






'''simple docstring'''
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
a_					=	logging.get_logger(__name__)





def 	_a(					UpperCamelCase__			:  Optional[int]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=DPTConfig()

      if "large" in checkpoint_url:
            SCREAMING_SNAKE_CASE__       :			Tuple													=1_0_2_4
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=4_0_9_6
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=2_4
            SCREAMING_SNAKE_CASE__       :			List[str]													=1_6
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=[5, 1_1, 1_7, 2_3]
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=[2_5_6, 5_1_2, 1_0_2_4, 1_0_2_4]
            SCREAMING_SNAKE_CASE__       :			Tuple													=(1, 3_8_4, 3_8_4)

      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=True

            SCREAMING_SNAKE_CASE__       :			List[Any]													=1_5_0
            SCREAMING_SNAKE_CASE__       :			List[Any]													='''huggingface/label-files'''
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''ade20k-id2label.json'''
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=json.load(open(cached_download(hf_hub_url(UpperCamelCase__,		UpperCamelCase__,		repo_type='''dataset'''							)							),		'''r'''							)							)
            SCREAMING_SNAKE_CASE__       :			List[str]													={int(UpperCamelCase__							): v for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE__       :			int													=idalabel
            SCREAMING_SNAKE_CASE__       :			Dict													={v: k for k, v in idalabel.items()}
            SCREAMING_SNAKE_CASE__       :			str													=[1, 1_5_0, 4_8_0, 4_8_0]

      return config, expected_shape





def 	_a(					UpperCamelCase__			:  Union[str, Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Tuple													=['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
      for k in ignore_keys:
            state_dict.pop(UpperCamelCase__,		UpperCamelCase__							)





def 	_a(					UpperCamelCase__			:  Union[str, Any]							):




      '''simple docstring'''






      if (
          "pretrained.model" in name
          and "cls_token" not in name
          and "pos_embed" not in name
          and "patch_embed" not in name
      ):
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=name.replace('''pretrained.model''',		'''dpt.encoder'''							)
      if "pretrained.model" in name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''pretrained.model''',		'''dpt.embeddings'''							)
      if "patch_embed" in name:
            SCREAMING_SNAKE_CASE__       :			int													=name.replace('''patch_embed''',		'''patch_embeddings'''							)
      if "pos_embed" in name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''pos_embed''',		'''position_embeddings'''							)
      if "attn.proj" in name:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=name.replace('''attn.proj''',		'''attention.output.dense'''							)
      if "proj" in name and "project" not in name:
            SCREAMING_SNAKE_CASE__       :			int													=name.replace('''proj''',		'''projection'''							)
      if "blocks" in name:
            SCREAMING_SNAKE_CASE__       :			List[Any]													=name.replace('''blocks''',		'''layer'''							)
      if "mlp.fc1" in name:
            SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''mlp.fc1''',		'''intermediate.dense'''							)
      if "mlp.fc2" in name:
            SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''mlp.fc2''',		'''output.dense'''							)
      if "norm1" in name:
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=name.replace('''norm1''',		'''layernorm_before'''							)
      if "norm2" in name:
            SCREAMING_SNAKE_CASE__       :			Tuple													=name.replace('''norm2''',		'''layernorm_after'''							)
      if "scratch.output_conv" in name:
            SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''scratch.output_conv''',		'''head'''							)
      if "scratch" in name:
            SCREAMING_SNAKE_CASE__       :			Tuple													=name.replace('''scratch''',		'''neck'''							)
      if "layer1_rn" in name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''layer1_rn''',		'''convs.0'''							)
      if "layer2_rn" in name:
            SCREAMING_SNAKE_CASE__       :			Dict													=name.replace('''layer2_rn''',		'''convs.1'''							)
      if "layer3_rn" in name:
            SCREAMING_SNAKE_CASE__       :			str													=name.replace('''layer3_rn''',		'''convs.2'''							)
      if "layer4_rn" in name:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.replace('''layer4_rn''',		'''convs.3'''							)
      if "refinenet" in name:
            SCREAMING_SNAKE_CASE__       :			str													=int(name[len('''neck.refinenet'''							) : len('''neck.refinenet'''							) + 1]							)
            # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
            SCREAMING_SNAKE_CASE__       :			int													=name.replace(f"refinenet{layer_idx}",		f"fusion_stage.layers.{abs(layer_idx-4							)}"							)
      if "out_conv" in name:
            SCREAMING_SNAKE_CASE__       :			Tuple													=name.replace('''out_conv''',		'''projection'''							)
      if "resConfUnit1" in name:
            SCREAMING_SNAKE_CASE__       :			Dict													=name.replace('''resConfUnit1''',		'''residual_layer1'''							)
      if "resConfUnit2" in name:
            SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''resConfUnit2''',		'''residual_layer2'''							)
      if "conv1" in name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''conv1''',		'''convolution1'''							)
      if "conv2" in name:
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=name.replace('''conv2''',		'''convolution2'''							)
      # readout blocks
      if "pretrained.act_postprocess1.0.project.0" in name:
            SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''pretrained.act_postprocess1.0.project.0''',		'''neck.reassemble_stage.readout_projects.0.0'''							)
      if "pretrained.act_postprocess2.0.project.0" in name:
            SCREAMING_SNAKE_CASE__       :			Tuple													=name.replace('''pretrained.act_postprocess2.0.project.0''',		'''neck.reassemble_stage.readout_projects.1.0'''							)
      if "pretrained.act_postprocess3.0.project.0" in name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''pretrained.act_postprocess3.0.project.0''',		'''neck.reassemble_stage.readout_projects.2.0'''							)
      if "pretrained.act_postprocess4.0.project.0" in name:
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=name.replace('''pretrained.act_postprocess4.0.project.0''',		'''neck.reassemble_stage.readout_projects.3.0'''							)
      # resize blocks
      if "pretrained.act_postprocess1.3" in name:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.replace('''pretrained.act_postprocess1.3''',		'''neck.reassemble_stage.layers.0.projection'''							)
      if "pretrained.act_postprocess1.4" in name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''pretrained.act_postprocess1.4''',		'''neck.reassemble_stage.layers.0.resize'''							)
      if "pretrained.act_postprocess2.3" in name:
            SCREAMING_SNAKE_CASE__       :			Tuple													=name.replace('''pretrained.act_postprocess2.3''',		'''neck.reassemble_stage.layers.1.projection'''							)
      if "pretrained.act_postprocess2.4" in name:
            SCREAMING_SNAKE_CASE__       :			str													=name.replace('''pretrained.act_postprocess2.4''',		'''neck.reassemble_stage.layers.1.resize'''							)
      if "pretrained.act_postprocess3.3" in name:
            SCREAMING_SNAKE_CASE__       :			str													=name.replace('''pretrained.act_postprocess3.3''',		'''neck.reassemble_stage.layers.2.projection'''							)
      if "pretrained.act_postprocess4.3" in name:
            SCREAMING_SNAKE_CASE__       :			List[str]													=name.replace('''pretrained.act_postprocess4.3''',		'''neck.reassemble_stage.layers.3.projection'''							)
      if "pretrained.act_postprocess4.4" in name:
            SCREAMING_SNAKE_CASE__       :			Dict													=name.replace('''pretrained.act_postprocess4.4''',		'''neck.reassemble_stage.layers.3.resize'''							)
      if "pretrained" in name:
            SCREAMING_SNAKE_CASE__       :			int													=name.replace('''pretrained''',		'''dpt'''							)
      if "bn" in name:
            SCREAMING_SNAKE_CASE__       :			Dict													=name.replace('''bn''',		'''batch_norm'''							)
      if "head" in name:
            SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''head''',		'''head.head'''							)
      if "encoder.norm" in name:
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=name.replace('''encoder.norm''',		'''layernorm'''							)
      if "auxlayer" in name:
            SCREAMING_SNAKE_CASE__       :			Any													=name.replace('''auxlayer''',		'''auxiliary_head.head'''							)

      return name





def 	_a(					UpperCamelCase__			:  List[Any],		UpperCamelCase__			:  List[Any]							):




      '''simple docstring'''






      for i in range(config.num_hidden_layers							):
            # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
            SCREAMING_SNAKE_CASE__       :			int													=state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.weight"							)
            SCREAMING_SNAKE_CASE__       :			Any													=state_dict.pop(f"dpt.encoder.layer.{i}.attn.qkv.bias"							)
            # next, add query, keys and values (in that order) to the state dict
            SCREAMING_SNAKE_CASE__       :			List[Any]													=in_proj_weight[: config.hidden_size, :]
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=in_proj_bias[: config.hidden_size]
            SCREAMING_SNAKE_CASE__       :			int													=in_proj_weight[
                config.hidden_size : config.hidden_size * 2, :
            ]
            SCREAMING_SNAKE_CASE__       :			List[Any]													=in_proj_bias[
                config.hidden_size : config.hidden_size * 2
            ]
            SCREAMING_SNAKE_CASE__       :			str													=in_proj_weight[
                -config.hidden_size :, :
            ]
            SCREAMING_SNAKE_CASE__       :			Tuple													=in_proj_bias[-config.hidden_size :]





def 	_a(					):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			str													='''http://images.cocodataset.org/val2017/000000039769.jpg'''
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=Image.open(requests.get(UpperCamelCase__,		stream=UpperCamelCase__							).raw							)
      return im





@torch.no_grad()
def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Optional[Any],		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  Optional[Any]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__						,		SCREAMING_SNAKE_CASE__       :			Any													=get_dpt_config(UpperCamelCase__							)
      # load original state_dict from URL
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=torch.hub.load_state_dict_from_url(UpperCamelCase__,		map_location='''cpu'''							)
      # remove certain keys
      remove_ignore_keys_(UpperCamelCase__							)
      # rename keys
      for key in state_dict.copy().keys():
            SCREAMING_SNAKE_CASE__       :			List[str]													=state_dict.pop(UpperCamelCase__							)
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=val
      # read in qkv matrices
      read_in_q_k_v(UpperCamelCase__,		UpperCamelCase__							)

      # load HuggingFace model
      SCREAMING_SNAKE_CASE__       :			List[Any]													=DPTForSemanticSegmentation(UpperCamelCase__							) if '''ade''' in checkpoint_url else DPTForDepthEstimation(UpperCamelCase__							)
      model.load_state_dict(UpperCamelCase__							)
      model.eval()

      # Check outputs on an image
      SCREAMING_SNAKE_CASE__       :			List[Any]													=4_8_0 if '''ade''' in checkpoint_url else 3_8_4
      SCREAMING_SNAKE_CASE__       :			str													=DPTImageProcessor(size=UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			int													=prepare_img()
      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=image_processor(UpperCamelCase__,		return_tensors='''pt'''							)

      # forward pass
      SCREAMING_SNAKE_CASE__       :			str													=model(**UpperCamelCase__							).logits if '''ade''' in checkpoint_url else model(**UpperCamelCase__							).predicted_depth

      # Assert logits
      SCREAMING_SNAKE_CASE__       :			Dict													=torch.tensor([[6.3_1_9_9, 6.3_6_2_9, 6.4_1_4_8], [6.3_8_5_0, 6.3_6_1_5, 6.4_1_6_6], [6.3_5_1_9, 6.3_1_7_6, 6.3_5_7_5]]							)
      if "ade" in checkpoint_url:
            SCREAMING_SNAKE_CASE__       :			str													=torch.tensor([[4.0_4_8_0, 4.2_4_2_0, 4.4_3_6_0], [4.3_1_2_4, 4.5_6_9_3, 4.8_2_6_1], [4.5_7_6_8, 4.8_9_6_5, 5.2_1_6_3]]							)
      assert outputs.shape == torch.Size(UpperCamelCase__							)
      assert (
          torch.allclose(outputs[0, 0, :3, :3],		UpperCamelCase__,		atol=1e-4							)
          if "ade" in checkpoint_url
          else torch.allclose(outputs[0, :3, :3],		UpperCamelCase__							)
      )

      Path(UpperCamelCase__							).mkdir(exist_ok=UpperCamelCase__							)
      print(f"Saving model to {pytorch_dump_folder_path}"							)
      model.save_pretrained(UpperCamelCase__							)
      print(f"Saving image processor to {pytorch_dump_folder_path}"							)
      image_processor.save_pretrained(UpperCamelCase__							)

      if push_to_hub:
            print('''Pushing model to hub...'''							)
            model.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase__,		UpperCamelCase__							),		organization='''nielsr''',		commit_message='''Add model''',		use_temp_dir=UpperCamelCase__,		)
            image_processor.push_to_hub(
                repo_path_or_name=Path(UpperCamelCase__,		UpperCamelCase__							),		organization='''nielsr''',		commit_message='''Add image processor''',		use_temp_dir=UpperCamelCase__,		)


if __name__ == "__main__":
  a_					=	argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '--checkpoint_url',
      default='https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt',
      type=str,
      help='URL of the original DPT checkpoint you\'d like to convert.',
  )
  parser.add_argument(
      '--pytorch_dump_folder_path',
      default=None,
      type=str,
      required=True,
      help='Path to the output PyTorch model directory.',
  )
  parser.add_argument(
      '--push_to_hub',
      action='store_true',
  )
  parser.add_argument(
      '--model_name',
      default='dpt-large',
      type=str,
      help='Name of the model, in case you\'re pushing to the hub.',
  )

  a_					=	parser.parse_args()
  convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)







'''simple docstring'''
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 	_a(					UpperCamelCase__			:  str,		UpperCamelCase__			:  Union[str, Any],		UpperCamelCase__			:  List[str]							):




      '''simple docstring'''






      SCREAMING_SNAKE_CASE__       :			Any													=AutoConfig.from_pretrained(UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=FlaxAutoModelForSeqaSeqLM.from_config(config=UpperCamelCase__							)
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=checkpoints.load_tax_checkpoint(UpperCamelCase__							)

      SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

      if config.model_type == "t5":
            SCREAMING_SNAKE_CASE__       :			str													='''SelfAttention'''
      if config.model_type == "longt5" and config.encoder_attention_type == "local":
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													='''LocalSelfAttention'''
      elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Dict													='''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							):
            SCREAMING_SNAKE_CASE__       :			Dict													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=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":
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''encoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			Dict													=tax_attention_layer_norm

            # Global input layer norm
            if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
                  SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_global_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			Any													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wo
            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=tax_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			Union[str, Any]													=flax_model_encoder_layer_block

      # Only for layer 0:
      SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
      SCREAMING_SNAKE_CASE__       :			Any													=tax_encoder_rel_embedding

      # Side/global relative position_bias + layer norm
      if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_encoder_global_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			int													=tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_layers							):
            SCREAMING_SNAKE_CASE__       :			int													=f"layers_{str(UpperCamelCase__							)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
                '''scale'''
            ]

            # Encoder-Decoder-Attention
            SCREAMING_SNAKE_CASE__       :			int													=tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_enc_dec_attention_module['''key''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			Any													=tax_enc_dec_attention_module['''out''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_enc_dec_attention_module['''query''']['''kernel''']
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_module['''value''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

            # MLP
            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			Tuple													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                  SCREAMING_SNAKE_CASE__       :			str													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
            else:
                  SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

            SCREAMING_SNAKE_CASE__       :			List[Any]													=tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

            # Layer Normalization
            SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

            # Assigning
            SCREAMING_SNAKE_CASE__       :			Tuple													=flax_model.params['''decoder''']['''block'''][str(UpperCamelCase__							)]['''layer''']
            SCREAMING_SNAKE_CASE__       :			Tuple													=tax_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_attention_out
            SCREAMING_SNAKE_CASE__       :			int													=tax_attention_query
            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_attention_value

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[str]													=tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE__       :			str													=tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE__       :			Optional[int]													=tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE__       :			int													=tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE__       :			List[str]													=tax_mlp_wi

            SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_mlp_wo

            SCREAMING_SNAKE_CASE__       :			str													=txa_mlp_layer_norm

            SCREAMING_SNAKE_CASE__       :			List[Any]													=flax_model_decoder_layer_block

      # Decoder Normalization
      SCREAMING_SNAKE_CASE__       :			Any													=tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
      SCREAMING_SNAKE_CASE__       :			Optional[int]													=txa_decoder_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE__       :			Dict													=tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
      SCREAMING_SNAKE_CASE__       :			Tuple													=tax_decoder_rel_embedding

      # Token Embeddings
      SCREAMING_SNAKE_CASE__       :			Optional[Any]													=tax_model['''target''']['''token_embedder''']['''embedding''']
      SCREAMING_SNAKE_CASE__       :			List[Any]													=txa_token_embeddings

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

      flax_model.save_pretrained(UpperCamelCase__							)
      print('''T5X Model was sucessfully converted!'''							)


if __name__ == "__main__":
  a_					=	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.'
  )
  a_					=	parser.parse_args()
  convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)