



"""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      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoints.load_tax_checkpoint(lowercase )
   SCREAMING_SNAKE_CASE_: List[Any]     			=flatten_dict(lowercase )
   return flax_params





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: int     			={}

   SCREAMING_SNAKE_CASE_: Optional[Any]     			={
       """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""",
   }

   SCREAMING_SNAKE_CASE_: Dict     			={
       """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
         SCREAMING_SNAKE_CASE_: int     			=""".""".join(key[1:] )

         # rename the key
         for old, new in CONVERSION_MAPPING.items():
            SCREAMING_SNAKE_CASE_: List[Any]     			=new_key.replace(lowercase							,			lowercase )

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               SCREAMING_SNAKE_CASE_: List[Any]     			=new_key.replace(lowercase							,			lowercase )

         if "layers" in new_key and "decoder" not in new_key:
            # use regex to replace the layer number
            SCREAMING_SNAKE_CASE_: Dict     			=re.sub(R"""layers_(\d+)"""							,			R"""layer.\1"""							,			lowercase )
            SCREAMING_SNAKE_CASE_: Dict     			=new_key.replace("""encoder"""							,			"""encoder.encoder""" )

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

         SCREAMING_SNAKE_CASE_: Tuple     			=flax_dict[key]

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

   return converted_torch_dict





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=False							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: Optional[int]     			=get_flax_param(lowercase )

   if not use_large:
      SCREAMING_SNAKE_CASE_: str     			=PixaStructVisionConfig()
      SCREAMING_SNAKE_CASE_: Optional[int]     			=PixaStructTextConfig()
   else:
      SCREAMING_SNAKE_CASE_: List[Any]     			=PixaStructVisionConfig(
          hidden_size=1536							,			d_ff=3968							,			num_attention_heads=24							,			num_hidden_layers=18 )
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=PixaStructTextConfig(hidden_size=1536							,			d_ff=3968							,			num_heads=24							,			num_layers=18 )
   SCREAMING_SNAKE_CASE_: List[Any]     			=PixaStructConfig(
       vision_config=encoder_config.to_dict()							,			text_config=decoder_config.to_dict()							,			is_vqa=lowercase )

   SCREAMING_SNAKE_CASE_: Tuple     			=PixaStructForConditionalGeneration(lowercase )

   SCREAMING_SNAKE_CASE_: Optional[int]     			=rename_and_convert_flax_params(lowercase )
   model.load_state_dict(lowercase )

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=AutoTokenizer.from_pretrained("""ybelkada/test-pix2struct-tokenizer""" )
   SCREAMING_SNAKE_CASE_: Dict     			=PixaStructImageProcessor()
   SCREAMING_SNAKE_CASE_: str     			=PixaStructProcessor(image_processor=lowercase							,			tokenizer=lowercase )

   if use_large:
      SCREAMING_SNAKE_CASE_: Tuple     			=4096

   SCREAMING_SNAKE_CASE_: Dict     			=True

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

   model.save_pretrained(lowercase )
   processor.save_pretrained(lowercase )

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


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
   )








"""simple docstring"""
from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable


_UpperCAmelCase     =			{"""configuration_dpt""": ["""DPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DPTConfig"""]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			["""DPTFeatureExtractor"""]
   _UpperCAmelCase     =			["""DPTImageProcessor"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase     =			[
       """DPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """DPTForDepthEstimation""",
       """DPTForSemanticSegmentation""",
       """DPTModel""",
       """DPTPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_dpt import DPTFeatureExtractor
      from .image_processing_dpt import DPTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_dpt import (
          DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
          DPTForDepthEstimation,
          DPTForSemanticSegmentation,
          DPTModel,
          DPTPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase     =			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



