



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__					:     Any    		=   logging.get_logger(__name__)



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

	__lowercase		       =			"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"
	__lowercase		       =			BitConfig(
	    conv_layer=__snake_case  ,	num_labels=1_0_0_0  ,	idalabel=__snake_case  ,	labelaid=__snake_case  ,	)

	return config



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):





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

	return name



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(__snake_case  ,	stream=__snake_case   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_config(__snake_case   )

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

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

	# load HuggingFace model
	__lowercase		       =			BitForImageClassification(__snake_case   )
	model.eval()
	model.load_state_dict(__snake_case   )

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

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

	__lowercase		       =			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()  ,	)

	__lowercase		       =			prepare_img()
	__lowercase		       =			transform(__snake_case   ).unsqueeze(0   )
	__lowercase		       =			processor(__snake_case  ,	return_tensors="pt"   ).pixel_values

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

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

	print("Logits:"  ,	logits[0, :3]   )
	print("Predicted class:"  ,	model.config.idalabel[logits.argmax(-1   ).item()]   )
	__lowercase		       =			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__":
		snake_case__					:     Tuple    		=   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__					:     str    		=   parser.parse_args()
		convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








# 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   _A   ( _lowercase	):


						'''simple docstring'''





						_snake_case			:     Dict      							=      """openai/whisper-base"""
						_snake_case			:     Union[str, Any]      							=      (
						    """This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
						    """transcribed text."""
						)
						_snake_case			:     Any      							=      """transcriber"""
						_snake_case			:     Any      							=      WhisperProcessor
						_snake_case			:     Optional[int]      							=      WhisperForConditionalGeneration

						_snake_case			:     str      							=      ["""audio"""]
						_snake_case			:     Optional[int]      							=      ["""text"""]



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Optional[int]	):






							'''simple docstring'''



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



						def     _snake_case	(	self	:							str							,      lowerCamelCase	:							List[Any]	):






							'''simple docstring'''



							return self.model.generate(inputs=lowerCamelCase	)



						def     _snake_case	(	self	:							List[str]							,      lowerCamelCase	:							Optional[Any]	):






							'''simple docstring'''



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



