




'''simple docstring'''





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()
_SCREAMING_SNAKE_CASE							:   List[Any]      		= logging.get_logger()




@dataclass
class  _snake_case		:
			lowerCAmelCase_     :      nn.Module
			lowerCAmelCase_     :      List[nn.Module]						       =    field(default_factory=lowercase_							)
			lowerCAmelCase_     :      list						       =    field(default_factory=lowercase_							)




			def    lowerCAmelCase__				(					self				,							a__				,							a__				,							a__							)				->				Tuple:

										'''simple docstring'''




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




			def __call__(					self				,							a__							)				->				List[str]:

										'''simple docstring'''




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




			@property
			def    lowerCAmelCase__				(					self							)				->				int:

										'''simple docstring'''




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




@dataclass
class  _snake_case		:
			lowerCAmelCase_     :      nn.Module
			lowerCAmelCase_     :      nn.Module
			lowerCAmelCase_     :      int						       =    1
			lowerCAmelCase_     :      List						       =    field(default_factory=lowercase_							)
			lowerCAmelCase_     :      List						       =    field(default_factory=lowercase_							)
			lowerCAmelCase_     :      bool						       =    True




			def __call__(					self				,							a__							)				->				Dict:

										'''simple docstring'''




										snake_case_						=      Tracker(self.dest							)(a__							).parametrized
										snake_case_						=      Tracker(self.src							)(a__							).parametrized

										snake_case_						=      list(filter(lambda a__							: type(a__							) not in self.src_skip				,							a__							)							)
										snake_case_						=      list(filter(lambda a__							: type(a__							) not in self.dest_skip				,							a__							)							)

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

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




class  _snake_case		(	nn.Module							):




			def __init__(					self				,							a__							)				->				str:

										'''simple docstring'''




										super().__init__()

										snake_case_						=      []
										# - 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}'
																	snake_case_						=      len(a__							) + 1
																	feature_blocks.append((F'res{block_index}', v)							)

										snake_case_						=      nn.ModuleDict(a__							)




			def    lowerCAmelCase__				(					self				,							a__							)				->				int:

										'''simple docstring'''




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




class  _snake_case		(	lowercase_							):




			def    lowerCAmelCase__				(					self				,							a__							)				->				str:

										'''simple docstring'''




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




			def __getitem__(					self				,							a__							)				->				Callable[[], Tuple[nn.Module, Dict]]:

										'''simple docstring'''




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

										else:
																	snake_case_						=      super().__getitem__(a__							)

										return val




class  _snake_case		(	lowercase_							):




			def __getitem__(					self				,							a__							)				->				Callable[[], nn.Module]:

										'''simple docstring'''




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


def      UpperCamelCase_(	snake_case							:       Tuple							,				snake_case							:       Dict							,				snake_case							:       List[Tuple[str, str]]     ):

							'''simple docstring'''






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


def      UpperCamelCase_(	snake_case							:       str							,				snake_case							:       Callable[[], nn.Module]							,				snake_case							:       Callable[[], nn.Module]							,				snake_case							:       RegNetConfig							,				snake_case							:       Path							,				snake_case							:       bool = True							,				):

							'''simple docstring'''






							print(f'Converting {name}...'     )
							with torch.no_grad():
														snake_case_					,      snake_case_						=      from_model_func()
														snake_case_						=      our_model_func(snake_case     ).eval()
														snake_case_						=      ModuleTransfer(src=snake_case							,				dest=snake_case							,				raise_if_mismatch=snake_case     )
														snake_case_						=      torch.randn((1, 3, 2_2_4, 2_2_4)     )
														module_transfer(snake_case     )

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

							snake_case_						=      our_model(snake_case							,				output_hidden_states=snake_case     )
							snake_case_						=      (
							    our_outputs.logits if isinstance(snake_case							,				snake_case     ) else our_outputs.last_hidden_state
							)

							snake_case_						=      from_model(snake_case     )
							snake_case_						=      from_output[-1] if type(snake_case     ) 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:
														snake_case_						=      our_outputs.hidden_states[-1]

							assert torch.allclose(snake_case							,				snake_case     ), "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=snake_case							,				)

														snake_case_						=      2_2_4 if "seer" not in name else 3_8_4
														# we can use the convnext one
														snake_case_						=      AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k"							,				size=snake_case     )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name							,				commit_message="Add image processor"							,				use_temp_dir=snake_case							,				)

														print(f'Pushed {name}'     )


def      UpperCamelCase_(	snake_case							:       Path							,				snake_case							:       str = None							,				snake_case							:       bool = True     ):

							'''simple docstring'''






							snake_case_						=      "imagenet-1k-id2label.json"
							snake_case_						=      1_0_0_0
							snake_case_						=      (1, num_labels)

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

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

							snake_case_						=      partial(snake_case							,				num_labels=snake_case							,				idalabel=snake_case							,				labelaid=snake_case     )

							snake_case_						=      {
							    "regnet-x-002": ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]							,				hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]							,				groups_width=8							,				layer_type="x"     ),
							    "regnet-x-004": ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 1_2]							,				hidden_sizes=[3_2, 6_4, 1_6_0, 3_8_4]							,				groups_width=1_6							,				layer_type="x"     ),
							    "regnet-x-006": ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]							,				hidden_sizes=[4_8, 9_6, 2_4_0, 5_2_8]							,				groups_width=2_4							,				layer_type="x"     ),
							    "regnet-x-008": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]							,				hidden_sizes=[6_4, 1_2_8, 2_8_8, 6_7_2]							,				groups_width=1_6							,				layer_type="x"     ),
							    "regnet-x-016": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 2]							,				hidden_sizes=[7_2, 1_6_8, 4_0_8, 9_1_2]							,				groups_width=2_4							,				layer_type="x"     ),
							    "regnet-x-032": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_5, 2]							,				hidden_sizes=[9_6, 1_9_2, 4_3_2, 1_0_0_8]							,				groups_width=4_8							,				layer_type="x"     ),
							    "regnet-x-040": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_4, 2]							,				hidden_sizes=[8_0, 2_4_0, 5_6_0, 1_3_6_0]							,				groups_width=4_0							,				layer_type="x"     ),
							    "regnet-x-064": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 1]							,				hidden_sizes=[1_6_8, 3_9_2, 7_8_4, 1_6_2_4]							,				groups_width=5_6							,				layer_type="x"     ),
							    "regnet-x-080": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_5, 1]							,				hidden_sizes=[8_0, 2_4_0, 7_2_0, 1_9_2_0]							,				groups_width=1_2_0							,				layer_type="x"     ),
							    "regnet-x-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_1, 1]							,				hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]							,				groups_width=1_1_2							,				layer_type="x"     ),
							    "regnet-x-160": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_3, 1]							,				hidden_sizes=[2_5_6, 5_1_2, 8_9_6, 2_0_4_8]							,				groups_width=1_2_8							,				layer_type="x"     ),
							    "regnet-x-320": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_3, 1]							,				hidden_sizes=[3_3_6, 6_7_2, 1_3_4_4, 2_5_2_0]							,				groups_width=1_6_8							,				layer_type="x"     ),
							    # y variant
							    "regnet-y-002": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]							,				hidden_sizes=[2_4, 5_6, 1_5_2, 3_6_8]							,				groups_width=8     ),
							    "regnet-y-004": ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]							,				hidden_sizes=[4_8, 1_0_4, 2_0_8, 4_4_0]							,				groups_width=8     ),
							    "regnet-y-006": ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]							,				hidden_sizes=[4_8, 1_1_2, 2_5_6, 6_0_8]							,				groups_width=1_6     ),
							    "regnet-y-008": ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]							,				hidden_sizes=[6_4, 1_2_8, 3_2_0, 7_6_8]							,				groups_width=1_6     ),
							    "regnet-y-016": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_7, 2]							,				hidden_sizes=[4_8, 1_2_0, 3_3_6, 8_8_8]							,				groups_width=2_4     ),
							    "regnet-y-032": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_3, 1]							,				hidden_sizes=[7_2, 2_1_6, 5_7_6, 1_5_1_2]							,				groups_width=2_4     ),
							    "regnet-y-040": ImageNetPreTrainedConfig(
							        depths=[2, 6, 1_2, 2]							,				hidden_sizes=[1_2_8, 1_9_2, 5_1_2, 1_0_8_8]							,				groups_width=6_4     ),
							    "regnet-y-064": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_4, 2]							,				hidden_sizes=[1_4_4, 2_8_8, 5_7_6, 1_2_9_6]							,				groups_width=7_2     ),
							    "regnet-y-080": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_0, 1]							,				hidden_sizes=[1_6_8, 4_4_8, 8_9_6, 2_0_1_6]							,				groups_width=5_6     ),
							    "regnet-y-120": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_1, 1]							,				hidden_sizes=[2_2_4, 4_4_8, 8_9_6, 2_2_4_0]							,				groups_width=1_1_2     ),
							    "regnet-y-160": ImageNetPreTrainedConfig(
							        depths=[2, 4, 1_1, 1]							,				hidden_sizes=[2_2_4, 4_4_8, 1_2_3_2, 3_0_2_4]							,				groups_width=1_1_2     ),
							    "regnet-y-320": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]							,				hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]							,				groups_width=2_3_2     ),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    "regnet-y-320-seer": RegNetConfig(depths=[2, 5, 1_2, 1]							,				hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]							,				groups_width=2_3_2     ),
							    "regnet-y-640-seer": RegNetConfig(depths=[2, 5, 1_2, 1]							,				hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]							,				groups_width=3_2_8     ),
							    "regnet-y-1280-seer": RegNetConfig(
							        depths=[2, 7, 1_7, 1]							,				hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]							,				groups_width=2_6_4     ),
							    "regnet-y-2560-seer": RegNetConfig(
							        depths=[3, 7, 1_6, 1]							,				hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]							,				groups_width=6_4_0     ),
							    "regnet-y-10b-seer": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]							,				hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]							,				groups_width=1_0_1_0     ),
							    # finetuned on imagenet
							    "regnet-y-320-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]							,				hidden_sizes=[2_3_2, 6_9_6, 1_3_9_2, 3_7_1_2]							,				groups_width=2_3_2     ),
							    "regnet-y-640-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 5, 1_2, 1]							,				hidden_sizes=[3_2_8, 9_8_4, 1_9_6_8, 4_9_2_0]							,				groups_width=3_2_8     ),
							    "regnet-y-1280-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]							,				hidden_sizes=[5_2_8, 1_0_5_6, 2_9_0_4, 7_3_9_2]							,				groups_width=2_6_4     ),
							    "regnet-y-2560-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[3, 7, 1_6, 1]							,				hidden_sizes=[6_4_0, 1_6_9_6, 2_5_4_4, 5_0_8_8]							,				groups_width=6_4_0     ),
							    "regnet-y-10b-seer-in1k": ImageNetPreTrainedConfig(
							        depths=[2, 7, 1_7, 1]							,				hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0]							,				groups_width=1_0_1_0     ),
							}

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

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

							# pretrained
							snake_case_						=      partial(
							    snake_case							,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"							,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,				)

							snake_case_						=      partial(
							    snake_case							,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"							,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,				)

							snake_case_						=      partial(
							    snake_case							,				"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()     )							,				)

							snake_case_						=      partial(
							    snake_case							,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"							,				lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=2_7							,				group_width=1_0_1_0							,				w_a=1_7_4_4							,				w_a=620.83							,				w_m=2.52     )     )     )							,				)

							# IN1K finetuned
							snake_case_						=      partial(
							    snake_case							,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"							,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,				)

							snake_case_						=      partial(
							    snake_case							,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"							,				lambda: FakeRegNetVisslWrapper(RegNetYaagf()     )							,				)

							snake_case_						=      partial(
							    snake_case							,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"							,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf()     )							,				)

							snake_case_						=      partial(
							    snake_case							,				"https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"							,				lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=2_7							,				group_width=1_0_1_0							,				w_a=1_7_4_4							,				w_a=620.83							,				w_m=2.52     )     )     )							,				)

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


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE							:   str      		= 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.",
		)

		_SCREAMING_SNAKE_CASE							:   Dict      		= parser.parse_args()

		_SCREAMING_SNAKE_CASE							:   Path      		= 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)






"""simple docstring"""



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 					_UpperCAmelCase      ( __lowerCamelCase   :       Tuple		)     ->					Optional[int]:
  _snake_case					     =       checkpoints.load_tax_checkpoint(__lowerCamelCase		)
  _snake_case					     =       flatten_dict(__lowerCamelCase		)
  return flax_params
def 					_UpperCAmelCase      ( __lowerCamelCase   :       Dict		)     ->					Optional[int]:
  _snake_case					     =       {}

  _snake_case					     =       {
      '''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''',
  }

  _snake_case					     =       {
      '''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
      _snake_case					     =       '''.'''.join(key[1:]		)

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

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

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

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

      _snake_case					     =       flax_dict[key]

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

  return converted_torch_dict
def 					_UpperCAmelCase      ( __lowerCamelCase   :       List[str] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       Any=False ,					__lowerCamelCase   :       Optional[int]=False		)     ->					int:
  _snake_case					     =       get_flax_param(__lowerCamelCase		)

  if not use_large:
    _snake_case					     =       PixaStructVisionConfig()
    _snake_case					     =       PixaStructTextConfig()
  else:
    _snake_case					     =       PixaStructVisionConfig(
        hidden_size=15_36 ,					d_ff=39_68 ,					num_attention_heads=24 ,					num_hidden_layers=18		)
    _snake_case					     =       PixaStructTextConfig(hidden_size=15_36 ,					d_ff=39_68 ,					num_heads=24 ,					num_layers=18		)
  _snake_case					     =       PixaStructConfig(
      vision_config=encoder_config.to_dict() ,					text_config=decoder_config.to_dict() ,					is_vqa=__lowerCamelCase		)

  _snake_case					     =       PixaStructForConditionalGeneration(__lowerCamelCase		)

  _snake_case					     =       rename_and_convert_flax_params(__lowerCamelCase		)
  model.load_state_dict(__lowerCamelCase		)

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

  if use_large:
    _snake_case					     =       40_96

  _snake_case					     =       True

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

  model.save_pretrained(__lowerCamelCase		)
  processor.save_pretrained(__lowerCamelCase		)

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


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
 )



