





'''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     a   (    __a				,      __a				,      __a				,      __a="attention"     )						->			Any:



					'''simple docstring'''






					UpperCamelCase__     :Optional[int]							      =				params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
					UpperCamelCase__     :List[str]							      =				params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
					UpperCamelCase__     :Optional[int]							      =				params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
					UpperCamelCase__     :Optional[Any]							      =				params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
					return k, o, q, v


def     a   (    __a				,      __a				,      __a				,      __a=False     )						->			Dict:



					'''simple docstring'''






					if split_mlp_wi:
										UpperCamelCase__     :Dict							      =				params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
										UpperCamelCase__     :Tuple							      =				params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
										UpperCamelCase__     :int							      =				(wi_a, wi_a)
					else:
										UpperCamelCase__     :Tuple							      =				params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

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


def     a   (    __a				,      __a				,      __a				,      __a     )						->			Union[str, Any]:



					'''simple docstring'''






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


def     a   (    __a				,      *, __a				,      __a     )						->			Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__     :str							      =				traverse_util.flatten_dict(variables['''target''']     )
					UpperCamelCase__     :List[Any]							      =				{'''/'''.join(UpperCAmelCase__     ): v for k, v in old.items()}

					# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
					UpperCamelCase__     :int							      =				'''encoder/layers_0/mlp/wi_0/kernel''' in old
					print('''Split MLP:'''				,      UpperCAmelCase__     )

					UpperCamelCase__     :List[str]							      =				collections.OrderedDict()

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

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

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

					UpperCamelCase__     :Optional[Any]							      =				old[
					    '''encoder/relpos_bias/rel_embedding'''
					].T
					UpperCamelCase__     :Optional[Any]							      =				old['''encoder/encoder_norm/scale''']

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

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

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

										UpperCamelCase__     :Tuple							      =				old['''decoder/decoder_norm/scale''']
										UpperCamelCase__     :int							      =				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:
															UpperCamelCase__     :Any							      =				old['''decoder/logits_dense/kernel'''].T

					return new


def     a   (    __a				,      __a     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :Any							      =				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:
										UpperCamelCase__     :str							      =				state_dict['''shared.weight''']

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

					return state_dict


def     a   (    __a				,      __a				,      __a				,      __a     )						->			Optional[Any]:



					'''simple docstring'''






					UpperCamelCase__     :Dict							      =				checkpoints.load_tax_checkpoint(UpperCAmelCase__     )
					UpperCamelCase__     :List[Any]							      =				convert_tax_to_pytorch(UpperCAmelCase__				,      num_layers=config.num_layers				,      is_encoder_only=UpperCAmelCase__     )
					UpperCamelCase__     :str							      =				make_state_dict(UpperCAmelCase__				,      UpperCAmelCase__     )
					model.load_state_dict(UpperCAmelCase__				,      strict=UpperCAmelCase__     )


def     a   (    __a				,      __a				,      __a				,      __a = False     )						->			Union[str, Any]:



					'''simple docstring'''






					UpperCamelCase__     :List[str]							      =				TaConfig.from_json_file(UpperCAmelCase__     )
					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:
										UpperCamelCase__     :Dict							      =				TaEncoderModel(UpperCAmelCase__     )
					else:
										UpperCamelCase__     :Union[str, Any]							      =				TaForConditionalGeneration(UpperCAmelCase__     )

					# Load weights from tf checkpoint
					load_tax_weights_in_ta(UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__				,      UpperCAmelCase__     )

					# Save pytorch-model
					print(f'''Save PyTorch model to {pytorch_dump_path}'''     )
					model.save_pretrained(UpperCAmelCase__     )

					# Verify that we can load the checkpoint.
					model.from_pretrained(UpperCAmelCase__     )
					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
		)



'''simple docstring'''
class      lowercase			:

		"""simple docstring"""

		def __init__(	self					,     UpperCamelCase_							):

							'''simple docstring'''


							UpperCamelCase__     :Union[str, Any]							      =				n
							UpperCamelCase__     :Tuple							      =				[None] * self.n
							UpperCamelCase__     :str							      =				0  # index of the first element
							UpperCamelCase__     :List[Any]							      =				0
							UpperCamelCase__     :Dict							      =				0




		def __len__(	self							):

							'''simple docstring'''


							return self.size




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return self.size == 0




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							return False if self.is_empty() else self.array[self.front]




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							if self.size >= self.n:
												raise Exception('''QUEUE IS FULL'''							)

							UpperCamelCase__     :List[Any]							      =				data
							UpperCamelCase__     :List[Any]							      =				(self.rear + 1) % self.n
							self.size += 1
							return self




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if self.size == 0:
												raise Exception('''UNDERFLOW'''							)

							UpperCamelCase__     :Dict							      =				self.array[self.front]
							UpperCamelCase__     :Union[str, Any]							      =				None
							UpperCamelCase__     :Union[str, Any]							      =				(self.front + 1) % self.n
							self.size -= 1
							return temp