





'''simple docstring'''




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCAmelCase            =       logging.get_logger(__name__)



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_=False	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			[]

		# fmt: off
		# stem:
		rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")	)
		rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")	)

		rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")	)
		rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")	)

		# backbone
		rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")	)
		rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")	)
		rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")	)

		for stage_idx in range(len(config.backbone_config.depths	)	):
				for layer_idx in range(config.backbone_config.depths[stage_idx]	):
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''')	)
						rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''')	)

				rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''')	)
				rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''')	)
				rename_keys.append((f'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', f'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''')	)

		# transformer encoder
		for i in range(config.num_hidden_layers	):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append((f'''blocks.{i}.norm1.weight''', f'''vit.encoder.layer.{i}.layernorm_before.weight''')	)
				rename_keys.append((f'''blocks.{i}.norm1.bias''', f'''vit.encoder.layer.{i}.layernorm_before.bias''')	)
				rename_keys.append((f'''blocks.{i}.attn.proj.weight''', f'''vit.encoder.layer.{i}.attention.output.dense.weight''')	)
				rename_keys.append((f'''blocks.{i}.attn.proj.bias''', f'''vit.encoder.layer.{i}.attention.output.dense.bias''')	)
				rename_keys.append((f'''blocks.{i}.norm2.weight''', f'''vit.encoder.layer.{i}.layernorm_after.weight''')	)
				rename_keys.append((f'''blocks.{i}.norm2.bias''', f'''vit.encoder.layer.{i}.layernorm_after.bias''')	)
				rename_keys.append((f'''blocks.{i}.mlp.fc1.weight''', f'''vit.encoder.layer.{i}.intermediate.dense.weight''')	)
				rename_keys.append((f'''blocks.{i}.mlp.fc1.bias''', f'''vit.encoder.layer.{i}.intermediate.dense.bias''')	)
				rename_keys.append((f'''blocks.{i}.mlp.fc2.weight''', f'''vit.encoder.layer.{i}.output.dense.weight''')	)
				rename_keys.append((f'''blocks.{i}.mlp.fc2.bias''', f'''vit.encoder.layer.{i}.output.dense.bias''')	)

		if base_model:
				# layernorm + pooler
				rename_keys.extend(
				    [
				        ("""norm.weight""", """layernorm.weight"""),
				        ("""norm.bias""", """layernorm.bias"""),
				        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
				        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
				    ]	)

				# if just the base model, we should remove "vit" from all keys that start with "vit"
				SCREAMING_SNAKE_CASE :				Tuple		=			[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""	) else pair for pair in rename_keys]
		else:
				# layernorm + classification head
				rename_keys.extend(
				    [
				        ("""norm.weight""", """vit.layernorm.weight"""),
				        ("""norm.bias""", """vit.layernorm.bias"""),
				        ("""head.weight""", """classifier.weight"""),
				        ("""head.bias""", """classifier.bias"""),
				    ]	)
		# fmt: on

		return rename_keys



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_=False	):


		"""simple docstring"""



		for i in range(config.num_hidden_layers	):
				if base_model:
						SCREAMING_SNAKE_CASE :				List[str]		=			""""""
				else:
						SCREAMING_SNAKE_CASE :				int		=			"""vit."""
				# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
				SCREAMING_SNAKE_CASE :				Optional[int]		=			state_dict.pop(f'''blocks.{i}.attn.qkv.weight'''	)
				SCREAMING_SNAKE_CASE :				int		=			state_dict.pop(f'''blocks.{i}.attn.qkv.bias'''	)
				# next, add query, keys and values (in that order) to the state dict
				SCREAMING_SNAKE_CASE :				int		=			in_proj_weight[
				    : config.hidden_size, :
				]
				SCREAMING_SNAKE_CASE :				Any		=			in_proj_bias[: config.hidden_size]
				SCREAMING_SNAKE_CASE :				str		=			in_proj_weight[
				    config.hidden_size : config.hidden_size * 2, :
				]
				SCREAMING_SNAKE_CASE :				Optional[Any]		=			in_proj_bias[
				    config.hidden_size : config.hidden_size * 2
				]
				SCREAMING_SNAKE_CASE :				Dict		=			in_proj_weight[
				    -config.hidden_size :, :
				]
				SCREAMING_SNAKE_CASE :				Union[str, Any]		=			in_proj_bias[-config.hidden_size :]



def 		__A		(  lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				List[Any]		=			["""head.weight""", """head.bias"""]
		for k in ignore_keys:
				state_dict.pop(lowerCamelCase_			,			lowerCamelCase_	)



def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			dct.pop(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Tuple		=			val



def 		__A		(  ):


		"""simple docstring"""



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



@torch.no_grad()
def 		__A		(  lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_=False	):


		"""simple docstring"""



		SCREAMING_SNAKE_CASE :				Any		=			BitConfig(
		    global_padding="""same"""			,			layer_type="""bottleneck"""			,			depths=(3, 4, 9)			,			out_features=["""stage3"""]			,			embedding_dynamic_padding=lowerCamelCase_			,			)
		SCREAMING_SNAKE_CASE :				List[Any]		=			ViTHybridConfig(backbone_config=lowerCamelCase_			,			image_size=3_84			,			num_labels=10_00	)
		SCREAMING_SNAKE_CASE :				Union[str, Any]		=			False

		# load original model from timm
		SCREAMING_SNAKE_CASE :				Dict		=			timm.create_model(lowerCamelCase_			,			pretrained=lowerCamelCase_	)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		SCREAMING_SNAKE_CASE :				List[Any]		=			timm_model.state_dict()
		if base_model:
				remove_classification_head_(lowerCamelCase_	)
		SCREAMING_SNAKE_CASE :				Optional[Any]		=			create_rename_keys(lowerCamelCase_			,			lowerCamelCase_	)
		for src, dest in rename_keys:
				rename_key(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)
		read_in_q_k_v(lowerCamelCase_			,			lowerCamelCase_			,			lowerCamelCase_	)

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

		# load HuggingFace model
		if vit_name[-5:] == "in21k":
				SCREAMING_SNAKE_CASE :				List[str]		=			ViTHybridModel(lowerCamelCase_	).eval()
		else:
				SCREAMING_SNAKE_CASE :				Dict		=			ViTHybridForImageClassification(lowerCamelCase_	).eval()
		model.load_state_dict(lowerCamelCase_	)

		# create image processor
		SCREAMING_SNAKE_CASE :				int		=			create_transform(**resolve_data_config({}			,			model=lowerCamelCase_	)	)
		SCREAMING_SNAKE_CASE :				List[Any]		=			transform.transforms

		SCREAMING_SNAKE_CASE :				Optional[int]		=			{
		    """bilinear""": PILImageResampling.BILINEAR,
		    """bicubic""": PILImageResampling.BICUBIC,
		    """nearest""": PILImageResampling.NEAREST,
		}

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

		SCREAMING_SNAKE_CASE :				Any		=			prepare_img()
		SCREAMING_SNAKE_CASE :				int		=			transform(lowerCamelCase_	).unsqueeze(0	)
		SCREAMING_SNAKE_CASE :				Any		=			processor(lowerCamelCase_			,			return_tensors="""pt"""	).pixel_values

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

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE :				Optional[int]		=			model(lowerCamelCase_	)
				SCREAMING_SNAKE_CASE :				Dict		=			outputs.logits

		print("""Predicted class:"""			,			logits.argmax(-1	).item()	)
		if base_model:
				SCREAMING_SNAKE_CASE :				Dict		=			timm_model.forward_features(lowerCamelCase_	)
				assert timm_pooled_output.shape == outputs.pooler_output.shape
				assert torch.allclose(lowerCamelCase_			,			outputs.pooler_output			,			atol=1E-3	)
		else:
				SCREAMING_SNAKE_CASE :				List[Any]		=			timm_model(lowerCamelCase_	)
				assert timm_logits.shape == outputs.logits.shape
				assert torch.allclose(lowerCamelCase_			,			outputs.logits			,			atol=1E-3	)
		print("""Looks ok!"""	)

		if pytorch_dump_folder_path is not None:
				Path(lowerCamelCase_	).mkdir(exist_ok=lowerCamelCase_	)
				print(f'''Saving model {vit_name} to {pytorch_dump_folder_path}'''	)
				model.save_pretrained(lowerCamelCase_	)
				print(f'''Saving processor to {pytorch_dump_folder_path}'''	)
				processor.save_pretrained(lowerCamelCase_	)

		if push_to_hub:
				print(f'''Pushing model and processor to the hub {vit_name}'''	)
				model.push_to_hub(f'''ybelkada/{vit_name}'''	)
				processor.push_to_hub(f'''ybelkada/{vit_name}'''	)


if __name__ == "__main__":
							__UpperCAmelCase            =       argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    """--vit_name""",
							    default="""vit_base_r50_s16_384""",
							    type=str,
							    help="""Name of the hybrid ViT 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 upload the model to the HuggingFace hub."""
							)

							__UpperCAmelCase            =       parser.parse_args()
							convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)










'''simple docstring'''




import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer




class      UpperCamelCase__					(					lowercase_			):




					"""simple docstring"""
					SCREAMING_SNAKE_CASE__				=	'''Wav2Vec2FeatureExtractor'''
					SCREAMING_SNAKE_CASE__				=	'''AutoTokenizer'''


					def __init__(						self				:			Optional[int]      ,						lowerCamelCase_				:			str      ,						lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							super().__init__(lowerCamelCase_      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				List[Any]		=			False


					@classmethod
					def lowerCamelCase_			(						cls				:			int      ,						lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Any						):

							'''simple docstring'''



							try:
									return super().from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
							except OSError:
									warnings.warn(
									    f'''Loading a tokenizer inside {cls.__name__} from a config that does not'''
									    """ include a `tokenizer_class` attribute is deprecated and will be """
									    """removed in v5. Please add `'tokenizer_class': 'Wav2Vec2CTCTokenizer'`"""
									    """ attribute to either your `config.json` or `tokenizer_config.json` """
									    """file to suppress this warning: """      ,						lowerCamelCase_      ,						)

									SCREAMING_SNAKE_CASE :				Optional[Any]		=			WavaVecaFeatureExtractor.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)
									SCREAMING_SNAKE_CASE :				Dict		=			WavaVecaCTCTokenizer.from_pretrained(lowerCamelCase_      ,						**lowerCamelCase_						)

									return cls(feature_extractor=lowerCamelCase_      ,						tokenizer=lowerCamelCase_						)


					def __call__(						self				:			List[Any]      ,						*lowerCamelCase_				:			str      ,						**lowerCamelCase_				:			int						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor(*lowerCamelCase_      ,						**lowerCamelCase_						)

							if "raw_speech" in kwargs:
									warnings.warn("""Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."""						)
									SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""raw_speech"""						)
							else:
									SCREAMING_SNAKE_CASE :				Any		=			kwargs.pop("""audio"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Tuple		=			kwargs.pop("""sampling_rate"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				int		=			kwargs.pop("""text"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Union[str, Any]		=			args[0]
									SCREAMING_SNAKE_CASE :				Dict		=			args[1:]

							if audio is None and text is None:
									raise ValueError("""You need to specify either an `audio` or `text` input to process."""						)

							if audio is not None:
									SCREAMING_SNAKE_CASE :				str		=			self.feature_extractor(lowerCamelCase_      ,						*lowerCamelCase_      ,						sampling_rate=lowerCamelCase_      ,						**lowerCamelCase_						)
							if text is not None:
									SCREAMING_SNAKE_CASE :				List[Any]		=			self.tokenizer(lowerCamelCase_      ,						**lowerCamelCase_						)

							if text is None:
									return inputs
							elif audio is None:
									return encodings
							else:
									SCREAMING_SNAKE_CASE :				List[str]		=			encodings["""input_ids"""]
									return inputs


					def lowerCamelCase_			(						self				:			Optional[Any]      ,						*lowerCamelCase_				:			Tuple      ,						**lowerCamelCase_				:			Dict						):

							'''simple docstring'''



							if self._in_target_context_manager:
									return self.current_processor.pad(*lowerCamelCase_      ,						**lowerCamelCase_						)

							SCREAMING_SNAKE_CASE :				str		=			kwargs.pop("""input_features"""      ,						lowerCamelCase_						)
							SCREAMING_SNAKE_CASE :				Dict		=			kwargs.pop("""labels"""      ,						lowerCamelCase_						)
							if len(lowerCamelCase_						) > 0:
									SCREAMING_SNAKE_CASE :				Optional[int]		=			args[0]
									SCREAMING_SNAKE_CASE :				Optional[Any]		=			args[1:]

							if input_features is not None:
									SCREAMING_SNAKE_CASE :				Any		=			self.feature_extractor.pad(lowerCamelCase_      ,						*lowerCamelCase_      ,						**lowerCamelCase_						)
							if labels is not None:
									SCREAMING_SNAKE_CASE :				int		=			self.tokenizer.pad(lowerCamelCase_      ,						**lowerCamelCase_						)

							if labels is None:
									return input_features
							elif input_features is None:
									return labels
							else:
									SCREAMING_SNAKE_CASE :				Dict		=			labels["""input_ids"""]
									return input_features


					def lowerCamelCase_			(						self				:			Optional[int]      ,						*lowerCamelCase_				:			List[str]      ,						**lowerCamelCase_				:			Optional[Any]						):

							'''simple docstring'''



							return self.tokenizer.batch_decode(*lowerCamelCase_      ,						**lowerCamelCase_						)


					def lowerCamelCase_			(						self				:			List[str]      ,						*lowerCamelCase_				:			Optional[int]      ,						**lowerCamelCase_				:			Optional[int]						):

							'''simple docstring'''



							return self.tokenizer.decode(*lowerCamelCase_      ,						**lowerCamelCase_						)



					@contextmanager
					def lowerCamelCase_			(						self				:			Tuple						):

							'''simple docstring'''



							warnings.warn(
							    """`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your """
							    """labels by using the argument `text` of the regular `__call__` method (either in the same call as """
							    """your audio inputs, or in a separate call."""						)
							SCREAMING_SNAKE_CASE :				int		=			True
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			self.tokenizer
							yield
							SCREAMING_SNAKE_CASE :				int		=			self.feature_extractor
							SCREAMING_SNAKE_CASE :				Optional[Any]		=			False



