





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 				_UpperCAmelCase			(    A				,							A				,							A     ):




   '''simple docstring'''

   UpperCAmelCase__						=AutoConfig.from_pretrained(A     )
   UpperCAmelCase__						=FlaxAutoModelForSeqaSeqLM.from_config(config=A     )
   UpperCAmelCase__						=checkpoints.load_tax_checkpoint(A     )

   UpperCAmelCase__						="wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

   if config.model_type == "t5":
      UpperCAmelCase__						="SelfAttention"
   if config.model_type == "longt5" and config.encoder_attention_type == "local":
      UpperCAmelCase__						="LocalSelfAttention"
   elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      UpperCAmelCase__						="TransientGlobalSelfAttention"
   else:
      raise ValueError(
          "Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"
          " attribute with a value from ['local', 'transient-global]."     )

   # Encoder
   for layer_index in range(config.num_layers     ):
      UpperCAmelCase__						=F"""layers_{str(A     )}"""

      # Self-Attention
      UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
      UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
      UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
      UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["attention"]["T5LayerNorm_0"]["scale"]

      # Layer Normalization
      UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

      if split_mlp_wi:
         UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

      UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      UpperCAmelCase__						=tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      UpperCAmelCase__						=flax_model.params["encoder"]["block"][str(A     )]["layer"]
      UpperCAmelCase__						=tax_attention_key
      UpperCAmelCase__						=tax_attention_out
      UpperCAmelCase__						=tax_attention_query
      UpperCAmelCase__						=tax_attention_value

      UpperCAmelCase__						=tax_attention_layer_norm

      # Global input layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
         UpperCAmelCase__						=tax_global_layer_norm

      if split_mlp_wi:
         UpperCAmelCase__						=tax_mlp_wi_a
         UpperCAmelCase__						=tax_mlp_wi_a
      else:
         UpperCAmelCase__						=tax_mlp_wi

      UpperCAmelCase__						=tax_mlp_wo
      UpperCAmelCase__						=tax_mlp_layer_norm

      UpperCAmelCase__						=flax_model_encoder_layer_block

   # Only for layer 0:
   UpperCAmelCase__						=tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
   UpperCAmelCase__						=tax_encoder_rel_embedding

   # Side/global relative position_bias + layer norm
   if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      UpperCAmelCase__						=tax_model["target"]["encoder"]["side_relpos_bias"]["rel_embedding"].T
      UpperCAmelCase__						=tax_encoder_global_rel_embedding

   # Assigning
   UpperCAmelCase__						=tax_model["target"]["encoder"]["encoder_norm"]["scale"]
   UpperCAmelCase__						=tax_encoder_norm

   # Decoder
   for layer_index in range(config.num_layers     ):
      UpperCAmelCase__						=F"""layers_{str(A     )}"""

      # Self-Attention
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

      # Layer Normalization
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
          "scale"
      ]

      # Encoder-Decoder-Attention
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]
      UpperCAmelCase__						=tax_enc_dec_attention_module["key"]["kernel"]
      UpperCAmelCase__						=tax_enc_dec_attention_module["out"]["kernel"]
      UpperCAmelCase__						=tax_enc_dec_attention_module["query"]["kernel"]
      UpperCAmelCase__						=tax_enc_dec_attention_module["value"]["kernel"]

      # Layer Normalization
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

      # MLP
      if split_mlp_wi:
         UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
         UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
      else:
         UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

      # Layer Normalization
      UpperCAmelCase__						=tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

      # Assigning
      UpperCAmelCase__						=flax_model.params["decoder"]["block"][str(A     )]["layer"]
      UpperCAmelCase__						=tax_attention_key
      UpperCAmelCase__						=tax_attention_out
      UpperCAmelCase__						=tax_attention_query
      UpperCAmelCase__						=tax_attention_value

      UpperCAmelCase__						=tax_pre_attention_layer_norm

      UpperCAmelCase__						=tax_enc_dec_attention_key
      UpperCAmelCase__						=tax_enc_dec_attention_out
      UpperCAmelCase__						=tax_enc_dec_attention_query
      UpperCAmelCase__						=tax_enc_dec_attention_value

      UpperCAmelCase__						=tax_cross_layer_norm

      if split_mlp_wi:
         UpperCAmelCase__						=tax_mlp_wi_a
         UpperCAmelCase__						=tax_mlp_wi_a
      else:
         UpperCAmelCase__						=tax_mlp_wi

      UpperCAmelCase__						=tax_mlp_wo

      UpperCAmelCase__						=txa_mlp_layer_norm

      UpperCAmelCase__						=flax_model_decoder_layer_block

   # Decoder Normalization
   UpperCAmelCase__						=tax_model["target"]["decoder"]["decoder_norm"]["scale"]
   UpperCAmelCase__						=txa_decoder_norm

   # Only for layer 0:
   UpperCAmelCase__						=tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
   UpperCAmelCase__						=tax_decoder_rel_embedding

   # Token Embeddings
   UpperCAmelCase__						=tax_model["target"]["token_embedder"]["embedding"]
   UpperCAmelCase__						=txa_token_embeddings

   # LM Head (only in v1.1 and LongT5 checkpoints)
   if "logits_dense" in tax_model["target"]["decoder"]:
      UpperCAmelCase__						=tax_model["target"]["decoder"]["logits_dense"]["kernel"]

   flax_model.save_pretrained(A     )
   print("T5X Model was sucessfully converted!"     )


if __name__ == "__main__":
    UpperCamelCase_												=	argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
    )
    parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
    parser.add_argument(
        '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
    )
    UpperCamelCase_												=	parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)










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


UpperCamelCase_												=	logging.get_logger(__name__)

UpperCamelCase_												=	{
    'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
    # See all Donut models at https://huggingface.co/models?filter=donut-swin
}

class     snake_case_		(   a							):






 '''simple docstring'''





 __UpperCamelCase       =      'donut-swin'

 __UpperCamelCase       =      {
     'num_attention_heads': 'num_heads',
     'num_hidden_layers': 'num_layers',
 }

 def __init__(  self,		A_=224,		A_=4,		A_=3,		A_=96,		A_=[2, 2, 6, 2],		A_=[3, 6, 12, 24],		A_=7,		A_=4.0,		A_=True,		A_=0.0,		A_=0.0,		A_=0.1,		A_="gelu",		A_=False,		A_=0.02,		A_=1E-5,		**A_,		)	->       List[str]:
    super().__init__(**A_		)

    UpperCAmelCase__						=image_size
    UpperCAmelCase__						=patch_size
    UpperCAmelCase__						=num_channels
    UpperCAmelCase__						=embed_dim
    UpperCAmelCase__						=depths
    UpperCAmelCase__						=len(A_		)
    UpperCAmelCase__						=num_heads
    UpperCAmelCase__						=window_size
    UpperCAmelCase__						=mlp_ratio
    UpperCAmelCase__						=qkv_bias
    UpperCAmelCase__						=hidden_dropout_prob
    UpperCAmelCase__						=attention_probs_dropout_prob
    UpperCAmelCase__						=drop_path_rate
    UpperCAmelCase__						=hidden_act
    UpperCAmelCase__						=use_absolute_embeddings
    UpperCAmelCase__						=layer_norm_eps
    UpperCAmelCase__						=initializer_range
    # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
    # this indicates the channel dimension after the last stage of the model
    UpperCAmelCase__						=int(embed_dim * 2 ** (len(A_		) - 1)		)



