"""simple docstring"""





import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
_UpperCamelCase   :						Optional[int]      =    logging.get_logger(__name__)

_UpperCamelCase   :						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",
    "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",
    "mask_emb": "masked_spec_embed",
}
_UpperCamelCase   :						Optional[Any]      =    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 				a_     (      _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Any   , _lowerCAmelCase  : Dict   , _lowerCAmelCase  : int   , _lowerCAmelCase  : List[str]     ):





       '''simple docstring'''

       for attribute in key.split('.'     ):
              lowercase__			:		Any       =	getattr(_lowerCAmelCase   , _lowerCAmelCase     )

       if weight_type is not None:
              lowercase__			:		str       =	getattr(_lowerCAmelCase   , _lowerCAmelCase     ).shape
       else:
              lowercase__			:		Optional[int]       =	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":
              lowercase__			:		Optional[int]       =	value
       elif weight_type == "weight_g":
              lowercase__			:		int       =	value
       elif weight_type == "weight_v":
              lowercase__			:		Optional[Any]       =	value
       elif weight_type == "bias":
              lowercase__			:		int       =	value
       else:
              lowercase__			:		Dict       =	value

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





def 				a_     (      _lowerCAmelCase  : Any   , _lowerCAmelCase  : Union[str, Any]     ):





       '''simple docstring'''

       lowercase__			:		str       =	[]
       lowercase__			:		int       =	fairseq_model.state_dict()

       lowercase__			:		Union[str, Any]       =	hf_model.feature_extractor
       lowercase__			:		Dict       =	hf_model.adapter

       for name, value in fairseq_dict.items():
              lowercase__			:		int       =	False
              if "conv_layers" in name:
                     load_conv_layer(
                         _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , hf_model.config.feat_extract_norm == 'group'   , )
                     lowercase__			:		Tuple       =	True
              elif any(x in name for x in ['adaptor', 'w2v_encoder.proj.', 'w2v_proj_ln.']     ):
                     load_adapter(_lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase   , _lowerCAmelCase     )
                     lowercase__			:		Dict       =	True
              else:
                     for key, mapped_key in MAPPING.items():
                            if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
                                   lowercase__			:		Optional[Any]       =	True
                                   if "*" in mapped_key:
                                          lowercase__			:		str       =	name.split(_lowerCAmelCase     )[0].split('.'     )[-2]
                                          lowercase__			:		Tuple       =	mapped_key.replace('*'   , _lowerCAmelCase     )
                                   if "weight_g" in name:
                                          lowercase__			:		Tuple       =	'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__			:		Any       =	'weight_v'
                                   elif "bias" in name:
                                          lowercase__			:		Tuple       =	'bias'
                                   elif "weight" in name:
                                          lowercase__			:		List[str]       =	'weight'
                                   else:
                                          lowercase__			:		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_     (      _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Optional[Any]     ):





       '''simple docstring'''

       lowercase__			:		Union[str, Any]       =	full_name.split('conv_layers.'     )[-1]
       lowercase__			:		str       =	name.split('.'     )
       lowercase__			:		Union[str, Any]       =	int(items[0]     )
       lowercase__			:		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."""
                     )
                     lowercase__			:		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."""
                     )
                     lowercase__			:		Optional[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."
                     )
                     lowercase__			:		Dict       =	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."""
                     )
                     lowercase__			:		int       =	value
                     logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""     )
       else:
              unused_weights.append(_lowerCAmelCase     )





def 				a_     (      _lowerCAmelCase  : Union[str, Any]   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : List[Any]   , _lowerCAmelCase  : Optional[int]     ):





       '''simple docstring'''

       lowercase__			:		Tuple       =	full_name.split('adaptor.'     )[-1]
       lowercase__			:		Dict       =	name.split('.'     )

       if items[1].isdigit():
              lowercase__			:		Dict       =	int(items[1]     )
       else:
              lowercase__			:		Union[str, Any]       =	None

       if "adaptor" not in full_name:
              if "proj_ln" in full_name:
                     # has to be layer norm
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.bias.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
                            lowercase__			:		Any       =	value
                            logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""     )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj_layer_norm.weight.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
                            lowercase__			:		List[Any]       =	value
              else:
                     # has to be projection layer
                     if "bias" in name:
                            assert (
                                value.shape == adapter.proj.bias.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
                            lowercase__			:		Optional[Any]       =	value
                            logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""     )
                     if "weight" in name:
                            assert (
                                value.shape == adapter.proj.weight.data.shape
                            ), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
                            lowercase__			:		Union[str, Any]       =	value
                            logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""     )
       elif isinstance(_lowerCAmelCase   , _lowerCAmelCase     ):
              if "bias" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.bias.data.shape
                     ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
                     lowercase__			:		int       =	value
                     logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""     )
              elif "weight" in name:
                     assert (
                         value.shape == adapter.layers[layer_id].conv.weight.data.shape
                     ), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
                     lowercase__			:		Dict       =	value
                     logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""     )
       else:
              unused_weights.append(_lowerCAmelCase     )





def 				a_     (      _lowerCAmelCase  : Any     ):





       '''simple docstring'''

       lowercase__    ,      lowercase__			:		Union[str, Any]       =	emb.weight.shape
       lowercase__			:		str       =	nn.Linear(_lowerCAmelCase   , _lowerCAmelCase   , bias=_lowerCAmelCase     )
       lowercase__			:		Optional[int]       =	emb.weight.data
       return lin_layer





@torch.no_grad()
def 				a_     (      _lowerCAmelCase  : List[str]   , _lowerCAmelCase  : Tuple   , _lowerCAmelCase  : Optional[int]   , _lowerCAmelCase  : int   , _lowerCAmelCase  : Any   , _lowerCAmelCase  : int   , _lowerCAmelCase  : Dict   , _lowerCAmelCase  : int   , _lowerCAmelCase  : int   , _lowerCAmelCase  : Optional[Any]   , _lowerCAmelCase  : Optional[int]   , ):





       '''simple docstring'''

       lowercase__			:		Optional[int]       =	WavaVecaConfig.from_pretrained(
           _lowerCAmelCase   , add_adapter=_lowerCAmelCase   , adapter_stride=_lowerCAmelCase   , adapter_kernel_size=_lowerCAmelCase   , use_auth_token=_lowerCAmelCase   , output_hidden_size=_lowerCAmelCase   , )
       lowercase__			:		List[str]       =	MBartConfig.from_pretrained(_lowerCAmelCase     )

       # load model
       lowercase__    ,      lowercase__    ,      lowercase__			:		Dict       =	fairseq.checkpoint_utils.load_model_ensemble_and_task(
           [checkpoint_path]   , arg_overrides={
               'config_yaml': config_yaml_path,
               'data': '/'.join(dict_path.split('/'     )[:-1]     ),
               'w2v_path': checkpoint_path,
               'load_pretrained_decoder_from': None,
           }   , )
       lowercase__			:		List[str]       =	model[0].eval()

       # load feature extractor
       lowercase__			:		Tuple       =	WavaVecaFeatureExtractor.from_pretrained(_lowerCAmelCase   , use_auth_token=_lowerCAmelCase     )

       # set weights for wav2vec2 encoder
       lowercase__			:		int       =	WavaVecaModel(_lowerCAmelCase     )

       recursively_load_weights_wavaveca(model.encoder   , _lowerCAmelCase     )

       # load decoder weights
       lowercase__			:		List[Any]       =	MBartForCausalLM(_lowerCAmelCase     )
       lowercase__    ,      lowercase__			:		Union[str, Any]       =	hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()   , strict=_lowerCAmelCase     )
       logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""     )
       logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""     )

       lowercase__			:		Optional[int]       =	SpeechEncoderDecoderModel(encoder=_lowerCAmelCase   , decoder=_lowerCAmelCase     )
       lowercase__			:		Dict       =	False

       lowercase__			:		Any       =	MBartaaTokenizer(_lowerCAmelCase     )
       tokenizer.save_pretrained(_lowerCAmelCase     )

       lowercase__			:		str       =	hf_wavavec.config.to_dict()
       lowercase__			:		Dict       =	tokenizer.pad_token_id
       lowercase__			:		str       =	tokenizer.bos_token_id
       lowercase__			:		Tuple       =	tokenizer.eos_token_id
       lowercase__			:		Any       =	'mbart50'
       lowercase__			:		Union[str, Any]       =	'wav2vec2'

       lowercase__			:		Optional[int]       =	tokenizer.eos_token_id
       lowercase__			:		Union[str, Any]       =	25_0004
       lowercase__			:		int       =	tokenizer.eos_token_id

       lowercase__			:		str       =	SpeechEncoderDecoderConfig.from_dict(_lowerCAmelCase     )

       hf_wavavec.save_pretrained(_lowerCAmelCase     )
       feature_extractor.save_pretrained(_lowerCAmelCase     )


if __name__ == "__main__":
       _UpperCamelCase   :						Dict      =    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_yaml_path", default=None, type=str, help="Path to yaml file of fine-tuned model")
       parser.add_argument(
           "--encoder_config_path",
           default="facebook/wav2vec2-xls-r-1b",
           type=str,
           help="Path to hf encoder wav2vec2 checkpoint config",
       )
       parser.add_argument(
           "--decoder_config_path",
           default="facebook/mbart-large-50-one-to-many-mmt",
           type=str,
           help="Path to hf decoder checkpoint config",
       )
       parser.add_argument("--add_adapter", default=True, type=bool, help="whethere to add model adapter layers")
       parser.add_argument("--adapter_stride", default=2, type=int, help="stride of adapter layers")
       parser.add_argument("--adapter_kernel_size", default=3, type=int, help="kernel size of adapter layers")
       parser.add_argument("--encoder_output_dim", default=10_24, type=int, help="encoder output dim")
       parser.add_argument("--start_token_id", default=25_00_04, type=int, help="`decoder_start_token_id` of model config")

       _UpperCamelCase   :						Any      =    parser.parse_args()
       convert_wavaveca_checkpoint(
           args.checkpoint_path,
           args.pytorch_dump_folder_path,
           args.dict_path,
           args.config_yaml_path,
           encoder_config_path=args.encoder_config_path,
           decoder_config_path=args.decoder_config_path,
           add_adapter=args.add_adapter,
           adapter_kernel_size=args.adapter_kernel_size,
           adapter_stride=args.adapter_stride,
           decoder_start_token_id=args.start_token_id,
           encoder_output_dim=args.encoder_output_dim,
       )




import argparse
import os
from io import BytesIO
from pathlib import Path

import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    1.5
							snake_case_							=    int(factor * num_class_images      )
							snake_case_							=    ClipClient(
							    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1      )

							os.makedirs(F'''{class_data_dir}/images'''   ,							exist_ok=UpperCamelCase__      )
							if len(list(Path(F'''{class_data_dir}/images'''      ).iterdir()      )      ) >= num_class_images:
														return

							while True:
														snake_case_							=    client.query(text=UpperCamelCase__      )
														if len(UpperCamelCase__      ) >= factor * num_class_images or num_images > 1E4:
																					break
														else:
																					snake_case_							=    int(factor * num_images      )
																					snake_case_							=    ClipClient(
																					    url='https://knn.laion.ai/knn-service'   ,							indice_name='laion_400m'   ,							num_images=UpperCamelCase__   ,							aesthetic_weight=0.1   ,							)

							snake_case_							=    0
							snake_case_							=    0
							snake_case_							=    tqdm(desc='downloading real regularization images'   ,							total=UpperCamelCase__      )

							with open(F'''{class_data_dir}/caption.txt'''   ,							'w'      ) as fa, open(F'''{class_data_dir}/urls.txt'''   ,							'w'      ) as fa, open(
							    F'''{class_data_dir}/images.txt'''   ,							'w'      ) as fa:
														while total < num_class_images:
																					snake_case_							=    class_images[count]
																					count += 1
																					try:
																												snake_case_							=    requests.get(images['url']      )
																												if img.status_code == 200:
																																			snake_case_							=    Image.open(BytesIO(img.content      )      )
																																			with open(F'''{class_data_dir}/images/{total}.jpg'''   ,							'wb'      ) as f:
																																										f.write(img.content      )
																																			fa.write(images['caption'] + '\n'      )
																																			fa.write(images['url'] + '\n'      )
																																			fa.write(F'''{class_data_dir}/images/{total}.jpg''' + '\n'      )
																																			total += 1
																																			pbar.update(1      )
																												else:
																																			continue
																					except Exception:
																												continue
							return





def 					__lowerCamelCase	( ):







							'''simple docstring'''






							snake_case_							=    argparse.ArgumentParser(''   ,							add_help=UpperCamelCase__      )
							parser.add_argument('--class_prompt'   ,							help='text prompt to retrieve images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--class_data_dir'   ,							help='path to save images'   ,							required=UpperCamelCase__   ,							type=UpperCamelCase__      )
							parser.add_argument('--num_class_images'   ,							help='number of images to download'   ,							default=200   ,							type=UpperCamelCase__      )
							return parser.parse_args()


if __name__ == "__main__":
					_UpperCAmelCase		:   Optional[int]										=       parse_args()
					retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)




