

'''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 	_A  (					A__  ,       A__  ,       A__  ,       A__="attention"   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       A__  ,       A__  ,       A__=False   ):






 """simple docstring"""



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

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


def 	_A  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



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


def 	_A  (					A__  ,       *, A__  ,       A__   ):






 """simple docstring"""



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

 __lowercase													=				collections.OrderedDict()

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

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

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

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

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

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

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

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

 return new


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



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

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

 return state_dict


def 	_A  (					A__  ,       A__  ,       A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				checkpoints.load_tax_checkpoint(A__   )
 __lowercase													=				convert_tax_to_pytorch(A__  ,       num_layers=config.num_layers  ,       is_encoder_only=A__   )
 __lowercase													=				make_state_dict(A__  ,       A__   )
 model.load_state_dict(A__  ,       strict=A__   )


def 	_A  (					A__  ,       A__  ,       A__  ,       A__ = False   ):






 """simple docstring"""



 __lowercase													=				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:
  __lowercase													=				TaEncoderModel(A__   )
 else:
  __lowercase													=				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__":
 lowerCAmelCase__      		=			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__      		=			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'''






from __future__ import annotations

import matplotlib.pyplot as plt  # type: ignore
import numpy

# initial triangle of Koch snowflake
lowerCAmelCase__      		=			numpy.array([0, 0])
lowerCAmelCase__      		=			numpy.array([0.5, 0.8_660_254])
lowerCAmelCase__      		=			numpy.array([1, 0])
lowerCAmelCase__      		=			[VECTOR_1, VECTOR_2, VECTOR_3, VECTOR_1]


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				initial_vectors
 for _ in range(A__   ):
  __lowercase													=				iteration_step(A__   )
 return vectors


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				[]
 for i, start_vector in enumerate(vectors[:-1]   ):
  __lowercase													=				vectors[i + 1]
  new_vectors.append(A__   )
  __lowercase													=				end_vector - start_vector
  new_vectors.append(start_vector + difference_vector / 3   )
  new_vectors.append(
      start_vector + difference_vector / 3 + rotate(difference_vector / 3  ,       60   )   )
  new_vectors.append(start_vector + difference_vector * 2 / 3   )
 new_vectors.append(vectors[-1]   )
 return new_vectors


def 	_A  (					A__  ,       A__   ):






 """simple docstring"""



 __lowercase													=				numpy.radians(A__   )
 __lowercase						,			__lowercase													=				numpy.cos(A__   ), numpy.sin(A__   )
 __lowercase													=				numpy.array(((c, -s), (s, c))   )
 return numpy.dot(A__  ,       A__   )


def 	_A  (					A__   ):






 """simple docstring"""



 __lowercase													=				plt.gca()
 axes.set_aspect('''equal'''   )

 # matplotlib.pyplot.plot takes a list of all x-coordinates and a list of all
 # y-coordinates as inputs, which are constructed from the vector-list using
 # zip()
 __lowercase						,			__lowercase													=				zip(*A__   )
 plt.plot(A__  ,       A__   )
 plt.show()


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCAmelCase__      		=			iterate(INITIAL_VECTORS, 5)
 plot(processed_vectors)

