
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__:   Union[str, Any]		,  snake_case__:   Tuple		,  snake_case__:   List[Any]   ):


				'''simple docstring'''
				return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]




def 							a		(				snake_case__:   Tuple		,  snake_case__:   List[Any]		,  snake_case__:   Dict		,  snake_case__:   str="attention"   ):


				'''simple docstring'''
				lowercase_	    =   lowercase_	    =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :]   )
				lowercase_	    =   k_tmp.reshape(k_tmp.shape[0]		,  k_tmp.shape[1] * k_tmp.shape[2]   )
				lowercase_	    =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :]   )
				lowercase_	    =   o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,  o_tmp.shape[2]   )
				lowercase_	    =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :]   )
				lowercase_	    =   q_tmp.reshape(q_tmp.shape[0]		,  q_tmp.shape[1] * q_tmp.shape[2]   )
				lowercase_	    =   np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :]   )
				lowercase_	    =   v_tmp.reshape(v_tmp.shape[0]		,  v_tmp.shape[1] * v_tmp.shape[2]   )
				return k, o, q, v




def 							a		(				snake_case__:   int		,  snake_case__:   int		,  snake_case__:   Any		,  snake_case__:   Optional[int]=False   ):


				'''simple docstring'''
				if split_mlp_wi:
								lowercase_	    =   params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
								lowercase_	    =   params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
								lowercase_	    =   (wi_a, wi_a)
				else:
								lowercase_	    =   params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

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




def 							a		(				snake_case__:   str		,  snake_case__:   Optional[Any]		,  snake_case__:   List[str]		,  snake_case__:   Tuple   ):


				'''simple docstring'''
				return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]




def 							a		(				snake_case__:   dict		,  *, snake_case__:   int		,  snake_case__:   bool		,  snake_case__:   bool = False   ):


				'''simple docstring'''
				lowercase_	    =   traverse_util.flatten_dict(variables['''target''']   )
				lowercase_	    =   {'''/'''.join(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
				lowercase_	    =   '''encoder/encoder/mlp/wi_0/kernel''' in old
				print('''Split MLP:'''		,  snake_case__   )

				lowercase_	    =   collections.OrderedDict()

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

				# Encoder.
				for i in range(snake_case__   ):
								# Block i, layer 0 (Self Attention).
								lowercase_	    =   tax_layer_norm_lookup(snake_case__		,  snake_case__		,  '''encoder'''		,  '''pre_attention_layer_norm'''   )
								lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   tax_attention_lookup(snake_case__		,  snake_case__		,  '''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(snake_case__		,  snake_case__		,  '''encoder'''		,  '''pre_mlp_layer_norm'''   )
								lowercase_    ,      lowercase_	    =   tax_mlp_lookup(snake_case__		,  snake_case__		,  '''encoder'''		,  snake_case__   )
								lowercase_	    =   layer_norm
								if split_mlp_wi:
												lowercase_	    =   wi[0].T
												lowercase_	    =   wi[1].T
								else:
												lowercase_	    =   wi.T
								lowercase_	    =   wo.T
								if scalable_attention:
												# convert the rel_embedding of each layer
												lowercase_	    =   tax_relpos_bias_lookup(
												    snake_case__		,  snake_case__		,  '''encoder'''   ).T

				lowercase_	    =   old['''encoder/encoder_norm/scale''']

				if not scalable_attention:
								lowercase_	    =   tax_relpos_bias_lookup(
								    snake_case__		,  0		,  '''encoder'''   ).T
								lowercase_	    =   tax_relpos_bias_lookup(
								    snake_case__		,  0		,  '''decoder'''   ).T

				if not is_encoder_only:
								# Decoder.
								for i in range(snake_case__   ):
												# Block i, layer 0 (Self Attention).
												lowercase_	    =   tax_layer_norm_lookup(snake_case__		,  snake_case__		,  '''decoder'''		,  '''pre_self_attention_layer_norm'''   )
												lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   tax_attention_lookup(snake_case__		,  snake_case__		,  '''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(snake_case__		,  snake_case__		,  '''decoder'''		,  '''pre_cross_attention_layer_norm'''   )
												lowercase_    ,      lowercase_    ,      lowercase_    ,      lowercase_	    =   tax_attention_lookup(snake_case__		,  snake_case__		,  '''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(snake_case__		,  snake_case__		,  '''decoder'''		,  '''pre_mlp_layer_norm'''   )
												lowercase_    ,      lowercase_	    =   tax_mlp_lookup(snake_case__		,  snake_case__		,  '''decoder'''		,  snake_case__   )
												lowercase_	    =   layer_norm
												if split_mlp_wi:
																lowercase_	    =   wi[0].T
																lowercase_	    =   wi[1].T
												else:
																lowercase_	    =   wi.T
												lowercase_	    =   wo.T

												if scalable_attention:
																# convert the rel_embedding of each layer
																lowercase_	    =   tax_relpos_bias_lookup(snake_case__		,  snake_case__		,  '''decoder'''   ).T

								lowercase_	    =   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_	    =   old['''decoder/logits_dense/kernel'''].T

				return new




def 							a		(				snake_case__:   List[str]		,  snake_case__:   bool   ):


				'''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		(				snake_case__:   Optional[int]		,  snake_case__:   int		,  snake_case__:   int		,  snake_case__:   int		,  snake_case__:   Union[str, Any]   ):


				'''simple docstring'''
				lowercase_	    =   checkpoints.load_tax_checkpoint(snake_case__   )
				lowercase_	    =   convert_tax_to_pytorch(
				    snake_case__		,  num_layers=config.num_layers		,  is_encoder_only=snake_case__		,  scalable_attention=snake_case__   )
				lowercase_	    =   make_state_dict(snake_case__		,  snake_case__   )
				model.load_state_dict(snake_case__		,  strict=snake_case__   )




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   List[Any]		,  snake_case__:   Optional[int]		,  snake_case__:   bool = False		,  snake_case__:   bool = False		,  ):


				'''simple docstring'''
				lowercase_	    =   MTaConfig.from_json_file(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:
								lowercase_	    =   UMTaEncoderModel(snake_case__   )
				else:
								lowercase_	    =   UMTaForConditionalGeneration(snake_case__   )

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

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

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


if __name__ == "__main__":
						__a         =   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,
						)
						__a         =   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'''





import functools





def   a_   (		_UpperCAmelCase    :      list[int] ,_UpperCAmelCase    :      list[int]				)    ->  int:

      # Validation
      if not isinstance(_UpperCAmelCase ,_UpperCAmelCase				) or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for day in days				):
            raise ValueError('The parameter days should be a list of integers'				)

      if len(_UpperCAmelCase				) != 3 or not all(isinstance(_UpperCAmelCase ,_UpperCAmelCase				) for cost in costs				):
            raise ValueError('The parameter costs should be a list of three integers'				)

      if len(_UpperCAmelCase				) == 0:
            return 0

      if min(_UpperCAmelCase				) <= 0:
            raise ValueError('All days elements should be greater than 0'				)

      if max(_UpperCAmelCase				) >= 3_66:
            raise ValueError('All days elements should be less than 366'				)

      __snake_case :   str													=    set(_UpperCAmelCase				)

      @functools.cache
      def dynamic_programming(_UpperCAmelCase    :      int				) -> int:
            if index > 3_65:
                  return 0

            if index not in days_set:
                  return dynamic_programming(index + 1				)

            return min(
                costs[0] + dynamic_programming(index + 1				) ,costs[1] + dynamic_programming(index + 7				) ,costs[2] + dynamic_programming(index + 30				) ,)

      return dynamic_programming(1				)


if __name__ == "__main__":
    import doctest

    doctest.testmod()



