




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      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]       ):






       """simple docstring"""





       UpperCamelCase    :	Union[str, Any]   	=		checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE_       )
       UpperCamelCase    :	Optional[int]   	=		flatten_dict(SCREAMING_SNAKE_CASE_       )
       return flax_params


def      a	(							SCREAMING_SNAKE_CASE_      :		List[str]       ):






       """simple docstring"""





       UpperCamelCase    :	Optional[int]   	=		{}

       UpperCamelCase    :	int   	=		{
           '''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''',
       }

       UpperCamelCase    :	int   	=		{
           '''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
                     UpperCamelCase    :	Any   	=		'''.'''.join(key[1:]       )

                     # rename the key
                     for old, new in CONVERSION_MAPPING.items():
                            UpperCamelCase    :	Optional[int]   	=		new_key.replace(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

                     if "decoder" in new_key:
                            for old, new in DECODER_CONVERSION_MAPPING.items():
                                   UpperCamelCase    :	int   	=		new_key.replace(SCREAMING_SNAKE_CASE_							,			SCREAMING_SNAKE_CASE_       )

                     if "layers" in new_key and "decoder" not in new_key:
                            # use regex to replace the layer number
                            UpperCamelCase    :	Union[str, Any]   	=		re.sub(R'''layers_(\d+)'''							,			R'''layer.\1'''							,			SCREAMING_SNAKE_CASE_       )
                            UpperCamelCase    :	Union[str, Any]   	=		new_key.replace('''encoder'''							,			'''encoder.encoder'''       )

                     elif "layers" in new_key and "decoder" in new_key:
                            # use regex to replace the layer number
                            UpperCamelCase    :	List[Any]   	=		re.sub(R'''layers_(\d+)'''							,			R'''layer.\1'''							,			SCREAMING_SNAKE_CASE_       )

                     UpperCamelCase    :	Optional[int]   	=		flax_dict[key]

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

       return converted_torch_dict


def      a	(							SCREAMING_SNAKE_CASE_      :		Optional[Any]							,			SCREAMING_SNAKE_CASE_      :		Any							,			SCREAMING_SNAKE_CASE_      :		List[str]=False							,			SCREAMING_SNAKE_CASE_      :		List[Any]=False       ):






       """simple docstring"""





       UpperCamelCase    :	Dict   	=		get_flax_param(SCREAMING_SNAKE_CASE_       )

       if not use_large:
              UpperCamelCase    :	List[str]   	=		PixaStructVisionConfig()
              UpperCamelCase    :	str   	=		PixaStructTextConfig()
       else:
              UpperCamelCase    :	List[Any]   	=		PixaStructVisionConfig(
                  hidden_size=1_5_3_6							,			d_ff=3_9_6_8							,			num_attention_heads=2_4							,			num_hidden_layers=1_8       )
              UpperCamelCase    :	str   	=		PixaStructTextConfig(hidden_size=1_5_3_6							,			d_ff=3_9_6_8							,			num_heads=2_4							,			num_layers=1_8       )
       UpperCamelCase    :	List[str]   	=		PixaStructConfig(
           vision_config=encoder_config.to_dict()							,			text_config=decoder_config.to_dict()							,			is_vqa=SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Tuple   	=		PixaStructForConditionalGeneration(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	int   	=		rename_and_convert_flax_params(SCREAMING_SNAKE_CASE_       )
       model.load_state_dict(SCREAMING_SNAKE_CASE_       )

       UpperCamelCase    :	Tuple   	=		AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''       )
       UpperCamelCase    :	Optional[Any]   	=		PixaStructImageProcessor()
       UpperCamelCase    :	Tuple   	=		PixaStructProcessor(image_processor=SCREAMING_SNAKE_CASE_							,			tokenizer=SCREAMING_SNAKE_CASE_       )

       if use_large:
              UpperCamelCase    :	Dict   	=		4_0_9_6

       UpperCamelCase    :	Union[str, Any]   	=		True

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

       model.save_pretrained(SCREAMING_SNAKE_CASE_       )
       processor.save_pretrained(SCREAMING_SNAKE_CASE_       )

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


if __name__ == "__main__":
  __UpperCAmelCase : int 			=				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 : Optional[int] 			=				parser.parse_args()

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





import copy
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING


__UpperCAmelCase : Optional[Any] 			=				logging.get_logger(__name__)

__UpperCAmelCase : List[str] 			=				{
    "microsoft/conditional-detr-resnet-50": (
        "https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
    ),
}



class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					"conditional_detr"
       __UpperCamelCase      :     Optional[Any]							=					["past_key_values"]
       __UpperCamelCase      :     Union[str, Any]							=					{
           "hidden_size": "d_model",
           "num_attention_heads": "encoder_attention_heads",
       }






       def __init__(     self ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=None ,					__SCREAMING_SNAKE_CASE=3 ,					__SCREAMING_SNAKE_CASE=300 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=6 ,					__SCREAMING_SNAKE_CASE=2_048 ,					__SCREAMING_SNAKE_CASE=8 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE="relu" ,					__SCREAMING_SNAKE_CASE=256 ,					__SCREAMING_SNAKE_CASE=0.1 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.0 ,					__SCREAMING_SNAKE_CASE=0.02 ,					__SCREAMING_SNAKE_CASE=1.0 ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE="sine" ,					__SCREAMING_SNAKE_CASE="resnet50" ,					__SCREAMING_SNAKE_CASE=True ,					__SCREAMING_SNAKE_CASE=False ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=1 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=5 ,					__SCREAMING_SNAKE_CASE=2 ,					__SCREAMING_SNAKE_CASE=0.25 ,					**__SCREAMING_SNAKE_CASE ,					):




              """simple docstring"""




              if backbone_config is not None and use_timm_backbone:
                     raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''						)

              if not use_timm_backbone:
                     if backbone_config is None:
                            logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''						)
                            UpperCamelCase    :	str   	=		CONFIG_MAPPING['''resnet'''](out_features=['''stage4''']						)
                     elif isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                            UpperCamelCase    :	Tuple   	=		backbone_config.get('''model_type'''						)
                            UpperCamelCase    :	Optional[Any]   	=		CONFIG_MAPPING[backbone_model_type]
                            UpperCamelCase    :	Any   	=		config_class.from_dict(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		use_timm_backbone
              UpperCamelCase    :	int   	=		backbone_config
              UpperCamelCase    :	Any   	=		num_channels
              UpperCamelCase    :	Optional[Any]   	=		num_queries
              UpperCamelCase    :	Tuple   	=		d_model
              UpperCamelCase    :	Optional[Any]   	=		encoder_ffn_dim
              UpperCamelCase    :	Optional[int]   	=		encoder_layers
              UpperCamelCase    :	Union[str, Any]   	=		encoder_attention_heads
              UpperCamelCase    :	Optional[Any]   	=		decoder_ffn_dim
              UpperCamelCase    :	Optional[int]   	=		decoder_layers
              UpperCamelCase    :	Optional[Any]   	=		decoder_attention_heads
              UpperCamelCase    :	Any   	=		dropout
              UpperCamelCase    :	List[Any]   	=		attention_dropout
              UpperCamelCase    :	List[Any]   	=		activation_dropout
              UpperCamelCase    :	List[str]   	=		activation_function
              UpperCamelCase    :	Optional[int]   	=		init_std
              UpperCamelCase    :	Optional[Any]   	=		init_xavier_std
              UpperCamelCase    :	Union[str, Any]   	=		encoder_layerdrop
              UpperCamelCase    :	Optional[Any]   	=		decoder_layerdrop
              UpperCamelCase    :	Tuple   	=		encoder_layers
              UpperCamelCase    :	Optional[Any]   	=		auxiliary_loss
              UpperCamelCase    :	Union[str, Any]   	=		position_embedding_type
              UpperCamelCase    :	Optional[int]   	=		backbone
              UpperCamelCase    :	Dict   	=		use_pretrained_backbone
              UpperCamelCase    :	Tuple   	=		dilation
              # Hungarian matcher
              UpperCamelCase    :	Union[str, Any]   	=		class_cost
              UpperCamelCase    :	List[Any]   	=		bbox_cost
              UpperCamelCase    :	Optional[Any]   	=		giou_cost
              # Loss coefficients
              UpperCamelCase    :	Optional[Any]   	=		mask_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		dice_loss_coefficient
              UpperCamelCase    :	Optional[Any]   	=		cls_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		bbox_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		giou_loss_coefficient
              UpperCamelCase    :	Optional[int]   	=		focal_alpha
              super().__init__(is_encoder_decoder=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.encoder_attention_heads






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return self.d_model






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		copy.deepcopy(self.__dict__						)
              if self.backbone_config is not None:
                     UpperCamelCase    :	List[Any]   	=		self.backbone_config.to_dict()
              UpperCamelCase    :	List[Any]   	=		self.__class__.model_type
              return output




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Dict							=					version.parse("1.11")






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return OrderedDict(
                  [
                      ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                      ('''pixel_mask''', {0: '''batch'''}),
                  ]						)






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 1e-5






       @property
       def  _lowercase   (     self						):




              """simple docstring"""




              return 12

