
"""simple docstring"""



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


UpperCAmelCase__          = logging.get_logger(__name__)

UpperCAmelCase__          = {
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}

class       lowerCAmelCase__				(						A_	):
     __a						=					"""open-llama"""







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Optional[int]=100000    ,      _lowerCamelCase			:			int=4096    ,      _lowerCamelCase			:			Optional[Any]=11008    ,      _lowerCamelCase			:			Union[str, Any]=32    ,      _lowerCamelCase			:			Optional[int]=32    ,      _lowerCamelCase			:			Union[str, Any]="silu"    ,      _lowerCamelCase			:			Dict=2048    ,      _lowerCamelCase			:			List[str]=0.0_2    ,      _lowerCamelCase			:			Optional[Any]=1e-6    ,      _lowerCamelCase			:			List[Any]=True    ,      _lowerCamelCase			:			str=0    ,      _lowerCamelCase			:			Any=1    ,      _lowerCamelCase			:			str=2    ,      _lowerCamelCase			:			Tuple=False    ,      _lowerCamelCase			:			Any=True    ,      _lowerCamelCase			:			int=0.1    ,      _lowerCamelCase			:			Union[str, Any]=0.1    ,      _lowerCamelCase			:			Optional[Any]=True    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			Dict=None    ,      **_lowerCamelCase			:			int    ,      ):
       _snake_case					     =       vocab_size
       _snake_case					     =       max_position_embeddings
       _snake_case					     =       hidden_size
       _snake_case					     =       intermediate_size
       _snake_case					     =       num_hidden_layers
       _snake_case					     =       num_attention_heads
       _snake_case					     =       hidden_act
       _snake_case					     =       initializer_range
       _snake_case					     =       rms_norm_eps
       _snake_case					     =       use_cache
       _snake_case					     =       kwargs.pop(
           '''use_memorry_efficient_attention'''    ,      _lowerCamelCase			)
       _snake_case					     =       hidden_dropout_prob
       _snake_case					     =       attention_dropout_prob
       _snake_case					     =       use_stable_embedding
       _snake_case					     =       shared_input_output_embedding
       _snake_case					     =       rope_scaling
       self._rope_scaling_validation()

       super().__init__(
           pad_token_id=_lowerCamelCase    ,      bos_token_id=_lowerCamelCase    ,      eos_token_id=_lowerCamelCase    ,      tie_word_embeddings=_lowerCamelCase    ,      **_lowerCamelCase    ,      )







     def      lowercase	(							self			:			Any			):
       if self.rope_scaling is None:
         return

       if not isinstance(self.rope_scaling    ,      _lowerCamelCase			) or len(self.rope_scaling			) != 2:
         raise ValueError(
             '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
             f'''got {self.rope_scaling}'''			)
       _snake_case					     =       self.rope_scaling.get('''type'''    ,      _lowerCamelCase			)
       _snake_case					     =       self.rope_scaling.get('''factor'''    ,      _lowerCamelCase			)
       if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
         raise ValueError(
             f'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''			)
       if rope_scaling_factor is None or not isinstance(_lowerCamelCase    ,      _lowerCamelCase			) or rope_scaling_factor <= 1.0:
         raise ValueError(f'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''			)





"""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
 )



