



"""simple docstring"""
import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   return params[f'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase="attention" ):
   SCREAMING_SNAKE_CASE_: Tuple     			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
   SCREAMING_SNAKE_CASE_: Tuple     			=k_tmp.reshape(k_tmp.shape[0]							,			k_tmp.shape[1] * k_tmp.shape[2] )
   SCREAMING_SNAKE_CASE_: str     			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
   SCREAMING_SNAKE_CASE_: Dict     			=o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]							,			o_tmp.shape[2] )
   SCREAMING_SNAKE_CASE_: Tuple     			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
   SCREAMING_SNAKE_CASE_: Any     			=q_tmp.reshape(q_tmp.shape[0]							,			q_tmp.shape[1] * q_tmp.shape[2] )
   SCREAMING_SNAKE_CASE_: Dict     			=np.ascontiguousarray(params[f'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
   SCREAMING_SNAKE_CASE_: List[str]     			=v_tmp.reshape(v_tmp.shape[0]							,			v_tmp.shape[1] * v_tmp.shape[2] )
   return k, o, q, v





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

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





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase ):
   return params[f'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]





def      __magic_name__				(							lowercase							,			*, lowercase							,			lowercase							,			lowercase = False ):
   SCREAMING_SNAKE_CASE_: List[Any]     			=traverse_util.flatten_dict(variables["""target"""] )
   SCREAMING_SNAKE_CASE_: Optional[int]     			={"""/""".join(lowercase ): v for k, v in old.items()}

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

   SCREAMING_SNAKE_CASE_: Tuple     			=collections.OrderedDict()

   # Shared embeddings.
   SCREAMING_SNAKE_CASE_: List[Any]     			=old["""token_embedder/embedding"""]

   # Encoder.
   for i in range(lowercase ):
      # Block i, layer 0 (Self Attention).
      SCREAMING_SNAKE_CASE_: int     			=tax_layer_norm_lookup(lowercase							,			lowercase							,			"""encoder"""							,			"""pre_attention_layer_norm""" )
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Dict     			=tax_attention_lookup(lowercase							,			lowercase							,			"""encoder"""							,			"""attention""" )
      SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=k.T
      SCREAMING_SNAKE_CASE_: Tuple     			=o.T
      SCREAMING_SNAKE_CASE_: Any     			=q.T
      SCREAMING_SNAKE_CASE_: Optional[int]     			=v.T

      # Block i, layer 1 (MLP).
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=tax_layer_norm_lookup(lowercase							,			lowercase							,			"""encoder"""							,			"""pre_mlp_layer_norm""" )
      SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Tuple     			=tax_mlp_lookup(lowercase							,			lowercase							,			"""encoder"""							,			lowercase )
      SCREAMING_SNAKE_CASE_: List[str]     			=layer_norm
      if split_mlp_wi:
         SCREAMING_SNAKE_CASE_: Dict     			=wi[0].T
         SCREAMING_SNAKE_CASE_: Optional[int]     			=wi[1].T
      else:
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=wi.T
      SCREAMING_SNAKE_CASE_: Tuple     			=wo.T
      if scalable_attention:
         # convert the rel_embedding of each layer
         SCREAMING_SNAKE_CASE_: List[str]     			=tax_relpos_bias_lookup(
             lowercase							,			lowercase							,			"""encoder""" ).T

   SCREAMING_SNAKE_CASE_: List[Any]     			=old["""encoder/encoder_norm/scale"""]

   if not scalable_attention:
      SCREAMING_SNAKE_CASE_: List[Any]     			=tax_relpos_bias_lookup(
          lowercase							,			0							,			"""encoder""" ).T
      SCREAMING_SNAKE_CASE_: Any     			=tax_relpos_bias_lookup(
          lowercase							,			0							,			"""decoder""" ).T

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

         # Block i, layer 1 (Cross Attention).
         SCREAMING_SNAKE_CASE_: Optional[int]     			=tax_layer_norm_lookup(lowercase							,			lowercase							,			"""decoder"""							,			"""pre_cross_attention_layer_norm""" )
         SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Optional[Any]     			=tax_attention_lookup(lowercase							,			lowercase							,			"""decoder"""							,			"""encoder_decoder_attention""" )
         SCREAMING_SNAKE_CASE_: List[Any]     			=layer_norm
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=k.T
         SCREAMING_SNAKE_CASE_: Tuple     			=o.T
         SCREAMING_SNAKE_CASE_: List[Any]     			=q.T
         SCREAMING_SNAKE_CASE_: Dict     			=v.T

         # Block i, layer 2 (MLP).
         SCREAMING_SNAKE_CASE_: int     			=tax_layer_norm_lookup(lowercase							,			lowercase							,			"""decoder"""							,			"""pre_mlp_layer_norm""" )
         SCREAMING_SNAKE_CASE_			,		SCREAMING_SNAKE_CASE_: Union[str, Any]     			=tax_mlp_lookup(lowercase							,			lowercase							,			"""decoder"""							,			lowercase )
         SCREAMING_SNAKE_CASE_: Optional[Any]     			=layer_norm
         if split_mlp_wi:
            SCREAMING_SNAKE_CASE_: int     			=wi[0].T
            SCREAMING_SNAKE_CASE_: Dict     			=wi[1].T
         else:
            SCREAMING_SNAKE_CASE_: List[Any]     			=wi.T
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=wo.T

         if scalable_attention:
            # convert the rel_embedding of each layer
            SCREAMING_SNAKE_CASE_: Any     			=tax_relpos_bias_lookup(lowercase							,			lowercase							,			"""decoder""" ).T

      SCREAMING_SNAKE_CASE_: str     			=old["""decoder/decoder_norm/scale"""]

      # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
      if "decoder/logits_dense/kernel" in old:
         SCREAMING_SNAKE_CASE_: Dict     			=old["""decoder/logits_dense/kernel"""].T

   return new





def      __magic_name__				(							lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: List[str]     			=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:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=state_dict["""shared.weight"""]

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         SCREAMING_SNAKE_CASE_: 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.""" )
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=state_dict["""shared.weight"""]

   return state_dict





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase ):
   SCREAMING_SNAKE_CASE_: str     			=checkpoints.load_tax_checkpoint(lowercase )
   SCREAMING_SNAKE_CASE_: Dict     			=convert_tax_to_pytorch(
       lowercase							,			num_layers=config.num_layers							,			is_encoder_only=lowercase							,			scalable_attention=lowercase )
   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=make_state_dict(lowercase							,			lowercase )
   model.load_state_dict(lowercase							,			strict=lowercase )





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase = False							,			lowercase = False							,			):
   SCREAMING_SNAKE_CASE_: Dict     			=MTaConfig.from_json_file(lowercase )
   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:
      SCREAMING_SNAKE_CASE_: Dict     			=UMTaEncoderModel(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Dict     			=UMTaForConditionalGeneration(lowercase )

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

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

   # Verify that we can load the checkpoint.
   model.from_pretrained(lowercase )
   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
   )
   parser.add_argument(
       """--scalable_attention""",
       action="""store_true""",
       help="""Whether the model uses scaled attention (umt5 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,
       args.scalable_attention,
   )








"""simple docstring"""
def      __magic_name__				(							lowercase							,			lowercase ):
   return int((input_a, input_a).count(0 ) == 0 )





def      __magic_name__				(							):
   assert and_gate(0							,			0 ) == 0
   assert and_gate(0							,			1 ) == 0
   assert and_gate(1							,			0 ) == 0
   assert and_gate(1							,			1 ) == 1


if __name__ == "__main__":
   test_and_gate()
   print(and_gate(1, 0))
   print(and_gate(0, 0))
   print(and_gate(0, 1))
   print(and_gate(1, 1))



