import argparse
import json

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

from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :List[Any]    						=	384
    if "tiny" in model_name:
        __UpperCamelCase  :Tuple    						=	[3, 3, 9, 3]
        __UpperCamelCase  :List[str]    						=	[96, 192, 384, 768]
    if "small" in model_name:
        __UpperCamelCase  :Optional[Any]    						=	[3, 3, 27, 3]
        __UpperCamelCase  :List[str]    						=	[96, 192, 384, 768]
    if "base" in model_name:
        __UpperCamelCase  :int    						=	[3, 3, 27, 3]
        __UpperCamelCase  :int    						=	[128, 256, 512, 1_024]
        __UpperCamelCase  :Dict    						=	512
    if "large" in model_name:
        __UpperCamelCase  :Union[str, Any]    						=	[3, 3, 27, 3]
        __UpperCamelCase  :List[Any]    						=	[192, 384, 768, 1_536]
        __UpperCamelCase  :Any    						=	768
    if "xlarge" in model_name:
        __UpperCamelCase  :List[Any]    						=	[3, 3, 27, 3]
        __UpperCamelCase  :Optional[Any]    						=	[256, 512, 1_024, 2_048]
        __UpperCamelCase  :Tuple    						=	1_024

    # set label information
    __UpperCamelCase  :Optional[Any]    						=	150
    __UpperCamelCase  :Optional[Any]    						=	'''huggingface/label-files'''
    __UpperCamelCase  :str    						=	'''ade20k-id2label.json'''
    __UpperCamelCase  :List[str]    						=	json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    repo_type='''dataset'''			)						,    '''r'''			)			)
    __UpperCamelCase  :int    						=	{int(SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
    __UpperCamelCase  :Optional[Any]    						=	{v: k for k, v in idalabel.items()}

    __UpperCamelCase  :Any    						=	ConvNextConfig(
        depths=SCREAMING_SNAKE_CASE						,    hidden_sizes=SCREAMING_SNAKE_CASE						,    out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']			)
    __UpperCamelCase  :Any    						=	UperNetConfig(
        backbone_config=SCREAMING_SNAKE_CASE						,    auxiliary_in_channels=SCREAMING_SNAKE_CASE						,    num_labels=SCREAMING_SNAKE_CASE						,    idalabel=SCREAMING_SNAKE_CASE						,    labelaid=SCREAMING_SNAKE_CASE						,    )

    return config



def lowerCamelCase						(     SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :str    						=	[]

    # fmt: off
    # stem
    rename_keys.append(('''backbone.downsample_layers.0.0.weight''', '''backbone.embeddings.patch_embeddings.weight''')			)
    rename_keys.append(('''backbone.downsample_layers.0.0.bias''', '''backbone.embeddings.patch_embeddings.bias''')			)
    rename_keys.append(('''backbone.downsample_layers.0.1.weight''', '''backbone.embeddings.layernorm.weight''')			)
    rename_keys.append(('''backbone.downsample_layers.0.1.bias''', '''backbone.embeddings.layernorm.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}.{j}.gamma""", f"""backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.depthwise_conv.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.dwconv.bias""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.norm.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.norm.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.layernorm.bias""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv1.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.weight""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight""")			)
            rename_keys.append((f"""backbone.stages.{i}.{j}.pointwise_conv2.bias""", f"""backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias""")			)
        if i > 0:
            rename_keys.append((f"""backbone.downsample_layers.{i}.0.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.weight""")			)
            rename_keys.append((f"""backbone.downsample_layers.{i}.0.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.0.bias""")			)
            rename_keys.append((f"""backbone.downsample_layers.{i}.1.weight""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.weight""")			)
            rename_keys.append((f"""backbone.downsample_layers.{i}.1.bias""", f"""backbone.encoder.stages.{i}.downsampling_layer.1.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 lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	dct.pop(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[Any]    						=	val



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	{
        '''upernet-convnext-tiny''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth''',
        '''upernet-convnext-small''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth''',
        '''upernet-convnext-base''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth''',
        '''upernet-convnext-large''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth''',
        '''upernet-convnext-xlarge''': '''https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth''',
    }
    __UpperCamelCase  :Optional[int]    						=	model_name_to_url[model_name]
    __UpperCamelCase  :List[Any]    						=	torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE						,    map_location='''cpu'''			)['''state_dict''']

    __UpperCamelCase  :List[Any]    						=	get_upernet_config(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :List[Any]    						=	UperNetForSemanticSegmentation(SCREAMING_SNAKE_CASE			)
    model.eval()

    # replace "bn" => "batch_norm"
    for key in state_dict.copy().keys():
        __UpperCamelCase  :List[Any]    						=	state_dict.pop(SCREAMING_SNAKE_CASE			)
        if "bn" in key:
            __UpperCamelCase  :List[Any]    						=	key.replace('''bn'''						,    '''batch_norm'''			)
        __UpperCamelCase  :Dict    						=	val

    # rename keys
    __UpperCamelCase  :Any    						=	create_rename_keys(SCREAMING_SNAKE_CASE			)
    for src, dest in rename_keys:
        rename_key(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    model.load_state_dict(SCREAMING_SNAKE_CASE			)

    # verify on image
    __UpperCamelCase  :List[Any]    						=	'''https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'''
    __UpperCamelCase  :Dict    						=	Image.open(requests.get(SCREAMING_SNAKE_CASE						,    stream=SCREAMING_SNAKE_CASE			).raw			).convert('''RGB'''			)

    __UpperCamelCase  :Dict    						=	SegformerImageProcessor()
    __UpperCamelCase  :Optional[Any]    						=	processor(SCREAMING_SNAKE_CASE						,    return_tensors='''pt'''			).pixel_values

    with torch.no_grad():
        __UpperCamelCase  :Dict    						=	model(SCREAMING_SNAKE_CASE			)

    if model_name == "upernet-convnext-tiny":
        __UpperCamelCase  :Union[str, Any]    						=	torch.tensor(
            [[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]]			)
    elif model_name == "upernet-convnext-small":
        __UpperCamelCase  :str    						=	torch.tensor(
            [[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]]			)
    elif model_name == "upernet-convnext-base":
        __UpperCamelCase  :Optional[Any]    						=	torch.tensor(
            [[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]]			)
    elif model_name == "upernet-convnext-large":
        __UpperCamelCase  :List[str]    						=	torch.tensor(
            [[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]]			)
    elif model_name == "upernet-convnext-xlarge":
        __UpperCamelCase  :Any    						=	torch.tensor(
            [[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]]			)
    print('''Logits:'''						,    outputs.logits[0, 0, :3, :3]			)
    assert torch.allclose(outputs.logits[0, 0, :3, :3]						,    SCREAMING_SNAKE_CASE						,    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(SCREAMING_SNAKE_CASE			)
        print(f"""Saving processor to {pytorch_dump_folder_path}"""			)
        processor.save_pretrained(SCREAMING_SNAKE_CASE			)

    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__":
    __lowercase											=      argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''upernet-convnext-tiny''',
        type=str,
        choices=[F'upernet-convnext-{size}' for size in ['''tiny''', '''small''', '''base''', '''large''', '''xlarge''']],
        help='''Name of the ConvNext 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.'''
    )

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






import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE="attention"			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
    __UpperCamelCase  :Optional[int]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
    __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
    __UpperCamelCase  :Union[str, Any]    						=	params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
    return k, o, q, v



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE=False			):
    '''simple docstring'''







    if split_mlp_wi:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
        __UpperCamelCase  :Dict    						=	params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
        __UpperCamelCase  :Optional[Any]    						=	(wi_a, wi_a)
    else:
        __UpperCamelCase  :List[str]    						=	params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]

    __UpperCamelCase  :int    						=	params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
    return wi, wo



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    *, SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Tuple    						=	traverse_util.flatten_dict(variables['''target''']			)
    __UpperCamelCase  :Optional[int]    						=	{'''/'''.join(SCREAMING_SNAKE_CASE			): v for k, v in old.items()}

    # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
    __UpperCamelCase  :Union[str, Any]    						=	'''encoder/layers_0/mlp/wi_0/kernel''' in old
    print('''Split MLP:'''						,    SCREAMING_SNAKE_CASE			)

    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict()

    # Shared embeddings.
    __UpperCamelCase  :Any    						=	old['''token_embedder/embedding''']

    # Encoder.
    for i in range(SCREAMING_SNAKE_CASE			):
        # Block i, layer 0 (Self Attention).
        __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_attention_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''attention'''			)
        __UpperCamelCase  :str    						=	layer_norm
        __UpperCamelCase  :str    						=	k.T
        __UpperCamelCase  :Optional[Any]    						=	o.T
        __UpperCamelCase  :Optional[int]    						=	q.T
        __UpperCamelCase  :Any    						=	v.T

        # Block i, layer 1 (MLP).
        __UpperCamelCase  :Union[str, Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    '''pre_mlp_layer_norm'''			)
        __UpperCamelCase						, __UpperCamelCase  :Optional[int]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''encoder'''						,    SCREAMING_SNAKE_CASE			)
        __UpperCamelCase  :Any    						=	layer_norm
        if split_mlp_wi:
            __UpperCamelCase  :Optional[Any]    						=	wi[0].T
            __UpperCamelCase  :int    						=	wi[1].T
        else:
            __UpperCamelCase  :Dict    						=	wi.T
        __UpperCamelCase  :str    						=	wo.T

    __UpperCamelCase  :Optional[Any]    						=	old[
        '''encoder/relpos_bias/rel_embedding'''
    ].T
    __UpperCamelCase  :Optional[int]    						=	old['''encoder/encoder_norm/scale''']

    if not is_encoder_only:
        # Decoder.
        for i in range(SCREAMING_SNAKE_CASE			):
            # Block i, layer 0 (Self Attention).
            __UpperCamelCase  :int    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_self_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :Dict    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''self_attention'''			)
            __UpperCamelCase  :str    						=	layer_norm
            __UpperCamelCase  :Optional[Any]    						=	k.T
            __UpperCamelCase  :List[str]    						=	o.T
            __UpperCamelCase  :Union[str, Any]    						=	q.T
            __UpperCamelCase  :int    						=	v.T

            # Block i, layer 1 (Cross Attention).
            __UpperCamelCase  :Optional[Any]    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_cross_attention_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase  :int    						=	tax_attention_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''encoder_decoder_attention'''			)
            __UpperCamelCase  :List[str]    						=	layer_norm
            __UpperCamelCase  :Any    						=	k.T
            __UpperCamelCase  :Any    						=	o.T
            __UpperCamelCase  :Optional[int]    						=	q.T
            __UpperCamelCase  :Union[str, Any]    						=	v.T

            # Block i, layer 2 (MLP).
            __UpperCamelCase  :Tuple    						=	tax_layer_norm_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    '''pre_mlp_layer_norm'''			)
            __UpperCamelCase						, __UpperCamelCase  :Optional[Any]    						=	tax_mlp_lookup(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    '''decoder'''						,    SCREAMING_SNAKE_CASE			)
            __UpperCamelCase  :Optional[Any]    						=	layer_norm
            if split_mlp_wi:
                __UpperCamelCase  :int    						=	wi[0].T
                __UpperCamelCase  :Any    						=	wi[1].T
            else:
                __UpperCamelCase  :Optional[Any]    						=	wi.T
            __UpperCamelCase  :Dict    						=	wo.T

        __UpperCamelCase  :Optional[int]    						=	old['''decoder/decoder_norm/scale''']
        __UpperCamelCase  :Optional[Any]    						=	old[
            '''decoder/relpos_bias/rel_embedding'''
        ].T

        # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
        if "decoder/logits_dense/kernel" in old:
            __UpperCamelCase  :List[str]    						=	old['''decoder/logits_dense/kernel'''].T

    return new



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :Optional[Any]    						=	collections.OrderedDict([(k, torch.from_numpy(v.copy()			)) for (k, v) in converted_params.items()]			)

    # Add what is missing.
    if "encoder.embed_tokens.weight" not in state_dict:
        __UpperCamelCase  :Union[str, Any]    						=	state_dict['''shared.weight''']

    if not is_encoder_only:
        if "decoder.embed_tokens.weight" not in state_dict:
            __UpperCamelCase  :Any    						=	state_dict['''shared.weight''']

        if "lm_head.weight" not in state_dict:  # For old 1.0 models.
            print('''Using shared word embeddings as lm_head.'''			)
            __UpperCamelCase  :Optional[int]    						=	state_dict['''shared.weight''']

    return state_dict



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			):
    '''simple docstring'''







    __UpperCamelCase  :int    						=	checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :str    						=	convert_tax_to_pytorch(SCREAMING_SNAKE_CASE						,    num_layers=config.num_layers						,    is_encoder_only=SCREAMING_SNAKE_CASE			)
    __UpperCamelCase  :Optional[int]    						=	make_state_dict(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)
    model.load_state_dict(SCREAMING_SNAKE_CASE						,    strict=SCREAMING_SNAKE_CASE			)



def lowerCamelCase						(     SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE = False			):
    '''simple docstring'''







    __UpperCamelCase  :Union[str, Any]    						=	TaConfig.from_json_file(SCREAMING_SNAKE_CASE			)
    print(f"""Building PyTorch model from configuration: {config}"""			)
    # Non-v1.1 checkpoints could also use T5Model, but this works for all.
    # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
    if is_encoder_only:
        __UpperCamelCase  :Dict    						=	TaEncoderModel(SCREAMING_SNAKE_CASE			)
    else:
        __UpperCamelCase  :str    						=	TaForConditionalGeneration(SCREAMING_SNAKE_CASE			)

    # Load weights from tf checkpoint
    load_tax_weights_in_ta(SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE						,    SCREAMING_SNAKE_CASE			)

    # Save pytorch-model
    print(f"""Save PyTorch model to {pytorch_dump_path}"""			)
    model.save_pretrained(SCREAMING_SNAKE_CASE			)

    # Verify that we can load the checkpoint.
    model.from_pretrained(SCREAMING_SNAKE_CASE			)
    print('''Done'''			)


if __name__ == "__main__":
    __lowercase											=      argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
    # Required parameters
    parser.add_argument(
        '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
    )
    parser.add_argument(
        '''--config_file''',
        default=None,
        type=str,
        required=True,
        help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
    )
    parser.add_argument(
        '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
    )
    parser.add_argument(
        '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
    )
    __lowercase											=      parser.parse_args()
    convert_tax_checkpoint_to_pytorch(
        args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
    )





