






"""simple docstring"""







import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__a       :	Any             = logging.get_logger(__name__)

__a       :	str             = {
    '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',
}
__a       :	Optional[int]             = [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					for attribute in key.split('''.'''):
										a__          =  getattr(lowerCamelCase_ ,     lowerCamelCase_)

					if weight_type is not None:
										a__          =  getattr(lowerCamelCase_ ,     lowerCamelCase_).shape
					else:
										a__          =  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":
										a__          =  value
					elif weight_type == "weight_g":
										a__          =  value
					elif weight_type == "weight_v":
										a__          =  value
					elif weight_type == "bias":
										a__          =  value
					else:
										a__          =  value

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





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  []
					a__          =  fairseq_model.state_dict()

					a__          =  hf_model.unispeech_sat.feature_extractor

					for name, value in fairseq_dict.items():
										a__          =  False
										if "conv_layers" in name:
															load_conv_layer(
															    lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     hf_model.config.feat_extract_norm == '''group''' ,     )
															a__          =  True
										else:
															for key, mapped_key in MAPPING.items():
																				a__          =  '''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
																									a__          =  True
																									if "*" in mapped_key:
																														a__          =  name.split(lowerCamelCase_)[0].split('''.''')[-2]
																														a__          =  mapped_key.replace('''*''' ,     lowerCamelCase_)
																									if "weight_g" in name:
																														a__          =  '''weight_g'''
																									elif "weight_v" in name:
																														a__          =  '''weight_v'''
																									elif "bias" in name:
																														a__          =  '''bias'''
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														a__          =  '''weight'''
																									else:
																														a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  full_name.split('''conv_layers.''')[-1]
					a__          =  name.split('''.''')
					a__          =  int(items[0])
					a__          =  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.')
															a__          =  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.')
															a__          =  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.')
															a__          =  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.')
															a__          =  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        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=None ,     lowerCamelCase_=None ,     lowerCamelCase_=True):
					if config_path is not None:
										a__          =  UniSpeechSatConfig.from_pretrained(lowerCamelCase_)
					else:
										a__          =  UniSpeechSatConfig()

					a__          =  ''''''

					if is_finetuned:
										a__          =  UniSpeechSatForCTC(lowerCamelCase_)
					else:
										a__          =  UniSpeechSatForPreTraining(lowerCamelCase_)

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

					recursively_load_weights(lowerCamelCase_ ,     lowerCamelCase_)

					hf_wavavec.save_pretrained(lowerCamelCase_)


if __name__ == "__main__":
	__a       :	int             = 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       :	Any             = 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 transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__a       :	int             = logging.get_logger(__name__)





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					a__          =  '''backbone.''' if is_semantic else ''''''

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

					# projection layer + position embeddings
					rename_keys.extend(
					    [
					        (f'{prefix}cls_token', '''beit.embeddings.cls_token'''),
					        (f'{prefix}patch_embed.proj.weight', '''beit.embeddings.patch_embeddings.projection.weight'''),
					        (f'{prefix}patch_embed.proj.bias', '''beit.embeddings.patch_embeddings.projection.bias'''),
					        (f'{prefix}pos_embed', '''beit.embeddings.position_embeddings'''),
					    ])

					if has_lm_head:
										# mask token + layernorm
										rename_keys.extend(
										    [
										        ('''mask_token''', '''beit.embeddings.mask_token'''),
										        ('''norm.weight''', '''layernorm.weight'''),
										        ('''norm.bias''', '''layernorm.bias'''),
										    ])
					else:
										# layernorm + classification head
										rename_keys.extend(
										    [
										        ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
										        ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
										        ('''head.weight''', '''classifier.weight'''),
										        ('''head.bias''', '''classifier.bias'''),
										    ])

					return rename_keys





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False ,     lowerCamelCase_=False):
					for i in range(config.num_hidden_layers):
										a__          =  '''backbone.''' if is_semantic else ''''''
										# queries, keys and values
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.attn.qkv.weight')
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.attn.q_bias')
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.attn.v_bias')

										a__          =  in_proj_weight[
										    : config.hidden_size, :
										]
										a__          =  q_bias
										a__          =  in_proj_weight[
										    config.hidden_size : config.hidden_size * 2, :
										]
										a__          =  in_proj_weight[
										    -config.hidden_size :, :
										]
										a__          =  v_bias

										# gamma_1 and gamma_2
										# we call them lambda because otherwise they are renamed when using .from_pretrained
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.gamma_1')
										a__          =  state_dict.pop(f'{prefix}blocks.{i}.gamma_2')

										a__          =  gamma_a
										a__          =  gamma_a





def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_):
					a__          =  dct.pop(lowerCamelCase_)
					a__          =  val





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





@torch.no_grad()
def        SCREAMING_SNAKE_CASE       (      lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_=False):
					a__          =  False if '''rvlcdip''' in checkpoint_url else True
					a__          =  BeitConfig(use_absolute_position_embeddings=lowerCamelCase_ ,     use_mask_token=lowerCamelCase_)

					# size of the architecture
					if "large" in checkpoint_url or "dit-l" in checkpoint_url:
										a__          =  1024
										a__          =  4096
										a__          =  24
										a__          =  16

					# labels
					if "rvlcdip" in checkpoint_url:
										a__          =  16
										a__          =  '''huggingface/label-files'''
										a__          =  '''rvlcdip-id2label.json'''
										a__          =  json.load(open(hf_hub_download(lowerCamelCase_ ,     lowerCamelCase_ ,     repo_type='''dataset''') ,     '''r'''))
										a__          =  {int(lowerCamelCase_): v for k, v in idalabel.items()}
										a__          =  idalabel
										a__          =  {v: k for k, v in idalabel.items()}

					# load state_dict of original model, remove and rename some keys
					a__          =  torch.hub.load_state_dict_from_url(lowerCamelCase_ ,     map_location='''cpu''')['''model''']

					a__          =  create_rename_keys(lowerCamelCase_ ,     has_lm_head=lowerCamelCase_)
					for src, dest in rename_keys:
										rename_key(lowerCamelCase_ ,     lowerCamelCase_ ,     lowerCamelCase_)
					read_in_q_k_v(lowerCamelCase_ ,     lowerCamelCase_ ,     has_lm_head=lowerCamelCase_)

					# load HuggingFace model
					a__          =  BeitForMaskedImageModeling(lowerCamelCase_) if has_lm_head else BeitForImageClassification(lowerCamelCase_)
					model.eval()
					model.load_state_dict(lowerCamelCase_)

					# Check outputs on an image
					a__          =  BeitImageProcessor(
					    size=config.image_size ,     resample=PILImageResampling.BILINEAR ,     do_center_crop=lowerCamelCase_)
					a__          =  prepare_img()

					a__          =  image_processor(images=lowerCamelCase_ ,     return_tensors='''pt''')
					a__          =  encoding['''pixel_values''']

					a__          =  model(lowerCamelCase_)
					a__          =  outputs.logits

					# verify logits
					a__          =  [1, 16] if '''rvlcdip''' in checkpoint_url else [1, 196, 8192]
					assert logits.shape == torch.Size(lowerCamelCase_), "Shape of logits not as expected"

					Path(lowerCamelCase_).mkdir(exist_ok=lowerCamelCase_)
					print(f'Saving model 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 push_to_hub:
										if has_lm_head:
															a__          =  '''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
										else:
															a__          =  '''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
										image_processor.push_to_hub(
										    repo_path_or_name=Path(lowerCamelCase_ ,     lowerCamelCase_) ,     organization='''nielsr''' ,     commit_message='''Add image processor''' ,     use_temp_dir=lowerCamelCase_ ,     )
										model.push_to_hub(
										    repo_path_or_name=Path(lowerCamelCase_ ,     lowerCamelCase_) ,     organization='''nielsr''' ,     commit_message='''Add model''' ,     use_temp_dir=lowerCamelCase_ ,     )


if __name__ == "__main__":
	__a       :	Dict             = argparse.ArgumentParser()

	parser.add_argument(
	    '--checkpoint_url',
	    default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
	    type=str,
	    help='URL 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.'
	)
	parser.add_argument(
	    '--push_to_hub',
	    action='store_true',
	)
	__a       :	List[Any]             = parser.parse_args()
	convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)


