



"""simple docstring"""






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      snake_case__   (		__lowerCamelCase				:    Union[str, Any]							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    List[Any]="attention"	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
			lowerCamelCase__		:		Dict			=params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
			lowerCamelCase__		:		List[str]			=params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
			lowerCamelCase__		:		Tuple			=params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
			return k, o, q, v






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Optional[Any]							,		__lowerCamelCase				:    Optional[Any]=False	):
			"""simple docstring"""
			if split_mlp_wi:
						lowerCamelCase__		:		Dict			=params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
						lowerCamelCase__		:		Tuple			=params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
						lowerCamelCase__		:		Optional[Any]			=(wi_a, wi_a)
			else:
						lowerCamelCase__		:		int			=params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

			lowerCamelCase__		:		Tuple			=params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
			return wi, wo






def      snake_case__   (		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Dict	):
			"""simple docstring"""
			return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']






def      snake_case__   (		__lowerCamelCase				:    dict							,		*, __lowerCamelCase				:    int							,		__lowerCamelCase				:    bool	):
			"""simple docstring"""
			lowerCamelCase__		:		Any			=traverse_util.flatten_dict(variables['''target''']	)
			lowerCamelCase__		:		Tuple			={'''/'''.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__		:		int			='''encoder/layers_0/mlp/wi_0/kernel''' in old
			print('''Split MLP:'''							,		__lowerCamelCase	)

			lowerCamelCase__		:		Any			=collections.OrderedDict()

			# Shared embeddings.
			lowerCamelCase__		:		Any			=old['''token_embedder/embedding''']

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

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

			lowerCamelCase__		:		str			=old[
			    '''encoder/relpos_bias/rel_embedding'''
			].T
			lowerCamelCase__		:		str			=old['''encoder/encoder_norm/scale''']

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

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

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

						lowerCamelCase__		:		Tuple			=old['''decoder/decoder_norm/scale''']
						lowerCamelCase__		:		Union[str, 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      snake_case__   (		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    bool	):
			"""simple docstring"""
			lowerCamelCase__		:		int			=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__		:		Union[str, Any]			=state_dict['''shared.weight''']

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCamelCase__		:		Union[str, Any]			=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__		:		List[Any]			=state_dict['''shared.weight''']

			return state_dict






def      snake_case__   (		__lowerCamelCase				:    List[str]							,		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    str							,		__lowerCamelCase				:    Union[str, Any]	):
			"""simple docstring"""
			lowerCamelCase__		:		str			=checkpoints.load_tax_checkpoint(__lowerCamelCase	)
			lowerCamelCase__		:		Any			=convert_tax_to_pytorch(__lowerCamelCase							,		num_layers=config.num_layers							,		is_encoder_only=__lowerCamelCase	)
			lowerCamelCase__		:		str			=make_state_dict(__lowerCamelCase							,		__lowerCamelCase	)
			model.load_state_dict(__lowerCamelCase							,		strict=__lowerCamelCase	)






def      snake_case__   (		__lowerCamelCase				:    Tuple							,		__lowerCamelCase				:    List[Any]							,		__lowerCamelCase				:    Dict							,		__lowerCamelCase				:    bool = False	):
			"""simple docstring"""
			lowerCamelCase__		:		Dict			=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[Any]			=TaEncoderModel(__lowerCamelCase	)
			else:
						lowerCamelCase__		:		str			=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__":
		_lowercase : List[str]      						=						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
		)
		_lowercase : int      						=						parser.parse_args()
		convert_tax_checkpoint_to_pytorch(
		    args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
		)









"""simple docstring"""






from __future__ import annotations






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			if b == 0:
						return (1, 0)
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		Any			=extended_euclid(__lowerCamelCase							,		a % b	)
			lowerCamelCase__		:		Optional[Any]			=a // b
			return (y, x - k * y)






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		Any			=extended_euclid(__lowerCamelCase							,		__lowerCamelCase	)
			lowerCamelCase__		:		List[Any]			=na * na
			lowerCamelCase__		:		Union[str, Any]			=ra * x * na + ra * y * na
			return (n % m + m) % m






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			((lowerCamelCase__)	,   (lowerCamelCase__))		:		int			=extended_euclid(__lowerCamelCase							,		__lowerCamelCase	)
			if b < 0:
						lowerCamelCase__		:		Any			=(b % n + n) % n
			return b






def      snake_case__   (		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int							,		__lowerCamelCase				:    int	):
			"""simple docstring"""
			lowerCamelCase__	,   lowerCamelCase__		:		Any			=invert_modulo(__lowerCamelCase							,		__lowerCamelCase	), invert_modulo(__lowerCamelCase							,		__lowerCamelCase	)
			lowerCamelCase__		:		Tuple			=na * na
			lowerCamelCase__		:		Optional[Any]			=ra * x * na + ra * y * na
			return (n % m + m) % m


if __name__ == "__main__":
		from doctest import testmod

		testmod(name="chinese_remainder_theorem", verbose=True)
		testmod(name="chinese_remainder_theorem2", verbose=True)
		testmod(name="invert_modulo", verbose=True)
		testmod(name="extended_euclid", verbose=True)




