


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 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE="attention" )->  Any:




      """simple docstring"""

      snake_case_	=				params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
      snake_case_	=				params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
      snake_case_	=				params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
      snake_case_	=				params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
      return k, o, q, v






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=False )->  Any:




      """simple docstring"""

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

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Union[str, Any]:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , *, SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

      snake_case_	=				traverse_util.flatten_dict(variables['''target'''] )
      snake_case_	=				{'''/'''.join(SCREAMING_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
      snake_case_	=				'''encoder/layers_0/mlp/wi_0/kernel''' in old
      print('''Split MLP:''' , SCREAMING_SNAKE_CASE )

      snake_case_	=				collections.OrderedDict()

      # Shared embeddings.
      snake_case_	=				old['''token_embedder/embedding''']

      # Encoder.
      for i in range(SCREAMING_SNAKE_CASE ):
            # Block i, layer 0 (Self Attention).
            snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , '''pre_attention_layer_norm''' )
            snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , '''attention''' )
            snake_case_	=				layer_norm
            snake_case_	=				k.T
            snake_case_	=				o.T
            snake_case_	=				q.T
            snake_case_	=				v.T

            # Block i, layer 1 (MLP).
            snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , '''pre_mlp_layer_norm''' )
            snake_case_	,   snake_case_	=				tax_mlp_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''encoder''' , SCREAMING_SNAKE_CASE )
            snake_case_	=				layer_norm
            if split_mlp_wi:
                  snake_case_	=				wi[0].T
                  snake_case_	=				wi[1].T
            else:
                  snake_case_	=				wi.T
            snake_case_	=				wo.T

      snake_case_	=				old[
          '''encoder/relpos_bias/rel_embedding'''
      ].T
      snake_case_	=				old['''encoder/encoder_norm/scale''']

      if not is_encoder_only:
            # Decoder.
            for i in range(SCREAMING_SNAKE_CASE ):
                  # Block i, layer 0 (Self Attention).
                  snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''pre_self_attention_layer_norm''' )
                  snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''self_attention''' )
                  snake_case_	=				layer_norm
                  snake_case_	=				k.T
                  snake_case_	=				o.T
                  snake_case_	=				q.T
                  snake_case_	=				v.T

                  # Block i, layer 1 (Cross Attention).
                  snake_case_	=				tax_layer_norm_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''pre_cross_attention_layer_norm''' )
                  snake_case_	,   snake_case_	,   snake_case_	,   snake_case_	=				tax_attention_lookup(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , '''decoder''' , '''encoder_decoder_attention''' )
                  snake_case_	=				layer_norm
                  snake_case_	=				k.T
                  snake_case_	=				o.T
                  snake_case_	=				q.T
                  snake_case_	=				v.T

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

            snake_case_	=				old['''decoder/decoder_norm/scale''']
            snake_case_	=				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:
                  snake_case_	=				old['''decoder/logits_dense/kernel'''].T

      return new






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      snake_case_	=				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:
            snake_case_	=				state_dict['''shared.weight''']

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

      return state_dict






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  str:




      """simple docstring"""

      snake_case_	=				checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE )
      snake_case_	=				convert_tax_to_pytorch(SCREAMING_SNAKE_CASE , num_layers=config.num_layers , is_encoder_only=SCREAMING_SNAKE_CASE )
      snake_case_	=				make_state_dict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      model.load_state_dict(SCREAMING_SNAKE_CASE , strict=SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False )->  Optional[Any]:




      """simple docstring"""

      snake_case_	=				TaConfig.from_json_file(SCREAMING_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:
            snake_case_	=				TaEncoderModel(SCREAMING_SNAKE_CASE )
      else:
            snake_case_	=				TaForConditionalGeneration(SCREAMING_SNAKE_CASE )

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

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

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


if __name__ == "__main__":
   UpperCAmelCase      =   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
   )
   UpperCAmelCase      =   parser.parse_args()
   convert_tax_checkpoint_to_pytorch(
       args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
   )



def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return x if y == 0 else greatest_common_divisor(SCREAMING_SNAKE_CASE , x % y )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

      return (x * y) // greatest_common_divisor(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE = 20 )->  int:




      """simple docstring"""

      snake_case_	=				1
      for i in range(1 , n + 1 ):
            snake_case_	=				lcm(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
      return g


if __name__ == "__main__":
   print(f'''{solution() = }''')