from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowerCAmelCase__     						=			logging.get_logger(__name__)


lowerCAmelCase__     						=			{
    'Salesforce/codegen-350M-nl': 'https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json',
    'Salesforce/codegen-350M-multi': 'https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json',
    'Salesforce/codegen-350M-mono': 'https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json',
    'Salesforce/codegen-2B-nl': 'https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json',
    'Salesforce/codegen-2B-multi': 'https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json',
    'Salesforce/codegen-2B-mono': 'https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json',
    'Salesforce/codegen-6B-nl': 'https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json',
    'Salesforce/codegen-6B-multi': 'https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json',
    'Salesforce/codegen-6B-mono': 'https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json',
    'Salesforce/codegen-16B-nl': 'https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json',
    'Salesforce/codegen-16B-multi': 'https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json',
    'Salesforce/codegen-16B-mono': 'https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json',
}
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= 'codegen'
 UpperCAmelCase_     			= {
     'max_position_embeddings': 'n_positions',
     'hidden_size': 'n_embd',
     'num_attention_heads': 'n_head',
     'num_hidden_layers': 'n_layer',
 }




 def __init__(      self :  int      ,   lowercase__ :  Any=50_400      ,   lowercase__ :  int=2_048      ,   lowercase__ :  Tuple=2_048      ,   lowercase__ :  Optional[Any]=4_096      ,   lowercase__ :  Dict=28      ,   lowercase__ :  List[str]=16      ,   lowercase__ :  Union[str, Any]=64      ,   lowercase__ :  str=None      ,   lowercase__ :  Tuple="gelu_new"      ,   lowercase__ :  str=0.0      ,   lowercase__ :  str=0.0      ,   lowercase__ :  Optional[Any]=0.0      ,   lowercase__ :  Any=1e-5      ,   lowercase__ :  Dict=0.02      ,   lowercase__ :  str=True      ,   lowercase__ :  str=50_256      ,   lowercase__ :  Optional[int]=50_256      ,   lowercase__ :  int=False      ,   **lowercase__ :  Optional[int]      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = vocab_size
        lowerCAmelCase__					     = n_ctx
        lowerCAmelCase__					     = n_positions
        lowerCAmelCase__					     = n_embd
        lowerCAmelCase__					     = n_layer
        lowerCAmelCase__					     = n_head
        lowerCAmelCase__					     = n_inner
        lowerCAmelCase__					     = rotary_dim
        lowerCAmelCase__					     = activation_function
        lowerCAmelCase__					     = resid_pdrop
        lowerCAmelCase__					     = embd_pdrop
        lowerCAmelCase__					     = attn_pdrop
        lowerCAmelCase__					     = layer_norm_epsilon
        lowerCAmelCase__					     = initializer_range
        lowerCAmelCase__					     = use_cache

        lowerCAmelCase__					     = bos_token_id
        lowerCAmelCase__					     = eos_token_id

        super().__init__(
            bos_token_id=lowercase__      ,   eos_token_id=lowercase__      ,   tie_word_embeddings=lowercase__      ,   **lowercase__)






class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 def __init__(      self :  Dict      ,   lowercase__ :  PretrainedConfig      ,   lowercase__ :  str = "default"      ,   lowercase__ :  List[PatchingSpec] = None      ,   lowercase__ :  bool = False      ,   ):







        '''simple docstring'''



        super().__init__(lowercase__      ,   task=lowercase__      ,   patching_specs=lowercase__      ,   use_past=lowercase__)
        if not getattr(self._config      ,   'pad_token_id'      ,   lowercase__):
               # TODO: how to do that better?
               lowerCAmelCase__					     = 0




 @property
 def 		__snake_case						(      self :  str):







        '''simple docstring'''



        lowerCAmelCase__					     = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}})
        if self.use_past:
               self.fill_with_past_key_values_(lowercase__      ,   direction='inputs')
               lowerCAmelCase__					     = {0: 'batch', 1: 'past_sequence + sequence'}
        else:
               lowerCAmelCase__					     = {0: 'batch', 1: 'sequence'}

        return common_inputs




 @property
 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        return self._config.n_layer




 @property
 def 		__snake_case						(      self :  Optional[int]):







        '''simple docstring'''



        return self._config.n_head




 def 		__snake_case						(      self :  Union[str, Any]      ,   lowercase__ :  PreTrainedTokenizer      ,   lowercase__ :  int = -1      ,   lowercase__ :  int = -1      ,   lowercase__ :  bool = False      ,   lowercase__ :  Optional[TensorType] = None      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = super(lowercase__      ,   self).generate_dummy_inputs(
            lowercase__      ,   batch_size=lowercase__      ,   seq_length=lowercase__      ,   is_pair=lowercase__      ,   framework=lowercase__)

        # We need to order the input in the way they appears in the forward()
        lowerCAmelCase__					     = OrderedDict({'input_ids': common_inputs['input_ids']})

        # Need to add the past_keys
        if self.use_past:
               if not is_torch_available():
                      raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.')
               else:
                      import torch

                      lowerCAmelCase__     , lowerCAmelCase__					     = common_inputs['input_ids'].shape
                      # Not using the same length for past_key_values
                      lowerCAmelCase__					     = seqlen + 2
                      lowerCAmelCase__					     = (
                          batch,
                          self.num_attention_heads,
                          past_key_values_length,
                          self._config.hidden_size // self.num_attention_heads,
                      )
                      lowerCAmelCase__					     = [
                          (torch.zeros(lowercase__), torch.zeros(lowercase__)) for _ in range(self.num_layers)
                      ]

        lowerCAmelCase__					     = common_inputs['attention_mask']
        if self.use_past:
               lowerCAmelCase__					     = ordered_inputs['attention_mask'].dtype
               lowerCAmelCase__					     = torch.cat(
                   [ordered_inputs['attention_mask'], torch.ones(lowercase__      ,   lowercase__      ,   dtype=lowercase__)]      ,   dim=1)

        return ordered_inputs




 @property
 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        return 13

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__     						=			logging.get_logger(__name__)






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

       lowerCAmelCase__					     = '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"
       lowerCAmelCase__					     = BitConfig(
           conv_layer=lowerCAmelCase__     ,  num_labels=1_0_0_0     ,  idalabel=lowerCAmelCase__     ,  labelaid=lowerCAmelCase__     ,  )

       return config






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

       return name






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






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=False     ):
       lowerCAmelCase__					     = get_config(lowerCAmelCase__     )

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

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

       # load HuggingFace model
       lowerCAmelCase__					     = BitForImageClassification(lowerCAmelCase__     )
       model.eval()
       model.load_state_dict(lowerCAmelCase__     )

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

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

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

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

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

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

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

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

       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__     						=			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__     						=			parser.parse_args()
    convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
