


'''simple docstring'''

import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase				:  Any    		=      logging.get_logger(__name__)

def _SCREAMING_SNAKE_CASE (A			)			->			Any:






       """simple docstring"""







       lowercase__      =					'''huggingface/label-files'''
       lowercase__      =					'''imagenet-1k-id2label.json'''
       lowercase__      =					json.load(open(hf_hub_download(A    ,					A    ,					repo_type='''dataset'''			)    ,					'''r'''			)			)
       lowercase__      =					{int(A			): v for k, v in idalabel.items()}
       lowercase__      =					{v: k for k, v in idalabel.items()}

       lowercase__      =					'''std_conv''' if '''bit''' in model_name else False

       # note that when using BiT as backbone for ViT-hybrid checkpoints,
       # one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
       # config.conv_layer = "std_conv_same"
       lowercase__      =					BitConfig(
           conv_layer=A    ,					num_labels=1_000    ,					idalabel=A    ,					labelaid=A    ,					)

       return config

def _SCREAMING_SNAKE_CASE (A			)			->			List[str]:






       """simple docstring"""







       if "stem.conv" in name:
              lowercase__      =					name.replace('''stem.conv'''    ,					'''bit.embedder.convolution'''			)
       if "blocks" in name:
              lowercase__      =					name.replace('''blocks'''    ,					'''layers'''			)
       if "head.fc" in name:
              lowercase__      =					name.replace('''head.fc'''    ,					'''classifier.1'''			)
       if name.startswith('''norm'''			):
              lowercase__      =					'''bit.''' + name
       if "bit" not in name and "classifier" not in name:
              lowercase__      =					'''bit.encoder.''' + name

       return name

def _SCREAMING_SNAKE_CASE ()			->			List[str]:






       """simple docstring"""







       lowercase__      =					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
       lowercase__      =					Image.open(requests.get(A    ,					stream=A			).raw			)
       return im

@torch.no_grad()
def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A=False			)			->			Tuple:






       """simple docstring"""







       lowercase__      =					get_config(A			)

       # load original model from timm
       lowercase__      =					create_model(A    ,					pretrained=A			)
       timm_model.eval()

       # load state_dict of original model
       lowercase__      =					timm_model.state_dict()
       for key in state_dict.copy().keys():
              lowercase__      =					state_dict.pop(A			)
              lowercase__      =					val.squeeze() if '''head''' in key else val

       # load HuggingFace model
       lowercase__      =					BitForImageClassification(A			)
       model.eval()
       model.load_state_dict(A			)

       # create image processor
       lowercase__      =					create_transform(**resolve_data_config({}    ,					model=A			)			)
       lowercase__      =					transform.transforms

       lowercase__      =					{
           '''bilinear''': PILImageResampling.BILINEAR,
           '''bicubic''': PILImageResampling.BICUBIC,
           '''nearest''': PILImageResampling.NEAREST,
       }

       lowercase__      =					BitImageProcessor(
           do_resize=A    ,					size={'''shortest_edge''': timm_transforms[0].size}    ,					resample=pillow_resamplings[timm_transforms[0].interpolation.value]    ,					do_center_crop=A    ,					crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]}    ,					do_normalize=A    ,					image_mean=timm_transforms[-1].mean.tolist()    ,					image_std=timm_transforms[-1].std.tolist()    ,					)

       lowercase__      =					prepare_img()
       lowercase__      =					transform(A			).unsqueeze(0			)
       lowercase__      =					processor(A    ,					return_tensors='''pt'''			).pixel_values

       # verify pixel values
       assert torch.allclose(A    ,					A			)

       # verify logits
       with torch.no_grad():
              lowercase__      =					model(A			)
              lowercase__      =					outputs.logits

       print('''Logits:'''    ,					logits[0, :3]			)
       print('''Predicted class:'''    ,					model.config.idalabel[logits.argmax(-1			).item()]			)
       lowercase__      =					timm_model(A			)
       assert timm_logits.shape == outputs.logits.shape
       assert torch.allclose(A    ,					outputs.logits    ,					atol=1E-3			)
       print('''Looks ok!'''			)

       if pytorch_dump_folder_path is not None:
              Path(A			).mkdir(exist_ok=A			)
              print(f"Saving model {model_name} and processor to {pytorch_dump_folder_path}"			)
              model.save_pretrained(A			)
              processor.save_pretrained(A			)

       if push_to_hub:
              print(f"Pushing model {model_name} and processor to the hub"			)
              model.push_to_hub(f"ybelkada/{model_name}"			)
              processor.push_to_hub(f"ybelkada/{model_name}"			)


if __name__ == "__main__":
 lowerCamelCase				:  Union[str, Any]    		=      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='resnetv2_50x1_bitm',
     type=str,
     help='Name of the BiT timm model you\'d like to convert.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub',
     action='store_true',
     help='Whether to push the model to the hub.',
 )

 lowerCamelCase				:  List[str]    		=      parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """simple docstring"""







       lowercase__      =					os.path.join(args.tf_model_dir    ,					'''parameters.json'''			)
       lowercase__      =					json.loads(open(A			).read()			)
       if not params:
              raise ValueError(
                  f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."			)
       if not args.output.endswith('''.pt'''			):
              lowercase__      =					args.output + '''.pt'''
       lowercase__      =					OrderedDict()
       with tf.device('''/CPU:0'''			):
              lowercase__      =					tf.train.load_checkpoint(args.tf_model_dir			)
              lowercase__      =					reader.get_variable_to_shape_map()
              for key_name in shapes.keys():
                     lowercase__      =					reader.get_tensor(A			).astype(np.floataa			)
                     if key_name.endswith('''/adam_m'''			) or key_name.endswith('''/adam_v'''			):
                            continue
                     if key_name.startswith('''pasts/'''			):
                            if key_name.startswith('''pasts/mlp'''			):
                                   lowercase__      =					int(key_name[9]			)
                            elif key_name.startswith('''pasts/out'''			):
                                   lowercase__      =					8
                            lowercase__      =					'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/moe'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/switch_gating/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/softmlp/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/wo/kernel'''			) or key_name.endswith('''/wi/kernel'''			):
                                   lowercase__      =					key_name[-9:-7]
                                   for i in range(16			):
                                          lowercase__      =					'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                                          lowercase__      =					(
                                              vnp[i].transpose([1, 0]			).copy()
                                          )  # In Mesh-Tensorflow, it is one array, so it is divided
                                          lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/mlp'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/p1/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p1/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/ln'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/att'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/qkv/kernel'''			):
                                   lowercase__      =					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                                   lowercase__      =					state[:, 0, :, :]
                                   lowercase__      =					state[:, 1, :, :]
                                   lowercase__      =					state[:, 2, :, :]
                                   lowercase__      =					(
                                       state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					(
                                       state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					(
                                       state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/o/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                                   lowercase__      =					(
                                       vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/an'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif (
                         key_name.startswith('''model/wte'''			)
                         or key_name.startswith('''model/wpe'''			)
                         or key_name.startswith('''model/ete'''			)
                     ):
                            lowercase__      =					{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                                key_name[-3:]
                            ]
                            lowercase__      =					'''model.%s.weight''' % nlayer
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					torch.tensor(A			)
                            if key_name.startswith('''model/wte'''			):
                                   lowercase__      =					'''lm_head.weight'''
                                   lowercase__      =					vnp.copy()  # same in embedded
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/wob'''			):
                            lowercase__      =					'''final_logits_bias'''
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					state.reshape((1, -1)			)
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense/kernel":
                            lowercase__      =					'''model.last_project.weight'''
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense_1/bias":
                            lowercase__      =					'''model.last_project.bias'''
                            lowercase__      =					vnp.copy()  # same because it is one dimensional
                            lowercase__      =					torch.tensor(A			)
       torch.save(A    ,					args.output			)


if __name__ == "__main__":
 lowerCamelCase				:  Optional[int]    		=      argparse.ArgumentParser(
     description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
 )
 parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
 parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
 lowerCamelCase				:  Dict    		=      parser.parse_args()
 convert_tf_gptsan_to_pt(args)




