     "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(lowerCamelCase__       ,     lowerCamelCase__       ,     "decoder"       ,     "pre_cross_attention_layer_norm"    )
			UpperCAmelCase_       				=			tax_attention_lookup(lowerCamelCase__       ,     lowerCamelCase__       ,     "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(lowerCamelCase__       ,     lowerCamelCase__       ,     "decoder"       ,     "pre_mlp_layer_norm"    )
			UpperCAmelCase_       				=			tax_mlp_lookup(lowerCamelCase__       ,     lowerCamelCase__       ,     "decoder"       ,     lowerCamelCase__    )
			UpperCAmelCase_       				=			layer_norm
			if split_mlp_wi:
				UpperCAmelCase_       				=			wi[0].T
				UpperCAmelCase_       				=			wi[1].T
			else:
				UpperCAmelCase_       				=			wi.T
			UpperCAmelCase_       				=			wo.T

		UpperCAmelCase_       				=			old["decoder/decoder_norm/scale"]
		UpperCAmelCase_       				=			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:
			UpperCAmelCase_       				=			old["decoder/logits_dense/kernel"].T

	return new



def       __lowerCAmelCase      (						A       ,     A    ):
	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       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A    ):
	UpperCAmelCase_       				=			checkpoints.load_tax_checkpoint(lowerCamelCase__    )
	UpperCAmelCase_       				=			convert_tax_to_pytorch(lowerCamelCase__       ,     num_layers=config.num_layers       ,     is_encoder_only=lowerCamelCase__    )
	UpperCAmelCase_       				=			make_state_dict(lowerCamelCase__       ,     lowerCamelCase__    )
	model.load_state_dict(lowerCamelCase__       ,     strict=lowerCamelCase__    )



def       __lowerCAmelCase      (						A       ,     A       ,     A       ,     A = False    ):
	UpperCAmelCase_       				=			TaConfig.from_json_file(lowerCamelCase__    )
	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_       				=			TaEncoderModel(lowerCamelCase__    )
	else:
		UpperCAmelCase_       				=			TaForConditionalGeneration(lowerCamelCase__    )

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

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

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


if __name__ == "__main__":
				_a:    Dict 							=			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
				)
				_a:    int 							=			parser.parse_args()
				convert_tax_checkpoint_to_pytorch(
				    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
				)


def       __lowerCAmelCase      (						A    ):
	if len(A    ) <= 1:
		return lst

	UpperCAmelCase_       				=			1

	while i < len(A    ):
		if lst[i - 1] <= lst[i]:
			i += 1
		else:
			UpperCAmelCase_				,   UpperCAmelCase_       				=			lst[i], lst[i - 1]
			i -= 1
			if i == 0:
				UpperCAmelCase_       				=			1

	return lst


if __name__ == "__main__":
				_a:    List[Any] 							=			input("""Enter numbers separated by a comma:\n""").strip()
				_a:    Optional[int] 							=			[int(item) for item in user_input.split(""",""")]
				print(gnome_sort(unsorted))