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 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: int				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Optional[int]="attention"			)							->      Tuple:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: List[Any]=False			)							->      Dict:







  """simple docstring"""





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

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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: Optional[int]				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Optional[int]			)							->      List[Any]:







  """simple docstring"""





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



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  *, __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: Optional[int]			)							->      int:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   traverse_util.flatten_dict(variables["""target"""]			)
  SCREAMING_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
  SCREAMING_SNAKE_CASE__    							=   """encoder/layers_0/mlp/wi_0/kernel""" in old
  print("""Split MLP:"""				,  SCREAMING_SNAKE_CASE_			)

  SCREAMING_SNAKE_CASE__    							=   collections.OrderedDict()

  # Shared embeddings.
  SCREAMING_SNAKE_CASE__    							=   old["""token_embedder/embedding"""]

  # Encoder.
  for i in range(SCREAMING_SNAKE_CASE_			):
    # Block i, layer 0 (Self Attention).
    SCREAMING_SNAKE_CASE__    							=   tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """encoder"""				,  """pre_attention_layer_norm"""			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   tax_attention_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """encoder"""				,  """attention"""			)
    SCREAMING_SNAKE_CASE__    							=   layer_norm
    SCREAMING_SNAKE_CASE__    							=   k.T
    SCREAMING_SNAKE_CASE__    							=   o.T
    SCREAMING_SNAKE_CASE__    							=   q.T
    SCREAMING_SNAKE_CASE__    							=   v.T

    # Block i, layer 1 (MLP).
    SCREAMING_SNAKE_CASE__    							=   tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """encoder"""				,  """pre_mlp_layer_norm"""			)
    SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   tax_mlp_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """encoder"""				,  SCREAMING_SNAKE_CASE_			)
    SCREAMING_SNAKE_CASE__    							=   layer_norm
    if split_mlp_wi:
      SCREAMING_SNAKE_CASE__    							=   wi[0].T
      SCREAMING_SNAKE_CASE__    							=   wi[1].T
    else:
      SCREAMING_SNAKE_CASE__    							=   wi.T
    SCREAMING_SNAKE_CASE__    							=   wo.T

  SCREAMING_SNAKE_CASE__    							=   old[
      """encoder/relpos_bias/rel_embedding"""
  ].T
  SCREAMING_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).
      SCREAMING_SNAKE_CASE__    							=   tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """decoder"""				,  """pre_self_attention_layer_norm"""			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   tax_attention_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """decoder"""				,  """self_attention"""			)
      SCREAMING_SNAKE_CASE__    							=   layer_norm
      SCREAMING_SNAKE_CASE__    							=   k.T
      SCREAMING_SNAKE_CASE__    							=   o.T
      SCREAMING_SNAKE_CASE__    							=   q.T
      SCREAMING_SNAKE_CASE__    							=   v.T

      # Block i, layer 1 (Cross Attention).
      SCREAMING_SNAKE_CASE__    							=   tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """decoder"""				,  """pre_cross_attention_layer_norm"""			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   tax_attention_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """decoder"""				,  """encoder_decoder_attention"""			)
      SCREAMING_SNAKE_CASE__    							=   layer_norm
      SCREAMING_SNAKE_CASE__    							=   k.T
      SCREAMING_SNAKE_CASE__    							=   o.T
      SCREAMING_SNAKE_CASE__    							=   q.T
      SCREAMING_SNAKE_CASE__    							=   v.T

      # Block i, layer 2 (MLP).
      SCREAMING_SNAKE_CASE__    							=   tax_layer_norm_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """decoder"""				,  """pre_mlp_layer_norm"""			)
      SCREAMING_SNAKE_CASE__							,  SCREAMING_SNAKE_CASE__    							=   tax_mlp_lookup(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_				,  """decoder"""				,  SCREAMING_SNAKE_CASE_			)
      SCREAMING_SNAKE_CASE__    							=   layer_norm
      if split_mlp_wi:
        SCREAMING_SNAKE_CASE__    							=   wi[0].T
        SCREAMING_SNAKE_CASE__    							=   wi[1].T
      else:
        SCREAMING_SNAKE_CASE__    							=   wi.T
      SCREAMING_SNAKE_CASE__    							=   wo.T

    SCREAMING_SNAKE_CASE__    							=   old["""decoder/decoder_norm/scale"""]
    SCREAMING_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:
      SCREAMING_SNAKE_CASE__    							=   old["""decoder/logits_dense/kernel"""].T

  return new



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: Union[str, Any]			)							->      Dict:







  """simple docstring"""





  SCREAMING_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:
    SCREAMING_SNAKE_CASE__    							=   state_dict["""shared.weight"""]

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

  return state_dict



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Dict				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: str				,  __UpperCamelCase					: Tuple			)							->      Dict:







  """simple docstring"""





  SCREAMING_SNAKE_CASE__    							=   checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_			)
  SCREAMING_SNAKE_CASE__    							=   convert_tax_to_pytorch(SCREAMING_SNAKE_CASE_				,  num_layers=config.num_layers				,  is_encoder_only=SCREAMING_SNAKE_CASE_			)
  SCREAMING_SNAKE_CASE__    							=   make_state_dict(SCREAMING_SNAKE_CASE_				,  SCREAMING_SNAKE_CASE_			)
  model.load_state_dict(SCREAMING_SNAKE_CASE_				,  strict=SCREAMING_SNAKE_CASE_			)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: Union[str, Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: List[Any]				,  __UpperCamelCase					: str = False			)							->      Optional[Any]:







  """simple docstring"""





  SCREAMING_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:
    SCREAMING_SNAKE_CASE__    							=   TaEncoderModel(SCREAMING_SNAKE_CASE_			)
  else:
    SCREAMING_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__":
  __lowerCamelCase    :				Optional[Any]							     =	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
  )
  __lowerCamelCase    :				int							     =	parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )

from math import log

from scipy.constants import Boltzmann, physical_constants

__lowerCamelCase    :				int							     =	300  # TEMPERATURE (unit = K)



def 			__SCREAMING_SNAKE_CASE						(      __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  __UpperCamelCase					: float				,  )							->      float:







  """simple docstring"""





  if donor_conc <= 0:
    raise ValueError("""Donor concentration should be positive"""			)
  elif acceptor_conc <= 0:
    raise ValueError("""Acceptor concentration should be positive"""			)
  elif intrinsic_conc <= 0:
    raise ValueError("""Intrinsic concentration should be positive"""			)
  elif donor_conc <= intrinsic_conc:
    raise ValueError(
        """Donor concentration should be greater than intrinsic concentration"""			)
  elif acceptor_conc <= intrinsic_conc:
    raise ValueError(
        """Acceptor concentration should be greater than intrinsic concentration"""			)
  else:
    return (
        Boltzmann
        * T
        * log((donor_conc * acceptor_conc) / intrinsic_conc**2			)
        / physical_constants["electron volt"][0]
    )


if __name__ == "__main__":
   import doctest

   doctest.testmod()
