



"""simple docstring"""







import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig


def  __SCREAMING_SNAKE_CASE (  __UpperCAmelCase   ,				__UpperCAmelCase   ,				__UpperCAmelCase      ):
		_lowercase				: int     =   TaConfig.from_pretrained(__UpperCAmelCase      )
		_lowercase				: Optional[int]     =   FlaxTaForConditionalGeneration(config=__UpperCAmelCase      )
		_lowercase				: List[Any]     =   checkpoints.load_tax_checkpoint(__UpperCAmelCase      )

		_lowercase				: Union[str, Any]     =   """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

		# Encoder
		for layer_index in range(config.num_layers      ):
				_lowercase				: Dict     =   F"""layers_{str(__UpperCAmelCase      )}"""

				# Self-Attention
				_lowercase				: Optional[Any]     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
				_lowercase				: Any     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
				_lowercase				: Optional[int]     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
				_lowercase				: Optional[int]     =   tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: List[Any]     =   tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

				if split_mlp_wi:
						_lowercase				: List[str]     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
						_lowercase				: List[Any]     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
				else:
						_lowercase				: Dict     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

				_lowercase				: Optional[int]     =   tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: Dict     =   tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

				# Assigning
				_lowercase				: int     =   tax_attention_key
				_lowercase				: List[Any]     =   tax_attention_out
				_lowercase				: Dict     =   tax_attention_query
				_lowercase				: Any     =   tax_attention_value

				_lowercase				: List[Any]     =   tax_attention_layer_norm

				if split_mlp_wi:
						_lowercase				: List[Any]     =   tax_mlp_wi_a
						_lowercase				: Optional[Any]     =   tax_mlp_wi_a
				else:
						_lowercase				: int     =   tax_mlp_wi

				_lowercase				: Optional[Any]     =   tax_mlp_wo
				_lowercase				: Optional[Any]     =   tax_mlp_layer_norm

		# Only for layer 0:
		_lowercase				: int     =   tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
		_lowercase				: Union[str, Any]     =   tax_encoder_rel_embedding

		# Assigning
		_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
		_lowercase				: List[Any]     =   tax_encoder_norm

		# Decoder
		for layer_index in range(config.num_decoder_layers      ):
				_lowercase				: List[str]     =   F"""layers_{str(__UpperCAmelCase      )}"""

				# Self-Attention
				_lowercase				: Any     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""key"""]["""kernel"""]
				_lowercase				: Optional[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""out"""]["""kernel"""]
				_lowercase				: int     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""query"""]["""kernel"""]
				_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""self_attention"""]["""value"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: Optional[int]     =   tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
				    """scale"""
				]

				# Encoder-Decoder-Attention
				_lowercase				: Optional[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
				    """kernel"""
				]
				_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
				    """kernel"""
				]
				_lowercase				: Any     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
				    """kernel"""
				]
				_lowercase				: List[str]     =   tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
				    """kernel"""
				]

				# Layer Normalization
				_lowercase				: int     =   tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

				# MLP
				if split_mlp_wi:
						_lowercase				: Optional[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
						_lowercase				: Optional[int]     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
				else:
						_lowercase				: Dict     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

				_lowercase				: List[Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

				# Layer Normalization
				_lowercase				: Union[str, Any]     =   tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

				# Assigning
				_lowercase				: List[Any]     =   tax_attention_key
				_lowercase				: Tuple     =   tax_attention_out
				_lowercase				: List[Any]     =   tax_attention_query
				_lowercase				: Any     =   tax_attention_value

				_lowercase				: Optional[int]     =   tax_pre_attention_layer_norm

				_lowercase				: List[str]     =   tax_enc_dec_attention_key
				_lowercase				: Tuple     =   tax_enc_dec_attention_out
				_lowercase				: List[Any]     =   tax_enc_dec_attention_query
				_lowercase				: Union[str, Any]     =   tax_enc_dec_attention_value

				_lowercase				: int     =   tax_cross_layer_norm

				if split_mlp_wi:
						_lowercase				: str     =   tax_mlp_wi_a
						_lowercase				: Optional[int]     =   tax_mlp_wi_a
				else:
						_lowercase				: Optional[Any]     =   tax_mlp_wi

				_lowercase				: int     =   tax_mlp_wo

				_lowercase				: int     =   txa_mlp_layer_norm

		# Decoder Normalization
		_lowercase				: Tuple     =   tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
		_lowercase				: Union[str, Any]     =   txa_decoder_norm

		# Only for layer 0:
		_lowercase				: List[str]     =   tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
		_lowercase				: Union[str, Any]     =   tax_decoder_rel_embedding

		# Token Embeddings
		_lowercase				: Tuple     =   tax_model["""target"""]["""token_embedder"""]["""embedding"""]
		_lowercase				: List[str]     =   txa_token_embeddings

		# LM Head (only in v1.1 checkpoints)
		if "logits_dense" in tax_model["target"]["decoder"]:
				_lowercase				: Any     =   tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

		flax_model.save_pretrained(__UpperCAmelCase      )
		print("""T5X Model was sucessfully converted!"""      )


if __name__ == "__main__":
		UpperCAmelCase:  Any			     =				argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the TX5 checkpoint."""
		)
		parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of T5 model.""")
		parser.add_argument(
		    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
		)
		UpperCAmelCase:  str			     =				parser.parse_args()
		convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)








"""simple docstring"""







import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


UpperCAmelCase:  Union[str, Any]			     =				namedtuple("""ExampleNamedTuple""", """a b c""")





class   UpperCamelCase	(     unittest.TestCase       ):




	"""simple docstring"""






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[Any]     =   torch.randn(5				,2   )
			_lowercase				: List[Any]     =   torch.device("""cuda"""   ) if torch.cuda.is_available() else torch.device("""cpu"""   )

			_lowercase				: int     =   send_to_device(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.cpu()				,UpperCAmelCase_   )   )

			_lowercase				: int     =   send_to_device((tensor, [tensor, tensor], 1)				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta[0].cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta[1]				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta[1][0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta[1][1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta[2]				,1   )

			_lowercase				: Any     =   send_to_device({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""a"""].cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta["""b"""]				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""b"""][0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta["""b"""][1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta["""c"""]				,1   )

			_lowercase				: Optional[int]     =   send_to_device(ExampleNamedTuple(a=UpperCAmelCase_				,b=[tensor, tensor]				,c=1   )				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.a.cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta.b				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta.b[0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta.b[1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta.c				,1   )

			_lowercase				: str     =   send_to_device(UserDict({"""a""": tensor, """b""": [tensor, tensor], """c""": 1}   )				,UpperCAmelCase_   )
			self.assertIsInstance(UpperCAmelCase_				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""a"""].cpu()				,UpperCAmelCase_   )   )
			self.assertIsInstance(resulta["""b"""]				,UpperCAmelCase_   )
			self.assertTrue(torch.equal(resulta["""b"""][0].cpu()				,UpperCAmelCase_   )   )
			self.assertTrue(torch.equal(resulta["""b"""][1].cpu()				,UpperCAmelCase_   )   )
			self.assertEqual(resulta["""c"""]				,1   )






	def    lowerCamelCase__					(     self   ):
			with self.assertRaises(UpperCAmelCase_   ) as cm:
					_lowercase				: str     =   recursively_apply(torch.tensor				,(torch.tensor(1   ), 1)				,error_on_other_type=UpperCAmelCase_   )
			self.assertEqual(
			    str(cm.exception   )				,"""Unsupported types (<class 'int'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed."""				,)






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[int]     =   torch.tensor([1, 2, 3, 4, 5]   )
			self.assertEqual(listify(UpperCAmelCase_   )				,[1, 2, 3, 4, 5]   )

			_lowercase				: Optional[int]     =   torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )
			self.assertEqual(listify(UpperCAmelCase_   )				,[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]   )

			_lowercase				: Optional[Any]     =   torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]   )
			self.assertEqual(
			    listify(UpperCAmelCase_   )				,[[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]   )






	def    lowerCamelCase__					(     self   ):
			with patch_environment(aa=1				,BB=2   ):
					self.assertEqual(os.environ.get("""AA"""   )				,"""1"""   )
					self.assertEqual(os.environ.get("""BB"""   )				,"""2"""   )

			self.assertNotIn("""AA"""				,os.environ   )
			self.assertNotIn("""BB"""				,os.environ   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: Optional[Any]     =   RegressionModel()
			_lowercase				: List[Any]     =   model.forward
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Any     =   extract_model_from_parallel(UpperCAmelCase_				,keep_fpaa_wrapper=UpperCAmelCase_   )
			_lowercase				: Tuple     =   pickle.dumps(UpperCAmelCase_   )






	@require_cuda
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   RegressionModel()
			_lowercase				: Any     =   model.forward
			_lowercase				: Any     =   torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Any     =   extract_model_from_parallel(UpperCAmelCase_				,keep_fpaa_wrapper=UpperCAmelCase_   )
			_lowercase				: Tuple     =   pickle.dumps(UpperCAmelCase_   )






	@require_cuda
	@require_torch_min_version(version="""2.0"""   )
	def    lowerCamelCase__					(     self   ):
			_lowercase				: str     =   RegressionModel()
			_lowercase				: Any     =   model.forward
			_lowercase				: Union[str, Any]     =   torch.cuda.amp.autocast(dtype=torch.floataa   )(model.forward   )
			_lowercase				: Optional[Any]     =   convert_outputs_to_fpaa(model.forward   )
			_lowercase				: Union[str, Any]     =   torch.compile(model.forward				,backend="""inductor"""   )
			_lowercase				: Any     =   torch.randn(4				,10   ).cuda()
			_lowercase				: int     =   model(UpperCAmelCase_   )






	def    lowerCamelCase__					(     self   ):
			_lowercase				: List[str]     =   RegressionModel()
			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase				: Any     =   torch.nn.parallel.DataParallel(UpperCAmelCase_   )
			_lowercase				: List[Any]     =   extract_model_from_parallel(UpperCAmelCase_   )

			self.assertEqual(UpperCAmelCase_				,UpperCAmelCase_   )






	@require_torch_min_version(version="""2.0"""   )
	def    lowerCamelCase__					(     self   ):
			_lowercase				: int     =   RegressionModel()
			_lowercase				: List[str]     =   torch.compile(UpperCAmelCase_   )

			# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
			_lowercase				: Optional[int]     =   torch.nn.parallel.DataParallel(UpperCAmelCase_   )
			_lowercase				: int     =   torch.compile(UpperCAmelCase_   )
			_lowercase				: Dict     =   extract_model_from_parallel(UpperCAmelCase_   )

			self.assertEqual(compiled_model._orig_mod				,compiled_model_unwrapped._orig_mod   )






	def    lowerCamelCase__					(     self   ):
			self.assertEqual(find_device([1, """a""", torch.tensor([1, 2, 3]   )]   )				,torch.device("""cpu"""   )   )
			self.assertEqual(find_device({"""a""": 1, """b""": torch.tensor([1, 2, 3]   )}   )				,torch.device("""cpu"""   )   )
			self.assertIsNone(find_device([1, """a"""]   )   )



