




import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase__				     = logging.get_logger()







@dataclass
class a :
						"""simple docstring"""

						UpperCamelCase_	:       nn.Module
						UpperCamelCase_	:       List[nn.Module]       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )
						UpperCamelCase_	:       list       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )






						def     UpperCAmelCase_      ( self :     str				,							lowerCamelCase__ :     Optional[Any]				,							lowerCamelCase__ :     Tensor				,							lowerCamelCase__ :     Tensor				)    ->						int:
													"""simple docstring"""


													__lowercase      =    len(list(m.modules()				)				) == 1 or isinstance(lowerCamelCase__				,							nn.Convad				) or isinstance(lowerCamelCase__				,							nn.BatchNormad				)
													if has_not_submodules:
																				self.traced.append(lowerCamelCase__				)






						def __call__( self :     Optional[int]				,							lowerCamelCase__ :     Tensor				)    ->						Union[str, Any]:
													"""simple docstring"""


													for m in self.module.modules():
																				self.handles.append(m.register_forward_hook(self._forward_hook				)				)
													self.module(lowerCamelCase__				)
													[x.remove() for x in self.handles]
													return self






						@property
						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						str:
													"""simple docstring"""


													return list(filter(lambda lowerCamelCase__				: len(list(x.state_dict().keys()				)				) > 0				,							self.traced				)				)







@dataclass
class a :
						"""simple docstring"""

						UpperCamelCase_	:       nn.Module
						UpperCamelCase_	:       nn.Module
						UpperCamelCase_	:       int       	=		1
						UpperCamelCase_	:       List       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )
						UpperCamelCase_	:       List       	=		field(default_factory=__SCREAMING_SNAKE_CASE  )
						UpperCamelCase_	:       bool       	=		True






						def __call__( self :     List[Any]				,							lowerCamelCase__ :     Tensor				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    Tracker(self.dest				)(lowerCamelCase__				).parametrized
													__lowercase      =    Tracker(self.src				)(lowerCamelCase__				).parametrized

													__lowercase      =    list(filter(lambda lowerCamelCase__				: type(lowerCamelCase__				) not in self.src_skip				,							lowerCamelCase__				)				)
													__lowercase      =    list(filter(lambda lowerCamelCase__				: type(lowerCamelCase__				) not in self.dest_skip				,							lowerCamelCase__				)				)

													if len(lowerCamelCase__				) != len(lowerCamelCase__				) and self.raise_if_mismatch:
																				raise Exception(
																				    f'Numbers of operations are different. Source module has {len(lowerCamelCase__				)} operations while'
																				    f' destination module has {len(lowerCamelCase__				)}.'				)

													for dest_m, src_m in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				dest_m.load_state_dict(src_m.state_dict()				)
																				if self.verbose == 1:
																											print(f'Transfered from={src_m} to={dest_m}'				)







class a (     nn.Module  ):
						"""simple docstring"""

						def __init__( self :     Tuple				,							lowerCamelCase__ :     nn.Module				)    ->						Any:
													"""simple docstring"""


													super().__init__()

													__lowercase      =    []
													# - get the stem
													feature_blocks.append(('''conv1''', model.stem)				)
													# - get all the feature blocks
													for k, v in model.trunk_output.named_children():
																				assert k.startswith('''block'''				), f'Unexpected layer name {k}'
																				__lowercase      =    len(lowerCamelCase__				) + 1
																				feature_blocks.append((f'res{block_index}', v)				)

													__lowercase      =    nn.ModuleDict(lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Tuple				,							lowerCamelCase__ :     Tensor				)    ->						List[Any]:
													"""simple docstring"""


													return get_trunk_forward_outputs(
													    lowerCamelCase__				,							out_feat_keys=lowerCamelCase__				,							feature_blocks=self._feature_blocks				,							)







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     str				)    ->						str:
													"""simple docstring"""


													__lowercase      =    x.split('''-'''				)
													return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)






						def __getitem__( self :     List[Any]				,							lowerCamelCase__ :     str				)    ->						Callable[[], Tuple[nn.Module, Dict]]:
													"""simple docstring"""


													if x not in self:
																				__lowercase      =    self.convert_name_to_timm(lowerCamelCase__				)
																				__lowercase      =    partial(lambda: (timm.create_model(lowerCamelCase__				,							pretrained=lowerCamelCase__				).eval(), None)				)

													else:
																				__lowercase      =    super().__getitem__(lowerCamelCase__				)

													return val







class a (     __SCREAMING_SNAKE_CASE  ):
						"""simple docstring"""

						def __getitem__( self :     int				,							lowerCamelCase__ :     str				)    ->						Callable[[], nn.Module]:
													"""simple docstring"""


													if "seer" in x and "in1k" not in x:
																				__lowercase      =    RegNetModel
													else:
																				__lowercase      =    RegNetForImageClassification
													return val







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			List[Tuple[str, str]]				)    -> List[Any]:
							'''simple docstring'''


							for from_key, to_key in keys:
														__lowercase      =    from_state_dict[from_key].clone()
														print(F'Copied key={from_key} to={to_key}'				)
							return to_state_dict







def 		_A(       UpperCamelCase__							:			str				,       UpperCamelCase__							:			Callable[[], nn.Module]				,       UpperCamelCase__							:			Callable[[], nn.Module]				,       UpperCamelCase__							:			RegNetConfig				,       UpperCamelCase__							:			Path				,       UpperCamelCase__							:			bool = True				,       )    -> Tuple:
							'''simple docstring'''


							print(F'Converting {name}...'				)
							with torch.no_grad():
														__lowercase     ,     __lowercase      =    from_model_func()
														__lowercase      =    our_model_func(UpperCamelCase__				).eval()
														__lowercase      =    ModuleTransfer(src=UpperCamelCase__				,       dest=UpperCamelCase__				,       raise_if_mismatch=UpperCamelCase__				)
														__lowercase      =    torch.randn((1, 3, 224, 224)				)
														module_transfer(UpperCamelCase__				)

							if from_state_dict is not None:
														__lowercase      =    []
														# for seer - in1k finetuned we have to manually copy the head
														if "seer" in name and "in1k" in name:
																					__lowercase      =    [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
														__lowercase      =    manually_copy_vissl_head(UpperCamelCase__				,       our_model.state_dict()				,       UpperCamelCase__				)
														our_model.load_state_dict(UpperCamelCase__				)

							__lowercase      =    our_model(UpperCamelCase__				,       output_hidden_states=UpperCamelCase__				)
							__lowercase      =    (
							    our_outputs.logits if isinstance(UpperCamelCase__				,       UpperCamelCase__				) else our_outputs.last_hidden_state
							)

							__lowercase      =    from_model(UpperCamelCase__				)
							__lowercase      =    from_output[-1] if type(UpperCamelCase__				) is list else from_output

							# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
							if "seer" in name and "in1k" in name:
														__lowercase      =    our_outputs.hidden_states[-1]

							assert torch.allclose(UpperCamelCase__				,       UpperCamelCase__				), "The model logits don't match the original one."

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / name				,       commit_message='''Add model'''				,       use_temp_dir=UpperCamelCase__				,       )

														__lowercase      =    224 if '''seer''' not in name else 384
														# we can use the convnext one
														__lowercase      =    AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''				,       size=UpperCamelCase__				)
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name				,       commit_message='''Add image processor'''				,       use_temp_dir=UpperCamelCase__				,       )

														print(F'Pushed {name}'				)







def 		_A(       UpperCamelCase__							:			Path				,       UpperCamelCase__							:			str = None				,       UpperCamelCase__							:			bool = True				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    '''imagenet-1k-id2label.json'''
							__lowercase      =    1000
							__lowercase      =    (1, num_labels)

							__lowercase      =    '''huggingface/label-files'''
							__lowercase      =    num_labels
							__lowercase      =    json.load(open(cached_download(hf_hub_url(UpperCamelCase__				,       UpperCamelCase__				,       repo_type='''dataset'''				)				)				,       '''r'''				)				)
							__lowercase      =    {int(UpperCamelCase__				): v for k, v in idalabel.items()}

							__lowercase      =    idalabel
							__lowercase      =    {v: k for k, v in idalabel.items()}

							__lowercase      =    partial(UpperCamelCase__				,       num_labels=UpperCamelCase__				,       idalabel=UpperCamelCase__				,       labelaid=UpperCamelCase__				)

							__lowercase      =    {
							    '''regnet-x-002''': ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 152, 368]				,       groups_width=8				,       layer_type='''x'''				),
							    '''regnet-x-004''': ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]				,       hidden_sizes=[32, 64, 160, 384]				,       groups_width=16				,       layer_type='''x'''				),
							    '''regnet-x-006''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]				,       hidden_sizes=[48, 96, 240, 528]				,       groups_width=24				,       layer_type='''x'''				),
							    '''regnet-x-008''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]				,       hidden_sizes=[64, 128, 288, 672]				,       groups_width=16				,       layer_type='''x'''				),
							    '''regnet-x-016''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]				,       hidden_sizes=[72, 168, 408, 912]				,       groups_width=24				,       layer_type='''x'''				),
							    '''regnet-x-032''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]				,       hidden_sizes=[96, 192, 432, 1008]				,       groups_width=48				,       layer_type='''x'''				),
							    '''regnet-x-040''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]				,       hidden_sizes=[80, 240, 560, 1360]				,       groups_width=40				,       layer_type='''x'''				),
							    '''regnet-x-064''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]				,       hidden_sizes=[168, 392, 784, 1624]				,       groups_width=56				,       layer_type='''x'''				),
							    '''regnet-x-080''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]				,       hidden_sizes=[80, 240, 720, 1920]				,       groups_width=120				,       layer_type='''x'''				),
							    '''regnet-x-120''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]				,       hidden_sizes=[224, 448, 896, 2240]				,       groups_width=112				,       layer_type='''x'''				),
							    '''regnet-x-160''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]				,       hidden_sizes=[256, 512, 896, 2048]				,       groups_width=128				,       layer_type='''x'''				),
							    '''regnet-x-320''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]				,       hidden_sizes=[336, 672, 1344, 2520]				,       groups_width=168				,       layer_type='''x'''				),
							    # y variant
							    '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]				,       hidden_sizes=[24, 56, 152, 368]				,       groups_width=8				),
							    '''regnet-y-004''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]				,       hidden_sizes=[48, 104, 208, 440]				,       groups_width=8				),
							    '''regnet-y-006''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]				,       hidden_sizes=[48, 112, 256, 608]				,       groups_width=16				),
							    '''regnet-y-008''': ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]				,       hidden_sizes=[64, 128, 320, 768]				,       groups_width=16				),
							    '''regnet-y-016''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]				,       hidden_sizes=[48, 120, 336, 888]				,       groups_width=24				),
							    '''regnet-y-032''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]				,       hidden_sizes=[72, 216, 576, 1512]				,       groups_width=24				),
							    '''regnet-y-040''': ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]				,       hidden_sizes=[128, 192, 512, 1088]				,       groups_width=64				),
							    '''regnet-y-064''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]				,       hidden_sizes=[144, 288, 576, 1296]				,       groups_width=72				),
							    '''regnet-y-080''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]				,       hidden_sizes=[168, 448, 896, 2016]				,       groups_width=56				),
							    '''regnet-y-120''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]				,       hidden_sizes=[224, 448, 896, 2240]				,       groups_width=112				),
							    '''regnet-y-160''': ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]				,       hidden_sizes=[224, 448, 1232, 3024]				,       groups_width=112				),
							    '''regnet-y-320''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]				,       hidden_sizes=[232, 696, 1392, 3712]				,       groups_width=232				),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[232, 696, 1392, 3712]				,       groups_width=232				),
							    '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]				,       hidden_sizes=[328, 984, 1968, 4920]				,       groups_width=328				),
							    '''regnet-y-1280-seer''': RegNetConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[528, 1056, 2904, 7392]				,       groups_width=264				),
							    '''regnet-y-2560-seer''': RegNetConfig(
							        depths=[3, 7, 16, 1]				,       hidden_sizes=[640, 1696, 2544, 5088]				,       groups_width=640				),
							    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]				,       groups_width=1010				),
							    # finetuned on imagenet
							    '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]				,       hidden_sizes=[232, 696, 1392, 3712]				,       groups_width=232				),
							    '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]				,       hidden_sizes=[328, 984, 1968, 4920]				,       groups_width=328				),
							    '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[528, 1056, 2904, 7392]				,       groups_width=264				),
							    '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]				,       hidden_sizes=[640, 1696, 2544, 5088]				,       groups_width=640				),
							    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]				,       hidden_sizes=[2020, 4040, 1_1110, 2_8280]				,       groups_width=1010				),
							}

							__lowercase      =    NameToOurModelFuncMap()
							__lowercase      =    NameToFromModelFuncMap()
							# add seer weights logic

							def load_using_classy_vision(UpperCamelCase__							:			str				,       UpperCamelCase__							:			Callable[[], nn.Module]				) -> Tuple[nn.Module, Dict]:
														__lowercase      =    torch.hub.load_state_dict_from_url(UpperCamelCase__				,       model_dir=str(UpperCamelCase__				)				,       map_location='''cpu'''				)
														__lowercase      =    model_func()
														# check if we have a head, if yes add it
														__lowercase      =    files['''classy_state_dict''']['''base_model''']['''model''']
														__lowercase      =    model_state_dict['''trunk''']
														model.load_state_dict(UpperCamelCase__				)
														return model.eval(), model_state_dict["heads"]

							# pretrained
							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''				,       lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27				,       group_width=1010				,       w_a=1744				,       w_a=620.83				,       w_m=2.52				)				)				)				,       )

							# IN1K finetuned
							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''				,       lambda: FakeRegNetVisslWrapper(RegNetYaaagf()				)				,       )

							__lowercase      =    partial(
							    UpperCamelCase__				,       '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''				,       lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27				,       group_width=1010				,       w_a=1744				,       w_a=620.83				,       w_m=2.52				)				)				)				,       )

							if model_name:
														convert_weight_and_push(
														    UpperCamelCase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       names_to_config[model_name]				,       UpperCamelCase__				,       UpperCamelCase__				,       )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    UpperCamelCase__				,       names_to_from_model_map[model_name]				,       names_to_ours_model_map[model_name]				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       )
							return config, expected_shape


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--model_name",
			    default=None,
			    type=str,
			    help=(
			        "The name of the model you wish to convert, it must be one of the supported regnet* architecture,"
			        " currently: regnetx-*, regnety-*. If `None`, all of them will the converted."
			    ),
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path",
			    default=None,
			    type=Path,
			    required=True,
			    help="Path to the output PyTorch model directory.",
			)
			parser.add_argument(
			    "--push_to_hub",
			    default=True,
			    type=bool,
			    required=False,
			    help="If True, push model and image processor to the hub.",
			)

			UpperCAmelCase__				     = parser.parse_args()

			UpperCAmelCase__				     = args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)












import argparse
import os
import re

import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints

from transformers import (
    AutoTokenizer,
    PixaStructConfig,
    PixaStructForConditionalGeneration,
    PixaStructImageProcessor,
    PixaStructProcessor,
    PixaStructTextConfig,
    PixaStructVisionConfig,
)







def 		_A(       UpperCamelCase__							:			Tuple				)    -> Tuple:
							'''simple docstring'''


							__lowercase      =    checkpoints.load_tax_checkpoint(UpperCamelCase__				)
							__lowercase      =    flatten_dict(UpperCamelCase__				)
							return flax_params







def 		_A(       UpperCamelCase__							:			str				)    -> Dict:
							'''simple docstring'''


							__lowercase      =    {}

							__lowercase      =    {
							    '''token_embedder''': '''embeddings''',
							    '''encoder_norm''': '''layernorm''',
							    '''kernel''': '''weight''',
							    '''.out''': '''.output''',
							    '''scale''': '''weight''',
							    '''embedders_0.pos_embedding''': '''row_embedder.weight''',
							    '''embedders_1.pos_embedding''': '''column_embedder.weight''',
							}

							__lowercase      =    {
							    '''query''': '''attention.query''',
							    '''key''': '''attention.key''',
							    '''value''': '''attention.value''',
							    '''output.dense''': '''output''',
							    '''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
							    '''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
							    '''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
							    '''mlp.''': '''mlp.DenseReluDense.''',
							    '''pre_mlp_layer_norm''': '''mlp.layer_norm''',
							    '''self_attention.o''': '''self_attention.attention.o''',
							    '''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
							    '''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
							    '''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
							    '''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
							}

							for key in flax_dict.keys():
														if "target" in key:
																					# remove the first prefix from the key
																					__lowercase      =    '''.'''.join(key[1:]				)

																					# rename the key
																					for old, new in CONVERSION_MAPPING.items():
																												__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "decoder" in new_key:
																												for old, new in DECODER_CONVERSION_MAPPING.items():
																																			__lowercase      =    new_key.replace(UpperCamelCase__				,       UpperCamelCase__				)

																					if "layers" in new_key and "decoder" not in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)
																												__lowercase      =    new_key.replace('''encoder'''				,       '''encoder.encoder'''				)

																					elif "layers" in new_key and "decoder" in new_key:
																												# use regex to replace the layer number
																												__lowercase      =    re.sub(r'''layers_(\d+)'''				,       r'''layer.\1'''				,       UpperCamelCase__				)

																					__lowercase      =    flax_dict[key]

							__lowercase      =    {}
							# convert converted_dict into torch format
							for key in converted_dict.keys():
														if ("embed_tokens" not in key) and ("embedder" not in key):
																					__lowercase      =    torch.from_numpy(converted_dict[key].T				)
														else:
																					__lowercase      =    torch.from_numpy(converted_dict[key]				)

							return converted_torch_dict







def 		_A(       UpperCamelCase__							:			Union[str, Any]				,       UpperCamelCase__							:			int				,       UpperCamelCase__							:			List[str]=False				,       UpperCamelCase__							:			List[str]=False				)    -> Any:
							'''simple docstring'''


							__lowercase      =    get_flax_param(UpperCamelCase__				)

							if not use_large:
														__lowercase      =    PixaStructVisionConfig()
														__lowercase      =    PixaStructTextConfig()
							else:
														__lowercase      =    PixaStructVisionConfig(
														    hidden_size=1536				,       d_ff=3968				,       num_attention_heads=24				,       num_hidden_layers=18				)
														__lowercase      =    PixaStructTextConfig(hidden_size=1536				,       d_ff=3968				,       num_heads=24				,       num_layers=18				)
							__lowercase      =    PixaStructConfig(
							    vision_config=encoder_config.to_dict()				,       text_config=decoder_config.to_dict()				,       is_vqa=UpperCamelCase__				)

							__lowercase      =    PixaStructForConditionalGeneration(UpperCamelCase__				)

							__lowercase      =    rename_and_convert_flax_params(UpperCamelCase__				)
							model.load_state_dict(UpperCamelCase__				)

							__lowercase      =    AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer'''				)
							__lowercase      =    PixaStructImageProcessor()
							__lowercase      =    PixaStructProcessor(image_processor=UpperCamelCase__				,       tokenizer=UpperCamelCase__				)

							if use_large:
														__lowercase      =    4096

							__lowercase      =    True

							# mkdir if needed
							os.makedirs(UpperCamelCase__				,       exist_ok=UpperCamelCase__				)

							model.save_pretrained(UpperCamelCase__				)
							processor.save_pretrained(UpperCamelCase__				)

							print('''Model saved in {}'''.format(UpperCamelCase__				)				)


if __name__ == "__main__":
			UpperCAmelCase__				     = argparse.ArgumentParser()
			parser.add_argument("--t5x_checkpoint_path", default=None, type=str, help="Path to the original T5x checkpoint.")
			parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
			parser.add_argument("--use_large", action="store_true", help="Use large model.")
			parser.add_argument("--is_vqa", action="store_true", help="Use large model.")
			UpperCAmelCase__				     = parser.parse_args()

			convert_pixastruct_original_pytorch_checkpoint_to_hf(
			    args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
			)






