

'''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 					_A		(	snake_case		,							snake_case		,							snake_case     )			->	List[str]:
   return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]







def 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case="attention"     )			->	int:
   _lowercase			:      Any			     =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]     )
   _lowercase			:      str			     =   k_tmp.reshape(k_tmp.shape[0]		,							k_tmp.shape[1] * k_tmp.shape[2]     )
   _lowercase			:      int			     =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]     )
   _lowercase			:      Any			     =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,							o_tmp.shape[2]     )
   _lowercase			:      List[Any]			     =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]     )
   _lowercase			:      Any			     =   q_tmp.reshape(q_tmp.shape[0]		,							q_tmp.shape[1] * q_tmp.shape[2]     )
   _lowercase			:      Tuple			     =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]     )
   _lowercase			:      Tuple			     =   v_tmp.reshape(v_tmp.shape[0]		,							v_tmp.shape[1] * v_tmp.shape[2]     )
   return k, o, q, v







def 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case=False     )			->	List[str]:






   if split_mlp_wi:
      _lowercase			:      Any			     =   params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
      _lowercase			:      Any			     =   params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
      _lowercase			:      List[Any]			     =   (wi_a, wi_a)
   else:
      _lowercase			:      Optional[Any]			     =   params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

   _lowercase			:      Optional[int]			     =   params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
   return wi, wo







def 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case     )			->	Union[str, Any]:
   return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]







def 					_A		(	snake_case		,							*, snake_case		,							snake_case		,							snake_case = False     )			->	str:
   _lowercase			:      str			     =   traverse_util.flatten_dict(variables["target"]     )
   _lowercase			:      Dict			     =   {'''/'''.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			:      str			     =   '''encoder/encoder/mlp/wi_0/kernel''' in old
   print("Split MLP:"		,							__a     )

   _lowercase			:      Any			     =   collections.OrderedDict()

   # Shared embeddings.
   _lowercase			:      List[str]			     =   old['''token_embedder/embedding''']

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

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

   _lowercase			:      str			     =   old['''encoder/encoder_norm/scale''']

   if not scalable_attention:
      _lowercase			:      Optional[int]			     =   tax_relpos_bias_lookup(
          __a		,							0		,							"encoder"     ).T
      _lowercase			:      int			     =   tax_relpos_bias_lookup(
          __a		,							0		,							"decoder"     ).T

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

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

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

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

      _lowercase			:      Dict			     =   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:
         _lowercase			:      Tuple			     =   old['''decoder/logits_dense/kernel'''].T

   return new







def 					_A		(	snake_case		,							snake_case     )			->	List[str]:
   _lowercase			:      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:
      _lowercase			:      List[str]			     =   state_dict['''shared.weight''']

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

   return state_dict







def 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case		,							snake_case     )			->	List[Any]:
   _lowercase			:      int			     =   checkpoints.load_tax_checkpoint(__a     )
   _lowercase			:      List[str]			     =   convert_tax_to_pytorch(
       __a		,							num_layers=config.num_layers		,							is_encoder_only=__a		,							scalable_attention=__a     )
   _lowercase			:      List[str]			     =   make_state_dict(__a		,							__a     )
   model.load_state_dict(__a		,							strict=__a     )







def 					_A		(	snake_case		,							snake_case		,							snake_case		,							snake_case = False		,							snake_case = False		,							)			->	List[Any]:
   _lowercase			:      int			     =   MTaConfig.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			:      Dict			     =   UMTaEncoderModel(__a     )
   else:
      _lowercase			:      Optional[int]			     =   UMTaForConditionalGeneration(__a     )

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(__a		,							__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__":
    _snake_case     =  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,
    )
    _snake_case     =  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'''
from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt


def 			a      (			__a   )  ->	bool:



					'''simple docstring'''






					UpperCamelCase__   :int												= int(number**0.5   )
					return number == sq * sq


def 			a      (			__a ,					__a ,					__a ,					__a ,					__a ,					__a   )  ->	tuple[int, int]:



					'''simple docstring'''






					UpperCamelCase__   :int												= x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
					UpperCamelCase__   :int												= x_den * y_den * z_den
					UpperCamelCase__   :int												= gcd(__a ,					__a   )
					top //= hcf
					bottom //= hcf
					return top, bottom


def 			a      (			__a = 35   )  ->	int:



					'''simple docstring'''






					UpperCamelCase__   :set												= set()
					UpperCamelCase__   :int
					UpperCamelCase__   :Fraction												= Fraction(0   )
					UpperCamelCase__   :tuple[int, int]

					for x_num in range(1 ,					order + 1   ):
										for x_den in range(x_num + 1 ,					order + 1   ):
															for y_num in range(1 ,					order + 1   ):
																				for y_den in range(y_num + 1 ,					order + 1   ):
																									# n=1
																									UpperCamelCase__   :int												= x_num * y_den + x_den * y_num
																									UpperCamelCase__   :Any												= x_den * y_den
																									UpperCamelCase__   :Tuple												= gcd(__a ,					__a   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														UpperCamelCase__   :Tuple												= add_three(
																														    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																														unique_s.add(__a   )

																									# n=2
																									UpperCamelCase__   :List[str]												= (
																									    x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
																									)
																									UpperCamelCase__   :Dict												= x_den * x_den * y_den * y_den
																									if is_sq(__a   ) and is_sq(__a   ):
																														UpperCamelCase__   :Any												= int(sqrt(__a   )   )
																														UpperCamelCase__   :Optional[int]												= int(sqrt(__a   )   )
																														UpperCamelCase__   :int												= gcd(__a ,					__a   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			UpperCamelCase__   :Tuple												= add_three(
																																			    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																																			unique_s.add(__a   )

                    # n=-1
																									UpperCamelCase__   :Tuple												= x_num * y_num
																									UpperCamelCase__   :Union[str, Any]												= x_den * y_num + x_num * y_den
																									UpperCamelCase__   :List[str]												= gcd(__a ,					__a   )
																									z_num //= hcf
																									z_den //= hcf
																									if 0 < z_num < z_den <= order:
																														UpperCamelCase__   :Union[str, Any]												= add_three(
																														    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																														unique_s.add(__a   )

																									# n=2
																									UpperCamelCase__   :Optional[Any]												= x_num * x_num * y_num * y_num
																									UpperCamelCase__   :Tuple												= (
																									    x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
																									)
																									if is_sq(__a   ) and is_sq(__a   ):
																														UpperCamelCase__   :str												= int(sqrt(__a   )   )
																														UpperCamelCase__   :Any												= int(sqrt(__a   )   )
																														UpperCamelCase__   :Dict												= gcd(__a ,					__a   )
																														z_num //= hcf
																														z_den //= hcf
																														if 0 < z_num < z_den <= order:
																																			UpperCamelCase__   :int												= add_three(
																																			    __a ,					__a ,					__a ,					__a ,					__a ,					__a   )
																																			unique_s.add(__a   )

					for num, den in unique_s:
										total += Fraction(__a ,					__a   )

					return total.denominator + total.numerator


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