


"""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()
__snake_case       =       logging.get_logger(__name__)







def     __lowerCAmelCase		(		lowercase    :    Union[str, Any]					)      ->		Optional[Any]:


    """simple docstring"""





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

    snake_case       : Dict    =       "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       : Optional[int]    =       BitConfig(
        conv_layer=lowercase		,				num_labels=1000		,				idalabel=lowercase		,				labelaid=lowercase		,				)

    return config







def     __lowerCAmelCase		(		lowercase    :    Any					)      ->		Optional[int]:


    """simple docstring"""





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

    return name







def     __lowerCAmelCase		(		)      ->		Dict:


    """simple docstring"""





    snake_case       : Any    =       "http://images.cocodataset.org/val2017/000000039769.jpg"
    snake_case       : List[str]    =       Image.open(requests.get(lowercase		,				stream=lowercase					).raw					)
    return im







@torch.no_grad()
def     __lowerCAmelCase		(		lowercase    :    Dict		,				lowercase    :    str		,				lowercase    :    Tuple=False					)      ->		Optional[int]:


    """simple docstring"""





    snake_case       : str    =       get_config(lowercase					)

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

    # load state_dict of original model
    snake_case       : Union[str, Any]    =       timm_model.state_dict()
    for key in state_dict.copy().keys():
        snake_case       : Optional[int]    =       state_dict.pop(lowercase					)
        snake_case       : Optional[int]    =       val.squeeze() if "head" in key else val

    # load HuggingFace model
    snake_case       : Any    =       BitForImageClassification(lowercase					)
    model.eval()
    model.load_state_dict(lowercase					)

    # create image processor
    snake_case       : Tuple    =       create_transform(**resolve_data_config({}		,				model=lowercase					)					)
    snake_case       : Any    =       transform.transforms

    snake_case       : Union[str, Any]    =       {
        "bilinear": PILImageResampling.BILINEAR,
        "bicubic": PILImageResampling.BICUBIC,
        "nearest": PILImageResampling.NEAREST,
    }

    snake_case       : Optional[int]    =       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()		,				)

    snake_case       : Optional[Any]    =       prepare_img()
    snake_case       : List[str]    =       transform(lowercase					).unsqueeze(0					)
    snake_case       : List[str]    =       processor(lowercase		,				return_tensors="pt"					).pixel_values

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

    # verify logits
    with torch.no_grad():
        snake_case       : Any    =       model(lowercase					)
        snake_case       : List[Any]    =       outputs.logits

    print("Logits:"		,				logits[0, :3]					)
    print("Predicted class:"		,				model.config.idalabel[logits.argmax(-1					).item()]					)
    snake_case       : Union[str, Any]    =       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__":
  __snake_case       =       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.""",
  )

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









"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool







class _lowerCAmelCase							( snake_case_      ):
   __UpperCAmelCase					:			List[str]				    =    '''openai/whisper-base'''
   __UpperCAmelCase					:			Any				    =    (
       '''This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '''
       '''transcribed text.'''
   )
   __UpperCAmelCase					:			Tuple				    =    '''transcriber'''
   __UpperCAmelCase					:			Union[str, Any]				    =    WhisperProcessor
   __UpperCAmelCase					:			str				    =    WhisperForConditionalGeneration

   __UpperCAmelCase					:			int				    =    ['''audio''']
   __UpperCAmelCase					:			Optional[int]				    =    ['''text''']







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Tuple:

       '''simple docstring'''




       return self.pre_processor(UpperCamelCase__		,      return_tensors="pt"		).input_features







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						int:

       '''simple docstring'''




       return self.model.generate(inputs=UpperCamelCase__		)







   def 					lowerCamelCase						(      self		,      UpperCamelCase__		)      ->						Optional[Any]:

       '''simple docstring'''




       return self.pre_processor.batch_decode(UpperCamelCase__		,      skip_special_tokens=UpperCamelCase__		)[0]





