


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     lowercase	(    a     , a     , a ):



		'''simple docstring'''




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


def     lowercase	(    a     , a     , a     , a="attention" ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/key/kernel"][:, i, :, :] )
		SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						k_tmp.reshape(k_tmp.shape[0]     , k_tmp.shape[1] * k_tmp.shape[2] )
		SCREAMING_SNAKE_CASE_				:str				     =						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/out/kernel"][:, i, :, :] )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]     , o_tmp.shape[2] )
		SCREAMING_SNAKE_CASE_				:Any				     =						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/query/kernel"][:, i, :, :] )
		SCREAMING_SNAKE_CASE_				:Union[str, Any]				     =						q_tmp.reshape(q_tmp.shape[0]     , q_tmp.shape[1] * q_tmp.shape[2] )
		SCREAMING_SNAKE_CASE_				:Tuple				     =						np.ascontiguousarray(params[F"{prefix}/{prefix}/{layer_name}/value/kernel"][:, i, :, :] )
		SCREAMING_SNAKE_CASE_				:Any				     =						v_tmp.reshape(v_tmp.shape[0]     , v_tmp.shape[1] * v_tmp.shape[2] )
		return k, o, q, v


def     lowercase	(    a     , a     , a     , a=False ):



		'''simple docstring'''




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

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


def     lowercase	(    a     , a     , a     , a ):



		'''simple docstring'''




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


def     lowercase	(    a     , *, a     , a     , a = False ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Dict				     =						traverse_util.flatten_dict(variables["target"] )
		SCREAMING_SNAKE_CASE_				:int				     =						{"/".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
		SCREAMING_SNAKE_CASE_				:str				     =						"encoder/encoder/mlp/wi_0/kernel" in old
		print("Split MLP:"     , snake_case__ )

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

		# Shared embeddings.
		SCREAMING_SNAKE_CASE_				:Tuple				     =						old["token_embedder/embedding"]

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

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

		SCREAMING_SNAKE_CASE_				:Any				     =						old["encoder/encoder_norm/scale"]

		if not scalable_attention:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						tax_relpos_bias_lookup(
				    snake_case__     , 0     , "encoder" ).T
				SCREAMING_SNAKE_CASE_				:Any				     =						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).
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						tax_layer_norm_lookup(snake_case__     , snake_case__     , "decoder"     , "pre_self_attention_layer_norm" )
						SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_     ,				SCREAMING_SNAKE_CASE_				:str				     =						tax_attention_lookup(snake_case__     , snake_case__     , "decoder"     , "self_attention" )
						SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						layer_norm
						SCREAMING_SNAKE_CASE_				:Tuple				     =						k.T
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						o.T
						SCREAMING_SNAKE_CASE_				:int				     =						q.T
						SCREAMING_SNAKE_CASE_				:List[Any]				     =						v.T

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

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

						if scalable_attention:
								# convert the rel_embedding of each layer
								SCREAMING_SNAKE_CASE_				:int				     =						tax_relpos_bias_lookup(snake_case__     , snake_case__     , "decoder" ).T

				SCREAMING_SNAKE_CASE_				:List[Any]				     =						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:
						SCREAMING_SNAKE_CASE_				:List[str]				     =						old["decoder/logits_dense/kernel"].T

		return new


def     lowercase	(    a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:int				     =						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:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						state_dict["shared.weight"]

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						SCREAMING_SNAKE_CASE_				:str				     =						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." )
						SCREAMING_SNAKE_CASE_				:Optional[int]				     =						state_dict["shared.weight"]

		return state_dict


def     lowercase	(    a     , a     , a     , a     , a ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:Dict				     =						checkpoints.load_tax_checkpoint(snake_case__ )
		SCREAMING_SNAKE_CASE_				:List[str]				     =						convert_tax_to_pytorch(
		    snake_case__     , num_layers=config.num_layers     , is_encoder_only=snake_case__     , scalable_attention=snake_case__ )
		SCREAMING_SNAKE_CASE_				:List[str]				     =						make_state_dict(snake_case__     , snake_case__ )
		model.load_state_dict(snake_case__     , strict=snake_case__ )


def     lowercase	(    a     , a     , a     , a = False     , a = False     , ):



		'''simple docstring'''




		SCREAMING_SNAKE_CASE_				:List[Any]				     =						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:
				SCREAMING_SNAKE_CASE_				:Optional[Any]				     =						UMTaEncoderModel(snake_case__ )
		else:
				SCREAMING_SNAKE_CASE_				:Optional[int]				     =						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__":
		SCREAMING_SNAKE_CASE__     					=			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,
		)
		SCREAMING_SNAKE_CASE__     					=			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"""




# flake8: noqa
# Lint as: python3
A_				:      List[str]           =			[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental


