




'''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
     )






'''simple docstring'''







import warnings

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


lowercase    :							int      =					logging.get_logger(__name__)

lowercase    :							Union[str, Any]      =					{
    "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/config.json",
    "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/config.json",
}


class 						__UpperCAmelCase      (					_lowerCamelCase						):
							__lowercase      		=       """xlnet"""
							__lowercase      		=       ["""mems"""]
							__lowercase      		=       {
							    """n_token""": """vocab_size""",  # Backward compatibility
							    """hidden_size""": """d_model""",
							    """num_attention_heads""": """n_head""",
							    """num_hidden_layers""": """n_layer""",
							}


							def __init__(       self    ,   lowerCAmelCase_=3_20_00    ,   lowerCAmelCase_=10_24    ,   lowerCAmelCase_=24    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=40_96    ,   lowerCAmelCase_="gelu"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="bi"    ,   lowerCAmelCase_=0.02    ,   lowerCAmelCase_=1E-12    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5_12    ,   lowerCAmelCase_=None    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=-1    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_="last"    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_="tanh"    ,   lowerCAmelCase_=0.1    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=5    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=2    ,   **lowerCAmelCase_    ,   ):





								"""simple docstring"""






								_snake_case   						=		vocab_size
								_snake_case   						=		d_model
								_snake_case   						=		n_layer
								_snake_case   						=		n_head
								if d_model % n_head != 0:
									raise ValueError(F'\'d_model % n_head\' ({d_model % n_head}) should be equal to 0'  )
								if "d_head" in kwargs:
									if kwargs["d_head"] != d_model // n_head:
										raise ValueError(
										    F'`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})'  )
								_snake_case   						=		d_model // n_head
								_snake_case   						=		ff_activation
								_snake_case   						=		d_inner
								_snake_case   						=		untie_r
								_snake_case   						=		attn_type

								_snake_case   						=		initializer_range
								_snake_case   						=		layer_norm_eps

								_snake_case   						=		dropout
								_snake_case   						=		mem_len
								_snake_case   						=		reuse_len
								_snake_case   						=		bi_data
								_snake_case   						=		clamp_len
								_snake_case   						=		same_length

								_snake_case   						=		summary_type
								_snake_case   						=		summary_use_proj
								_snake_case   						=		summary_activation
								_snake_case   						=		summary_last_dropout
								_snake_case   						=		start_n_top
								_snake_case   						=		end_n_top

								_snake_case   						=		bos_token_id
								_snake_case   						=		pad_token_id
								_snake_case   						=		eos_token_id

								if "use_cache" in kwargs:
									warnings.warn(
									    'The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`'
									    ' instead.'    ,   lowerCAmelCase_    ,   )
									_snake_case   						=		kwargs['use_cache']

								_snake_case   						=		use_mems_eval
								_snake_case   						=		use_mems_train
								super().__init__(pad_token_id=lowerCAmelCase_    ,   bos_token_id=lowerCAmelCase_    ,   eos_token_id=lowerCAmelCase_    ,   **lowerCAmelCase_  )


							@property
							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.'  )
								return -1




							@max_position_embeddings.setter
							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								raise NotImplementedError(
								    F'The model {self.model_type} is one of the few models that has no sequence length limit.'  )

