






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       (			snake_case,	snake_case,	snake_case,	snake_case="attention"			):







				"""simple docstring"""






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

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case=False			):







				"""simple docstring"""






				if split_mlp_wi:
								__magic_name__		:List[str]   =   params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
								__magic_name__		:Union[str, Any]   =   params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
								__magic_name__		:List[Any]   =   (wi_a, wi_a)
				else:
								__magic_name__		:List[Any]   =   params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

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

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






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

def   __lowercase       (			snake_case,	*, snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Tuple   =   traverse_util.flatten_dict(variables['''target''']			)
				__magic_name__		:Dict   =   {'''/'''.join(snake_case			): v for k, v in old.items()}

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

				__magic_name__		:Optional[int]   =   collections.OrderedDict()

				# Shared embeddings.
				__magic_name__		:Union[str, Any]   =   old['''token_embedder/embedding''']

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

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

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

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

												# Block i, layer 1 (Cross Attention).
												__magic_name__		:int   =   tax_layer_norm_lookup(snake_case,	snake_case,	'''decoder''',	'''pre_cross_attention_layer_norm'''			)
												__magic_name__  ,					__magic_name__  ,					__magic_name__  ,					__magic_name__		:Any   =   tax_attention_lookup(snake_case,	snake_case,	'''decoder''',	'''encoder_decoder_attention'''			)
												__magic_name__		:Union[str, Any]   =   layer_norm
												__magic_name__		:int   =   k.T
												__magic_name__		:Tuple   =   o.T
												__magic_name__		:int   =   q.T
												__magic_name__		:Union[str, Any]   =   v.T

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

								__magic_name__		:Dict   =   old['''decoder/decoder_norm/scale''']
								__magic_name__		:List[str]   =   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:
												__magic_name__		:Optional[int]   =   old['''decoder/logits_dense/kernel'''].T

				return new

def   __lowercase       (			snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:Dict   =   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:
								__magic_name__		:int   =   state_dict['''shared.weight''']

				if not is_encoder_only:
								if "decoder.embed_tokens.weight" not in state_dict:
												__magic_name__		:Optional[int]   =   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.'''			)
												__magic_name__		:Any   =   state_dict['''shared.weight''']

				return state_dict

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:int   =   checkpoints.load_tax_checkpoint(snake_case			)
				__magic_name__		:Dict   =   convert_tax_to_pytorch(snake_case,	num_layers=config.num_layers,	is_encoder_only=snake_case			)
				__magic_name__		:str   =   make_state_dict(snake_case,	snake_case			)
				model.load_state_dict(snake_case,	strict=snake_case			)

def   __lowercase       (			snake_case,	snake_case,	snake_case,	snake_case = False			):







				"""simple docstring"""






				__magic_name__		:Optional[Any]   =   TaConfig.from_json_file(snake_case			)
				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:
								__magic_name__		:Optional[Any]   =   TaEncoderModel(snake_case			)
				else:
								__magic_name__		:Union[str, Any]   =   TaForConditionalGeneration(snake_case			)

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

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

				# Verify that we can load the checkpoint.
				model.from_pretrained(snake_case			)
				print('''Done'''			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      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
					)
					SCREAMING_SNAKE_CASE__   :      Optional[Any]				=		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 multiprocessing import Lock, Pipe, Process

# lock used to ensure that two processes do not access a pipe at the same time
__UpperCamelCase   :							Optional[int]         =   Lock()



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						Tuple,       SCREAMING_SNAKE_CASE__:						str,       SCREAMING_SNAKE_CASE__:						Optional[Any],       SCREAMING_SNAKE_CASE__:						Any,       SCREAMING_SNAKE_CASE__:						List[Any]     )	->      int:

   """simple docstring"""
   global process_lock

   # we perform n swaps since after n swaps we know we are sorted
   # we *could* stop early if we are sorted already, but it takes as long to
   # find out we are sorted as it does to sort the list with this algorithm
   for i in range(0,       10     ):
      if (i + position) % 2 == 0 and r_send is not None:
         # send your value to your right neighbor
         process_lock.acquire()
         r_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your right neighbor's value
         process_lock.acquire()
         __a					  =     rr_cv[0].recv()
         process_lock.release()

         # take the lower value since you are on the left
         __a					  =     min(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
      elif (i + position) % 2 != 0 and l_send is not None:
         # send your value to your left neighbor
         process_lock.acquire()
         l_send[1].send(SCREAMING_SNAKE_CASE__     )
         process_lock.release()

         # receive your left neighbor's value
         process_lock.acquire()
         __a					  =     lr_cv[0].recv()
         process_lock.release()

         # take the higher value since you are on the right
         __a					  =     max(SCREAMING_SNAKE_CASE__,       SCREAMING_SNAKE_CASE__     )
    # after all swaps are performed, send the values back to main
   result_pipe[1].send(SCREAMING_SNAKE_CASE__     )



def  __UpperCAmelCase						(					SCREAMING_SNAKE_CASE__:						Tuple     )	->      Optional[Any]:

   """simple docstring"""
   __a					  =     []
   __a					  =     []
   # initialize the list of pipes where the values will be retrieved
   for _ in arr:
      result_pipe.append(Pipe()     )
   # creates the processes
   # the first and last process only have one neighbor so they are made outside
   # of the loop
   __a					  =     Pipe()
   __a					  =     Pipe()
   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(0, arr[0], None, temp_rs, None, temp_rr, result_pipe[0]),       )     )
   __a					  =     temp_rs
   __a					  =     temp_rr

   for i in range(1,       len(SCREAMING_SNAKE_CASE__     ) - 1     ):
      __a					  =     Pipe()
      __a					  =     Pipe()
      process_array_.append(
          Process(
              target=SCREAMING_SNAKE_CASE__,       args=(i, arr[i], temp_ls, temp_rs, temp_lr, temp_rr, result_pipe[i]),       )     )
      __a					  =     temp_rs
      __a					  =     temp_rr

   process_array_.append(
       Process(
           target=SCREAMING_SNAKE_CASE__,       args=(
               len(SCREAMING_SNAKE_CASE__     ) - 1,
               arr[len(SCREAMING_SNAKE_CASE__     ) - 1],
               temp_ls,
               None,
               temp_lr,
               None,
               result_pipe[len(SCREAMING_SNAKE_CASE__     ) - 1],
           ),       )     )

   # start the processes
   for p in process_array_:
      p.start()

   # wait for the processes to end and write their values to the list
   for p in range(0,       len(SCREAMING_SNAKE_CASE__     )     ):
      __a					  =     result_pipe[p][0].recv()
      process_array_[p].join()
   return arr



def  __UpperCAmelCase						(					)	->      Any:

   """simple docstring"""
   __a					  =     list(range(10,       0,       -1     )     )
   print('Initial List'     )
   print(*SCREAMING_SNAKE_CASE__     )
   __a					  =     odd_even_transposition(SCREAMING_SNAKE_CASE__     )
   print('Sorted List\n'     )
   print(*SCREAMING_SNAKE_CASE__     )


if __name__ == "__main__":
 main()