
'''simple docstring'''


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch



def 			__snake_case  (     _UpperCAmelCase       :							Tuple):
   UpperCamelCase	  =	os.path.join(args.tf_model_dir,   '''parameters.json''')
   UpperCamelCase	  =	json.loads(open(_UpperCAmelCase).read())
   if not params:
      raise ValueError(
          f'It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.')
   if not args.output.endswith('''.pt'''):
      UpperCamelCase	  =	args.output + '.pt'
   UpperCamelCase	  =	OrderedDict()
   with tf.device('''/CPU:0'''):
      UpperCamelCase	  =	tf.train.load_checkpoint(args.tf_model_dir)
      UpperCamelCase	  =	reader.get_variable_to_shape_map()
      for key_name in shapes.keys():
         UpperCamelCase	  =	reader.get_tensor(_UpperCAmelCase).astype(np.floataa)
         if key_name.endswith('''/adam_m''') or key_name.endswith('''/adam_v'''):
            continue
         if key_name.startswith('''pasts/'''):
            if key_name.startswith('''pasts/mlp'''):
               UpperCamelCase	  =	int(key_name[9])
            elif key_name.startswith('''pasts/out'''):
               UpperCamelCase	  =	8
            UpperCamelCase	  =	'model.sqout.%d.weight' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
            UpperCamelCase	  =	vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
            UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name.startswith('''model/moe'''):
            UpperCamelCase	  =	int(key_name[9:].split('''/''')[0])
            if key_name.endswith('''/switch_gating/kernel'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.mlp.router.classifier.weight' % player
               UpperCamelCase	  =	vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/softmlp/kernel'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.soft_bypass_mlp.weight' % player
               UpperCamelCase	  =	vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/wo/kernel''') or key_name.endswith('''/wi/kernel'''):
               UpperCamelCase	  =	key_name[-9:-7]
               for i in range(16):
                  UpperCamelCase	  =	'model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight' % (player, i, nlayer)
                  UpperCamelCase	  =	(
                      vnp[i].transpose([1, 0]).copy()
                  )  # In Mesh-Tensorflow, it is one array, so it is divided
                  UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name.startswith('''model/mlp'''):
            UpperCamelCase	  =	int(key_name[9:].split('''/''')[0])
            if key_name.endswith('''/p1/kernel'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.mlp.wi.weight' % player
               UpperCamelCase	  =	vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/p1/bias'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.mlp.wi.bias' % player
               UpperCamelCase	  =	vnp.copy()  # same because it is one dimensional
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/p2/kernel'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.mlp.wo.weight' % player
               UpperCamelCase	  =	vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/p2/bias'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.mlp.wo.bias' % player
               UpperCamelCase	  =	vnp.copy()  # same because it is one dimensional
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name.startswith('''model/ln'''):
            UpperCamelCase	  =	int(key_name[8:].split('''/''')[0])
            if key_name.endswith('''/b'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.norm.bias' % player
               UpperCamelCase	  =	vnp.copy()  # same because it is one dimensional
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/g'''):
               UpperCamelCase	  =	'model.blocks.%d.feed_forward.norm.weight' % player
               UpperCamelCase	  =	vnp.copy()  # same because it is one dimensional
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name.startswith('''model/att'''):
            UpperCamelCase	  =	int(key_name[9:].split('''/''')[0])
            if key_name.endswith('''/qkv/kernel'''):
               UpperCamelCase	  =	vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
               UpperCamelCase	  =	state[:, 0, :, :]
               UpperCamelCase	  =	state[:, 1, :, :]
               UpperCamelCase	  =	state[:, 2, :, :]
               UpperCamelCase	  =	(
                   state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]])
                   .transpose([1, 0])
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	(
                   state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]])
                   .transpose([1, 0])
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	(
                   state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]])
                   .transpose([1, 0])
                   .copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	'model.blocks.%d.self_attn.self_attn.q_proj.weight' % player
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
               UpperCamelCase	  =	'model.blocks.%d.self_attn.self_attn.k_proj.weight' % player
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
               UpperCamelCase	  =	'model.blocks.%d.self_attn.self_attn.v_proj.weight' % player
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/o/kernel'''):
               UpperCamelCase	  =	'model.blocks.%d.self_attn.self_attn.out_proj.weight' % player
               UpperCamelCase	  =	(
                   vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]).transpose([1, 0]).copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name.startswith('''model/an'''):
            UpperCamelCase	  =	int(key_name[8:].split('''/''')[0])
            if key_name.endswith('''/b'''):
               UpperCamelCase	  =	'model.blocks.%d.self_attn.norm.bias' % player
               UpperCamelCase	  =	vnp.copy()  # same because it is one dimensional
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            elif key_name.endswith('''/g'''):
               UpperCamelCase	  =	'model.blocks.%d.self_attn.norm.weight' % player
               UpperCamelCase	  =	vnp.copy()  # same because it is one dimensional
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif (
             key_name.startswith('''model/wte''')
             or key_name.startswith('''model/wpe''')
             or key_name.startswith('''model/ete''')
         ):
            UpperCamelCase	  =	{'wte': 'embed_tokens', 'wpe': 'position_embeddings', 'ete': 'extra_position_embeddings'}[
                key_name[-3:]
            ]
            UpperCamelCase	  =	'model.%s.weight' % nlayer
            UpperCamelCase	  =	vnp.copy()  # same in embedded
            UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
            if key_name.startswith('''model/wte'''):
               UpperCamelCase	  =	'lm_head.weight'
               UpperCamelCase	  =	vnp.copy()  # same in embedded
               UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name.startswith('''model/wob'''):
            UpperCamelCase	  =	'final_logits_bias'
            UpperCamelCase	  =	vnp.copy()  # same in embedded
            UpperCamelCase	  =	state.reshape((1, -1))
            UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name == "model/dense/kernel":
            UpperCamelCase	  =	'model.last_project.weight'
            UpperCamelCase	  =	vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
            UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
         elif key_name == "model/dense_1/bias":
            UpperCamelCase	  =	'model.last_project.bias'
            UpperCamelCase	  =	vnp.copy()  # same because it is one dimensional
            UpperCamelCase	  =	torch.tensor(_UpperCAmelCase)
   torch.save(_UpperCAmelCase,   args.output)


if __name__ == "__main__":
   snake_case_			:	Union[str, Any] 	=       argparse.ArgumentParser(
       description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
   )
   parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
   parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
   snake_case_			:	Optional[Any] 	=       parser.parse_args()
   convert_tf_gptsan_to_pt(args)













"""simple docstring"""
from typing import List, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase__    						=			logging.get_logger(__name__)

lowerCAmelCase__    						=			{
    'huggingface/time-series-transformer-tourism-monthly': (
        'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
    ),
    # See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}


class _lowerCAmelCase						(    __UpperCAmelCase       ):
						SCREAMING_SNAKE_CASE_:					Any 					=				'time_series_transformer'
						SCREAMING_SNAKE_CASE_:					List[str] 					=				{
						    'hidden_size': 'd_model',
						    'num_attention_heads': 'encoder_attention_heads',
						    'num_hidden_layers': 'encoder_layers',
						}







						def __init__(    self   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = "student_t"   ,				lowerCAmelCase_ = "nll"   ,				lowerCAmelCase_ = 1   ,				lowerCAmelCase_ = [1, 2, 3, 4, 5, 6, 7]   ,				lowerCAmelCase_ = "mean"   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = 0   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = None   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 3_2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = 2   ,				lowerCAmelCase_ = True   ,				lowerCAmelCase_ = "gelu"   ,				lowerCAmelCase_ = 6_4   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 0.1   ,				lowerCAmelCase_ = 1_0_0   ,				lowerCAmelCase_ = 0.02   ,				lowerCAmelCase_=True   ,				**lowerCAmelCase_   ,				)		->							str:
										# time series specific configuration
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    prediction_length
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    context_length or prediction_length
										_SCREAMING_SNAKE_CASE   :		Any       		=    distribution_output
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    loss
										_SCREAMING_SNAKE_CASE   :		Any       		=    input_size
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_time_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    lags_sequence
										_SCREAMING_SNAKE_CASE   :		Dict       		=    scaling
										_SCREAMING_SNAKE_CASE   :		Any       		=    num_dynamic_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_real_features
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    num_static_categorical_features
										if cardinality and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The cardinality should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    cardinality
										else:
														_SCREAMING_SNAKE_CASE   :		Dict       		=    [0]
										if embedding_dimension and num_static_categorical_features > 0:
														if len(lowerCAmelCase_							) != num_static_categorical_features:
																		raise ValueError(
																		    'The embedding dimension should be a list of the same length as `num_static_categorical_features`'							)
														_SCREAMING_SNAKE_CASE   :		int       		=    embedding_dimension
										else:
														_SCREAMING_SNAKE_CASE   :		List[str]       		=    [min(5_0   ,				(cat + 1) // 2							) for cat in self.cardinality]
										_SCREAMING_SNAKE_CASE   :		str       		=    num_parallel_samples

										# Transformer architecture configuration
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    input_size * len(lowerCAmelCase_							) + self._number_of_features
										_SCREAMING_SNAKE_CASE   :		int       		=    d_model
										_SCREAMING_SNAKE_CASE   :		str       		=    encoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		Optional[Any]       		=    decoder_attention_heads
										_SCREAMING_SNAKE_CASE   :		List[Any]       		=    encoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Dict       		=    decoder_ffn_dim
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layers
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    decoder_layers

										_SCREAMING_SNAKE_CASE   :		Dict       		=    dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    attention_dropout
										_SCREAMING_SNAKE_CASE   :		Tuple       		=    activation_dropout
										_SCREAMING_SNAKE_CASE   :		Union[str, Any]       		=    encoder_layerdrop
										_SCREAMING_SNAKE_CASE   :		List[str]       		=    decoder_layerdrop

										_SCREAMING_SNAKE_CASE   :		Dict       		=    activation_function
										_SCREAMING_SNAKE_CASE   :		str       		=    init_std

										_SCREAMING_SNAKE_CASE   :		Tuple       		=    use_cache

										super().__init__(is_encoder_decoder=lowerCAmelCase_   ,				**lowerCAmelCase_							)







						@property
						def 			A      (    self							)		->							int:
										return (
										    sum(self.embedding_dimension							)
										    + self.num_dynamic_real_features
										    + self.num_time_features
										    + self.num_static_real_features
										    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
										)

