



"""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_: Optional[Any]     			=checkpoints.load_tax_checkpoint(A_ )
   SCREAMING_SNAKE_CASE_: Dict     			=flatten_dict(A_ )
   return flax_params





def      __magic_name__				(							lowercase ):
   SCREAMING_SNAKE_CASE_: List[str]     			={}

   SCREAMING_SNAKE_CASE_: List[str]     			={
       """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_: Optional[Any]     			={
       """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_: Optional[int]     			=""".""".join(key[1:] )

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

         if "decoder" in new_key:
            for old, new in DECODER_CONVERSION_MAPPING.items():
               SCREAMING_SNAKE_CASE_: Optional[int]     			=new_key.replace(A_							,			A_ )

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

         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=flax_dict[key]

   SCREAMING_SNAKE_CASE_: 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):
         SCREAMING_SNAKE_CASE_: List[Any]     			=torch.from_numpy(converted_dict[key].T )
      else:
         SCREAMING_SNAKE_CASE_: Tuple     			=torch.from_numpy(converted_dict[key] )

   return converted_torch_dict





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase=False							,			lowercase=False ):
   SCREAMING_SNAKE_CASE_: int     			=get_flax_param(A_ )

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

   SCREAMING_SNAKE_CASE_: int     			=PixaStructForConditionalGeneration(A_ )

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

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

   if use_large:
      SCREAMING_SNAKE_CASE_: Union[str, Any]     			=4096

   SCREAMING_SNAKE_CASE_: int     			=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
   )








"""simple docstring"""
import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser(
       description=(
           """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
           """ Distillation"""
       )
   )
   parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
   parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
   parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
   parser.add_argument("""--vocab_transform""", action="""store_true""")
   _UpperCAmelCase     =			parser.parse_args()

   if args.model_type == "bert":
      _UpperCAmelCase     =			BertForMaskedLM.from_pretrained(args.model_name)
      _UpperCAmelCase     =			"""bert"""
   else:
      raise ValueError("""args.model_type should be \"bert\".""")

   _UpperCAmelCase     =			model.state_dict()
   _UpperCAmelCase     =			{}

   for w in ["word_embeddings", "position_embeddings"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.{w}.weight"""]
   for w in ["weight", "bias"]:
      _UpperCAmelCase     =			state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

   _UpperCAmelCase     =			0
   for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
         ]

         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
         ]
         _UpperCAmelCase     =			state_dict[
             f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
         ]
      std_idx += 1

   _UpperCAmelCase     =			state_dict["""cls.predictions.decoder.weight"""]
   _UpperCAmelCase     =			state_dict["""cls.predictions.bias"""]
   if args.vocab_transform:
      for w in ["weight", "bias"]:
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.dense.{w}"""]
         _UpperCAmelCase     =			state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

   print(f"""N layers selected for distillation: {std_idx}""")
   print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

   print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
   torch.save(compressed_sd, args.dump_checkpoint)



