
"""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 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  int      ,     lowercase__		:  Tuple		)  ->				Any:


			'''simple docstring'''





			return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]






def 						_snake_case       (	lowercase__		:  Union[str, Any]      ,     lowercase__		:  List[Any]      ,     lowercase__		:  Any      ,     lowercase__		:  Tuple="attention"		)  ->				Union[str, Any]:


			'''simple docstring'''





			lowerCAmelCase_    :Optional[Any]			     =  np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]		)
			lowerCAmelCase_    :str			     =  k_tmp.reshape(k_tmp.shape[0]      ,     k_tmp.shape[1] * k_tmp.shape[2]		)
			lowerCAmelCase_    :Any			     =  np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]		)
			lowerCAmelCase_    :Tuple			     =  o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]      ,     o_tmp.shape[2]		)
			lowerCAmelCase_    :str			     =  np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]		)
			lowerCAmelCase_    :Dict			     =  q_tmp.reshape(q_tmp.shape[0]      ,     q_tmp.shape[1] * q_tmp.shape[2]		)
			lowerCAmelCase_    :Optional[Any]			     =  np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]		)
			lowerCAmelCase_    :Optional[Any]			     =  v_tmp.reshape(v_tmp.shape[0]      ,     v_tmp.shape[1] * v_tmp.shape[2]		)
			return k, o, q, v






def 						_snake_case       (	lowercase__		:  Tuple      ,     lowercase__		:  Tuple      ,     lowercase__		:  Dict      ,     lowercase__		:  Optional[int]=False		)  ->				List[Any]:


			'''simple docstring'''





			if split_mlp_wi:
						lowerCAmelCase_    :Tuple			     =  params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
						lowerCAmelCase_    :Optional[Any]			     =  params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
						lowerCAmelCase_    :List[Any]			     =  (wi_a, wi_a)
			else:
						lowerCAmelCase_    :List[str]			     =  params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

			lowerCAmelCase_    :Union[str, Any]			     =  params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
			return wi, wo






def 						_snake_case       (	lowercase__		:  int      ,     lowercase__		:  Dict      ,     lowercase__		:  List[str]      ,     lowercase__		:  Optional[int]		)  ->				List[str]:


			'''simple docstring'''





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






def 						_snake_case       (	lowercase__		:  dict      ,     *, lowercase__		:  int      ,     lowercase__		:  bool      ,     lowercase__		:  bool = False		)  ->				List[str]:


			'''simple docstring'''





			lowerCAmelCase_    :int			     =  traverse_util.flatten_dict(variables["""target"""]		)
			lowerCAmelCase_    :Optional[int]			     =  {"""/""".join(lowercase__		): v for k, v in old.items()}

			# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
			lowerCAmelCase_    :Union[str, Any]			     =  """encoder/encoder/mlp/wi_0/kernel""" in old
			print("""Split MLP:"""      ,     lowercase__		)

			lowerCAmelCase_    :List[Any]			     =  collections.OrderedDict()

			# Shared embeddings.
			lowerCAmelCase_    :Tuple			     =  old["""token_embedder/embedding"""]

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

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

			lowerCAmelCase_    :Tuple			     =  old["""encoder/encoder_norm/scale"""]

			if not scalable_attention:
						lowerCAmelCase_    :List[str]			     =  tax_relpos_bias_lookup(
						    lowercase__      ,     0      ,     """encoder"""		).T
						lowerCAmelCase_    :Dict			     =  tax_relpos_bias_lookup(
						    lowercase__      ,     0      ,     """decoder"""		).T

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

									# Block i, layer 1 (Cross Attention).
									lowerCAmelCase_    :Tuple			     =  tax_layer_norm_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     """pre_cross_attention_layer_norm"""		)
									lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_     ,       lowerCAmelCase_    :Union[str, Any]			     =  tax_attention_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     """encoder_decoder_attention"""		)
									lowerCAmelCase_    :str			     =  layer_norm
									lowerCAmelCase_    :Optional[int]			     =  k.T
									lowerCAmelCase_    :Optional[int]			     =  o.T
									lowerCAmelCase_    :Dict			     =  q.T
									lowerCAmelCase_    :Union[str, Any]			     =  v.T

									# Block i, layer 2 (MLP).
									lowerCAmelCase_    :Optional[int]			     =  tax_layer_norm_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     """pre_mlp_layer_norm"""		)
									lowerCAmelCase_     ,       lowerCAmelCase_    :str			     =  tax_mlp_lookup(lowercase__      ,     lowercase__      ,     """decoder"""      ,     lowercase__		)
									lowerCAmelCase_    :str			     =  layer_norm
									if split_mlp_wi:
												lowerCAmelCase_    :Optional[int]			     =  wi[0].T
												lowerCAmelCase_    :Tuple			     =  wi[1].T
									else:
												lowerCAmelCase_    :str			     =  wi.T
									lowerCAmelCase_    :Tuple			     =  wo.T

									if scalable_attention:
												# convert the rel_embedding of each layer
												lowerCAmelCase_    :Dict			     =  tax_relpos_bias_lookup(lowercase__      ,     lowercase__      ,     """decoder"""		).T

						lowerCAmelCase_    :int			     =  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:
									lowerCAmelCase_    :Dict			     =  old["""decoder/logits_dense/kernel"""].T

			return new






def 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  bool		)  ->				str:


			'''simple docstring'''





			lowerCAmelCase_    :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:
						lowerCAmelCase_    :Any			     =  state_dict["""shared.weight"""]

			if not is_encoder_only:
						if "decoder.embed_tokens.weight" not in state_dict:
									lowerCAmelCase_    :Union[str, 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."""		)
									lowerCAmelCase_    :Tuple			     =  state_dict["""shared.weight"""]

			return state_dict






def 						_snake_case       (	lowercase__		:  Optional[int]      ,     lowercase__		:  Union[str, Any]      ,     lowercase__		:  str      ,     lowercase__		:  int      ,     lowercase__		:  Union[str, Any]		)  ->				Tuple:


			'''simple docstring'''





			lowerCAmelCase_    :Tuple			     =  checkpoints.load_tax_checkpoint(lowercase__		)
			lowerCAmelCase_    :List[Any]			     =  convert_tax_to_pytorch(
			    lowercase__      ,     num_layers=config.num_layers      ,     is_encoder_only=lowercase__      ,     scalable_attention=lowercase__		)
			lowerCAmelCase_    :Optional[Any]			     =  make_state_dict(lowercase__      ,     lowercase__		)
			model.load_state_dict(lowercase__      ,     strict=lowercase__		)






def 						_snake_case       (	lowercase__		:  Any      ,     lowercase__		:  Dict      ,     lowercase__		:  Union[str, Any]      ,     lowercase__		:  bool = False      ,     lowercase__		:  bool = False      ,     )  ->				Tuple:


			'''simple docstring'''





			lowerCAmelCase_    :Any			     =  MTaConfig.from_json_file(lowercase__		)
			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:
						lowerCAmelCase_    :Tuple			     =  UMTaEncoderModel(lowercase__		)
			else:
						lowerCAmelCase_    :Union[str, Any]			     =  UMTaForConditionalGeneration(lowercase__		)

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

			# Save pytorch-model
			print(f"""Save PyTorch model to {pytorch_dump_path}"""		)
			model.save_pretrained(lowercase__		)

			# Verify that we can load the checkpoint.
			model.from_pretrained(lowercase__		)
			print("""Done"""		)


if __name__ == "__main__":
				__UpperCAmelCase       =  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,
				)
				__UpperCAmelCase       =  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 math


class 				a :


    def    lowerCAmelCase_     (    self		:     Tuple      , __lowerCAmelCase		:     list[list[float]]      , __lowerCAmelCase		:     list[int]    ):
      _UpperCAmelCase         =					0.0
      _UpperCAmelCase         =					0.0
      for i in range(len(__lowerCAmelCase    )    ):
        da += math.pow((sample[i] - weights[0][i])      , 2    )
        da += math.pow((sample[i] - weights[1][i])      , 2    )
        return 0 if da > da else 1
      return 0


    def    lowerCAmelCase_     (    self		:     Union[str, Any]      , __lowerCAmelCase		:     list[list[int | float]]      , __lowerCAmelCase		:     list[int]      , __lowerCAmelCase		:     int      , __lowerCAmelCase		:     float    ):
      for i in range(len(__lowerCAmelCase    )    ):
        weights[j][i] += alpha * (sample[i] - weights[j][i])
      return weights






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  # Training Examples ( m, n )
  _UpperCAmelCase         =					[[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

  # weight initialization ( n, C )
  _UpperCAmelCase         =					[[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

  # training
  _UpperCAmelCase         =					SelfOrganizingMap()
  _UpperCAmelCase         =					3
  _UpperCAmelCase         =					0.5

  for _ in range(lowercase						):
    for j in range(len(lowercase						)						):
      # training sample
      _UpperCAmelCase         =					training_samples[j]

      # Compute the winning vector
      _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

      # Update the winning vector
      _UpperCAmelCase         =					self_organizing_map.update(lowercase      ,lowercase      ,lowercase      ,lowercase						)

    # classify test sample
  _UpperCAmelCase         =					[0, 0, 0, 1]
  _UpperCAmelCase         =					self_organizing_map.get_winner(lowercase      ,lowercase						)

  # results
  print(f'''Clusters that the test sample belongs to : {winner}'''						)
  print(f'''Weights that have been trained : {weights}'''						)


# running the main() function
if __name__ == "__main__":
    main()
