





import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any				)  ->							List[Any]:
    UpperCAmelCase_										=					384
    UpperCAmelCase_										=					7
    if "tiny" in model_name:
        UpperCAmelCase_										=					96
        UpperCAmelCase_										=					(2, 2, 6, 2)
        UpperCAmelCase_										=					(3, 6, 12, 24)
    elif "small" in model_name:
        UpperCAmelCase_										=					96
        UpperCAmelCase_										=					(2, 2, 18, 2)
        UpperCAmelCase_										=					(3, 6, 12, 24)
    elif "base" in model_name:
        UpperCAmelCase_										=					128
        UpperCAmelCase_										=					(2, 2, 18, 2)
        UpperCAmelCase_										=					(4, 8, 16, 32)
        UpperCAmelCase_										=					12
        UpperCAmelCase_										=					512
    elif "large" in model_name:
        UpperCAmelCase_										=					192
        UpperCAmelCase_										=					(2, 2, 18, 2)
        UpperCAmelCase_										=					(6, 12, 24, 48)
        UpperCAmelCase_										=					12
        UpperCAmelCase_										=					768

    # set label information
    UpperCAmelCase_										=					150
    UpperCAmelCase_										=					'''huggingface/label-files'''
    UpperCAmelCase_										=					'''ade20k-id2label.json'''
    UpperCAmelCase_										=					json.load(open(hf_hub_download(A__	,					A__	,					repo_type='''dataset'''				)	,					'''r'''				)				)
    UpperCAmelCase_										=					{int(A__				): v for k, v in idalabel.items()}
    UpperCAmelCase_										=					{v: k for k, v in idalabel.items()}

    UpperCAmelCase_										=					SwinConfig(
        embed_dim=A__	,					depths=A__	,					num_heads=A__	,					window_size=A__	,					out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']	,					)
    UpperCAmelCase_										=					UperNetConfig(
        backbone_config=A__	,					auxiliary_in_channels=A__	,					num_labels=A__	,					idalabel=A__	,					labelaid=A__	,					)

    return config




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						int				)  ->							Optional[int]:
    UpperCAmelCase_										=					[]

    # fmt: off
    # stem
    rename_keys.append(('''backbone.patch_embed.projection.weight''', '''backbone.embeddings.patch_embeddings.projection.weight''')				)
    rename_keys.append(('''backbone.patch_embed.projection.bias''', '''backbone.embeddings.patch_embeddings.projection.bias''')				)
    rename_keys.append(('''backbone.patch_embed.norm.weight''', '''backbone.embeddings.norm.weight''')				)
    rename_keys.append(('''backbone.patch_embed.norm.bias''', '''backbone.embeddings.norm.bias''')				)
    # stages
    for i in range(len(config.backbone_config.depths				)				):
        for j in range(config.backbone_config.depths[i]				):
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.weight')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_before.bias')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_bias_table', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.relative_position_index', f'backbone.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.weight', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.attn.w_msa.proj.bias', f'backbone.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.weight', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.weight')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.norm2.bias', f'backbone.encoder.layers.{i}.blocks.{j}.layernorm_after.bias')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.weight', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.0.0.bias', f'backbone.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.weight', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.weight')				)
            rename_keys.append((f'backbone.stages.{i}.blocks.{j}.ffn.layers.1.bias', f'backbone.encoder.layers.{i}.blocks.{j}.output.dense.bias')				)

        if i < 3:
            rename_keys.append((f'backbone.stages.{i}.downsample.reduction.weight', f'backbone.encoder.layers.{i}.downsample.reduction.weight')				)
            rename_keys.append((f'backbone.stages.{i}.downsample.norm.weight', f'backbone.encoder.layers.{i}.downsample.norm.weight')				)
            rename_keys.append((f'backbone.stages.{i}.downsample.norm.bias', f'backbone.encoder.layers.{i}.downsample.norm.bias')				)
        rename_keys.append((f'backbone.norm{i}.weight', f'backbone.hidden_states_norms.stage{i+1}.weight')				)
        rename_keys.append((f'backbone.norm{i}.bias', f'backbone.hidden_states_norms.stage{i+1}.bias')				)

    # decode head
    rename_keys.extend(
        [
            ('''decode_head.conv_seg.weight''', '''decode_head.classifier.weight'''),
            ('''decode_head.conv_seg.bias''', '''decode_head.classifier.bias'''),
            ('''auxiliary_head.conv_seg.weight''', '''auxiliary_head.classifier.weight'''),
            ('''auxiliary_head.conv_seg.bias''', '''auxiliary_head.classifier.bias'''),
        ]				)
    # fmt: on

    return rename_keys




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]				)  ->							Any:
    UpperCAmelCase_										=					dct.pop(A__				)
    UpperCAmelCase_										=					val




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]	,					__UpperCamelCase      :						Any				)  ->							int:
    UpperCAmelCase_										=					[int(backbone_config.embed_dim * 2**i				) for i in range(len(backbone_config.depths				)				)]
    for i in range(len(backbone_config.depths				)				):
        UpperCAmelCase_										=					num_features[i]
        for j in range(backbone_config.depths[i]				):
            # fmt: off
            # read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
            UpperCAmelCase_										=					state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.weight'				)
            UpperCAmelCase_										=					state_dict.pop(f'backbone.stages.{i}.blocks.{j}.attn.w_msa.qkv.bias'				)
            # next, add query, keys and values (in that order) to the state dict
            UpperCAmelCase_										=					in_proj_weight[:dim, :]
            UpperCAmelCase_										=					in_proj_bias[: dim]
            UpperCAmelCase_										=					in_proj_weight[
                dim : dim * 2, :
            ]
            UpperCAmelCase_										=					in_proj_bias[
                dim : dim * 2
            ]
            UpperCAmelCase_										=					in_proj_weight[
                -dim :, :
            ]
            UpperCAmelCase_										=					in_proj_bias[-dim :]
            # fmt: on




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict				)  ->							str:
    UpperCAmelCase_			,							UpperCAmelCase_										=					x.shape
    UpperCAmelCase_										=					x.reshape(A__	,					4	,					in_channel // 4				)
    UpperCAmelCase_										=					x[:, [0, 2, 1, 3], :].transpose(1	,					2				).reshape(A__	,					A__				)
    return x




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str				)  ->							Any:
    UpperCAmelCase_			,							UpperCAmelCase_										=					x.shape
    UpperCAmelCase_										=					x.reshape(A__	,					in_channel // 4	,					4				)
    UpperCAmelCase_										=					x[:, :, [0, 2, 1, 3]].transpose(1	,					2				).reshape(A__	,					A__				)

    return x




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[int]				)  ->							Union[str, Any]:
    UpperCAmelCase_										=					x.shape[0]
    UpperCAmelCase_										=					x.reshape(4	,					in_channel // 4				)
    UpperCAmelCase_										=					x[[0, 2, 1, 3], :].transpose(0	,					1				).reshape(A__				)
    return x




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Optional[Any]				)  ->							Optional[Any]:
    UpperCAmelCase_										=					x.shape[0]
    UpperCAmelCase_										=					x.reshape(in_channel // 4	,					4				)
    UpperCAmelCase_										=					x[:, [0, 2, 1, 3]].transpose(0	,					1				).reshape(A__				)
    return x




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Any	,					__UpperCamelCase      :						int	,					__UpperCamelCase      :						int				)  ->							List[Any]:
    UpperCAmelCase_										=					{
        '''upernet-swin-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_tiny_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210531_112542-e380ad3e.pth''',
        '''upernet-swin-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K/upernet_swin_small_patch4_window7_512x512_160k_ade20k_pretrain_224x224_1K_20210526_192015-ee2fff1c.pth''',
        '''upernet-swin-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K/upernet_swin_base_patch4_window12_512x512_160k_ade20k_pretrain_384x384_22K_20210531_125459-429057bf.pth''',
        '''upernet-swin-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/swin/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k/upernet_swin_large_patch4_window12_512x512_pretrain_384x384_22K_160k_ade20k_20220318_091743-9ba68901.pth''',
    }
    UpperCAmelCase_										=					model_name_to_url[model_name]
    UpperCAmelCase_										=					torch.hub.load_state_dict_from_url(A__	,					map_location='''cpu'''	,					file_name=A__				)[
        '''state_dict'''
    ]

    for name, param in state_dict.items():
        print(A__	,					param.shape				)

    UpperCAmelCase_										=					get_upernet_config(A__				)
    UpperCAmelCase_										=					UperNetForSemanticSegmentation(A__				)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        UpperCAmelCase_										=					state_dict.pop(A__				)
        if "bn" in key:
            UpperCAmelCase_										=					key.replace('''bn'''	,					'''batch_norm'''				)
        UpperCAmelCase_										=					val

    # rename keys
    UpperCAmelCase_										=					create_rename_keys(A__				)
    for src, dest in rename_keys:
        rename_key(A__	,					A__	,					A__				)
    read_in_q_k_v(A__	,					config.backbone_config				)

    # fix downsample parameters
    for key, value in state_dict.items():
        if "downsample" in key:
            if "reduction" in key:
                UpperCAmelCase_										=					reverse_correct_unfold_reduction_order(A__				)
            if "norm" in key:
                UpperCAmelCase_										=					reverse_correct_unfold_norm_order(A__				)

    model.load_state_dict(A__				)

    # verify on image
    UpperCAmelCase_										=					'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    UpperCAmelCase_										=					Image.open(requests.get(A__	,					stream=A__				).raw				).convert('''RGB'''				)

    UpperCAmelCase_										=					SegformerImageProcessor()
    UpperCAmelCase_										=					processor(A__	,					return_tensors='''pt'''				).pixel_values

    with torch.no_grad():
        UpperCAmelCase_										=					model(A__				)
        UpperCAmelCase_										=					outputs.logits

    print(logits.shape				)
    print('''First values of logits:'''	,					logits[0, 0, :3, :3]				)
    # assert values
    if model_name == "upernet-swin-tiny":
        UpperCAmelCase_										=					torch.tensor(
            [[-7.5_958, -7.5_958, -7.4_302], [-7.5_958, -7.5_958, -7.4_302], [-7.4_797, -7.4_797, -7.3_068]]				)
    elif model_name == "upernet-swin-small":
        UpperCAmelCase_										=					torch.tensor(
            [[-7.1_921, -7.1_921, -6.9_532], [-7.1_921, -7.1_921, -6.9_532], [-7.0_908, -7.0_908, -6.8_534]]				)
    elif model_name == "upernet-swin-base":
        UpperCAmelCase_										=					torch.tensor(
            [[-6.5_851, -6.5_851, -6.4_330], [-6.5_851, -6.5_851, -6.4_330], [-6.4_763, -6.4_763, -6.3_254]]				)
    elif model_name == "upernet-swin-large":
        UpperCAmelCase_										=					torch.tensor(
            [[-7.5_297, -7.5_297, -7.3_802], [-7.5_297, -7.5_297, -7.3_802], [-7.4_044, -7.4_044, -7.2_586]]				)
    print('''Logits:'''	,					outputs.logits[0, 0, :3, :3]				)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]	,					A__	,					atol=1e-4				)
    print('''Looks ok!'''				)

    if pytorch_dump_folder_path is not None:
        print(f'Saving model {model_name} to {pytorch_dump_folder_path}'				)
        model.save_pretrained(A__				)
        print(f'Saving processor to {pytorch_dump_folder_path}'				)
        processor.save_pretrained(A__				)

    if push_to_hub:
        print(f'Pushing model and processor for {model_name} to hub'				)
        model.push_to_hub(f'openmmlab/{model_name}'				)
        processor.push_to_hub(f'openmmlab/{model_name}'				)


if __name__ == "__main__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='upernet-swin-tiny',
     type=str,
     choices=[F"upernet-swin-{size}" for size in ['tiny', 'small', 'base', 'large']],
     help='Name of the Swin + UperNet model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

 _lowerCamelCase      			=						parser.parse_args()
 convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)











import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Tuple	,					__UpperCamelCase      :						List[str]	,					__UpperCamelCase      :						str				)  ->							str:
    UpperCAmelCase_										=					AutoConfig.from_pretrained(__UpperCamelCase				)
    UpperCAmelCase_										=					FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCamelCase				)
    UpperCAmelCase_										=					checkpoints.load_tax_checkpoint(__UpperCamelCase				)

    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(__UpperCamelCase				)}'

        # 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(__UpperCamelCase				)]['''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(__UpperCamelCase				)}'

        # 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(__UpperCamelCase				)]['''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(__UpperCamelCase				)
    print('''T5X Model was sucessfully converted!'''				)


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





