



import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def 				A			(		__UpperCAmelCase							,					__UpperCAmelCase							,					__UpperCAmelCase	)       ->       str:



						'''simple docstring'''






						UpperCAmelCase_     							=     TaConfig.from_pretrained(__UpperCAmelCase	)
						UpperCAmelCase_     							=     FlaxTaForConditionalGeneration(config=__UpperCAmelCase	)
						UpperCAmelCase_     							=     checkpoints.load_tax_checkpoint(__UpperCAmelCase	)

						UpperCAmelCase_     							=     '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

						# Encoder
						for layer_index in range(config.num_layers	):
												UpperCAmelCase_     							=     f"layers_{str(__UpperCAmelCase	)}"

												# Self-Attention
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												UpperCAmelCase_     							=     tax_attention_key
												UpperCAmelCase_     							=     tax_attention_out
												UpperCAmelCase_     							=     tax_attention_query
												UpperCAmelCase_     							=     tax_attention_value

												UpperCAmelCase_     							=     tax_attention_layer_norm

												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_mlp_wi_a
																		UpperCAmelCase_     							=     tax_mlp_wi_a
												else:
																		UpperCAmelCase_     							=     tax_mlp_wi

												UpperCAmelCase_     							=     tax_mlp_wo
												UpperCAmelCase_     							=     tax_mlp_layer_norm

						# Only for layer 0:
						UpperCAmelCase_     							=     tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
						UpperCAmelCase_     							=     tax_encoder_rel_embedding

						# Assigning
						UpperCAmelCase_     							=     tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
						UpperCAmelCase_     							=     tax_encoder_norm

						# Decoder
						for layer_index in range(config.num_decoder_layers	):
												UpperCAmelCase_     							=     f"layers_{str(__UpperCAmelCase	)}"

												# Self-Attention
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
												    '''scale'''
												]

												# Encoder-Decoder-Attention
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''key'''][
												    '''kernel'''
												]
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''out'''][
												    '''kernel'''
												]
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''query'''][
												    '''kernel'''
												]
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']['''value'''][
												    '''kernel'''
												]

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

												# MLP
												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
																		UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
												else:
																		UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

												# Layer Normalization
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

												# Assigning
												UpperCAmelCase_     							=     tax_attention_key
												UpperCAmelCase_     							=     tax_attention_out
												UpperCAmelCase_     							=     tax_attention_query
												UpperCAmelCase_     							=     tax_attention_value

												UpperCAmelCase_     							=     tax_pre_attention_layer_norm

												UpperCAmelCase_     							=     tax_enc_dec_attention_key
												UpperCAmelCase_     							=     tax_enc_dec_attention_out
												UpperCAmelCase_     							=     tax_enc_dec_attention_query
												UpperCAmelCase_     							=     tax_enc_dec_attention_value

												UpperCAmelCase_     							=     tax_cross_layer_norm

												if split_mlp_wi:
																		UpperCAmelCase_     							=     tax_mlp_wi_a
																		UpperCAmelCase_     							=     tax_mlp_wi_a
												else:
																		UpperCAmelCase_     							=     tax_mlp_wi

												UpperCAmelCase_     							=     tax_mlp_wo

												UpperCAmelCase_     							=     txa_mlp_layer_norm

						# Decoder Normalization
						UpperCAmelCase_     							=     tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
						UpperCAmelCase_     							=     txa_decoder_norm

						# Only for layer 0:
						UpperCAmelCase_     							=     tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
						UpperCAmelCase_     							=     tax_decoder_rel_embedding

						# Token Embeddings
						UpperCAmelCase_     							=     tax_model['''target''']['''token_embedder''']['''embedding''']
						UpperCAmelCase_     							=     txa_token_embeddings

						# LM Head (only in v1.1 checkpoints)
						if "logits_dense" in tax_model["target"]["decoder"]:
												UpperCAmelCase_     							=     tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

						flax_model.save_pretrained(__UpperCAmelCase	)
						print('''T5X Model was sucessfully converted!'''	)


if __name__ == "__main__":
							UpperCamelCase_	       =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
							)
							parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
							parser.add_argument(
							    "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
							)
							UpperCamelCase_	       =   parser.parse_args()
							convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











from __future__ import annotations

import sys






class 	A	:
     """simple docstring"""

     def __init__(			self     :      str		,     A__     :      str		,     A__     :      int	) ->				str:
           lowerCamelCase__					     = letter
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = {}




     def __repr__(			self     :      Any	) ->				str:
           return F"""{self.letter}:{self.freq}"""






class 	A	:
     """simple docstring"""

     def __init__(			self     :      Optional[Any]		,     A__     :      int		,     A__     :      Letter | TreeNode		,     A__     :      Letter | TreeNode	) ->				Any:
           lowerCamelCase__					     = freq
           lowerCamelCase__					     = left
           lowerCamelCase__					     = right

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      lowerCamelCase__					     = {}
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  lowerCamelCase__					     = chars[c] + 1 if c in chars else 1
      return sorted((Letter(lowerCamelCase   , lowerCamelCase  ) for c, f in chars.items())   , key=lambda lowerCamelCase  : x.freq  )

def 	lowerCAmelCase_	(     lowerCamelCase:				list[Letter]  )    ->	Letter | TreeNode:


      '''simple docstring'''




      lowerCamelCase__					     = letters  # type: ignore
      while len(lowerCamelCase  ) > 1:
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = response.pop(0  )
            lowerCamelCase__					     = left.freq + right.freq
            lowerCamelCase__					     = TreeNode(lowerCamelCase   , lowerCamelCase   , lowerCamelCase  )
            response.append(lowerCamelCase  )
            response.sort(key=lambda lowerCamelCase  : x.freq  )
      return response[0]

def 	lowerCAmelCase_	(     lowerCamelCase:				Letter | TreeNode   , lowerCamelCase:				str  )    ->	list[Letter]:


      '''simple docstring'''




      if isinstance(lowerCamelCase   , lowerCamelCase  ):
            lowerCamelCase__					     = bitstring
            return [root]
      lowerCamelCase__					     = root  # type: ignore
      lowerCamelCase__					     = []
      letters += traverse_tree(treenode.left   , bitstring + '0'  )
      letters += traverse_tree(treenode.right   , bitstring + '1'  )
      return letters

def 	lowerCAmelCase_	(     lowerCamelCase:				str  )    ->	None:


      '''simple docstring'''




      lowerCamelCase__					     = parse_file(lowerCamelCase  )
      lowerCamelCase__					     = build_tree(lowerCamelCase  )
      lowerCamelCase__					     = {
          k: v for letter in traverse_tree(lowerCamelCase   , ''  ) for k, v in letter.bitstring.items()
      }
      print(f"""Huffman Coding  of {file_path}: """  )
      with open(lowerCamelCase  ) as f:
            while True:
                  lowerCamelCase__					     = f.read(1  )
                  if not c:
                        break
                  print(letters[c]   , end=' '  )
      print()


if __name__ == "__main__":
      # pass the file path to the huffman function
      huffman(sys.argv[1])