

"""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__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__="attention"     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   SCREAMING_SNAKE_CASE__										=   np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]     )
			SCREAMING_SNAKE_CASE__										=   k_tmp.reshape(k_tmp.shape[0]		,					k_tmp.shape[1] * k_tmp.shape[2]     )
			SCREAMING_SNAKE_CASE__										=   np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]     )
			SCREAMING_SNAKE_CASE__										=   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,					o_tmp.shape[2]     )
			SCREAMING_SNAKE_CASE__										=   np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]     )
			SCREAMING_SNAKE_CASE__										=   q_tmp.reshape(q_tmp.shape[0]		,					q_tmp.shape[1] * q_tmp.shape[2]     )
			SCREAMING_SNAKE_CASE__										=   np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]     )
			SCREAMING_SNAKE_CASE__										=   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     ):




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE__										=   params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
			return wi, wo

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


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

def 						A							(  snake_case__		,					*, snake_case__		,					snake_case__		,					snake_case__ = False     ):




			'''simple docstring'''


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

			SCREAMING_SNAKE_CASE__										=   collections.OrderedDict()

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

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

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

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

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

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

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

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

						SCREAMING_SNAKE_CASE__										=   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__										=   old["""decoder/logits_dense/kernel"""].T

			return new

def 						A							(  snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_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:
						SCREAMING_SNAKE_CASE__										=   state_dict["""shared.weight"""]

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

			return state_dict

def 						A							(  snake_case__		,					snake_case__		,					snake_case__		,					snake_case__		,					snake_case__     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   checkpoints.load_tax_checkpoint(snake_case__     )
			SCREAMING_SNAKE_CASE__										=   convert_tax_to_pytorch(
			    snake_case__		,					num_layers=config.num_layers		,					is_encoder_only=snake_case__		,					scalable_attention=snake_case__     )
			SCREAMING_SNAKE_CASE__										=   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		,					):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__										=   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__										=   UMTaEncoderModel(snake_case__     )
			else:
						SCREAMING_SNAKE_CASE__										=   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__":
						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
						)
						parser.add_argument(
						    "--scalable_attention",
						    action="store_true",
						    help="Whether the model uses scaled attention (umt5 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,
						    args.scalable_attention,
						)





"""simple docstring"""




import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class  lowerCamelCase    (unittest.TestCase      ):

			def        SCREAMING_SNAKE_CASE					(       self					:    Dict							) ->     str:
						SCREAMING_SNAKE_CASE__										=   torch.nn.Linear(1_0     ,					1_0							)
						SCREAMING_SNAKE_CASE__										=   torch.optim.SGD(model.parameters()     ,					0.1							)
						SCREAMING_SNAKE_CASE__										=   Accelerator()
						SCREAMING_SNAKE_CASE__										=   accelerator.prepare(__UpperCAmelCase							)
						try:
									pickle.loads(pickle.dumps(__UpperCAmelCase							)							)
						except Exception as e:
									self.fail(F"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()


