






'''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 transformers import DeiTConfig, DeiTForImageClassificationWithTeacher, DeiTImageProcessor
from transformers.utils import logging


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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_=False)							->     Tuple:
		UpperCamelCase__				:					Optional[int]					=					[]
		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'deit.encoder.layer.{i}.layernorm_before.weight'))
				rename_keys.append((f'blocks.{i}.norm1.bias', f'deit.encoder.layer.{i}.layernorm_before.bias'))
				rename_keys.append((f'blocks.{i}.attn.proj.weight', f'deit.encoder.layer.{i}.attention.output.dense.weight'))
				rename_keys.append((f'blocks.{i}.attn.proj.bias', f'deit.encoder.layer.{i}.attention.output.dense.bias'))
				rename_keys.append((f'blocks.{i}.norm2.weight', f'deit.encoder.layer.{i}.layernorm_after.weight'))
				rename_keys.append((f'blocks.{i}.norm2.bias', f'deit.encoder.layer.{i}.layernorm_after.bias'))
				rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'deit.encoder.layer.{i}.intermediate.dense.weight'))
				rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'deit.encoder.layer.{i}.intermediate.dense.bias'))
				rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'deit.encoder.layer.{i}.output.dense.weight'))
				rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'deit.encoder.layer.{i}.output.dense.bias'))

		# projection layer + position embeddings
		rename_keys.extend(
		    [
		        ('cls_token', 'deit.embeddings.cls_token'),
		        ('dist_token', 'deit.embeddings.distillation_token'),
		        ('patch_embed.proj.weight', 'deit.embeddings.patch_embeddings.projection.weight'),
		        ('patch_embed.proj.bias', 'deit.embeddings.patch_embeddings.projection.bias'),
		        ('pos_embed', 'deit.embeddings.position_embeddings'),
		    ])

		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 "deit" from all keys that start with "deit"
				UpperCamelCase__				:					List[Any]					=					[(pair[0], pair[1][4:]) if pair[1].startswith('deit') else pair for pair in rename_keys]
		else:
				# layernorm + classification heads
				rename_keys.extend(
				    [
				        ('norm.weight', 'deit.layernorm.weight'),
				        ('norm.bias', 'deit.layernorm.bias'),
				        ('head.weight', 'cls_classifier.weight'),
				        ('head.bias', 'cls_classifier.bias'),
				        ('head_dist.weight', 'distillation_classifier.weight'),
				        ('head_dist.bias', 'distillation_classifier.bias'),
				    ])

		return rename_keys







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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     int:
		UpperCamelCase__				:					Union[str, Any]					=					dct.pop(lowerCamelCase_)
		UpperCamelCase__				:					Optional[Any]					=					val







def       __UpperCAmelCase						(  )							->     str:
		UpperCamelCase__				:					Union[str, Any]					=					'http://images.cocodataset.org/val2017/000000039769.jpg'
		UpperCamelCase__				:					List[str]					=					Image.open(requests.get(lowerCamelCase_     ,      stream=lowerCamelCase_).raw)
		return im







@torch.no_grad()
def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					Optional[int]					=					DeiTConfig()
		# all deit models have fine-tuned heads
		UpperCamelCase__				:					Tuple					=					False
		# dataset (fine-tuned on ImageNet 2012), patch_size and image_size
		UpperCamelCase__				:					Tuple					=					1_000
		UpperCamelCase__				:					List[Any]					=					'huggingface/label-files'
		UpperCamelCase__				:					Any					=					'imagenet-1k-id2label.json'
		UpperCamelCase__				:					int					=					json.load(open(hf_hub_download(lowerCamelCase_     ,      lowerCamelCase_     ,      repo_type='dataset')     ,      'r'))
		UpperCamelCase__				:					str					=					{int(lowerCamelCase_): v for k, v in idalabel.items()}
		UpperCamelCase__				:					Tuple					=					idalabel
		UpperCamelCase__				:					int					=					{v: k for k, v in idalabel.items()}
		UpperCamelCase__				:					int					=					int(deit_name[-6:-4])
		UpperCamelCase__				:					int					=					int(deit_name[-3:])
		# size of the architecture
		if deit_name[9:].startswith('tiny'):
				UpperCamelCase__				:					Union[str, Any]					=					192
				UpperCamelCase__				:					int					=					768
				UpperCamelCase__				:					List[str]					=					12
				UpperCamelCase__				:					Union[str, Any]					=					3
		elif deit_name[9:].startswith('small'):
				UpperCamelCase__				:					str					=					384
				UpperCamelCase__				:					Union[str, Any]					=					1_536
				UpperCamelCase__				:					List[str]					=					12
				UpperCamelCase__				:					List[Any]					=					6
		if deit_name[9:].startswith('base'):
				pass
		elif deit_name[4:].startswith('large'):
				UpperCamelCase__				:					List[Any]					=					1_024
				UpperCamelCase__				:					Union[str, Any]					=					4_096
				UpperCamelCase__				:					List[Any]					=					24
				UpperCamelCase__				:					str					=					16

		# load original model from timm
		UpperCamelCase__				:					Optional[Any]					=					timm.create_model(lowerCamelCase_     ,      pretrained=lowerCamelCase_)
		timm_model.eval()

		# load state_dict of original model, remove and rename some keys
		UpperCamelCase__				:					List[Any]					=					timm_model.state_dict()
		UpperCamelCase__				:					List[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_)

		# load HuggingFace model
		UpperCamelCase__				:					List[str]					=					DeiTForImageClassificationWithTeacher(lowerCamelCase_).eval()
		model.load_state_dict(lowerCamelCase_)

		# Check outputs on an image, prepared by DeiTImageProcessor
		UpperCamelCase__				:					Dict					=					int(
		    (256 / 224) * config.image_size)  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
		UpperCamelCase__				:					int					=					DeiTImageProcessor(size=lowerCamelCase_     ,      crop_size=config.image_size)
		UpperCamelCase__				:					List[Any]					=					image_processor(images=prepare_img()     ,      return_tensors='pt')
		UpperCamelCase__				:					Optional[Any]					=					encoding['pixel_values']
		UpperCamelCase__				:					str					=					model(lowerCamelCase_)

		UpperCamelCase__				:					List[str]					=					timm_model(lowerCamelCase_)
		assert timm_logits.shape == outputs.logits.shape
		assert torch.allclose(lowerCamelCase_     ,      outputs.logits     ,      atol=1e-3)

		Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
		print(f'Saving model {deit_name} to {pytorch_dump_folder_path}')
		model.save_pretrained(lowerCamelCase_)
		print(f'Saving image processor to {pytorch_dump_folder_path}')
		image_processor.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
		lowerCAmelCase__          =						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--deit_name',
		    default='vit_deit_base_distilled_patch16_224',
		    type=str,
		    help='Name of the DeiT 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.'
		)

		lowerCAmelCase__          =						parser.parse_args()
		convert_deit_checkpoint(args.deit_name, args.pytorch_dump_folder_path)












'''simple docstring'''




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    UniSpeechConfig,
    UniSpeechForCTC,
    UniSpeechForPreTraining,
    WavaVecaFeatureExtractor,
    WavaVecaPhonemeCTCTokenizer,
    WavaVecaProcessor,
    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',
    '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': 'ctc_proj',
    'mask_emb': 'masked_spec_embed',
}
lowerCAmelCase__          =						[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     str:
		for attribute in key.split('.'):
				if is_finetuned:
						if attribute in ["quantizer", "project_q", "project_hid"]:
								# those layers are only relevant for pretraining and should be dropped
								return

						if attribute == "ctc_proj":
								# we should rename `ctc_proj` to `lm_head` for fine-tuned phoneme models
								UpperCamelCase__				:					str					=					'lm_head'

				UpperCamelCase__				:					Optional[Any]					=					getattr(lowerCamelCase_     ,      lowerCamelCase_)

		if weight_type is not None:
				UpperCamelCase__				:					List[Any]					=					getattr(lowerCamelCase_     ,      lowerCamelCase_).shape
		else:
				UpperCamelCase__				:					List[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":
				UpperCamelCase__				:					Optional[Any]					=					value
		elif weight_type == "weight_g":
				UpperCamelCase__				:					Union[str, Any]					=					value
		elif weight_type == "weight_v":
				UpperCamelCase__				:					List[Any]					=					value
		elif weight_type == "bias":
				UpperCamelCase__				:					Any					=					value
		else:
				UpperCamelCase__				:					Optional[int]					=					value

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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     List[Any]:
		UpperCamelCase__				:					List[Any]					=					[]
		UpperCamelCase__				:					int					=					fairseq_model.state_dict()

		UpperCamelCase__				:					int					=					hf_model.unispeech.feature_extractor

		for name, value in fairseq_dict.items():
				UpperCamelCase__				:					Union[str, Any]					=					False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      hf_model.config.feat_extract_norm == 'group'     ,      )
						UpperCamelCase__				:					List[Any]					=					True
				else:
						for key, mapped_key in MAPPING.items():
								UpperCamelCase__				:					List[Any]					=					'unispeech.' + 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]:
										UpperCamelCase__				:					Any					=					True
										if "*" in mapped_key:
												UpperCamelCase__				:					Any					=					name.split(lowerCamelCase_)[0].split('.')[-2]
												UpperCamelCase__				:					Union[str, Any]					=					mapped_key.replace('*'     ,      lowerCamelCase_)
										if "weight_g" in name:
												UpperCamelCase__				:					int					=					'weight_g'
										elif "weight_v" in name:
												UpperCamelCase__				:					Any					=					'weight_v'
										elif "bias" in name:
												UpperCamelCase__				:					Union[str, Any]					=					'bias'
										elif "weight" in name:
												# TODO: don't match quantizer.weight_proj
												UpperCamelCase__				:					Any					=					'weight'
										else:
												UpperCamelCase__				:					Tuple					=					None
										set_recursively(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)
								continue
				if not is_used:
						unused_weights.append(lowerCamelCase_)

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







def       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)							->     Tuple:
		UpperCamelCase__				:					Dict					=					full_name.split('conv_layers.')[-1]
		UpperCamelCase__				:					List[Any]					=					name.split('.')
		UpperCamelCase__				:					Any					=					int(items[0])
		UpperCamelCase__				:					int					=					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.'
						)
						UpperCamelCase__				:					Tuple					=					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.'
						)
						UpperCamelCase__				:					int					=					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."
						)
						UpperCamelCase__				:					Optional[Any]					=					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.'
						)
						UpperCamelCase__				:					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       __UpperCAmelCase						(  lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_=None     ,      lowerCamelCase_=None     ,      lowerCamelCase_=True)							->     Tuple:
		if config_path is not None:
				UpperCamelCase__				:					Optional[Any]					=					UniSpeechConfig.from_pretrained(lowerCamelCase_)
		else:
				UpperCamelCase__				:					int					=					UniSpeechConfig()

		if is_finetuned:
				if dict_path:
						UpperCamelCase__				:					Union[str, Any]					=					Dictionary.load_from_json(lowerCamelCase_)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						UpperCamelCase__				:					List[Any]					=					target_dict.pad_index
						UpperCamelCase__				:					Dict					=					target_dict.bos_index
						UpperCamelCase__				:					Union[str, Any]					=					target_dict.eos_index
						UpperCamelCase__				:					Tuple					=					len(target_dict.symbols)
						UpperCamelCase__				:					Dict					=					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_)
						UpperCamelCase__				:					Optional[int]					=					target_dict.indices

						# fairseq has the <pad> and <s> switched
						UpperCamelCase__				:					Any					=					42
						UpperCamelCase__				:					List[str]					=					43
						with open(lowerCamelCase_     ,      'w'     ,      encoding='utf-8') as vocab_handle:
								json.dump(lowerCamelCase_     ,      lowerCamelCase_)
						UpperCamelCase__				:					Optional[int]					=					WavaVecaPhonemeCTCTokenizer(
						    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_     ,      )
						UpperCamelCase__				:					Optional[Any]					=					True if config.feat_extract_norm == 'layer' else False
						UpperCamelCase__				:					Union[str, Any]					=					WavaVecaFeatureExtractor(
						    feature_size=1     ,      sampling_rate=16_000     ,      padding_value=0     ,      do_normalize=lowerCamelCase_     ,      return_attention_mask=lowerCamelCase_     ,      )
						UpperCamelCase__				:					Tuple					=					WavaVecaProcessor(feature_extractor=lowerCamelCase_     ,      tokenizer=lowerCamelCase_)
						processor.save_pretrained(lowerCamelCase_)

				UpperCamelCase__				:					Dict					=					UniSpeechForCTC(lowerCamelCase_)
		else:
				UpperCamelCase__				:					List[Any]					=					UniSpeechForPreTraining(lowerCamelCase_)

		if is_finetuned:
				UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					int					=					fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]     ,      arg_overrides={'data': '/'.join(dict_path.split('/')[:-1]), 'w2v_path': checkpoint_path})
		else:
				UpperCamelCase__,	UpperCamelCase__,	UpperCamelCase__				:					str					=					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path])

		UpperCamelCase__				:					int					=					model[0].eval()

		recursively_load_weights(lowerCamelCase_     ,      lowerCamelCase_     ,      lowerCamelCase_)

		hf_unispeech.save_pretrained(lowerCamelCase_)


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_checkpoint(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
		)




