





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 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase="attention"  ):


			'''simple docstring'''







			__lowercase 					=   params[F'{prefix}/layers_{i}/{layer_name}/key/kernel']
			__lowercase 					=   params[F'{prefix}/layers_{i}/{layer_name}/out/kernel']
			__lowercase 					=   params[F'{prefix}/layers_{i}/{layer_name}/query/kernel']
			__lowercase 					=   params[F'{prefix}/layers_{i}/{layer_name}/value/kernel']
			return k, o, q, v


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase=False  ):


			'''simple docstring'''







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

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


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return params[F'{prefix}/layers_{i}/{layer_name}/scale']


def 		lowercase_  (     _UpperCamelCase       ,			*, _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   traverse_util.flatten_dict(variables['''target''']  )
			__lowercase 					=   {"/".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
			__lowercase 					=   "encoder/layers_0/mlp/wi_0/kernel" in old
			print('''Split MLP:'''       ,			_lowerCAmelCase  )

			__lowercase 					=   collections.OrderedDict()

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

			# Encoder.
			for i in range(_lowerCAmelCase  ):
						# Block i, layer 0 (Self Attention).
						__lowercase 					=   tax_layer_norm_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''encoder'''       ,			'''pre_attention_layer_norm'''  )
						__lowercase 					=   tax_attention_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''encoder'''       ,			'''attention'''  )
						__lowercase 					=   layer_norm
						__lowercase 					=   k.T
						__lowercase 					=   o.T
						__lowercase 					=   q.T
						__lowercase 					=   v.T

						# Block i, layer 1 (MLP).
						__lowercase 					=   tax_layer_norm_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''encoder'''       ,			'''pre_mlp_layer_norm'''  )
						__lowercase 					=   tax_mlp_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''encoder'''       ,			_lowerCAmelCase  )
						__lowercase 					=   layer_norm
						if split_mlp_wi:
									__lowercase 					=   wi[0].T
									__lowercase 					=   wi[1].T
						else:
									__lowercase 					=   wi.T
						__lowercase 					=   wo.T

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

			if not is_encoder_only:
						# Decoder.
						for i in range(_lowerCAmelCase  ):
									# Block i, layer 0 (Self Attention).
									__lowercase 					=   tax_layer_norm_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''decoder'''       ,			'''pre_self_attention_layer_norm'''  )
									__lowercase 					=   tax_attention_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''decoder'''       ,			'''self_attention'''  )
									__lowercase 					=   layer_norm
									__lowercase 					=   k.T
									__lowercase 					=   o.T
									__lowercase 					=   q.T
									__lowercase 					=   v.T

									# Block i, layer 1 (Cross Attention).
									__lowercase 					=   tax_layer_norm_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''decoder'''       ,			'''pre_cross_attention_layer_norm'''  )
									__lowercase 					=   tax_attention_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''decoder'''       ,			'''encoder_decoder_attention'''  )
									__lowercase 					=   layer_norm
									__lowercase 					=   k.T
									__lowercase 					=   o.T
									__lowercase 					=   q.T
									__lowercase 					=   v.T

									# Block i, layer 2 (MLP).
									__lowercase 					=   tax_layer_norm_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''decoder'''       ,			'''pre_mlp_layer_norm'''  )
									__lowercase 					=   tax_mlp_lookup(_lowerCAmelCase       ,			_lowerCAmelCase       ,			'''decoder'''       ,			_lowerCAmelCase  )
									__lowercase 					=   layer_norm
									if split_mlp_wi:
												__lowercase 					=   wi[0].T
												__lowercase 					=   wi[1].T
									else:
												__lowercase 					=   wi.T
									__lowercase 					=   wo.T

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

			return new


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   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:
						__lowercase 					=   state_dict["shared.weight"]

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

			return state_dict


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   checkpoints.load_tax_checkpoint(_lowerCAmelCase  )
			__lowercase 					=   convert_tax_to_pytorch(_lowerCAmelCase       ,			num_layers=config.num_layers       ,			is_encoder_only=_lowerCAmelCase  )
			__lowercase 					=   make_state_dict(_lowerCAmelCase       ,			_lowerCAmelCase  )
			model.load_state_dict(_lowerCAmelCase       ,			strict=_lowerCAmelCase  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = False  ):


			'''simple docstring'''







			__lowercase 					=   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:
						__lowercase 					=   TaEncoderModel(_lowerCAmelCase  )
			else:
						__lowercase 					=   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 : Union[str, Any]		=				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 : Optional[int]		=				parser.parse_args()
	convert_tax_checkpoint_to_pytorch(
	    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
	)






import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType
class 	lowerCamelCase_       :



					'''simple docstring'''







					def __init__(				self ,       snake_case_=2 ,       snake_case_=3 ,       snake_case_=6_4 ,       snake_case_=None						)   ->			List[str]:







								'''simple docstring'''

								__lowercase 					=   np.random.default_rng(snake_case_						)
								__lowercase 					=   length
								__lowercase 					=   rng.normal(size=(length,)						).astype(np.floataa						)
								__lowercase 					=   a * self.x + b + rng.normal(scale=0.1 ,       size=(length,)						).astype(np.floataa						)






					def __len__(				self						)   ->			Dict:







								'''simple docstring'''

								return self.length






					def __getitem__(				self ,       snake_case_						)   ->			Union[str, Any]:







								'''simple docstring'''

								return {"x": self.x[i], "y": self.y[i]}




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			Optional[Any]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor([2, 3]						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			List[Any]:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a[0] + self.b[0]




class 	lowerCamelCase_       (				torch.nn.Module				):



					'''simple docstring'''







					def __init__(				self ,       snake_case_=0 ,       snake_case_=0 ,       snake_case_=False						)   ->			List[str]:







								'''simple docstring'''

								super().__init__()
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   torch.nn.Parameter(torch.tensor(snake_case_						).float()						)
								__lowercase 					=   True






					def A      (				self ,       snake_case_=None						)   ->			str:







								'''simple docstring'''

								if self.first_batch:
											print(F'Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'						)
											__lowercase 					=   False
								return x * self.a + self.b




def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = 16  ):


			'''simple docstring'''







			from datasets import load_dataset
			from transformers import AutoTokenizer

			__lowercase 					=   AutoTokenizer.from_pretrained('''bert-base-cased'''  )
			__lowercase 					=   {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
			__lowercase 					=   load_dataset('''csv'''       ,			data_files=_UpperCamelCase  )
			__lowercase 					=   datasets['''train'''].unique('''label'''  )

			__lowercase 					=   {v: i for i, v in enumerate(_UpperCamelCase  )}

			def tokenize_function(_UpperCamelCase  ):
						# max_length=None => use the model max length (it's actually the default)
						__lowercase 					=   tokenizer(
						    examples['''sentence1''']       ,			examples['''sentence2''']       ,			truncation=_UpperCamelCase       ,			max_length=_UpperCamelCase       ,			padding='''max_length'''  )
						if "label" in examples:
									__lowercase 					=   [label_to_id[l] for l in examples['''label''']]
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			__lowercase 					=   datasets.map(
			    _UpperCamelCase       ,			batched=_UpperCamelCase       ,			remove_columns=['''sentence1''', '''sentence2''', '''label''']       ,			)

			def collate_fn(_UpperCamelCase  ):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(_UpperCamelCase       ,			padding='''max_length'''       ,			max_length=1_28       ,			return_tensors='''pt'''  )
						return tokenizer.pad(_UpperCamelCase       ,			padding='''longest'''       ,			return_tensors='''pt'''  )

			# Instantiate dataloaders.
			__lowercase 					=   DataLoader(tokenized_datasets['''train''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=2  )
			__lowercase 					=   DataLoader(tokenized_datasets['''validation''']       ,			shuffle=_UpperCamelCase       ,			collate_fn=_UpperCamelCase       ,			batch_size=1  )

			return train_dataloader, eval_dataloader


