'''simple docstring'''




import argparse
import json
from collections import OrderedDict
from pathlib import Path

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

from transformers import (
    SegformerConfig,
    SegformerForImageClassification,
    SegformerForSemanticSegmentation,
    SegformerImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case	=      logging.get_logger(__name__)







def       A_ (			_lowerCamelCase      :				Any	,	_lowerCamelCase      :				List[Any]=False     ):
      _lowerCAmelCase		 = OrderedDict()
      for key, value in state_dict.items():
            if encoder_only and not key.startswith('head'     ):
                  _lowerCAmelCase		 = """segformer.encoder.""" + key
            if key.startswith('backbone'     ):
                  _lowerCAmelCase		 = key.replace('backbone'	,	'segformer.encoder'     )
            if "patch_embed" in key:
                  # replace for example patch_embed1 by patch_embeddings.0
                  _lowerCAmelCase		 = key[key.find('patch_embed'     ) + len('patch_embed'     )]
                  _lowerCAmelCase		 = key.replace(F"patch_embed{idx}"	,	F"patch_embeddings.{int(__lowerCAmelCase     )-1}"     )
            if "norm" in key:
                  _lowerCAmelCase		 = key.replace('norm'	,	'layer_norm'     )
            if "segformer.encoder.layer_norm" in key:
                  # replace for example layer_norm1 by layer_norm.0
                  _lowerCAmelCase		 = key[key.find('segformer.encoder.layer_norm'     ) + len('segformer.encoder.layer_norm'     )]
                  _lowerCAmelCase		 = key.replace(F"layer_norm{idx}"	,	F"layer_norm.{int(__lowerCAmelCase     )-1}"     )
            if "layer_norm1" in key:
                  _lowerCAmelCase		 = key.replace('layer_norm1'	,	'layer_norm_1'     )
            if "layer_norm2" in key:
                  _lowerCAmelCase		 = key.replace('layer_norm2'	,	'layer_norm_2'     )
            if "block" in key:
                  # replace for example block1 by block.0
                  _lowerCAmelCase		 = key[key.find('block'     ) + len('block'     )]
                  _lowerCAmelCase		 = key.replace(F"block{idx}"	,	F"block.{int(__lowerCAmelCase     )-1}"     )
            if "attn.q" in key:
                  _lowerCAmelCase		 = key.replace('attn.q'	,	'attention.self.query'     )
            if "attn.proj" in key:
                  _lowerCAmelCase		 = key.replace('attn.proj'	,	'attention.output.dense'     )
            if "attn" in key:
                  _lowerCAmelCase		 = key.replace('attn'	,	'attention.self'     )
            if "fc1" in key:
                  _lowerCAmelCase		 = key.replace('fc1'	,	'dense1'     )
            if "fc2" in key:
                  _lowerCAmelCase		 = key.replace('fc2'	,	'dense2'     )
            if "linear_pred" in key:
                  _lowerCAmelCase		 = key.replace('linear_pred'	,	'classifier'     )
            if "linear_fuse" in key:
                  _lowerCAmelCase		 = key.replace('linear_fuse.conv'	,	'linear_fuse'     )
                  _lowerCAmelCase		 = key.replace('linear_fuse.bn'	,	'batch_norm'     )
            if "linear_c" in key:
                  # replace for example linear_c4 by linear_c.3
                  _lowerCAmelCase		 = key[key.find('linear_c'     ) + len('linear_c'     )]
                  _lowerCAmelCase		 = key.replace(F"linear_c{idx}"	,	F"linear_c.{int(__lowerCAmelCase     )-1}"     )
            if key.startswith('head'     ):
                  _lowerCAmelCase		 = key.replace('head'	,	'classifier'     )
            _lowerCAmelCase		 = value

      return new_state_dict







def       A_ (			_lowerCamelCase      :				int	,	_lowerCamelCase      :				Union[str, Any]     ):
      # for each of the encoder blocks:
      for i in range(config.num_encoder_blocks     ):
            for j in range(config.depths[i]     ):
                  # read in weights + bias of keys and values (which is a single matrix in the original implementation)
                  _lowerCAmelCase		 = state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.weight"     )
                  _lowerCAmelCase		 = state_dict.pop(F"segformer.encoder.block.{i}.{j}.attention.self.kv.bias"     )
                  # next, add keys and values (in that order) to the state dict
                  _lowerCAmelCase		 = kv_weight[
                      : config.hidden_sizes[i], :
                  ]
                  _lowerCAmelCase		 = kv_bias[: config.hidden_sizes[i]]
                  _lowerCAmelCase		 = kv_weight[
                      config.hidden_sizes[i] :, :
                  ]
                  _lowerCAmelCase		 = kv_bias[
                      config.hidden_sizes[i] :
                  ]







def       A_ (			):
      _lowerCAmelCase		 = """http://images.cocodataset.org/val2017/000000039769.jpg"""
      _lowerCAmelCase		 = Image.open(requests.get(__lowerCAmelCase	,	stream=__lowerCAmelCase     ).raw     )

      return image







@torch.no_grad()
def       A_ (			_lowerCamelCase      :				List[str]	,	_lowerCamelCase      :				List[Any]	,	_lowerCamelCase      :				Optional[int]     ):
      _lowerCAmelCase		 = SegformerConfig()
      _lowerCAmelCase		 = False

      # set attributes based on model_name
      _lowerCAmelCase		 = """huggingface/label-files"""
      if "segformer" in model_name:
            _lowerCAmelCase		 = model_name[len('segformer.'     ) : len('segformer.'     ) + 2]
            if "ade" in model_name:
                  _lowerCAmelCase		 = 150
                  _lowerCAmelCase		 = """ade20k-id2label.json"""
                  _lowerCAmelCase		 = (1, 150, 128, 128)
            elif "city" in model_name:
                  _lowerCAmelCase		 = 19
                  _lowerCAmelCase		 = """cityscapes-id2label.json"""
                  _lowerCAmelCase		 = (1, 19, 128, 128)
            else:
                  raise ValueError(F"Model {model_name} not supported"     )
      elif "mit" in model_name:
            _lowerCAmelCase		 = True
            _lowerCAmelCase		 = model_name[4:6]
            _lowerCAmelCase		 = 1_000
            _lowerCAmelCase		 = """imagenet-1k-id2label.json"""
            _lowerCAmelCase		 = (1, 1_000)
      else:
            raise ValueError(F"Model {model_name} not supported"     )

      # set config attributes
      _lowerCAmelCase		 = json.load(open(hf_hub_download(__lowerCAmelCase	,	__lowerCAmelCase	,	repo_type='dataset'     )	,	'r'     )     )
      _lowerCAmelCase		 = {int(__lowerCAmelCase     ): v for k, v in idalabel.items()}
      _lowerCAmelCase		 = idalabel
      _lowerCAmelCase		 = {v: k for k, v in idalabel.items()}
      if size == "b0":
            pass
      elif size == "b1":
            _lowerCAmelCase		 = [64, 128, 320, 512]
            _lowerCAmelCase		 = 256
      elif size == "b2":
            _lowerCAmelCase		 = [64, 128, 320, 512]
            _lowerCAmelCase		 = 768
            _lowerCAmelCase		 = [3, 4, 6, 3]
      elif size == "b3":
            _lowerCAmelCase		 = [64, 128, 320, 512]
            _lowerCAmelCase		 = 768
            _lowerCAmelCase		 = [3, 4, 18, 3]
      elif size == "b4":
            _lowerCAmelCase		 = [64, 128, 320, 512]
            _lowerCAmelCase		 = 768
            _lowerCAmelCase		 = [3, 8, 27, 3]
      elif size == "b5":
            _lowerCAmelCase		 = [64, 128, 320, 512]
            _lowerCAmelCase		 = 768
            _lowerCAmelCase		 = [3, 6, 40, 3]
      else:
            raise ValueError(F"Size {size} not supported"     )

      # load image processor (only resize + normalize)
      _lowerCAmelCase		 = SegformerImageProcessor(
          image_scale=(512, 512)	,	keep_ratio=__lowerCAmelCase	,	align=__lowerCAmelCase	,	do_random_crop=__lowerCAmelCase     )

      # prepare image
      _lowerCAmelCase		 = prepare_img()
      _lowerCAmelCase		 = image_processor(images=__lowerCAmelCase	,	return_tensors='pt'     ).pixel_values

      logger.info(F"Converting model {model_name}..."     )

      # load original state dict
      if encoder_only:
            _lowerCAmelCase		 = torch.load(__lowerCAmelCase	,	map_location=torch.device('cpu'     )     )
      else:
            _lowerCAmelCase		 = torch.load(__lowerCAmelCase	,	map_location=torch.device('cpu'     )     )["""state_dict"""]

      # rename keys
      _lowerCAmelCase		 = rename_keys(__lowerCAmelCase	,	encoder_only=__lowerCAmelCase     )
      if not encoder_only:
            del state_dict["decode_head.conv_seg.weight"]
            del state_dict["decode_head.conv_seg.bias"]

      # key and value matrices need special treatment
      read_in_k_v(__lowerCAmelCase	,	__lowerCAmelCase     )

      # create HuggingFace model and load state dict
      if encoder_only:
            _lowerCAmelCase		 = False
            _lowerCAmelCase		 = SegformerForImageClassification(__lowerCAmelCase     )
      else:
            _lowerCAmelCase		 = SegformerForSemanticSegmentation(__lowerCAmelCase     )
      model.load_state_dict(__lowerCAmelCase     )
      model.eval()

      # forward pass
      _lowerCAmelCase		 = model(__lowerCAmelCase     )
      _lowerCAmelCase		 = outputs.logits

      # set expected_slice based on model name
      # ADE20k checkpoints
      if model_name == "segformer.b0.512x512.ade.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-4.6310, -5.5232, -6.2356], [-5.1921, -6.1444, -6.5996], [-5.4424, -6.2790, -6.7574]],
                    [[-12.1391, -13.3122, -13.9554], [-12.8732, -13.9352, -14.3563], [-12.9438, -13.8226, -14.2513]],
                    [[-12.5134, -13.4686, -14.4915], [-12.8669, -14.4343, -14.7758], [-13.2523, -14.5819, -15.0694]],
                ]     )
      elif model_name == "segformer.b1.512x512.ade.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-7.5820, -8.7231, -8.3215], [-8.0600, -10.3529, -10.0304], [-7.5208, -9.4103, -9.6239]],
                    [[-12.6918, -13.8994, -13.7137], [-13.3196, -15.7523, -15.4789], [-12.9343, -14.8757, -14.9689]],
                    [[-11.1911, -11.9421, -11.3243], [-11.3342, -13.6839, -13.3581], [-10.3909, -12.1832, -12.4858]],
                ]     )
      elif model_name == "segformer.b2.512x512.ade.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-11.8173, -14.3850, -16.3128], [-14.5648, -16.5804, -18.6568], [-14.7223, -15.7387, -18.4218]],
                    [[-15.7290, -17.9171, -19.4423], [-18.3105, -19.9448, -21.4661], [-17.9296, -18.6497, -20.7910]],
                    [[-15.0783, -17.0336, -18.2789], [-16.8771, -18.6870, -20.1612], [-16.2454, -17.1426, -19.5055]],
                ]     )
      elif model_name == "segformer.b3.512x512.ade.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-9.0878, -10.2081, -10.1891], [-9.3144, -10.7941, -10.9843], [-9.2294, -10.3855, -10.5704]],
                    [[-12.2316, -13.9068, -13.6102], [-12.9161, -14.3702, -14.3235], [-12.5233, -13.7174, -13.7932]],
                    [[-14.6275, -15.2490, -14.9727], [-14.3400, -15.9687, -16.2827], [-14.1484, -15.4033, -15.8937]],
                ]     )
      elif model_name == "segformer.b4.512x512.ade.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-12.3144, -13.2447, -14.0802], [-13.3614, -14.5816, -15.6117], [-13.3340, -14.4433, -16.2219]],
                    [[-19.2781, -20.4128, -20.7506], [-20.6153, -21.6566, -22.0998], [-19.9800, -21.0430, -22.1494]],
                    [[-18.8739, -19.7804, -21.1834], [-20.1233, -21.6765, -23.2944], [-20.0315, -21.2641, -23.6944]],
                ]     )
      elif model_name == "segformer.b5.640x640.ade.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-9.5524, -12.0835, -11.7348], [-10.5229, -13.6446, -14.5662], [-9.5842, -12.8851, -13.9414]],
                    [[-15.3432, -17.5323, -17.0818], [-16.3330, -18.9255, -19.2101], [-15.1340, -17.7848, -18.3971]],
                    [[-12.6072, -14.9486, -14.6631], [-13.7629, -17.0907, -17.7745], [-12.7899, -16.1695, -17.1671]],
                ]     )
      # Cityscapes checkpoints
      elif model_name == "segformer.b0.1024x1024.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-11.9295, -13.4057, -14.8106], [-13.3431, -14.8179, -15.3781], [-14.2836, -15.5942, -16.1588]],
                    [[-11.4906, -12.8067, -13.6564], [-13.1189, -14.0500, -14.1543], [-13.8748, -14.5136, -14.8789]],
                    [[0.5374, 0.1067, -0.4742], [0.1141, -0.2255, -0.7099], [-0.3000, -0.5924, -1.3105]],
                ]     )
      elif model_name == "segformer.b0.512x1024.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-7.8217, -9.8767, -10.1717], [-9.4438, -10.9058, -11.4047], [-9.7939, -12.3495, -12.1079]],
                    [[-7.1514, -9.5336, -10.0860], [-9.7776, -11.6822, -11.8439], [-10.1411, -12.7655, -12.8972]],
                    [[0.3021, 0.0805, -0.2310], [-0.0328, -0.1605, -0.2714], [-0.1408, -0.5477, -0.6976]],
                ]     )
      elif model_name == "segformer.b0.640x1280.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [
                        [-1.1_3_7_2e0_1, -1.2_7_8_7e0_1, -1.3_4_7_7e0_1],
                        [-1.2_5_3_6e0_1, -1.4_1_9_4e0_1, -1.4_4_0_9e0_1],
                        [-1.3_2_1_7e0_1, -1.4_8_8_8e0_1, -1.5_3_2_7e0_1],
                    ],
                    [
                        [-1.4_7_9_1e0_1, -1.7_1_2_2e0_1, -1.8_2_7_7e0_1],
                        [-1.7_1_6_3e0_1, -1.9_1_9_2e0_1, -1.9_5_3_3e0_1],
                        [-1.7_8_9_7e0_1, -1.9_9_9_1e0_1, -2.0_3_1_5e0_1],
                    ],
                    [
                        [7.6_7_2_3e-0_1, 4.1_9_2_1e-0_1, -7.7_8_7_8e-0_2],
                        [4.7_7_7_2e-0_1, 9.5_5_5_7e-0_3, -2.8_0_8_2e-0_1],
                        [3.6_0_3_2e-0_1, -2.4_8_2_6e-0_1, -5.1_1_6_8e-0_1],
                    ],
                ]     )
      elif model_name == "segformer.b0.768x768.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-9.4959, -11.3087, -11.7479], [-11.0025, -12.6540, -12.3319], [-11.4064, -13.0487, -12.9905]],
                    [[-9.8905, -11.3084, -12.0854], [-11.1726, -12.7698, -12.9583], [-11.5985, -13.3278, -14.1774]],
                    [[0.2213, 0.0192, -0.2466], [-0.1731, -0.4213, -0.4874], [-0.3126, -0.6541, -1.1389]],
                ]     )
      elif model_name == "segformer.b1.1024x1024.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-13.5748, -13.9111, -12.6500], [-14.3500, -15.3683, -14.2328], [-14.7532, -16.0424, -15.6087]],
                    [[-17.1651, -15.8725, -12.9653], [-17.2580, -17.3718, -14.8223], [-16.6058, -16.8783, -16.7452]],
                    [[-3.6456, -3.0209, -1.4203], [-3.0797, -3.1959, -2.0000], [-1.8757, -1.9217, -1.6997]],
                ]     )
      elif model_name == "segformer.b2.1024x1024.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-16.0976, -16.4856, -17.3962], [-16.6234, -19.0342, -19.7685], [-16.0900, -18.0661, -19.1180]],
                    [[-18.4750, -18.8488, -19.5074], [-19.4030, -22.1570, -22.5977], [-19.1191, -20.8486, -22.3783]],
                    [[-4.5178, -5.5037, -6.5109], [-5.0884, -7.2174, -8.0334], [-4.4156, -5.8117, -7.2970]],
                ]     )
      elif model_name == "segformer.b3.1024x1024.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-14.2081, -14.4732, -14.1977], [-14.5867, -16.4423, -16.6356], [-13.4441, -14.9685, -16.8696]],
                    [[-14.4576, -14.7073, -15.0451], [-15.0816, -17.6237, -17.9873], [-14.4213, -16.0199, -18.5992]],
                    [[-4.7349, -4.9588, -5.0966], [-4.3210, -6.9325, -7.2591], [-3.4312, -4.7484, -7.1917]],
                ]     )
      elif model_name == "segformer.b4.1024x1024.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-11.7737, -11.9526, -11.3273], [-13.6692, -14.4574, -13.8878], [-13.8937, -14.6924, -15.9345]],
                    [[-14.6706, -14.5330, -14.1306], [-16.1502, -16.8180, -16.4269], [-16.8338, -17.8939, -20.1746]],
                    [[1.0491, 0.8289, 1.0310], [1.1044, 0.5219, 0.8055], [1.0899, 0.6926, 0.5590]],
                ]     )
      elif model_name == "segformer.b5.1024x1024.city.160k":
            _lowerCAmelCase		 = torch.tensor(
                [
                    [[-12.5641, -13.4777, -13.0684], [-13.9587, -15.8983, -16.6557], [-13.3109, -15.7350, -16.3141]],
                    [[-14.7074, -15.4352, -14.5944], [-16.6353, -18.1663, -18.6120], [-15.1702, -18.0329, -18.1547]],
                    [[-1.7990, -2.0951, -1.7784], [-2.6397, -3.8245, -3.9686], [-1.5264, -2.8126, -2.9316]],
                ]     )
      else:
            _lowerCAmelCase		 = logits.argmax(-1     ).item()
            print('Predicted class:'	,	model.config.idalabel[predicted_class_idx]     )

      # verify logits
      if not encoder_only:
            assert logits.shape == expected_shape
            assert torch.allclose(logits[0, :3, :3, :3]	,	__lowerCAmelCase	,	atol=1e-2     )

      # finally, save model and image processor
      logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."     )
      Path(__lowerCAmelCase     ).mkdir(exist_ok=__lowerCAmelCase     )
      model.save_pretrained(__lowerCAmelCase     )
      image_processor.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
 snake_case	=      argparse.ArgumentParser()

 parser.add_argument(
     '''--model_name''',
     default='''segformer.b0.512x512.ade.160k''',
     type=str,
     help='''Name of the model you\'d like to convert.''',
 )
 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
 )
 parser.add_argument(
     '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
 )
 snake_case	=      parser.parse_args()
 convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)













import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


UpperCamelCase    							=      logging.get_logger(__name__)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					List[Any]=False							)     ->  Union[str, Any]:
   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          """Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"""
          """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
          """ instructions."""							)
      raise

   if not is_sharded:
      __UpperCamelCase	:			Tuple					=					os.path.abspath(__lowerCAmelCase							)
      logger.info(f'Loading PyTorch weights from {pt_path}'							)

      __UpperCamelCase	:			int					=					torch.load(__lowerCAmelCase					,							map_location="""cpu"""							)
      logger.info(f'PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()							):,} parameters.'							)

      __UpperCamelCase	:			List[Any]					=					convert_pytorch_state_dict_to_flax(__lowerCAmelCase					,							__lowerCAmelCase							)
   else:
      # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
      __UpperCamelCase	:			List[str]					=					convert_pytorch_sharded_state_dict_to_flax(__lowerCAmelCase					,							__lowerCAmelCase							)
   return flax_state_dict


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Tuple[str]					,							__lowerCAmelCase							:					np.ndarray					,							__lowerCAmelCase							:					Dict[str, jnp.ndarray]					,							__lowerCAmelCase							:					str					,							)     ->  (Tuple[str], np.ndarray):

   def is_key_or_prefix_key_in_dict(__lowerCAmelCase							:					Tuple[str]							) -> bool:
      return len(set(__lowerCAmelCase							) & {key, (model_prefix,) + key}							) > 0

   # layer norm
   __UpperCamelCase	:			Optional[int]					=					pt_tuple_key[:-1] + ("""scale""",)
   if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(__lowerCAmelCase							):
      return renamed_pt_tuple_key, pt_tensor

   # batch norm layer mean
   __UpperCamelCase	:			Optional[Any]					=					pt_tuple_key[:-1] + ("""mean""",)
   if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase							):
      return renamed_pt_tuple_key, pt_tensor

   # batch norm layer var
   __UpperCamelCase	:			Optional[Any]					=					pt_tuple_key[:-1] + ("""var""",)
   if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase							):
      return renamed_pt_tuple_key, pt_tensor

   # embedding
   __UpperCamelCase	:			Union[str, Any]					=					pt_tuple_key[:-1] + ("""embedding""",)
   if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(__lowerCAmelCase							):
      return renamed_pt_tuple_key, pt_tensor

   # conv layer
   __UpperCamelCase	:			Tuple					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(__lowerCAmelCase							):
      __UpperCamelCase	:			Optional[int]					=					pt_tensor.transpose(2					,							3					,							1					,							0							)
      return renamed_pt_tuple_key, pt_tensor

   # linear layer
   __UpperCamelCase	:			int					=					pt_tuple_key[:-1] + ("""kernel""",)
   if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(__lowerCAmelCase							):
      __UpperCamelCase	:			Optional[int]					=					pt_tensor.T
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm weight
   __UpperCamelCase	:			Tuple					=					pt_tuple_key[:-1] + ("""weight""",)
   if pt_tuple_key[-1] == "gamma":
      return renamed_pt_tuple_key, pt_tensor

   # old PyTorch layer norm bias
   __UpperCamelCase	:			str					=					pt_tuple_key[:-1] + ("""bias""",)
   if pt_tuple_key[-1] == "beta":
      return renamed_pt_tuple_key, pt_tensor

   # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
   __UpperCamelCase	:			Optional[int]					=					None
   if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
      __UpperCamelCase	:			Tuple					=					pt_tuple_key[-2] + """_g"""
   elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
      __UpperCamelCase	:			Dict					=					pt_tuple_key[-2] + """_v"""
   if name is not None:
      __UpperCamelCase	:			Optional[Any]					=					pt_tuple_key[:-3] + (name,)
      return renamed_pt_tuple_key, pt_tensor

   return pt_tuple_key, pt_tensor


def 				__lowerCamelCase     (							__lowerCAmelCase							:					int					,							__lowerCAmelCase							:					int							)     ->  Union[str, Any]:
   # convert pytorch tensor to numpy
   __UpperCamelCase	:			Optional[Any]					=					{k: v.numpy() for k, v in pt_state_dict.items()}

   __UpperCamelCase	:			List[Any]					=					flax_model.base_model_prefix

   # use params dict if the model contains batch norm layers
   if "params" in flax_model.params:
      __UpperCamelCase	:			Union[str, Any]					=					flax_model.params["""params"""]
   else:
      __UpperCamelCase	:			List[Any]					=					flax_model.params
   __UpperCamelCase	:			List[Any]					=					flatten_dict(__lowerCAmelCase							)

   # add batch_stats keys,values to dict
   if "batch_stats" in flax_model.params:
      __UpperCamelCase	:			List[str]					=					flatten_dict(flax_model.params["""batch_stats"""]							)
      random_flax_state_dict.update(__lowerCAmelCase							)

   __UpperCamelCase	:			List[Any]					=					{}

   __UpperCamelCase	:			int					=					(model_prefix not in flax_model_params) and (
       model_prefix in {k.split("""."""							)[0] for k in pt_state_dict.keys()}
   )
   __UpperCamelCase	:			int					=					(model_prefix in flax_model_params) and (
       model_prefix not in {k.split("""."""							)[0] for k in pt_state_dict.keys()}
   )

   # Need to change some parameters name to match Flax names
   for pt_key, pt_tensor in pt_state_dict.items():
      __UpperCamelCase	:			List[str]					=					tuple(pt_key.split("""."""							)							)

      # remove base model prefix if necessary
      __UpperCamelCase	:			Optional[Any]					=					pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
         __UpperCamelCase	:			List[str]					=					pt_tuple_key[1:]

      # Correctly rename weight parameters
      __UpperCamelCase	,     __UpperCamelCase	:			Optional[int]					=					rename_key_and_reshape_tensor(
          __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)

      # add model prefix if necessary
      __UpperCamelCase	:			Optional[Any]					=					(model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
         __UpperCamelCase	:			Dict					=					(model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
         if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
            raise ValueError(
                f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)

        # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
         if "mean" in flax_key[-1] or "var" in flax_key[-1]:
            __UpperCamelCase	:			int					=					jnp.asarray(__lowerCAmelCase							)
            continue
         # remove num_batches_tracked key
         if "num_batches_tracked" in flax_key[-1]:
            flax_state_dict.pop(__lowerCAmelCase					,							__lowerCAmelCase							)
            continue

         # also add unexpected weight so that warning is thrown
         __UpperCamelCase	:			Optional[Any]					=					jnp.asarray(__lowerCAmelCase							)

      else:
         # also add unexpected weight so that warning is thrown
         __UpperCamelCase	:			Optional[int]					=					jnp.asarray(__lowerCAmelCase							)

   return unflatten_dict(__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					int							)     ->  Dict:
   import torch

   # Load the index
   __UpperCamelCase	:			List[str]					=					{}
   for shard_file in shard_filenames:
      # load using msgpack utils
      __UpperCamelCase	:			List[str]					=					torch.load(__lowerCAmelCase							)
      __UpperCamelCase	:			int					=					{k: v.numpy() for k, v in pt_state_dict.items()}

      __UpperCamelCase	:			List[str]					=					flax_model.base_model_prefix

      # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
      if "batch_stats" in flax_model.params:
         __UpperCamelCase	:			Optional[Any]					=					flax_model.params["""params"""]

         __UpperCamelCase	:			int					=					flatten_dict(__lowerCAmelCase							)
         random_flax_state_dict.update(flatten_dict(flax_model.params["""batch_stats"""]							)							)
      else:
         __UpperCamelCase	:			Dict					=					flax_model.params
         __UpperCamelCase	:			int					=					flatten_dict(__lowerCAmelCase							)

      __UpperCamelCase	:			Union[str, Any]					=					(model_prefix not in flax_model_params) and (
          model_prefix in {k.split("""."""							)[0] for k in pt_state_dict.keys()}
      )
      __UpperCamelCase	:			Any					=					(model_prefix in flax_model_params) and (
          model_prefix not in {k.split("""."""							)[0] for k in pt_state_dict.keys()}
      )
      # Need to change some parameters name to match Flax names
      for pt_key, pt_tensor in pt_state_dict.items():
         __UpperCamelCase	:			Any					=					tuple(pt_key.split("""."""							)							)

         # remove base model prefix if necessary
         __UpperCamelCase	:			Dict					=					pt_tuple_key[0] == model_prefix
         if load_model_with_head_into_base_model and has_base_model_prefix:
            __UpperCamelCase	:			str					=					pt_tuple_key[1:]

         # Correctly rename weight parameters
         __UpperCamelCase	,     __UpperCamelCase	:			Optional[Any]					=					rename_key_and_reshape_tensor(
             __lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase					,							__lowerCAmelCase							)
         # add model prefix if necessary
         __UpperCamelCase	:			Tuple					=					(model_prefix,) + flax_key in random_flax_state_dict
         if load_base_model_into_model_with_head and require_base_model_prefix:
            __UpperCamelCase	:			Optional[Any]					=					(model_prefix,) + flax_key

         if flax_key in random_flax_state_dict:
            if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
               raise ValueError(
                   f'PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '
                   f'{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)

            # add batch stats if the model contains batchnorm layers
         if "batch_stats" in flax_model.params:
            if "mean" in flax_key[-1]:
               __UpperCamelCase	:			List[Any]					=					jnp.asarray(__lowerCAmelCase							)
               continue
            if "var" in flax_key[-1]:
               __UpperCamelCase	:			Optional[Any]					=					jnp.asarray(__lowerCAmelCase							)
               continue
            # remove num_batches_tracked key
            if "num_batches_tracked" in flax_key[-1]:
               flax_state_dict.pop(__lowerCAmelCase					,							__lowerCAmelCase							)
               continue

            # also add unexpected weight so that warning is thrown
            __UpperCamelCase	:			int					=					jnp.asarray(__lowerCAmelCase							)

         else:
            # also add unexpected weight so that warning is thrown
            __UpperCamelCase	:			int					=					jnp.asarray(__lowerCAmelCase							)
   return unflatten_dict(__lowerCAmelCase							)


def 				__lowerCamelCase     (							__lowerCAmelCase							:					Any					,							__lowerCAmelCase							:					Union[str, Any]							)     ->  Union[str, Any]:
   __UpperCamelCase	:			List[Any]					=					os.path.abspath(__lowerCAmelCase							)
   logger.info(f'Loading Flax weights from {flax_checkpoint_path}'							)

   # import correct flax class
   __UpperCamelCase	:			str					=					getattr(__lowerCAmelCase					,							"""Flax""" + model.__class__.__name__							)

   # load flax weight dict
   with open(__lowerCAmelCase					,							"""rb"""							) as state_f:
      try:
         __UpperCamelCase	:			Optional[int]					=					from_bytes(__lowerCAmelCase					,							state_f.read()							)
      except UnpicklingError:
         raise EnvironmentError(f'Unable to convert {flax_checkpoint_path} to Flax deserializable object. '							)

   return load_flax_weights_in_pytorch_model(__lowerCAmelCase					,							__lowerCAmelCase							)



def 				__lowerCamelCase     (							__lowerCAmelCase							:					Optional[int]					,							__lowerCAmelCase							:					Optional[int]							)     ->  List[Any]:

   try:
      import torch  # noqa: F401
   except ImportError:
      logger.error(
          """Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"""
          """ https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"""
          """ instructions."""							)
      raise

   # check if we have bf16 weights
   __UpperCamelCase	:			Tuple					=					flatten_dict(jax.tree_util.tree_map(lambda __lowerCAmelCase							: x.dtype == jnp.bfloataa					,							__lowerCAmelCase							)							).values()
   if any(__lowerCAmelCase							):
      # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
      # and bf16 is not fully supported in PT yet.
      logger.warning(
          """Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` """
          """before loading those in PyTorch model."""							)
      __UpperCamelCase	:			List[Any]					=					jax.tree_util.tree_map(
          lambda __lowerCAmelCase							: params.astype(np.floataa							) if params.dtype == jnp.bfloataa else params					,							__lowerCAmelCase							)

   __UpperCamelCase	:			Tuple					=					flatten_dict(__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					pt_model.state_dict()

   __UpperCamelCase	:			Tuple					=					(pt_model.base_model_prefix in flax_state) and (
       pt_model.base_model_prefix not in {k.split("""."""							)[0] for k in pt_model_dict.keys()}
   )
   __UpperCamelCase	:			Any					=					(pt_model.base_model_prefix not in flax_state) and (
       pt_model.base_model_prefix in {k.split("""."""							)[0] for k in pt_model_dict.keys()}
   )

   # keep track of unexpected & missing keys
   __UpperCamelCase	:			Dict					=					[]
   __UpperCamelCase	:			List[str]					=					set(pt_model_dict.keys()							)

   for flax_key_tuple, flax_tensor in flax_state_dict.items():
      __UpperCamelCase	:			Union[str, Any]					=					flax_key_tuple[0] == pt_model.base_model_prefix
      __UpperCamelCase	:			Optional[Any]					=					""".""".join((pt_model.base_model_prefix,) + flax_key_tuple							) in pt_model_dict

      # adapt flax_key to prepare for loading from/to base model only
      if load_model_with_head_into_base_model and has_base_model_prefix:
         __UpperCamelCase	:			List[Any]					=					flax_key_tuple[1:]
      elif load_base_model_into_model_with_head and require_base_model_prefix:
         __UpperCamelCase	:			Tuple					=					(pt_model.base_model_prefix,) + flax_key_tuple

      # rename flax weights to PyTorch format
      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(__lowerCAmelCase							) not in pt_model_dict:
         # conv layer
         __UpperCamelCase	:			Optional[int]					=					flax_key_tuple[:-1] + ("""weight""",)
         __UpperCamelCase	:			Dict					=					jnp.transpose(__lowerCAmelCase					,							(3, 2, 0, 1)							)
      elif flax_key_tuple[-1] == "kernel" and ".".join(__lowerCAmelCase							) not in pt_model_dict:
         # linear layer
         __UpperCamelCase	:			Optional[int]					=					flax_key_tuple[:-1] + ("""weight""",)
         __UpperCamelCase	:			List[Any]					=					flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
         __UpperCamelCase	:			Union[str, Any]					=					flax_key_tuple[:-1] + ("""weight""",)

      # adding batch stats from flax batch norm to pt
      elif "mean" in flax_key_tuple[-1]:
         __UpperCamelCase	:			List[str]					=					flax_key_tuple[:-1] + ("""running_mean""",)
      elif "var" in flax_key_tuple[-1]:
         __UpperCamelCase	:			Tuple					=					flax_key_tuple[:-1] + ("""running_var""",)

      if "batch_stats" in flax_state:
         __UpperCamelCase	:			Optional[int]					=					""".""".join(flax_key_tuple[1:]							)  # Remove the params/batch_stats header
      else:
         __UpperCamelCase	:			Optional[int]					=					""".""".join(__lowerCAmelCase							)

      # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
      __UpperCamelCase	:			Union[str, Any]					=					{}
      # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
      for key in pt_model_dict:
         __UpperCamelCase	:			int					=					key.split("""."""							)
         __UpperCamelCase	:			str					=					None
         if key_components[-3::2] == ["parametrizations", "original0"]:
            __UpperCamelCase	:			Union[str, Any]					=					key_components[-2] + """_g"""
         elif key_components[-3::2] == ["parametrizations", "original1"]:
            __UpperCamelCase	:			Tuple					=					key_components[-2] + """_v"""
         if name is not None:
            __UpperCamelCase	:			Optional[Any]					=					key_components[:-3] + [name]
            __UpperCamelCase	:			int					=					""".""".join(__lowerCAmelCase							)
            __UpperCamelCase	:			List[str]					=					key

      if flax_key in special_pt_names:
         __UpperCamelCase	:			Any					=					special_pt_names[flax_key]

      if flax_key in pt_model_dict:
         if flax_tensor.shape != pt_model_dict[flax_key].shape:
            raise ValueError(
                f'Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '
                f'to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'							)
         else:
            # add weight to pytorch dict
            __UpperCamelCase	:			str					=					np.asarray(__lowerCAmelCase							) if not isinstance(__lowerCAmelCase					,							np.ndarray							) else flax_tensor
            __UpperCamelCase	:			Optional[int]					=					torch.from_numpy(__lowerCAmelCase							)
            # remove from missing keys
            missing_keys.remove(__lowerCAmelCase							)
      else:
         # weight is not expected by PyTorch model
         unexpected_keys.append(__lowerCAmelCase							)

   pt_model.load_state_dict(__lowerCAmelCase							)

   # re-transform missing_keys to list
   __UpperCamelCase	:			Union[str, Any]					=					list(__lowerCAmelCase							)

   if len(__lowerCAmelCase							) > 0:
      logger.warning(
          """Some weights of the Flax model were not used when initializing the PyTorch model"""
          f' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'
          f' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'
          """ (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"""
          f' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'
          """ to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"""
          """ FlaxBertForSequenceClassification model)."""							)
   else:
      logger.warning(f'All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'							)
   if len(__lowerCAmelCase							) > 0:
      logger.warning(
          f'Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'
          f' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'
          """ use it for predictions and inference."""							)
   else:
      logger.warning(
          f'All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'
          """If your task is similar to the task the model of the checkpoint was trained on, """
          f'you can already use {pt_model.__class__.__name__} for predictions without further training.'							)

   return pt_model



