




"""simple docstring"""




def     a__ (				snake_case__				)						->					int:
			if not isinstance(snake_case__       , snake_case__				):
						raise TypeError("""Input value must be an 'int' type"""				)

			lowerCamelCase			      =					0
			while number:
						position += 1
						number >>= 1

			return position


if __name__ == "__main__":
					import doctest

					doctest.testmod()









"""simple docstring"""




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__ (				snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__="attention"				)						->					List[Any]:
			lowerCamelCase			      =					lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					k_tmp.reshape(k_tmp.shape[0]       , k_tmp.shape[1] * k_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]       , o_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					q_tmp.reshape(q_tmp.shape[0]       , q_tmp.shape[1] * q_tmp.shape[2]				)
			lowerCamelCase			      =					np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :]				)
			lowerCamelCase			      =					v_tmp.reshape(v_tmp.shape[0]       , v_tmp.shape[1] * v_tmp.shape[2]				)
			return k, o, q, v



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__=False				)						->					List[str]:
			if split_mlp_wi:
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
						lowerCamelCase			      =					(wi_a, wi_a)
			else:
						lowerCamelCase			      =					params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					Tuple:
			return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]



def     a__ (				snake_case__       , *, snake_case__       , snake_case__       , snake_case__ = False				)						->					Dict:
			lowerCamelCase			      =					traverse_util.flatten_dict(variables["""target"""]				)
			lowerCamelCase			      =					{"""/""".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
			lowerCamelCase			      =					"""encoder/encoder/mlp/wi_0/kernel""" in old
			print("""Split MLP:"""       , snake_case__				)

			lowerCamelCase			      =					collections.OrderedDict()

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

			# Encoder.
			for i in range(snake_case__				):
						# Block i, layer 0 (Self Attention).
						lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """encoder"""       , """pre_attention_layer_norm"""				)
						lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """encoder"""       , """attention"""				)
						lowerCamelCase			      =					layer_norm
						lowerCamelCase			      =					k.T
						lowerCamelCase			      =					o.T
						lowerCamelCase			      =					q.T
						lowerCamelCase			      =					v.T

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

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

			if not scalable_attention:
						lowerCamelCase			      =					tax_relpos_bias_lookup(
						    snake_case__       , 0       , """encoder"""				).T
						lowerCamelCase			      =					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).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_self_attention_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """decoder"""       , """self_attention"""				)
									lowerCamelCase			      =					layer_norm
									lowerCamelCase			      =					k.T
									lowerCamelCase			      =					o.T
									lowerCamelCase			      =					q.T
									lowerCamelCase			      =					v.T

									# Block i, layer 1 (Cross Attention).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_cross_attention_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase      ,   lowerCamelCase			      =					tax_attention_lookup(snake_case__       , snake_case__       , """decoder"""       , """encoder_decoder_attention"""				)
									lowerCamelCase			      =					layer_norm
									lowerCamelCase			      =					k.T
									lowerCamelCase			      =					o.T
									lowerCamelCase			      =					q.T
									lowerCamelCase			      =					v.T

									# Block i, layer 2 (MLP).
									lowerCamelCase			      =					tax_layer_norm_lookup(snake_case__       , snake_case__       , """decoder"""       , """pre_mlp_layer_norm"""				)
									lowerCamelCase      ,   lowerCamelCase			      =					tax_mlp_lookup(snake_case__       , snake_case__       , """decoder"""       , snake_case__				)
									lowerCamelCase			      =					layer_norm
									if split_mlp_wi:
												lowerCamelCase			      =					wi[0].T
												lowerCamelCase			      =					wi[1].T
									else:
												lowerCamelCase			      =					wi.T
									lowerCamelCase			      =					wo.T

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

						lowerCamelCase			      =					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:
									lowerCamelCase			      =					old["""decoder/logits_dense/kernel"""].T

			return new



def     a__ (				snake_case__       , snake_case__				)						->					Optional[int]:
			lowerCamelCase			      =					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			      =					state_dict["""shared.weight"""]

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCamelCase			      =					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			      =					state_dict["""shared.weight"""]

			return state_dict



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__       , snake_case__				)						->					List[Any]:
			lowerCamelCase			      =					checkpoints.load_tax_checkpoint(snake_case__				)
			lowerCamelCase			      =					convert_tax_to_pytorch(
			    snake_case__       , num_layers=config.num_layers       , is_encoder_only=snake_case__       , scalable_attention=snake_case__				)
			lowerCamelCase			      =					make_state_dict(snake_case__       , snake_case__				)
			model.load_state_dict(snake_case__       , strict=snake_case__				)



def     a__ (				snake_case__       , snake_case__       , snake_case__       , snake_case__ = False       , snake_case__ = False       , )						->					str:
			lowerCamelCase			      =					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:
						lowerCamelCase			      =					UMTaEncoderModel(snake_case__				)
			else:
						lowerCamelCase			      =					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							:			Optional[int]     							=							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							:			int     							=							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,
					)



