





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 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_="attention"		)   ->     Optional[Any]:
		lowerCamelCase   : List[Any]								=params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
		lowerCamelCase   : str								=params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
		lowerCamelCase   : int								=params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
		lowerCamelCase   : Optional[int]								=params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
		return k, o, q, v


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_=False		)   ->     Dict:
		if split_mlp_wi:
				lowerCamelCase   : Dict								=params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
				lowerCamelCase   : Dict								=params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
				lowerCamelCase   : str								=(wi_a, wi_a)
		else:
				lowerCamelCase   : Optional[int]								=params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

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


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Any:
		return params[F"{prefix}/layers_{i}/{layer_name}/scale"]


def 					A__	(       SCREAMING_SNAKE_CASE_							,       *, SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     List[Any]:
		lowerCamelCase   : List[str]								=traverse_util.flatten_dict(variables['''target''']		)
		lowerCamelCase   : Any								={'''/'''.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
		lowerCamelCase   : str								='''encoder/layers_0/mlp/wi_0/kernel''' in old
		print('''Split MLP:'''							,       lowerCAmelCase__		)

		lowerCamelCase   : int								=collections.OrderedDict()

		# Shared embeddings.
		lowerCamelCase   : Dict								=old['''token_embedder/embedding''']

		# Encoder.
		for i in range(lowerCAmelCase__		):
				# Block i, layer 0 (Self Attention).
				lowerCamelCase   : str								=tax_layer_norm_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''encoder'''							,       '''pre_attention_layer_norm'''		)
				lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : int								=tax_attention_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''encoder'''							,       '''attention'''		)
				lowerCamelCase   : List[Any]								=layer_norm
				lowerCamelCase   : Optional[Any]								=k.T
				lowerCamelCase   : List[Any]								=o.T
				lowerCamelCase   : str								=q.T
				lowerCamelCase   : Dict								=v.T

				# Block i, layer 1 (MLP).
				lowerCamelCase   : Optional[Any]								=tax_layer_norm_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''encoder'''							,       '''pre_mlp_layer_norm'''		)
				lowerCamelCase		,   lowerCamelCase   : Dict								=tax_mlp_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''encoder'''							,       lowerCAmelCase__		)
				lowerCamelCase   : Any								=layer_norm
				if split_mlp_wi:
						lowerCamelCase   : Optional[int]								=wi[0].T
						lowerCamelCase   : str								=wi[1].T
				else:
						lowerCamelCase   : List[str]								=wi.T
				lowerCamelCase   : Tuple								=wo.T

		lowerCamelCase   : Optional[int]								=old[
		    '''encoder/relpos_bias/rel_embedding'''
		].T
		lowerCamelCase   : Dict								=old['''encoder/encoder_norm/scale''']

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCAmelCase__		):
						# Block i, layer 0 (Self Attention).
						lowerCamelCase   : int								=tax_layer_norm_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''decoder'''							,       '''pre_self_attention_layer_norm'''		)
						lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : int								=tax_attention_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''decoder'''							,       '''self_attention'''		)
						lowerCamelCase   : List[Any]								=layer_norm
						lowerCamelCase   : Union[str, Any]								=k.T
						lowerCamelCase   : List[Any]								=o.T
						lowerCamelCase   : Tuple								=q.T
						lowerCamelCase   : List[Any]								=v.T

						# Block i, layer 1 (Cross Attention).
						lowerCamelCase   : List[Any]								=tax_layer_norm_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''decoder'''							,       '''pre_cross_attention_layer_norm'''		)
						lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase		,   lowerCamelCase   : Any								=tax_attention_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''decoder'''							,       '''encoder_decoder_attention'''		)
						lowerCamelCase   : Optional[Any]								=layer_norm
						lowerCamelCase   : Optional[Any]								=k.T
						lowerCamelCase   : Optional[Any]								=o.T
						lowerCamelCase   : Dict								=q.T
						lowerCamelCase   : Any								=v.T

						# Block i, layer 2 (MLP).
						lowerCamelCase   : Any								=tax_layer_norm_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''decoder'''							,       '''pre_mlp_layer_norm'''		)
						lowerCamelCase		,   lowerCamelCase   : List[Any]								=tax_mlp_lookup(lowerCAmelCase__							,       lowerCAmelCase__							,       '''decoder'''							,       lowerCAmelCase__		)
						lowerCamelCase   : Any								=layer_norm
						if split_mlp_wi:
								lowerCamelCase   : Any								=wi[0].T
								lowerCamelCase   : int								=wi[1].T
						else:
								lowerCamelCase   : Union[str, Any]								=wi.T
						lowerCamelCase   : Optional[Any]								=wo.T

				lowerCamelCase   : Union[str, Any]								=old['''decoder/decoder_norm/scale''']
				lowerCamelCase   : Any								=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:
						lowerCamelCase   : Union[str, Any]								=old['''decoder/logits_dense/kernel'''].T

		return new


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Optional[Any]:
		lowerCamelCase   : Tuple								=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   : str								=state_dict['''shared.weight''']

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

		return state_dict


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_		)   ->     Union[str, Any]:
		lowerCamelCase   : List[str]								=checkpoints.load_tax_checkpoint(lowerCAmelCase__		)
		lowerCamelCase   : str								=convert_tax_to_pytorch(lowerCAmelCase__							,       num_layers=config.num_layers							,       is_encoder_only=lowerCAmelCase__		)
		lowerCamelCase   : List[str]								=make_state_dict(lowerCAmelCase__							,       lowerCAmelCase__		)
		model.load_state_dict(lowerCAmelCase__							,       strict=lowerCAmelCase__		)


def 					A__	(       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_ = False		)   ->     List[str]:
		lowerCamelCase   : int								=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:
				lowerCamelCase   : List[str]								=TaEncoderModel(lowerCAmelCase__		)
		else:
				lowerCamelCase   : Optional[Any]								=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__":
				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
				)
				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
				)








from abc import ABC, abstractmethod
from typing import Optional, Union

from .. import Dataset, DatasetDict, Features, IterableDataset, IterableDatasetDict, NamedSplit
from ..utils.typing import NestedDataStructureLike, PathLike
class        snake_case_		(			_A):







		def __init__(		self			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = None			,     __lowercase = False			,     __lowercase = False			,     __lowercase = None			,     **__lowercase			,     )				->   List[Any]:
				lowerCamelCase   : Dict								=path_or_paths
				lowerCamelCase   : Optional[int]								=split if split or isinstance(__lowercase			,     __lowercase   ) else '''train'''
				lowerCamelCase   : Tuple								=features
				lowerCamelCase   : Tuple								=cache_dir
				lowerCamelCase   : Optional[Any]								=keep_in_memory
				lowerCamelCase   : Any								=streaming
				lowerCamelCase   : Dict								=num_proc
				lowerCamelCase   : List[Any]								=kwargs







		@abstractmethod
		def 			__lowercase   (		self   )				->   Union[Dataset, DatasetDict, IterableDataset, IterableDatasetDict]:
				pass

class        snake_case_		(			_A):







		def __init__(		self			,     __lowercase = None			,     __lowercase = None			,     __lowercase = False			,     __lowercase = False			,     __lowercase = None			,     **__lowercase			,     )				->   List[str]:
				lowerCamelCase   : Tuple								=features
				lowerCamelCase   : Union[str, Any]								=cache_dir
				lowerCamelCase   : Optional[int]								=keep_in_memory
				lowerCamelCase   : List[str]								=streaming
				lowerCamelCase   : List[str]								=num_proc
				lowerCamelCase   : List[str]								=kwargs







		@abstractmethod
		def 			__lowercase   (		self   )				->   Union[Dataset, IterableDataset]:
				pass

