




'''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      a_							(      __snake_case							:			Any   )							->						int:



       """simple docstring"""
       lowerCamelCase_							=checkpoints.load_tax_checkpoint(__snake_case   )
       lowerCamelCase_							=flatten_dict(__snake_case   )
       return flax_params





def      a_							(      __snake_case							:			Dict   )							->						Optional[int]:



       """simple docstring"""
       lowerCamelCase_							={}

       lowerCamelCase_							={
           '''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''',
       }

       lowerCamelCase_							={
           '''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
                     lowerCamelCase_							='''.'''.join(key[1:]   )

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

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

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

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

                     lowerCamelCase_							=flax_dict[key]

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

       return converted_torch_dict





def      a_							(      __snake_case							:			Optional[Any]   ,       __snake_case							:			List[str]   ,       __snake_case							:			Any=False   ,       __snake_case							:			Optional[int]=False   )							->						Union[str, Any]:



       """simple docstring"""
       lowerCamelCase_							=get_flax_param(__snake_case   )

       if not use_large:
              lowerCamelCase_							=PixaStructVisionConfig()
              lowerCamelCase_							=PixaStructTextConfig()
       else:
              lowerCamelCase_							=PixaStructVisionConfig(
                  hidden_size=1536   ,       d_ff=3968   ,       num_attention_heads=24   ,       num_hidden_layers=18   )
              lowerCamelCase_							=PixaStructTextConfig(hidden_size=1536   ,       d_ff=3968   ,       num_heads=24   ,       num_layers=18   )
       lowerCamelCase_							=PixaStructConfig(
           vision_config=encoder_config.to_dict()   ,       text_config=decoder_config.to_dict()   ,       is_vqa=__snake_case   )

       lowerCamelCase_							=PixaStructForConditionalGeneration(__snake_case   )

       lowerCamelCase_							=rename_and_convert_flax_params(__snake_case   )
       model.load_state_dict(__snake_case   )

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

       if use_large:
              lowerCamelCase_							=4096

       lowerCamelCase_							=True

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

       model.save_pretrained(__snake_case   )
       processor.save_pretrained(__snake_case   )

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


if __name__ == "__main__":
     a_      :		Optional[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.""")
     a_      :		Tuple							      =       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 typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
						from ... import PreTrainedTokenizerBase, TensorType

_snake_case		=  logging.get_logger(__name__)



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		'vision-encoder-decoder'
							lowerCamelCase__   			=		True







							def __init__(   self,			**__a):

														'''simple docstring'''

														super().__init__(**__a)
														if "encoder" not in kwargs or "decoder" not in kwargs:
																					raise ValueError(
																					    f"A configuraton of type {self.model_type} cannot be instantiated because "
																					    f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")

														_lowerCAmelCase     :							str    =    kwargs.pop("encoder")
														_lowerCAmelCase     :							Any    =    encoder_config.pop("model_type")
														_lowerCAmelCase     :							str    =    kwargs.pop("decoder")
														_lowerCAmelCase     :							List[str]    =    decoder_config.pop("model_type")

														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[Any]    =    AutoConfig.for_model(__a,			**__a)
														_lowerCAmelCase     :							Optional[int]    =    True







							@classmethod
							def    snake_case__      (   cls,			__a,			__a,			**__a):

														'''simple docstring'''

														logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
														_lowerCAmelCase     :							Optional[Any]    =    True
														_lowerCAmelCase     :							str    =    True

														return cls(encoder=encoder_config.to_dict(),			decoder=decoder_config.to_dict(),			**__a)







							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							int    =    copy.deepcopy(self.__dict__)
														_lowerCAmelCase     :							List[str]    =    self.encoder.to_dict()
														_lowerCAmelCase     :							List[str]    =    self.decoder.to_dict()
														_lowerCAmelCase     :							Any    =    self.__class__.model_type
														return output



class   UpperCAmelCase_    (       a):
							lowerCamelCase__   			=		version.parse('1.11')







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

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







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return 1E-4







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()
														_lowerCAmelCase     :							Any    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							List[str]    =    {0: "batch", 1: "past_decoder_sequence + sequence"}
														_lowerCAmelCase     :							Optional[Any]    =    {0: "batch", 1: "encoder_sequence"}

														return common_inputs







							def    snake_case__      (   self,			__a,			__a = -1,			__a = -1,			__a = False,			__a = None,			):

														'''simple docstring'''

														import torch

														_lowerCAmelCase     :							Optional[Any]    =    OrderedDict()

														_lowerCAmelCase     :							List[str]    =    super().generate_dummy_inputs(
														    __a,			batch_size=__a,			seq_length=__a,			is_pair=__a,			framework=__a)

														_lowerCAmelCase   ,    _lowerCAmelCase     :							Optional[Any]    =    dummy_input["input_ids"].shape
														_lowerCAmelCase     :							str    =    (batch, encoder_sequence, self._config.encoder_hidden_size)
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("input_ids")
														_lowerCAmelCase     :							List[str]    =    dummy_input.pop("attention_mask")
														_lowerCAmelCase     :							Optional[int]    =    torch.zeros(__a)

														return common_inputs



class   UpperCAmelCase_    (       a):







							@property
							def    snake_case__      (   self):

														'''simple docstring'''

														pass







							def    snake_case__      (   self,			__a):

														'''simple docstring'''

														return VisionEncoderDecoderEncoderOnnxConfig(__a)







							def    snake_case__      (   self,			__a,			__a,			__a = "default"):

														'''simple docstring'''

														_lowerCAmelCase     :							Dict    =    encoder_config.hidden_size
														return VisionEncoderDecoderDecoderOnnxConfig(__a,			__a)
