


import argparse
import json

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			__lowercase			       = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
			__lowercase			       = True if '''large''' in model_name or '''huge''' in model_name else False
			__lowercase			       = True if '''large''' in model_name or '''huge''' in model_name else False
			__lowercase			       = True if '''large''' in model_name or '''huge''' in model_name else False

			if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
						if "fl3" in model_name:
									__lowercase			       = [3, 3, 3, 3]
									__lowercase			       = [5, 5, 5, 5]
						elif "fl4" in model_name:
									__lowercase			       = [4, 4, 4, 4]
									__lowercase			       = [3, 3, 3, 3]

			if "tiny" in model_name or "small" in model_name or "base" in model_name:
						__lowercase			       = [3, 3, 3, 3]
						if "lrf" in model_name:
									__lowercase			       = [3, 3, 3, 3]
						else:
									__lowercase			       = [2, 2, 2, 2]

			if "tiny" in model_name:
						__lowercase			       = 96
			elif "small" in model_name:
						__lowercase			       = 96
			elif "base" in model_name:
						__lowercase			       = 1_28
			elif "large" in model_name:
						__lowercase			       = 1_92
			elif "xlarge" in model_name:
						__lowercase			       = 2_56
			elif "huge" in model_name:
						__lowercase			       = 3_52

			# set label information
			__lowercase			       = '''huggingface/label-files'''
			if "large" in model_name or "huge" in model_name:
						__lowercase			       = '''imagenet-22k-id2label.json'''
			else:
						__lowercase			       = '''imagenet-1k-id2label.json'''

			__lowercase			       = json.load(open(hf_hub_download(_UpperCamelCase							,      _UpperCamelCase							,      repo_type='''dataset'''	)							,      '''r'''	)	)
			__lowercase			       = {int(_UpperCamelCase	): v for k, v in idalabel.items()}
			__lowercase			       = {v: k for k, v in idalabel.items()}

			__lowercase			       = FocalNetConfig(
			    embed_dim=_UpperCamelCase							,      depths=_UpperCamelCase							,      focal_levels=_UpperCamelCase							,      focal_windows=_UpperCamelCase							,      use_conv_embed=_UpperCamelCase							,      idalabel=_UpperCamelCase							,      labelaid=_UpperCamelCase							,      use_post_layernorm=_UpperCamelCase							,      use_layerscale=_UpperCamelCase							,      )

			return config


def 		lowercase_	(  _UpperCamelCase	):


			'''simple docstring'''







			if "patch_embed.proj" in name:
						__lowercase			       = name.replace('''patch_embed.proj'''							,      '''embeddings.patch_embeddings.projection'''	)
			if "patch_embed.norm" in name:
						__lowercase			       = name.replace('''patch_embed.norm'''							,      '''embeddings.norm'''	)
			if "layers" in name:
						__lowercase			       = '''encoder.''' + name
			if "encoder.layers" in name:
						__lowercase			       = name.replace('''encoder.layers'''							,      '''encoder.stages'''	)
			if "downsample.proj" in name:
						__lowercase			       = name.replace('''downsample.proj'''							,      '''downsample.projection'''	)
			if "blocks" in name:
						__lowercase			       = name.replace('''blocks'''							,      '''layers'''	)
			if "modulation.f.weight" in name or "modulation.f.bias" in name:
						__lowercase			       = name.replace('''modulation.f'''							,      '''modulation.projection_in'''	)
			if "modulation.h.weight" in name or "modulation.h.bias" in name:
						__lowercase			       = name.replace('''modulation.h'''							,      '''modulation.projection_context'''	)
			if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
						__lowercase			       = name.replace('''modulation.proj'''							,      '''modulation.projection_out'''	)

			if name == "norm.weight":
						__lowercase			       = '''layernorm.weight'''
			if name == "norm.bias":
						__lowercase			       = '''layernorm.bias'''

			if "head" in name:
						__lowercase			       = name.replace('''head'''							,      '''classifier'''	)
			else:
						__lowercase			       = '''focalnet.''' + name

			return name


def 		lowercase_	(  _UpperCamelCase							,      _UpperCamelCase							,      _UpperCamelCase=False	):


			'''simple docstring'''







			__lowercase			       = {
			    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
			    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
			    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
			    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
			    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
			    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
			    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
			    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
			    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
			    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
			}
			# fmt: on

			__lowercase			       = model_name_to_url[model_name]
			print('''Checkpoint URL: '''							,      _UpperCamelCase	)
			__lowercase			       = torch.hub.load_state_dict_from_url(_UpperCamelCase							,      map_location='''cpu'''	)['''model''']

			# rename keys
			for key in state_dict.copy().keys():
						__lowercase			       = state_dict.pop(_UpperCamelCase	)
						__lowercase			       = val

			__lowercase			       = get_focalnet_config(_UpperCamelCase	)
			__lowercase			       = FocalNetForImageClassification(_UpperCamelCase	)
			model.eval()

			# load state dict
			model.load_state_dict(_UpperCamelCase	)

			# verify conversion
			__lowercase			       = '''http://images.cocodataset.org/val2017/000000039769.jpg'''

			__lowercase			       = BitImageProcessor(
			    do_resize=_UpperCamelCase							,      size={'''shortest_edge''': 2_56}							,      resample=PILImageResampling.BILINEAR							,      do_center_crop=_UpperCamelCase							,      crop_size=2_24							,      do_normalize=_UpperCamelCase							,      image_mean=_UpperCamelCase							,      image_std=_UpperCamelCase							,      )
			__lowercase			       = Image.open(requests.get(_UpperCamelCase							,      stream=_UpperCamelCase	).raw	)
			__lowercase			       = processor(images=_UpperCamelCase							,      return_tensors='''pt'''	)

			__lowercase			       = transforms.Compose(
			    [
			        transforms.Resize(2_56	),
			        transforms.CenterCrop(2_24	),
			        transforms.ToTensor(),
			        transforms.Normalize(mean=[0.485, 0.456, 0.406]							,      std=[0.229, 0.224, 0.225]	),
			    ]	)

			__lowercase			       = image_transforms(_UpperCamelCase	).unsqueeze(0	)

			# verify pixel_values
			assert torch.allclose(inputs.pixel_values							,      _UpperCamelCase							,      atol=1E-4	)

			__lowercase			       = model(**_UpperCamelCase	)

			__lowercase			       = outputs.logits.argmax(-1	).item()
			print('''Predicted class:'''							,      model.config.idalabel[predicted_class_idx]	)

			print('''First values of logits:'''							,      outputs.logits[0, :3]	)

			if model_name == "focalnet-tiny":
						__lowercase			       = torch.tensor([0.2166, -0.4368, 0.2191]	)
			elif model_name == "focalnet-tiny-lrf":
						__lowercase			       = torch.tensor([1.1669, 0.0125, -0.1695]	)
			elif model_name == "focalnet-small":
						__lowercase			       = torch.tensor([0.4917, -0.0430, 0.1341]	)
			elif model_name == "focalnet-small-lrf":
						__lowercase			       = torch.tensor([-0.2588, -0.5342, -0.2331]	)
			elif model_name == "focalnet-base":
						__lowercase			       = torch.tensor([-0.1655, -0.4090, -0.1730]	)
			elif model_name == "focalnet-base-lrf":
						__lowercase			       = torch.tensor([0.5306, -0.0483, -0.3928]	)
			assert torch.allclose(outputs.logits[0, :3]							,      _UpperCamelCase							,      atol=1E-4	)
			print('''Looks ok!'''	)

			if pytorch_dump_folder_path is not None:
						print(F'Saving model and processor of {model_name} to {pytorch_dump_folder_path}'	)
						model.save_pretrained(_UpperCamelCase	)
						processor.save_pretrained(_UpperCamelCase	)

			if push_to_hub:
						print(F'Pushing model and processor of {model_name} to the hub...'	)
						model.push_to_hub(F'{model_name}'	)
						processor.push_to_hub(F'{model_name}'	)


if __name__ == "__main__":
	a			:			Dict												=					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '''--model_name''',
	    default='''focalnet-tiny''',
	    type=str,
	    help='''Name of the FocalNet 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 and processor to the hub.''',
	)

	a			:			Union[str, Any]												=					parser.parse_args()
	convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase__					)
class 						lowerCamelCase_							(					lowerCAmelCase__					):



					'''simple docstring'''







					__UpperCAmelCase							  =					field(default="automatic-speech-recognition"  ,  metadata={"include_in_asdict_even_if_is_default": True}					)
					__UpperCAmelCase							  =					Features({"audio": Audio()}					)
					__UpperCAmelCase							  =					Features({"transcription": Value("string"					)}					)
					__UpperCAmelCase							  =					"audio"
					__UpperCAmelCase							  =					"transcription"






					def A   (					self			,							snake_case_    ) ->      Any:







								'''simple docstring'''

								if self.audio_column not in features:
											raise ValueError(F'Column {self.audio_column} is not present in features.'    )
								if not isinstance(features[self.audio_column]			,							snake_case_    ):
											raise ValueError(F'Column {self.audio_column} is not an Audio type.'    )
								__lowercase			       = copy.deepcopy(self    )
								__lowercase			       = self.input_schema.copy()
								__lowercase			       = features[self.audio_column]
								__lowercase			       = input_schema
								return task_template






					@property
					def A   (					self    ) ->      Dict[str, str]:







								'''simple docstring'''

								return {self.audio_column: "audio", self.transcription_column: "transcription"}


