'''simple docstring'''

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 __UpperCamelCase( _A       :					List[str]	,      _A       :					List[str]	,      _A       :					List[str]	,      _A       :					int="attention"     ):
   '''simple docstring'''
   UpperCAmelCase__				:					int 					=	params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
   UpperCAmelCase__				:					Union[str, Any] 					=	params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
   UpperCAmelCase__				:					str 					=	params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
   UpperCAmelCase__				:					Union[str, Any] 					=	params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
   return k, o, q, v







def __UpperCamelCase( _A       :					List[Any]	,      _A       :					Dict	,      _A       :					int	,      _A       :					Any=False     ):
   '''simple docstring'''
   if split_mlp_wi:
      UpperCAmelCase__				:					List[str] 					=	params[F'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
      UpperCAmelCase__				:					Tuple 					=	params[F'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
      UpperCAmelCase__				:					int 					=	(wi_a, wi_a)
   else:
      UpperCAmelCase__				:					Tuple 					=	params[F'''{prefix}/layers_{i}/mlp/wi/kernel''']

   UpperCAmelCase__				:					Any 					=	params[F'''{prefix}/layers_{i}/mlp/wo/kernel''']
   return wi, wo







def __UpperCamelCase( _A       :					Any	,      _A       :					Dict	,      _A       :					Optional[int]	,      _A       :					Tuple     ):
   '''simple docstring'''
   return params[F'''{prefix}/layers_{i}/{layer_name}/scale''']







def __UpperCamelCase( _A       :					dict	,      *, _A       :					int	,      _A       :					bool     ):
   '''simple docstring'''
   UpperCAmelCase__				:					Any 					=	traverse_util.flatten_dict(variables['''target''']     )
   UpperCAmelCase__				:					List[str] 					=	{'''/'''.join(_A     ): v for k, v in old.items()}

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

   UpperCAmelCase__				:					Dict 					=	collections.OrderedDict()

   # Shared embeddings.
   UpperCAmelCase__				:					Dict 					=	old['''token_embedder/embedding''']

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

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

   UpperCAmelCase__				:					int 					=	old[
       '''encoder/relpos_bias/rel_embedding'''
   ].T
   UpperCAmelCase__				:					Union[str, Any] 					=	old['''encoder/encoder_norm/scale''']

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

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

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

      UpperCAmelCase__				:					int 					=	old['''decoder/decoder_norm/scale''']
      UpperCAmelCase__				:					Any 					=	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:
         UpperCAmelCase__				:					int 					=	old['''decoder/logits_dense/kernel'''].T

   return new







def __UpperCamelCase( _A       :					Optional[int]	,      _A       :					bool     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[Any] 					=	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:
      UpperCAmelCase__				:					Tuple 					=	state_dict['''shared.weight''']

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

   return state_dict







def __UpperCamelCase( _A       :					Dict	,      _A       :					List[str]	,      _A       :					Optional[int]	,      _A       :					Tuple     ):
   '''simple docstring'''
   UpperCAmelCase__				:					List[str] 					=	checkpoints.load_tax_checkpoint(_A     )
   UpperCAmelCase__				:					Dict 					=	convert_tax_to_pytorch(_A	,      num_layers=config.num_layers	,      is_encoder_only=_A     )
   UpperCAmelCase__				:					List[str] 					=	make_state_dict(_A	,      _A     )
   model.load_state_dict(_A	,      strict=_A     )







def __UpperCamelCase( _A       :					Optional[int]	,      _A       :					int	,      _A       :					List[str]	,      _A       :					bool = False     ):
   '''simple docstring'''
   UpperCAmelCase__				:					str 					=	TaConfig.from_json_file(_A     )
   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:
      UpperCAmelCase__				:					int 					=	TaEncoderModel(_A     )
   else:
      UpperCAmelCase__				:					Union[str, Any] 					=	TaForConditionalGeneration(_A     )

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

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

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


if __name__ == "__main__":
  UpperCamelCase__  :						Tuple					=      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__  :						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'''

import numpy as np







def __UpperCamelCase( _A       :					np.ndarray	,      _A       :					np.ndarray	,      _A       :					float = 1e-12	,      _A       :					int = 1_00	,      ):
   '''simple docstring'''
   assert np.shape(_A     )[0] == np.shape(_A     )[1]
   # Ensure proper dimensionality.
   assert np.shape(_A     )[0] == np.shape(_A     )[0]
   # Ensure inputs are either both complex or both real
   assert np.iscomplexobj(_A     ) == np.iscomplexobj(_A     )
   UpperCAmelCase__				:					int 					=	np.iscomplexobj(_A     )
   if is_complex:
      # Ensure complex input_matrix is Hermitian
      assert np.array_equal(_A	,      input_matrix.conj().T     )

   # Set convergence to False. Will define convergence when we exceed max_iterations
   # or when we have small changes from one iteration to next.

   UpperCAmelCase__				:					Any 					=	False
   UpperCAmelCase__				:					str 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	0
   UpperCAmelCase__				:					Optional[int] 					=	1e12

   while not convergence:
      # Multiple matrix by the vector.
      UpperCAmelCase__				:					str 					=	np.dot(_A	,      _A     )
      # Normalize the resulting output vector.
      UpperCAmelCase__				:					int 					=	w / np.linalg.norm(_A     )
      # Find rayleigh quotient
      # (faster than usual b/c we know vector is normalized already)
      UpperCAmelCase__				:					int 					=	vector.conj().T if is_complex else vector.T
      UpperCAmelCase__				:					Any 					=	np.dot(_A	,      np.dot(_A	,      _A     )     )

      # Check convergence.
      UpperCAmelCase__				:					List[Any] 					=	np.abs(lambda_ - lambda_previous     ) / lambda_
      iterations += 1

      if error <= error_tol or iterations >= max_iterations:
         UpperCAmelCase__				:					Optional[Any] 					=	True

      UpperCAmelCase__				:					Optional[int] 					=	lambda_

   if is_complex:
      UpperCAmelCase__				:					Tuple 					=	np.real(lambda_     )

   return lambda_, vector







def __UpperCamelCase( ):
   '''simple docstring'''
   UpperCAmelCase__				:					Optional[int] 					=	np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]]     )
   UpperCAmelCase__				:					int 					=	np.array([41, 4, 20]     )
   UpperCAmelCase__				:					str 					=	real_input_matrix.astype(np.complexaaa     )
   UpperCAmelCase__				:					Union[str, Any] 					=	np.triu(1J * complex_input_matrix	,      1     )
   complex_input_matrix += imag_matrix
   complex_input_matrix += -1 * imag_matrix.T
   UpperCAmelCase__				:					Tuple 					=	np.array([41, 4, 20]     ).astype(np.complexaaa     )

   for problem_type in ["real", "complex"]:
      if problem_type == "real":
         UpperCAmelCase__				:					Optional[int] 					=	real_input_matrix
         UpperCAmelCase__				:					int 					=	real_vector
      elif problem_type == "complex":
         UpperCAmelCase__				:					int 					=	complex_input_matrix
         UpperCAmelCase__				:					Tuple 					=	complex_vector

      # Our implementation.
      UpperCAmelCase__					,   UpperCAmelCase__				:					str 					=	power_iteration(_A	,      _A     )

      # Numpy implementation.

      # Get eigenvalues and eigenvectors using built-in numpy
      # eigh (eigh used for symmetric or hermetian matrices).
      UpperCAmelCase__					,   UpperCAmelCase__				:					Tuple 					=	np.linalg.eigh(_A     )
      # Last eigenvalue is the maximum one.
      UpperCAmelCase__				:					Dict 					=	eigen_values[-1]
      # Last column in this matrix is eigenvector corresponding to largest eigenvalue.
      UpperCAmelCase__				:					Union[str, Any] 					=	eigen_vectors[:, -1]

      # Check our implementation and numpy gives close answers.
      assert np.abs(eigen_value - eigen_value_max     ) <= 1e-6
      # Take absolute values element wise of each eigenvector.
      # as they are only unique to a minus sign.
      assert np.linalg.norm(np.abs(_A     ) - np.abs(_A     )     ) <= 1e-6


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  test_power_iteration()




