



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__  :  List[Any]								=					logging.get_logger(__name__)


def 			UpperCamelCase(  __UpperCamelCase							:		List[str] ):
		lowerCAmelCase_			:     Any									= DPTConfig(embedding_type='''hybrid''' )

		if "large" in checkpoint_url:
				lowerCAmelCase_			:     Any									= 1024
				lowerCAmelCase_			:     Tuple									= 4096
				lowerCAmelCase_			:     List[Any]									= 24
				lowerCAmelCase_			:     int									= 16
				lowerCAmelCase_			:     Tuple									= [5, 11, 17, 23]
				lowerCAmelCase_			:     Optional[Any]									= [256, 512, 1024, 1024]
				lowerCAmelCase_			:     str									= (1, 384, 384)

		if "nyu" or "midas" in checkpoint_url:
				lowerCAmelCase_			:     Union[str, Any]									= 768
				lowerCAmelCase_			:     int									= [1, 1, 1, 0.5]
				lowerCAmelCase_			:     List[str]									= [256, 512, 768, 768]
				lowerCAmelCase_			:     int									= 150
				lowerCAmelCase_			:     Any									= 16
				lowerCAmelCase_			:     Any									= (1, 384, 384)
				lowerCAmelCase_			:     List[str]									= False
				lowerCAmelCase_			:     Union[str, Any]									= '''project'''

		if "ade" in checkpoint_url:
				lowerCAmelCase_			:     Any									= True
				lowerCAmelCase_			:     Any									= 768
				lowerCAmelCase_			:     Optional[int]									= [1, 1, 1, 0.5]
				lowerCAmelCase_			:     Union[str, Any]									= 150
				lowerCAmelCase_			:     Any									= 16
				lowerCAmelCase_			:     Any									= '''huggingface/label-files'''
				lowerCAmelCase_			:     Any									= '''ade20k-id2label.json'''
				lowerCAmelCase_			:     str									= json.load(open(cached_download(hf_hub_url(__UpperCamelCase     ,__UpperCamelCase     ,repo_type='''dataset''' ) )     ,'''r''' ) )
				lowerCAmelCase_			:     Optional[Any]									= {int(__UpperCamelCase ): v for k, v in idalabel.items()}
				lowerCAmelCase_			:     Optional[Any]									= idalabel
				lowerCAmelCase_			:     Optional[int]									= {v: k for k, v in idalabel.items()}
				lowerCAmelCase_			:     List[Any]									= [1, 150, 480, 480]

		return config, expected_shape


def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int] ):
		lowerCAmelCase_			:     int									= ['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
		for k in ignore_keys:
				state_dict.pop(__UpperCamelCase     ,__UpperCamelCase )


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

		# resize blocks
		if "pretrained.act_postprocess1.3" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''pretrained.act_postprocess1.3'''     ,'''neck.reassemble_stage.layers.0.projection''' )
		if "pretrained.act_postprocess1.4" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''pretrained.act_postprocess1.4'''     ,'''neck.reassemble_stage.layers.0.resize''' )
		if "pretrained.act_postprocess2.3" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''pretrained.act_postprocess2.3'''     ,'''neck.reassemble_stage.layers.1.projection''' )
		if "pretrained.act_postprocess2.4" in name:
				lowerCAmelCase_			:     List[str]									= name.replace('''pretrained.act_postprocess2.4'''     ,'''neck.reassemble_stage.layers.1.resize''' )
		if "pretrained.act_postprocess3.3" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''pretrained.act_postprocess3.3'''     ,'''neck.reassemble_stage.layers.2.projection''' )
		if "pretrained.act_postprocess4.3" in name:
				lowerCAmelCase_			:     List[str]									= name.replace('''pretrained.act_postprocess4.3'''     ,'''neck.reassemble_stage.layers.3.projection''' )
		if "pretrained.act_postprocess4.4" in name:
				lowerCAmelCase_			:     Optional[Any]									= name.replace('''pretrained.act_postprocess4.4'''     ,'''neck.reassemble_stage.layers.3.resize''' )
		if "pretrained" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''pretrained'''     ,'''dpt''' )
		if "bn" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''bn'''     ,'''batch_norm''' )
		if "head" in name:
				lowerCAmelCase_			:     Any									= name.replace('''head'''     ,'''head.head''' )
		if "encoder.norm" in name:
				lowerCAmelCase_			:     Tuple									= name.replace('''encoder.norm'''     ,'''layernorm''' )
		if "auxlayer" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''auxlayer'''     ,'''auxiliary_head.head''' )
		if "backbone" in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''backbone'''     ,'''backbone.bit.encoder''' )

		if ".." in name:
				lowerCAmelCase_			:     List[Any]									= name.replace('''..'''     ,'''.''' )

		if "stem.conv" in name:
				lowerCAmelCase_			:     str									= name.replace('''stem.conv'''     ,'''bit.embedder.convolution''' )
		if "blocks" in name:
				lowerCAmelCase_			:     List[str]									= name.replace('''blocks'''     ,'''layers''' )
		if "convolution" in name and "backbone" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''convolution'''     ,'''conv''' )
		if "layer" in name and "backbone" in name:
				lowerCAmelCase_			:     Optional[int]									= name.replace('''layer'''     ,'''layers''' )
		if "backbone.bit.encoder.bit" in name:
				lowerCAmelCase_			:     Union[str, Any]									= name.replace('''backbone.bit.encoder.bit'''     ,'''backbone.bit''' )
		if "embedder.conv" in name:
				lowerCAmelCase_			:     str									= name.replace('''embedder.conv'''     ,'''embedder.convolution''' )
		if "backbone.bit.encoder.stem.norm" in name:
				lowerCAmelCase_			:     Dict									= name.replace('''backbone.bit.encoder.stem.norm'''     ,'''backbone.bit.embedder.norm''' )
		return name


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


def 			UpperCamelCase(  ):
		lowerCAmelCase_			:     str									= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
		lowerCAmelCase_			:     Dict									= Image.open(requests.get(__UpperCamelCase     ,stream=__UpperCamelCase ).raw )
		return im


@torch.no_grad()
def 			UpperCamelCase(  __UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Dict     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		Optional[int]     ,__UpperCamelCase							:		str ):
		lowerCAmelCase_					,  lowerCAmelCase_			:     Any									= get_dpt_config(__UpperCamelCase )
		# load original state_dict from URL
		# state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
		lowerCAmelCase_			:     List[str]									= torch.load(__UpperCamelCase     ,map_location='''cpu''' )
		# remove certain keys
		remove_ignore_keys_(__UpperCamelCase )
		# rename keys
		for key in state_dict.copy().keys():
				lowerCAmelCase_			:     Any									= state_dict.pop(__UpperCamelCase )
				lowerCAmelCase_			:     Optional[Any]									= val
		# read in qkv matrices
		read_in_q_k_v(__UpperCamelCase     ,__UpperCamelCase )

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

		# Check outputs on an image
		lowerCAmelCase_			:     Tuple									= 480 if '''ade''' in checkpoint_url else 384
		lowerCAmelCase_			:     Optional[int]									= DPTImageProcessor(size=__UpperCamelCase )

		lowerCAmelCase_			:     Union[str, Any]									= prepare_img()
		lowerCAmelCase_			:     str									= image_processor(__UpperCamelCase     ,return_tensors='''pt''' )

		# forward pass
		lowerCAmelCase_			:     Tuple									= model(**__UpperCamelCase ).logits if '''ade''' in checkpoint_url else model(**__UpperCamelCase ).predicted_depth

		if show_prediction:
				lowerCAmelCase_			:     Optional[Any]									= (
				    torch.nn.functional.interpolate(
				        outputs.unsqueeze(1 )     ,size=(image.size[1], image.size[0])     ,mode='''bicubic'''     ,align_corners=__UpperCamelCase     ,)
				    .squeeze()
				    .cpu()
				    .numpy()
				)

				Image.fromarray((prediction / prediction.max()) * 255 ).show()

		if pytorch_dump_folder_path is not None:
				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:
				model.push_to_hub('''ybelkada/dpt-hybrid-midas''' )
				image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas''' )


if __name__ == "__main__":
						A__  :  Optional[int]								=					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=False,
						    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.''',
						)
						parser.add_argument(
						    '''--show_prediction''',
						    action='''store_true''',
						)

						A__  :  Union[str, Any]								=					parser.parse_args()
						convert_dpt_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
						)









'''simple docstring'''



import argparse
from typing import Dict

import tensorflow as tf
import torch
from tqdm import tqdm

from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration


A__		:					str												=  [
    # tf -> hf
    ("""/""", """."""),
    ("""layer_""", """layers."""),
    ("""kernel""", """weight"""),
    ("""beta""", """bias"""),
    ("""gamma""", """weight"""),
    ("""pegasus""", """model"""),
]
A__		:					Dict												=  [
    (""".output.dense""", """.fc2"""),
    ("""intermediate.LayerNorm""", """final_layer_norm"""),
    ("""intermediate.dense""", """fc1"""),
]

A__		:					int												=  (
    INIT_COMMON
    + [
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.out_proj"""),
        ("""attention.self""", """self_attn"""),
        ("""attention.encdec.LayerNorm""", """encoder_attn_layer_norm"""),
        ("""attention.encdec_output.dense""", """encoder_attn.out_proj"""),
        ("""attention.encdec""", """encoder_attn"""),
        ("""key""", """k_proj"""),
        ("""value""", """v_proj"""),
        ("""query""", """q_proj"""),
        ("""decoder.LayerNorm""", """decoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  (
    INIT_COMMON
    + [
        ("""embeddings.word_embeddings""", """shared.weight"""),
        ("""embeddings.position_embeddings""", """embed_positions.weight"""),
        ("""attention.self.LayerNorm""", """self_attn_layer_norm"""),
        ("""attention.output.dense""", """self_attn.output"""),
        ("""attention.self""", """self_attn.self"""),
        ("""encoder.LayerNorm""", """encoder.layernorm_embedding"""),
    ]
    + END_COMMON
)

A__		:					Tuple												=  [
    """encdec/key/bias""",
    """encdec/query/bias""",
    """encdec/value/bias""",
    """self/key/bias""",
    """self/query/bias""",
    """self/value/bias""",
    """encdec_output/dense/bias""",
    """attention/output/dense/bias""",
]



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]      ,    UpperCAmelCase_    :				Any							)     ->		List[str]:
     for tf_name, hf_name in patterns:
          __lowerCamelCase   :	Optional[int]								=		k.replace(UpperCAmelCase_      ,    UpperCAmelCase_							)
     return k



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				dict      ,    UpperCAmelCase_    :				dict							)     ->		BigBirdPegasusForConditionalGeneration:
     __lowerCamelCase   :	int								=		BigBirdPegasusConfig(**UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		BigBirdPegasusForConditionalGeneration(UpperCAmelCase_							)
     __lowerCamelCase   :	Union[str, Any]								=		torch_model.state_dict()
     __lowerCamelCase   :	Tuple								=		{}

     # separating decoder weights
     __lowerCamelCase   :	Dict								=		{k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder'							)}
     __lowerCamelCase   :	str								=		{k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder'							)}

     for k, v in tqdm(decoder_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Tuple								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Tuple								=		DECODER_PATTERNS
          __lowerCamelCase   :	Optional[int]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict:
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	Union[str, Any]								=		v.T
          __lowerCamelCase   :	str								=		torch.from_numpy(UpperCAmelCase_							)
          assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     for k, v in tqdm(remaining_weights.items()      ,    'tf -> hf conversion'							):
          __lowerCamelCase   :	Optional[Any]								=		[k.endswith(UpperCAmelCase_							) for ending in KEYS_TO_IGNORE]
          if any(UpperCAmelCase_							):
               continue
          __lowerCamelCase   :	Dict								=		REMAINING_PATTERNS
          __lowerCamelCase   :	List[str]								=		rename_state_dict_key(UpperCAmelCase_      ,    UpperCAmelCase_							)
          if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
               raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})'							)
          if any(True if i in k else False for i in ['dense', 'query', 'key', 'value']							):
               __lowerCamelCase   :	List[str]								=		v.T
          __lowerCamelCase   :	List[str]								=		torch.from_numpy(UpperCAmelCase_							)
          if k != "pegasus/embeddings/position_embeddings":
               assert v.shape == state_dict[new_k].shape, F'{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'

     __lowerCamelCase   :	Any								=		mapping['model.embed_positions.weight']
     __lowerCamelCase   :	Union[str, Any]								=		mapping.pop('model.embed_positions.weight'							)
     __lowerCamelCase				,       __lowerCamelCase   :	List[str]								=		torch_model.load_state_dict(UpperCAmelCase_      ,    strict=UpperCAmelCase_							)
     __lowerCamelCase   :	Any								=		[
         k
         for k in missing
         if k
         not in [
             'final_logits_bias',
             'model.encoder.embed_tokens.weight',
             'model.decoder.embed_tokens.weight',
             'lm_head.weight',
         ]
     ]
     assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
     assert extra == [], F'no matches found for the following tf keys {extra}'
     return torch_model



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				Union[str, Any]							)     ->		Dict:
     __lowerCamelCase   :	int								=		tf.train.list_variables(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		{}
     __lowerCamelCase   :	List[Any]								=		['global_step']
     for name, shape in tqdm(UpperCAmelCase_      ,    desc='converting tf checkpoint to dict'							):
          __lowerCamelCase   :	Any								=		any(pat in name for pat in ignore_name							)
          if skip_key:
               continue
          __lowerCamelCase   :	Dict								=		tf.train.load_variable(UpperCAmelCase_      ,    UpperCAmelCase_							)
          __lowerCamelCase   :	List[Any]								=		array
     return tf_weights



def 			UpperCAmelCase__					(	UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				str      ,    UpperCAmelCase_    :				dict							)     ->		Union[str, Any]:
     __lowerCamelCase   :	Optional[int]								=		get_tf_weights_as_numpy(UpperCAmelCase_							)
     __lowerCamelCase   :	List[str]								=		convert_bigbird_pegasus(UpperCAmelCase_      ,    UpperCAmelCase_							)
     torch_model.save_pretrained(UpperCAmelCase_							)


if __name__ == "__main__":
      A__		:					Any												=  argparse.ArgumentParser()
      parser.add_argument("""--tf_ckpt_path""", type=str, help="""passed to tf.train.list_variables""")
      parser.add_argument("""--save_dir""", default=None, type=str, help="""Path to the output PyTorch model.""")
      A__		:					List[str]												=  parser.parse_args()
      A__		:					Optional[int]												=  {}
      convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)






