


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_tf_available():
      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
      import torch

      from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

__UpperCAmelCase    :str				       =     logging.get_logger(__name__)



@add_end_docstrings(_a )
class 			a       (	_a ):


     """simple docstring"""






     def __init__(       self  :	List[str]							,				*snake_case  :	Optional[Any]							,				**snake_case  :	List[str]    )		->							List[Any]:
          super().__init__(*snake_case							,				**snake_case    )
          requires_backends(self							,				'''vision'''    )
          self.check_model_type(
              TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING    )

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	List[str]=None							,				snake_case  :	Any=None							,				snake_case  :	Optional[int]=None    )		->							Dict:
          __UpperCAmelCase			:				Optional[int]													=    {}
          __UpperCAmelCase			:				Optional[int]													=    {}

          if prompt is not None:
               __UpperCAmelCase			:				Any													=    prompt

          if generate_kwargs is not None:
               __UpperCAmelCase			:				Optional[Any]													=    generate_kwargs
          if max_new_tokens is not None:
               if "generate_kwargs" not in forward_kwargs:
                    __UpperCAmelCase			:				Union[str, Any]													=    {}
               if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                    raise ValueError(
                        '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
                        ''' please use only one'''    )
               __UpperCAmelCase			:				List[Any]													=    max_new_tokens
          return preprocess_params, forward_kwargs, {}

     def __call__(       self  :	Optional[int]							,				snake_case  :	Union[str, List[str], "Image.Image", List["Image.Image"]]							,				**snake_case  :	List[Any]    )		->							List[str]:
          return super().__call__(snake_case							,				**snake_case    )

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Union[str, Any]							,				snake_case  :	List[Any]=None    )		->							str:
          __UpperCAmelCase			:				Optional[int]													=    load_image(snake_case    )

          if prompt is not None:
               if not isinstance(snake_case							,				snake_case    ):
                    raise ValueError(
                        f'Received an invalid text input, got - {type(snake_case    )} - but expected a single string. '
                        '''Note also that one single text can be provided for conditional image to text generation.'''    )

               __UpperCAmelCase			:				Tuple													=    self.model.config.model_type

               if model_type == "git":
                    __UpperCAmelCase			:				List[Any]													=    self.image_processor(images=snake_case							,				return_tensors=self.framework    )
                    __UpperCAmelCase			:				List[str]													=    self.tokenizer(text=snake_case							,				add_special_tokens=snake_case    ).input_ids
                    __UpperCAmelCase			:				Any													=    [self.tokenizer.cls_token_id] + input_ids
                    __UpperCAmelCase			:				Dict													=    torch.tensor(snake_case    ).unsqueeze(0    )
                    model_inputs.update({'''input_ids''': input_ids}    )

               elif model_type == "pix2struct":
                    __UpperCAmelCase			:				int													=    self.image_processor(images=snake_case							,				header_text=snake_case							,				return_tensors=self.framework    )

               elif model_type != "vision-encoder-decoder":
                    # vision-encoder-decoder does not support conditional generation
                    __UpperCAmelCase			:				Tuple													=    self.image_processor(images=snake_case							,				return_tensors=self.framework    )
                    __UpperCAmelCase			:				List[Any]													=    self.tokenizer(snake_case							,				return_tensors=self.framework    )
                    model_inputs.update(snake_case    )

               else:
                    raise ValueError(f'Model type {model_type} does not support conditional text generation'    )

          else:
               __UpperCAmelCase			:				Tuple													=    self.image_processor(images=snake_case							,				return_tensors=self.framework    )

          if self.model.config.model_type == "git" and prompt is None:
               __UpperCAmelCase			:				Any													=    None

          return model_inputs

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	Optional[int]							,				snake_case  :	List[Any]=None    )		->							Optional[Any]:
          # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
          # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
          if (
              "input_ids" in model_inputs
              and isinstance(model_inputs['''input_ids''']							,				snake_case    )
              and all(x is None for x in model_inputs['''input_ids''']    )
          ):
               __UpperCAmelCase			:				int													=    None

          if generate_kwargs is None:
               __UpperCAmelCase			:				Any													=    {}
          # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
          #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
          #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
          #  in the `_prepare_model_inputs` method.
          __UpperCAmelCase			:				int													=    model_inputs.pop(self.model.main_input_name    )
          __UpperCAmelCase			:				int													=    self.model.generate(snake_case							,				**snake_case							,				**snake_case    )
          return model_outputs






     def 				lowerCamelCase__				(       self  :	int							,				snake_case  :	List[Any]    )		->							List[Any]:
          __UpperCAmelCase			:				Union[str, Any]													=    []
          for output_ids in model_outputs:
               __UpperCAmelCase			:				Optional[Any]													=    {
                   '''generated_text''': self.tokenizer.decode(
                       snake_case							,				skip_special_tokens=snake_case							,				)
               }
               records.append(snake_case    )
          return records



'''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			(		_lowercase      :			Tuple						):





     '''simple docstring'''






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

     __UpperCAmelCase			:				Optional[Any]													=    '''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			:				int													=    BitConfig(
         conv_layer=_lowercase    ,      num_labels=1000    ,      idalabel=_lowercase    ,      labelaid=_lowercase    ,      )

     return config




def 			_a			(		_lowercase      :			Tuple						):





     '''simple docstring'''






     if "stem.conv" in name:
          __UpperCAmelCase			:				Any													=    name.replace('''stem.conv'''    ,      '''bit.embedder.convolution'''						)
     if "blocks" in name:
          __UpperCAmelCase			:				Optional[int]													=    name.replace('''blocks'''    ,      '''layers'''						)
     if "head.fc" in name:
          __UpperCAmelCase			:				Dict													=    name.replace('''head.fc'''    ,      '''classifier.1'''						)
     if name.startswith('''norm'''						):
          __UpperCAmelCase			:				List[Any]													=    '''bit.''' + name
     if "bit" not in name and "classifier" not in name:
          __UpperCAmelCase			:				List[str]													=    '''bit.encoder.''' + name

     return name




def 			_a			(		):





     '''simple docstring'''






     __UpperCAmelCase			:				Optional[Any]													=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''
     __UpperCAmelCase			:				Optional[Any]													=    Image.open(requests.get(_lowercase    ,      stream=_lowercase						).raw						)
     return im




@torch.no_grad()
def 			_a			(		_lowercase      :			Tuple    ,      _lowercase      :			Dict    ,      _lowercase      :			Tuple=False						):





     '''simple docstring'''






     __UpperCAmelCase			:				Any													=    get_config(_lowercase						)

     # load original model from timm
     __UpperCAmelCase			:				Tuple													=    create_model(_lowercase    ,      pretrained=_lowercase						)
     timm_model.eval()

     # load state_dict of original model
     __UpperCAmelCase			:				List[Any]													=    timm_model.state_dict()
     for key in state_dict.copy().keys():
          __UpperCAmelCase			:				Optional[Any]													=    state_dict.pop(_lowercase						)
          __UpperCAmelCase			:				int													=    val.squeeze() if '''head''' in key else val

     # load HuggingFace model
     __UpperCAmelCase			:				Any													=    BitForImageClassification(_lowercase						)
     model.eval()
     model.load_state_dict(_lowercase						)

     # create image processor
     __UpperCAmelCase			:				Optional[int]													=    create_transform(**resolve_data_config({}    ,      model=_lowercase						)						)
     __UpperCAmelCase			:				Union[str, Any]													=    transform.transforms

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

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

     __UpperCAmelCase			:				Optional[Any]													=    prepare_img()
     __UpperCAmelCase			:				Any													=    transform(_lowercase						).unsqueeze(0						)
     __UpperCAmelCase			:				List[Any]													=    processor(_lowercase    ,      return_tensors='''pt'''						).pixel_values

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

     # verify logits
     with torch.no_grad():
          __UpperCAmelCase			:				Union[str, Any]													=    model(_lowercase						)
          __UpperCAmelCase			:				Union[str, Any]													=    outputs.logits

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

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

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