
"""simple docstring"""




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowerCAmelCase__				=					{
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    'encoder.layer_norm': 'encoder.layer_norm',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    'w2v_model.layer_norm': 'feature_projection.layer_norm',
    'quantizer.weight_proj': 'quantizer.weight_proj',
    'quantizer.vars': 'quantizer.codevectors',
    'project_q': 'project_q',
    'final_proj': 'project_hid',
    'w2v_encoder.proj': 'lm_head',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
lowerCAmelCase__				=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a     ):
		for attribute in key.split('''.'''     ):
				SCREAMING_SNAKE_CASE_    			=			getattr(__a,				__a     )

		if weight_type is not None:
				SCREAMING_SNAKE_CASE_    			=			getattr(__a,				__a     ).shape
		else:
				SCREAMING_SNAKE_CASE_    			=			hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
				    F' {value.shape} for {full_name}'     )

		if weight_type == "weight":
				SCREAMING_SNAKE_CASE_    			=			value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE_    			=			value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE_    			=			value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE_    			=			value
		else:
				SCREAMING_SNAKE_CASE_    			=			value

		logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'     )



def 							_lowerCamelCase			(			__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			[]
		SCREAMING_SNAKE_CASE_    			=			fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE_    			=			hf_model.unispeech_sat.feature_extractor

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE_    			=			False
				if "conv_layers" in name:
						load_conv_layer(
						    __a,				__a,				__a,				__a,				hf_model.config.feat_extract_norm == '''group''',				)
						SCREAMING_SNAKE_CASE_    			=			True
				else:
						for key, mapped_key in MAPPING.items():
								SCREAMING_SNAKE_CASE_    			=			'''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
								if key in name or key.split('''w2v_model.'''     )[-1] == name.split('''.'''     )[0]:
										if "layer_norm_for_extract" in name and (".".join(name.split('''.'''     )[:-1]     ) != key):
												# special case since naming is very similar
												continue
										SCREAMING_SNAKE_CASE_    			=			True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE_    			=			name.split(__a     )[0].split('''.'''     )[-2]
												SCREAMING_SNAKE_CASE_    			=			mapped_key.replace('''*''',				__a     )
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE_    			=			'''weight_g'''
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE_    			=			'''weight_v'''
										elif "bias" in name:
												SCREAMING_SNAKE_CASE_    			=			'''bias'''
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												SCREAMING_SNAKE_CASE_    			=			'''weight'''
										else:
												SCREAMING_SNAKE_CASE_    			=			None
										set_recursively(__a,				__a,				__a,				__a,				__a     )
								continue
				if not is_used:
						unused_weights.append(__a     )

		logger.warning(F'Unused weights: {unused_weights}'     )



def 							_lowerCamelCase			(			__a,				__a,				__a,				__a,				__a     ):
		SCREAMING_SNAKE_CASE_    			=			full_name.split('''conv_layers.'''     )[-1]
		SCREAMING_SNAKE_CASE_    			=			name.split('''.'''     )
		SCREAMING_SNAKE_CASE_    			=			int(items[0]     )
		SCREAMING_SNAKE_CASE_    			=			int(items[1]     )

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'     )
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'     )
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'     )
		elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.'     )
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    F'{full_name} has size {value.shape}, but'
								    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'     )
						SCREAMING_SNAKE_CASE_    			=			value
						logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'     )
		else:
				unused_weights.append(__a     )



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=None,				__a=None,				__a=True     ):
		if config_path is not None:
				SCREAMING_SNAKE_CASE_    			=			UniSpeechSatConfig.from_pretrained(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			UniSpeechSatConfig()

		SCREAMING_SNAKE_CASE_    			=			''''''

		if is_finetuned:
				SCREAMING_SNAKE_CASE_    			=			UniSpeechSatForCTC(__a     )
		else:
				SCREAMING_SNAKE_CASE_    			=			UniSpeechSatForPreTraining(__a     )

		SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_			,SCREAMING_SNAKE_CASE_    			=			fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path],				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''     )[:-1]     )}     )
		SCREAMING_SNAKE_CASE_    			=			model[0].eval()

		recursively_load_weights(__a,				__a     )

		hf_wavavec.save_pretrained(__a     )


if __name__ == "__main__":
						lowerCAmelCase__				=					argparse.ArgumentParser()
						parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
						parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to fairseq checkpoint')
						parser.add_argument('--dict_path', default=None, type=str, help='Path to dict of fine-tuned model')
						parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
						parser.add_argument(
						    '--not_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
						)
						lowerCAmelCase__				=					parser.parse_args()
						convert_unispeech_sat_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)

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



def 							_lowerCamelCase			(			__a     ):
		SCREAMING_SNAKE_CASE_    			=			'''huggingface/label-files'''
		SCREAMING_SNAKE_CASE_    			=			'''imagenet-1k-id2label.json'''
		SCREAMING_SNAKE_CASE_    			=			json.load(open(hf_hub_download(__a,				__a,				repo_type='''dataset'''     ),				'''r'''     )     )
		SCREAMING_SNAKE_CASE_    			=			{int(__a     ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_    			=			{v: k for k, v in idalabel.items()}

		SCREAMING_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"
		SCREAMING_SNAKE_CASE_    			=			BitConfig(
		    conv_layer=__a,				num_labels=1_000,				idalabel=__a,				labelaid=__a,				)

		return config



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

		return name



def 							_lowerCamelCase			(			):
		SCREAMING_SNAKE_CASE_    			=			'''http://images.cocodataset.org/val2017/000000039769.jpg'''
		SCREAMING_SNAKE_CASE_    			=			Image.open(requests.get(__a,				stream=__a     ).raw     )
		return im



@torch.no_grad()
def 							_lowerCamelCase			(			__a,				__a,				__a=False     ):
		SCREAMING_SNAKE_CASE_    			=			get_config(__a     )

		# load original model from timm
		SCREAMING_SNAKE_CASE_    			=			create_model(__a,				pretrained=__a     )
		timm_model.eval()

		# load state_dict of original model
		SCREAMING_SNAKE_CASE_    			=			timm_model.state_dict()
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_    			=			state_dict.pop(__a     )
				SCREAMING_SNAKE_CASE_    			=			val.squeeze() if '''head''' in key else val

		# load HuggingFace model
		SCREAMING_SNAKE_CASE_    			=			BitForImageClassification(__a     )
		model.eval()
		model.load_state_dict(__a     )

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

		SCREAMING_SNAKE_CASE_    			=			{
		    '''bilinear''': PILImageResampling.BILINEAR,
		    '''bicubic''': PILImageResampling.BICUBIC,
		    '''nearest''': PILImageResampling.NEAREST,
		}

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

		SCREAMING_SNAKE_CASE_    			=			prepare_img()
		SCREAMING_SNAKE_CASE_    			=			transform(__a     ).unsqueeze(0     )
		SCREAMING_SNAKE_CASE_    			=			processor(__a,				return_tensors='''pt'''     ).pixel_values

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

		# verify logits
		with torch.no_grad():
				SCREAMING_SNAKE_CASE_    			=			model(__a     )
				SCREAMING_SNAKE_CASE_    			=			outputs.logits

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

		if pytorch_dump_folder_path is not None:
				Path(__a     ).mkdir(exist_ok=__a     )
				print(F'Saving model {model_name} and processor to {pytorch_dump_folder_path}'     )
				model.save_pretrained(__a     )
				processor.save_pretrained(__a     )

		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__":
						lowerCAmelCase__				=					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.',
						)

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