

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 						lowerCamelCase__      ( __A    :str		):


       """simple docstring"""




       __snake_case       	=							checkpoints.load_tax_checkpoint(__A		)
       __snake_case       	=							flatten_dict(__A		)
       return flax_params







def 						lowerCamelCase__      ( __A    :Dict		):


       """simple docstring"""




       __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(__A	,__A		)

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

                     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"""	,__A		)
                            __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"""	,__A		)

                     __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 						lowerCamelCase__      ( __A    :Union[str, Any]	,__A    :Dict	,__A    :List[Any]=False	,__A    :str=False		):


       """simple docstring"""




       __snake_case       	=							get_flax_param(__A		)

       if not use_large:
              __snake_case       	=							PixaStructVisionConfig()
              __snake_case       	=							PixaStructTextConfig()
       else:
              __snake_case       	=							PixaStructVisionConfig(
                  hidden_size=1_5_3_6	,d_ff=3_9_6_8	,num_attention_heads=2_4	,num_hidden_layers=1_8		)
              __snake_case       	=							PixaStructTextConfig(hidden_size=1_5_3_6	,d_ff=3_9_6_8	,num_heads=2_4	,num_layers=1_8		)
       __snake_case       	=							PixaStructConfig(
           vision_config=encoder_config.to_dict()	,text_config=decoder_config.to_dict()	,is_vqa=__A		)

       __snake_case       	=							PixaStructForConditionalGeneration(__A		)

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

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

       if use_large:
              __snake_case       	=							4_0_9_6

       __snake_case       	=							True

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

       model.save_pretrained(__A		)
       processor.save_pretrained(__A		)

       print("""Model saved in {}""".format(__A		)		)


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
    )




import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


UpperCamelCase__		 =     get_logger(__name__)

class     __snake_case    (				enum.Enum  ):
       """simple docstring"""







       __SCREAMING_SNAKE_CASE				    =							"all_checks"
       __SCREAMING_SNAKE_CASE				    =							"basic_checks"
       __SCREAMING_SNAKE_CASE				    =							"no_checks"






class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict	,__A    :Any=None		):


       """simple docstring"""




       if expected_checksums is None:
              logger.info("""Unable to verify checksums."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreDownloadedFiles(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedDownloadedFile(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
       __snake_case       	=							""" for """ + verification_name if verification_name is not None else """"""
       if len(__A		) > 0:
              raise NonMatchingChecksumError(
                  F'Checksums didn\'t match{for_verification_name}:\n'
                  F'{bad_urls}\n'
                  """Set `verification_mode='no_checks'` to skip checksums verification and ignore this error"""		)
       logger.info("""All the checksums matched successfully""" + for_verification_name		)

class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""













class     __snake_case    (				snake_case__  ):
       """simple docstring"""














def 						lowerCamelCase__      ( __A    :Optional[dict]	,__A    :dict		):


       """simple docstring"""




       if expected_splits is None:
              logger.info("""Unable to verify splits sizes."""		)
              return
       if len(set(__A		) - set(__A		)		) > 0:
              raise ExpectedMoreSplits(str(set(__A		) - set(__A		)		)		)
       if len(set(__A		) - set(__A		)		) > 0:
              raise UnexpectedSplits(str(set(__A		) - set(__A		)		)		)
       __snake_case       	=							[
           {"""expected""": expected_splits[name], """recorded""": recorded_splits[name]}
           for name in expected_splits
           if expected_splits[name].num_examples != recorded_splits[name].num_examples
       ]
       if len(__A		) > 0:
              raise NonMatchingSplitsSizesError(str(__A		)		)
       logger.info("""All the splits matched successfully."""		)







def 						lowerCamelCase__      ( __A    :str	,__A    :bool = True		):


       """simple docstring"""




       if record_checksum:
              __snake_case       	=							shaaaa()
              with open(__A	,"""rb"""		) as f:
                     for chunk in iter(lambda: f.read(1 << 2_0		)	,b""""""		):
                            m.update(__A		)
                     __snake_case       	=							m.hexdigest()
       else:
              __snake_case       	=							None
       return {"num_bytes": os.path.getsize(__A		), "checksum": checksum}







def 						lowerCamelCase__      ( __A    :Optional[int]		):


       """simple docstring"""




       if dataset_size and config.IN_MEMORY_MAX_SIZE:
              return dataset_size < config.IN_MEMORY_MAX_SIZE
       else:
              return False

