


'''simple docstring'''

from typing import List, Optional, Union

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


a_  :				List[Any]						= logging.get_logger(__name__)

a_  :				List[Any]						= {
    """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      snake_case    (    A_			):





  """simple docstring"""




  _lowerCamelCase		    =  "time_series_transformer"
  _lowerCamelCase		    =  {
      "hidden_size": "d_model",
      "num_attention_heads": "encoder_attention_heads",
      "num_hidden_layers": "encoder_layers",
  }






  def __init__(    self			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = "student_t"			,  UpperCamelCase = "nll"			,  UpperCamelCase = 1			,  UpperCamelCase = [1, 2, 3, 4, 5, 6, 7]			,  UpperCamelCase = "mean"			,  UpperCamelCase = 0			,  UpperCamelCase = 0			,  UpperCamelCase = 0			,  UpperCamelCase = 0			,  UpperCamelCase = None			,  UpperCamelCase = None			,  UpperCamelCase = 32			,  UpperCamelCase = 32			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  UpperCamelCase = 2			,  UpperCamelCase = True			,  UpperCamelCase = "gelu"			,  UpperCamelCase = 64			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 0.1			,  UpperCamelCase = 100			,  UpperCamelCase = 0.02			,  UpperCamelCase=True			,  **UpperCamelCase			,  ):







   """simple docstring"""
   # time series specific configuration
   lowerCamelCase_      =  prediction_length
   lowerCamelCase_      =  context_length or prediction_length
   lowerCamelCase_      =  distribution_output
   lowerCamelCase_      =  loss
   lowerCamelCase_      =  input_size
   lowerCamelCase_      =  num_time_features
   lowerCamelCase_      =  lags_sequence
   lowerCamelCase_      =  scaling
   lowerCamelCase_      =  num_dynamic_real_features
   lowerCamelCase_      =  num_static_real_features
   lowerCamelCase_      =  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`"     )
    lowerCamelCase_      =  cardinality
   else:
    lowerCamelCase_      =  [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`"     )
    lowerCamelCase_      =  embedding_dimension
   else:
    lowerCamelCase_      =  [min(50			,  (cat + 1) // 2     ) for cat in self.cardinality]
   lowerCamelCase_      =  num_parallel_samples

   # Transformer architecture configuration
   lowerCamelCase_      =  input_size * len(_lowerCamelCase     ) + self._number_of_features
   lowerCamelCase_      =  d_model
   lowerCamelCase_      =  encoder_attention_heads
   lowerCamelCase_      =  decoder_attention_heads
   lowerCamelCase_      =  encoder_ffn_dim
   lowerCamelCase_      =  decoder_ffn_dim
   lowerCamelCase_      =  encoder_layers
   lowerCamelCase_      =  decoder_layers

   lowerCamelCase_      =  dropout
   lowerCamelCase_      =  attention_dropout
   lowerCamelCase_      =  activation_dropout
   lowerCamelCase_      =  encoder_layerdrop
   lowerCamelCase_      =  decoder_layerdrop

   lowerCamelCase_      =  activation_function
   lowerCamelCase_      =  init_std

   lowerCamelCase_      =  use_cache

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







  @property
  def   snake_case		(    self     ):







   """simple docstring"""
   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
   )





"""simple docstring"""



import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Optional[int]:
  _snake_case					     =       checkpoints.load_tax_checkpoint(__lowerCamelCase		)
  _snake_case					     =       flatten_dict(__lowerCamelCase		)
  return flax_params
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Optional[int]:
  _snake_case					     =       {}

  _snake_case					     =       {
      '''token_embedder''': '''embeddings''',
      '''encoder_norm''': '''layernorm''',
      '''kernel''': '''weight''',
      '''.out''': '''.output''',
      '''scale''': '''weight''',
      '''embedders_0.pos_embedding''': '''row_embedder.weight''',
      '''embedders_1.pos_embedding''': '''column_embedder.weight''',
  }

  _snake_case					     =       {
      '''query''': '''attention.query''',
      '''key''': '''attention.key''',
      '''value''': '''attention.value''',
      '''output.dense''': '''output''',
      '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
      '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
      '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
      '''mlp.''': '''mlp.DenseReluDense.''',
      '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
      '''self_attention.o''': '''self_attention.attention.o''',
      '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
      '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
      '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
      '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
  }

  for key in flax_dict.keys():
    if "target" in key:
      # remove the first prefix from the key
      _snake_case					     =       '''.'''.join(key[1:]		)

      # rename the key
      for old, new in CONVERSION_MAPPING.items():
        _snake_case					     =       new_key.replace(__lowerCamelCase ,					__lowerCamelCase		)

      if "decoder" in new_key:
        for old, new in DECODER_CONVERSION_MAPPING.items():
          _snake_case					     =       new_key.replace(__lowerCamelCase ,					__lowerCamelCase		)

      if "layers" in new_key and "decoder" not in new_key:
        # use regex to replace the layer number
        _snake_case					     =       re.sub(R'''layers_(\d+)''' ,					R'''layer.\1''' ,					__lowerCamelCase		)
        _snake_case					     =       new_key.replace('''encoder''' ,					'''encoder.encoder'''		)

      elif "layers" in new_key and "decoder" in new_key:
        # use regex to replace the layer number
        _snake_case					     =       re.sub(R'''layers_(\d+)''' ,					R'''layer.\1''' ,					__lowerCamelCase		)

      _snake_case					     =       flax_dict[key]

  _snake_case					     =       {}
  # convert converted_dict into torch format
  for key in converted_dict.keys():
    if ("embed_tokens" not in key) and ("embedder" not in key):
      _snake_case					     =       torch.from_numpy(converted_dict[key].T		)
    else:
      _snake_case					     =       torch.from_numpy(converted_dict[key]		)

  return converted_torch_dict
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any=False ,					__lowerCamelCase   :       Optional[int]=False		)     ->					int:
  _snake_case					     =       get_flax_param(__lowerCamelCase		)

  if not use_large:
    _snake_case					     =       PixaStructVisionConfig()
    _snake_case					     =       PixaStructTextConfig()
  else:
    _snake_case					     =       PixaStructVisionConfig(
        hidden_size=15_36 ,					d_ff=39_68 ,					num_attention_heads=24 ,					num_hidden_layers=18		)
    _snake_case					     =       PixaStructTextConfig(hidden_size=15_36 ,					d_ff=39_68 ,					num_heads=24 ,					num_layers=18		)
  _snake_case					     =       PixaStructConfig(
      vision_config=encoder_config.to_dict() ,					text_config=decoder_config.to_dict() ,					is_vqa=__lowerCamelCase		)

  _snake_case					     =       PixaStructForConditionalGeneration(__lowerCamelCase		)

  _snake_case					     =       rename_and_convert_flax_params(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)

  _snake_case					     =       AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''		)
  _snake_case					     =       PixaStructImageProcessor()
  _snake_case					     =       PixaStructProcessor(image_processor=__lowerCamelCase ,					tokenizer=__lowerCamelCase		)

  if use_large:
    _snake_case					     =       40_96

  _snake_case					     =       True

  # mkdir if needed
  os.makedirs(__lowerCamelCase ,					exist_ok=__lowerCamelCase		)

  model.save_pretrained(__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

  print('''Model saved in {}'''.format(__lowerCamelCase		)		)


if __name__ == "__main__":
 UpperCAmelCase__          = argparse.ArgumentParser()
 parser.add_argument('--t5x_checkpoint_path', default=None, type=str, help='Path to the original T5x checkpoint.')
 parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
 parser.add_argument('--use_large', action='store_true', help='Use large model.')
 parser.add_argument('--is_vqa', action='store_true', help='Use large model.')
 UpperCAmelCase__          = parser.parse_args()

 convert_pixastruct_original_pytorch_checkpoint_to_hf(
     args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
 )



