

"""simple docstring"""


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__A						       =      {
    "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",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		for attribute in key.split("."			):
				SCREAMING_SNAKE_CASE:	str   				=      getattr(_lowerCAmelCase ,			_lowerCAmelCase			)

		if weight_type is not None:
				SCREAMING_SNAKE_CASE:	Optional[int]   				=      getattr(_lowerCAmelCase ,			_lowerCAmelCase			).shape
		else:
				SCREAMING_SNAKE_CASE:	str   				=      hf_pointer.shape

		assert hf_shape == value.shape, (
		    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:	Optional[int]   				=      value
		elif weight_type == "weight_g":
				SCREAMING_SNAKE_CASE:	List[Any]   				=      value
		elif weight_type == "weight_v":
				SCREAMING_SNAKE_CASE:	Union[str, Any]   				=      value
		elif weight_type == "bias":
				SCREAMING_SNAKE_CASE:	Optional[int]   				=      value
		else:
				SCREAMING_SNAKE_CASE:	str   				=      value

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		SCREAMING_SNAKE_CASE:	Dict   				=      []
		SCREAMING_SNAKE_CASE:	Optional[int]   				=      fairseq_model.state_dict()

		SCREAMING_SNAKE_CASE:	Optional[int]   				=      hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

		for name, value in fairseq_dict.items():
				SCREAMING_SNAKE_CASE:	Dict   				=      False
				if "conv_layers" in name:
						load_conv_layer(
						    _lowerCAmelCase ,			_lowerCAmelCase ,			_lowerCAmelCase ,			_lowerCAmelCase ,			hf_model.config.feat_extract_norm == "group" ,			)
						SCREAMING_SNAKE_CASE:	str   				=      True
				else:
						for key, mapped_key in MAPPING.items():
								SCREAMING_SNAKE_CASE:	Tuple   				=      """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key

								if key in name or (key.split("w2v_model."			)[-1] == name.split("."			)[0] and not is_finetuned):
										SCREAMING_SNAKE_CASE:	Optional[Any]   				=      True
										if "*" in mapped_key:
												SCREAMING_SNAKE_CASE:	Dict   				=      name.split(_lowerCAmelCase			)[0].split("."			)[-2]
												SCREAMING_SNAKE_CASE:	Any   				=      mapped_key.replace("*" ,			_lowerCAmelCase			)
										if "weight_g" in name:
												SCREAMING_SNAKE_CASE:	List[Any]   				=      """weight_g"""
										elif "weight_v" in name:
												SCREAMING_SNAKE_CASE:	Optional[Any]   				=      """weight_v"""
										elif "weight" in name:
												SCREAMING_SNAKE_CASE:	str   				=      """weight"""
										elif "bias" in name:
												SCREAMING_SNAKE_CASE:	List[str]   				=      """bias"""
										else:
												SCREAMING_SNAKE_CASE:	str   				=      None
										set_recursively(_lowerCAmelCase ,			_lowerCAmelCase ,			_lowerCAmelCase ,			_lowerCAmelCase ,			_lowerCAmelCase			)
								continue
				if not is_used:
						unused_weights.append(_lowerCAmelCase			)

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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[int]:
		SCREAMING_SNAKE_CASE:	Optional[int]   				=      full_name.split("conv_layers."			)[-1]
		SCREAMING_SNAKE_CASE:	List[str]   				=      name.split("."			)
		SCREAMING_SNAKE_CASE:	List[str]   				=      int(items[0]			)
		SCREAMING_SNAKE_CASE:	List[Any]   				=      int(items[1]			)

		if type_id == 0:
				if "bias" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
						    F"{full_name} has size {value.shape}, but"
						    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
						)
						SCREAMING_SNAKE_CASE:	Optional[int]   				=      value
						logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
						    F"{full_name} has size {value.shape}, but"
						    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
						)
						SCREAMING_SNAKE_CASE:	Dict   				=      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:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
						    F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
						    " found."
						)
						SCREAMING_SNAKE_CASE:	List[str]   				=      value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
				elif "weight" in name:
						assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
						    F"{full_name} has size {value.shape}, but"
						    F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
						)
						SCREAMING_SNAKE_CASE:	List[Any]   				=      value
						logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."			)
		else:
				unused_weights.append(_lowerCAmelCase			)






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=True			)     ->     List[Any]:
		if config_path is not None:
				SCREAMING_SNAKE_CASE:	Optional[Any]   				=      HubertConfig.from_pretrained(_lowerCAmelCase			)
		else:
				SCREAMING_SNAKE_CASE:	str   				=      HubertConfig()

		if is_finetuned:
				if dict_path:
						SCREAMING_SNAKE_CASE:	Optional[int]   				=      Dictionary.load(_lowerCAmelCase			)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						SCREAMING_SNAKE_CASE:	Optional[int]   				=      target_dict.pad_index
						SCREAMING_SNAKE_CASE:	int   				=      target_dict.bos_index
						SCREAMING_SNAKE_CASE:	Tuple   				=      target_dict.eos_index
						SCREAMING_SNAKE_CASE:	List[Any]   				=      len(target_dict.symbols			)
						SCREAMING_SNAKE_CASE:	int   				=      os.path.join(_lowerCAmelCase ,			"vocab.json"			)
						if not os.path.isdir(_lowerCAmelCase			):
								logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowerCAmelCase			)			)
								return
						os.makedirs(_lowerCAmelCase ,			exist_ok=_lowerCAmelCase			)
						with open(_lowerCAmelCase ,			"w" ,			encoding="utf-8"			) as vocab_handle:
								json.dump(target_dict.indices ,			_lowerCAmelCase			)
						SCREAMING_SNAKE_CASE:	List[Any]   				=      WavaVecaCTCTokenizer(
						    _lowerCAmelCase ,			unk_token=target_dict.unk_word ,			pad_token=target_dict.pad_word ,			bos_token=target_dict.bos_word ,			eos_token=target_dict.eos_word ,			word_delimiter_token="|" ,			do_lower_case=_lowerCAmelCase ,			)
						SCREAMING_SNAKE_CASE:	List[str]   				=      True if config.feat_extract_norm == """layer""" else False
						SCREAMING_SNAKE_CASE:	str   				=      WavaVecaFeatureExtractor(
						    feature_size=1 ,			sampling_rate=1_6_0_0_0 ,			padding_value=0 ,			do_normalize=_lowerCAmelCase ,			return_attention_mask=_lowerCAmelCase ,			)
						SCREAMING_SNAKE_CASE:	List[Any]   				=      WavaVecaProcessor(feature_extractor=_lowerCAmelCase ,			tokenizer=_lowerCAmelCase			)
						processor.save_pretrained(_lowerCAmelCase			)

				SCREAMING_SNAKE_CASE:	Optional[Any]   				=      HubertForCTC(_lowerCAmelCase			)
		else:
				SCREAMING_SNAKE_CASE:	List[str]   				=      HubertModel(_lowerCAmelCase			)

		if is_finetuned:
				SCREAMING_SNAKE_CASE:	Optional[Any]   				=      fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path] ,			arg_overrides={"data": "/".join(dict_path.split("/"			)[:-1]			)}			)
		else:
				SCREAMING_SNAKE_CASE:	Tuple   				=      fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			)

		SCREAMING_SNAKE_CASE:	List[Any]   				=      model[0].eval()

		recursively_load_weights(_lowerCAmelCase ,			_lowerCAmelCase ,			_lowerCAmelCase			)

		hf_wavavec.save_pretrained(_lowerCAmelCase			)


if __name__ == "__main__":
			__A						       =      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"
			)
			__A						       =      parser.parse_args()
			convert_hubert_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 collections import OrderedDict
from pathlib import Path

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

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		__lowerCAmelCase:	Dict   				=      original_name.split("."			)[0]
		__lowerCAmelCase:	Any   				=      key.split("."			)
		__lowerCAmelCase:	Union[str, Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 2]			)
		__lowerCAmelCase:	List[Any]   				=      int(key_list[key_list.index(__SCREAMING_SNAKE_CASE			) - 1]			)
		__lowerCAmelCase:	List[str]   				=      orig_block_num - offset

		__lowerCAmelCase:	Tuple   				=      key.replace(F"{orig_block_num}.{layer_num}.{original_name}" ,			F"block.{new_block_num}.{layer_num}.{new_name}"			)
		return key






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		__lowerCAmelCase:	List[Any]   				=      OrderedDict()
		__lowerCAmelCase				,			__lowerCAmelCase:	Optional[int]   				=      0, 0
		for key, value in state_dict.items():
				if key.startswith("network"			):
						__lowerCAmelCase:	Dict   				=      key.replace("network" ,			"poolformer.encoder"			)
				if "proj" in key:
						# Works for the first embedding as well as the internal embedding layers
						if key.endswith("bias"			) and "patch_embed" not in key:
								patch_emb_offset += 1
						__lowerCAmelCase:	int   				=      key[: key.find("proj"			)]
						__lowerCAmelCase:	Dict   				=      key.replace(__SCREAMING_SNAKE_CASE ,			F"patch_embeddings.{total_embed_found}."			)
						__lowerCAmelCase:	Optional[int]   				=      key.replace("proj" ,			"projection"			)
						if key.endswith("bias"			):
								total_embed_found += 1
				if "patch_embeddings" in key:
						__lowerCAmelCase:	int   				=      "poolformer.encoder." + key
				if "mlp.fc1" in key:
						__lowerCAmelCase:	Optional[Any]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc1" ,			"output.conv1"			)
				if "mlp.fc2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"mlp.fc2" ,			"output.conv2"			)
				if "norm1" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm1" ,			"before_norm"			)
				if "norm2" in key:
						__lowerCAmelCase:	Dict   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"norm2" ,			"after_norm"			)
				if "layer_scale_1" in key:
						__lowerCAmelCase:	Optional[int]   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_1" ,			"layer_scale_1"			)
				if "layer_scale_2" in key:
						__lowerCAmelCase:	Any   				=      replace_key_with_offset(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			"layer_scale_2" ,			"layer_scale_2"			)
				if "head" in key:
						__lowerCAmelCase:	int   				=      key.replace("head" ,			"classifier"			)
				__lowerCAmelCase:	Tuple   				=      value
		return new_state_dict






def    a__   (   )     ->     Tuple:
		__lowerCAmelCase:	Union[str, Any]   				=      "http://images.cocodataset.org/val2017/000000039769.jpg"
		__lowerCAmelCase:	int   				=      Image.open(requests.get(__SCREAMING_SNAKE_CASE ,			stream=__SCREAMING_SNAKE_CASE			).raw			)

		return image






@torch.no_grad()
def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		__lowerCAmelCase:	Any   				=      PoolFormerConfig()

		# set attributes based on model_name
		__lowerCAmelCase:	Any   				=      "huggingface/label-files"
		__lowerCAmelCase:	int   				=      model_name[-3:]
		__lowerCAmelCase:	List[Any]   				=      1_0_0_0
		__lowerCAmelCase:	Tuple   				=      "imagenet-1k-id2label.json"
		__lowerCAmelCase:	str   				=      (1, 1_0_0_0)

		# set config attributes
		__lowerCAmelCase:	Dict   				=      json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			repo_type="dataset"			) ,			"r"			)			)
		__lowerCAmelCase:	List[str]   				=      {int(__SCREAMING_SNAKE_CASE			): v for k, v in idalabel.items()}
		__lowerCAmelCase:	Any   				=      idalabel
		__lowerCAmelCase:	Any   				=      {v: k for k, v in idalabel.items()}
		if size == "s12":
				__lowerCAmelCase:	Dict   				=      [2, 2, 6, 2]
				__lowerCAmelCase:	str   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Optional[Any]   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      0.9
		elif size == "s24":
				__lowerCAmelCase:	Tuple   				=      [4, 4, 1_2, 4]
				__lowerCAmelCase:	List[str]   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	Tuple   				=      4.0
				__lowerCAmelCase:	Optional[int]   				=      0.9
		elif size == "s36":
				__lowerCAmelCase:	int   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	int   				=      [6_4, 1_2_8, 3_2_0, 5_1_2]
				__lowerCAmelCase:	List[str]   				=      4.0
				__lowerCAmelCase:	Dict   				=      1E-6
				__lowerCAmelCase:	List[Any]   				=      0.9
		elif size == "m36":
				__lowerCAmelCase:	Dict   				=      [6, 6, 1_8, 6]
				__lowerCAmelCase:	Dict   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	Union[str, Any]   				=      1E-6
				__lowerCAmelCase:	Union[str, Any]   				=      0.95
		elif size == "m48":
				__lowerCAmelCase:	str   				=      [8, 8, 2_4, 8]
				__lowerCAmelCase:	Optional[int]   				=      [9_6, 1_9_2, 3_8_4, 7_6_8]
				__lowerCAmelCase:	str   				=      4.0
				__lowerCAmelCase:	int   				=      1E-6
				__lowerCAmelCase:	str   				=      0.95
		else:
				raise ValueError(F"Size {size} not supported"			)

		# load image processor
		__lowerCAmelCase:	Union[str, Any]   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)

		# Prepare image
		__lowerCAmelCase:	int   				=      prepare_img()
		__lowerCAmelCase:	Tuple   				=      image_processor(images=__SCREAMING_SNAKE_CASE ,			return_tensors="pt"			).pixel_values

		logger.info(F"Converting model {model_name}..."			)

		# load original state dict
		__lowerCAmelCase:	Optional[int]   				=      torch.load(__SCREAMING_SNAKE_CASE ,			map_location=torch.device("cpu"			)			)

		# rename keys
		__lowerCAmelCase:	Any   				=      rename_keys(__SCREAMING_SNAKE_CASE			)

		# create HuggingFace model and load state dict
		__lowerCAmelCase:	str   				=      PoolFormerForImageClassification(__SCREAMING_SNAKE_CASE			)
		model.load_state_dict(__SCREAMING_SNAKE_CASE			)
		model.eval()

		# Define image processor
		__lowerCAmelCase:	Any   				=      PoolFormerImageProcessor(crop_pct=__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Any   				=      image_processor(images=prepare_img() ,			return_tensors="pt"			).pixel_values

		# forward pass
		__lowerCAmelCase:	int   				=      model(__SCREAMING_SNAKE_CASE			)
		__lowerCAmelCase:	Union[str, Any]   				=      outputs.logits

		# define expected logit slices for different models
		if size == "s12":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.3045, -0.6758, -0.4869]			)
		elif size == "s24":
				__lowerCAmelCase:	Optional[int]   				=      torch.tensor([0.4402, -0.1374, -0.8045]			)
		elif size == "s36":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([-0.6080, -0.5133, -0.5898]			)
		elif size == "m36":
				__lowerCAmelCase:	Union[str, Any]   				=      torch.tensor([0.3952, 0.2263, -1.2668]			)
		elif size == "m48":
				__lowerCAmelCase:	List[str]   				=      torch.tensor([0.1167, -0.0656, -0.3423]			)
		else:
				raise ValueError(F"Size {size} not supported"			)

		# verify logits
		assert logits.shape == expected_shape
		assert torch.allclose(logits[0, :3] ,			__SCREAMING_SNAKE_CASE ,			atol=1E-2			)

		# finally, save model and image processor
		logger.info(F"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."			)
		Path(__SCREAMING_SNAKE_CASE			).mkdir(exist_ok=__SCREAMING_SNAKE_CASE			)
		model.save_pretrained(__SCREAMING_SNAKE_CASE			)
		print(F"Saving image processor to {pytorch_dump_folder_path}"			)
		image_processor.save_pretrained(__SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
			__A						       =      argparse.ArgumentParser()

			parser.add_argument(
			    "--model_name",
			    default="poolformer_s12",
			    type=str,
			    help="Name of the model you'd like to convert.",
			)
			parser.add_argument(
			    "--checkpoint_path", default=None, type=str, help="Path to the original PyTorch checkpoint (.pth file)."
			)
			parser.add_argument(
			    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
			)
			__A						       =      parser.parse_args()
			convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)


