
"""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()
UpperCamelCase			:       List[Any]									=				logging.get_logger(__name__)






def 							A		(       snake_case   :Union[str, Any]	)   ->			Tuple:
 __UpperCamelCase       						= 'huggingface/label-files'
 __UpperCamelCase       						= 'imagenet-1k-id2label.json'
 __UpperCamelCase       						= json.load(open(hf_hub_download(snake_case       ,      snake_case       ,      repo_type='dataset'	)       ,      'r'	)	)
 __UpperCamelCase       						= {int(snake_case	): v for k, v in idalabel.items()}
 __UpperCamelCase       						= {v: k for k, v in idalabel.items()}

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

 return config






def 							A		(       snake_case   :Dict	)   ->			Dict:
 if "stem.conv" in name:
  __UpperCamelCase       						= name.replace('stem.conv'       ,      'bit.embedder.convolution'	)
 if "blocks" in name:
  __UpperCamelCase       						= name.replace('blocks'       ,      'layers'	)
 if "head.fc" in name:
  __UpperCamelCase       						= name.replace('head.fc'       ,      'classifier.1'	)
 if name.startswith('norm'	):
  __UpperCamelCase       						= 'bit.' + name
 if "bit" not in name and "classifier" not in name:
  __UpperCamelCase       						= 'bit.encoder.' + name

 return name






def 							A		(       )   ->			Dict:
 __UpperCamelCase       						= 'http://images.cocodataset.org/val2017/000000039769.jpg'
 __UpperCamelCase       						= Image.open(requests.get(snake_case       ,      stream=snake_case	).raw	)
 return im






@torch.no_grad()
def 							A		(       snake_case   :int       ,      snake_case   :int       ,      snake_case   :List[str]=False	)   ->			List[str]:
 __UpperCamelCase       						= get_config(snake_case	)

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

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

 # load HuggingFace model
 __UpperCamelCase       						= BitForImageClassification(snake_case	)
 model.eval()
 model.load_state_dict(snake_case	)

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

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

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

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

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

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

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

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

 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__":
      UpperCamelCase			:       Optional[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.",
      )

      UpperCamelCase			:       Tuple									=				parser.parse_args()
      convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""

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


UpperCamelCase			:       Optional[int]									=				logging.get_logger(__name__)

UpperCamelCase			:       int									=				{
    "EleutherAI/gpt-j-6B": "https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}

class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):
     lowercase		     =		"gptj"
     lowercase		     =		{
         "max_position_embeddings": "n_positions",
         "hidden_size": "n_embd",
         "num_attention_heads": "n_head",
         "num_hidden_layers": "n_layer",
     }





     def __init__(			self			,  __UpperCAmelCase=5_0400			,  __UpperCAmelCase=2048			,  __UpperCAmelCase=4096			,  __UpperCAmelCase=28			,  __UpperCAmelCase=16			,  __UpperCAmelCase=64			,  __UpperCAmelCase=None			,  __UpperCAmelCase="gelu_new"			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=0.0			,  __UpperCAmelCase=1E-5			,  __UpperCAmelCase=0.0_2			,  __UpperCAmelCase=True			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=5_0256			,  __UpperCAmelCase=False			,  **__UpperCAmelCase			,  ):

      '''simple docstring'''




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

      __UpperCamelCase       						= bos_token_id
      __UpperCamelCase       						= eos_token_id

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


class __lowerCAmelCase	(   __SCREAMING_SNAKE_CASE    ):





     def __init__(			self			,  __UpperCAmelCase			,  __UpperCAmelCase = "default"			,  __UpperCAmelCase = None			,  __UpperCAmelCase = False			,  ):

      '''simple docstring'''




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





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




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

      return common_inputs





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_layer





     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return self._config.n_head





     def 		UpperCAmelCase      (			self			,  __UpperCAmelCase			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = -1			,  __UpperCAmelCase = False			,  __UpperCAmelCase = None			,  ):

      '''simple docstring'''




      __UpperCamelCase       						= super(__UpperCAmelCase			,  self   ).generate_dummy_inputs(
          __UpperCAmelCase			,  batch_size=__UpperCAmelCase			,  seq_length=__UpperCAmelCase			,  is_pair=__UpperCAmelCase			,  framework=__UpperCAmelCase   )

      # We need to order the input in the way they appears in the forward()
      __UpperCamelCase       						= 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

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

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

      return ordered_inputs







     @property
     def 		UpperCAmelCase      (			self   ):

      '''simple docstring'''




      return 13



