





'''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()
__magic_name__   :						str    =			logging.get_logger(__name__)




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  _snake_case     		=				"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"
  _snake_case     		=				BitConfig(
      conv_layer=SCREAMING_SNAKE_CASE__					,	num_labels=10_00					,	idalabel=SCREAMING_SNAKE_CASE__					,	labelaid=SCREAMING_SNAKE_CASE__					,	)

  return config




def 				snake_case_      (		SCREAMING_SNAKE_CASE__       ):







  '''simple docstring'''



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

  return name




def 				snake_case_      (		):







  '''simple docstring'''



  _snake_case     		=				"http://images.cocodataset.org/val2017/000000039769.jpg"
  _snake_case     		=				Image.open(requests.get(SCREAMING_SNAKE_CASE__					,	stream=SCREAMING_SNAKE_CASE__       ).raw       )
  return im




@torch.no_grad()
def 				snake_case_      (		SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=False       ):







  '''simple docstring'''



  _snake_case     		=				get_config(SCREAMING_SNAKE_CASE__       )

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

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

  # load HuggingFace model
  _snake_case     		=				BitForImageClassification(SCREAMING_SNAKE_CASE__       )
  model.eval()
  model.load_state_dict(SCREAMING_SNAKE_CASE__       )

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

  _snake_case     		=				{
      "bilinear": PILImageResampling.BILINEAR,
      "bicubic": PILImageResampling.BICUBIC,
      "nearest": PILImageResampling.NEAREST,
  }

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

  _snake_case     		=				prepare_img()
  _snake_case     		=				transform(SCREAMING_SNAKE_CASE__       ).unsqueeze(0       )
  _snake_case     		=				processor(SCREAMING_SNAKE_CASE__					,	return_tensors="pt"       ).pixel_values

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

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

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

  if pytorch_dump_folder_path is not None:
    Path(SCREAMING_SNAKE_CASE__       ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__       )
    print(f'''Saving model {model_name} and processor to {pytorch_dump_folder_path}'''       )
    model.save_pretrained(SCREAMING_SNAKE_CASE__       )
    processor.save_pretrained(SCREAMING_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__":
 __magic_name__   :						List[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.""",
 )

 __magic_name__   :						Optional[Any]    =			parser.parse_args()
 convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









'''simple docstring'''



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

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


@add_end_docstrings(__UpperCamelCase			)
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       super().__init__(*lowerCamelCase						,   **lowerCamelCase	)
       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 	UpperCamelCase(					self						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       _snake_case     		=				{}
       _snake_case     		=				{}

       if prompt is not None:
         _snake_case     		=				prompt

       if generate_kwargs is not None:
         _snake_case     		=				generate_kwargs
       if max_new_tokens is not None:
         if "generate_kwargs" not in forward_kwargs:
           _snake_case     		=				{}
         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"	)
         _snake_case     		=				max_new_tokens
       return preprocess_params, forward_kwargs, {}

     def __call__(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       return super().__call__(lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       _snake_case     		=				load_image(lowerCamelCase	)

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

         _snake_case     		=				self.model.config.model_type

         if model_type == "git":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(text=lowerCamelCase						,   add_special_tokens=lowerCamelCase	).input_ids
           _snake_case     		=				[self.tokenizer.cls_token_id] + input_ids
           _snake_case     		=				torch.tensor(lowerCamelCase	).unsqueeze(0	)
           model_inputs.update({"input_ids": input_ids}	)

         elif model_type == "pix2struct":
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   header_text=lowerCamelCase						,   return_tensors=self.framework	)

         elif model_type != "vision-encoder-decoder":
           # vision-encoder-decoder does not support conditional generation
           _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)
           _snake_case     		=				self.tokenizer(lowerCamelCase						,   return_tensors=self.framework	)
           model_inputs.update(lowerCamelCase	)

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

       else:
         _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)

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

       return model_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       # 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"]						,   lowerCamelCase	)
           and all(x is None for x in model_inputs["input_ids"]	)
       ):
         _snake_case     		=				None

       if generate_kwargs is None:
         _snake_case     		=				{}
       # 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.
       _snake_case     		=				model_inputs.pop(self.model.main_input_name	)
       _snake_case     		=				self.model.generate(lowerCamelCase						,   **lowerCamelCase						,   **lowerCamelCase	)
       return model_outputs






     def 	UpperCamelCase(					self						,   lowerCamelCase	):
       _snake_case     		=				[]
       for output_ids in model_outputs:
         _snake_case     		=				{
             "generated_text": self.tokenizer.decode(
                 lowerCamelCase						,   skip_special_tokens=lowerCamelCase						,   )
         }
         records.append(lowerCamelCase	)
       return records


