import argparse
import os

import torch

from transformers import FlavaConfig, FlavaForPreTraining
from transformers.models.flava.convert_dalle_to_flava_codebook import convert_dalle_checkpoint






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       return sum(param.float().sum() if 'encoder.embeddings' not in key else 0 for key, param in state_dict.items()     )






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = {}

       for key, value in state_dict.items():
              if "text_encoder.embeddings" in key or "image_encoder.embeddings" in key:
                     continue

              lowerCAmelCase__					     = key.replace('heads.cmd.mim_head.cls.predictions'     ,  'mmm_image_head'     )
              lowerCAmelCase__					     = key.replace('heads.cmd.mlm_head.cls.predictions'     ,  'mmm_text_head'     )
              lowerCAmelCase__					     = key.replace('heads.cmd.itm_head.cls'     ,  'itm_head'     )
              lowerCAmelCase__					     = key.replace('heads.cmd.itm_head.pooler'     ,  'itm_head.pooler'     )
              lowerCAmelCase__					     = key.replace('heads.cmd.clip_head.logit_scale'     ,  'flava.logit_scale'     )
              lowerCAmelCase__					     = key.replace('heads.fairseq_mlm.cls.predictions'     ,  'mlm_head'     )
              lowerCAmelCase__					     = key.replace('heads.imagenet.mim_head.cls.predictions'     ,  'mim_head'     )
              lowerCAmelCase__					     = key.replace('mm_text_projection'     ,  'flava.text_to_mm_projection'     )
              lowerCAmelCase__					     = key.replace('mm_image_projection'     ,  'flava.image_to_mm_projection'     )
              lowerCAmelCase__					     = key.replace('image_encoder.module'     ,  'flava.image_model'     )
              lowerCAmelCase__					     = key.replace('text_encoder.module'     ,  'flava.text_model'     )
              lowerCAmelCase__					     = key.replace('mm_encoder.module.encoder.cls_token'     ,  'flava.multimodal_model.cls_token'     )
              lowerCAmelCase__					     = key.replace('mm_encoder.module'     ,  'flava.multimodal_model'     )
              lowerCAmelCase__					     = key.replace('text_projection'     ,  'flava.text_projection'     )
              lowerCAmelCase__					     = key.replace('image_projection'     ,  'flava.image_projection'     )

              lowerCAmelCase__					     = value.float()

       for key, value in codebook_state_dict.items():
              lowerCAmelCase__					     = value

       return upgrade






@torch.no_grad()
def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ):






       if config_path is not None:
              lowerCAmelCase__					     = FlavaConfig.from_pretrained(SCREAMING_SNAKE_CASE_     )
       else:
              lowerCAmelCase__					     = FlavaConfig()

       lowerCAmelCase__					     = FlavaForPreTraining(SCREAMING_SNAKE_CASE_     ).eval()

       lowerCAmelCase__					     = convert_dalle_checkpoint(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  save_checkpoint=SCREAMING_SNAKE_CASE_     )

       if os.path.exists(SCREAMING_SNAKE_CASE_     ):
              lowerCAmelCase__					     = torch.load(SCREAMING_SNAKE_CASE_     ,  map_location='cpu'     )
       else:
              lowerCAmelCase__					     = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_     ,  map_location='cpu'     )

       lowerCAmelCase__					     = upgrade_state_dict(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     )
       hf_model.load_state_dict(SCREAMING_SNAKE_CASE_     )
       lowerCAmelCase__					     = hf_model.state_dict()
       lowerCAmelCase__					     = count_parameters(SCREAMING_SNAKE_CASE_     )
       lowerCAmelCase__					     = count_parameters(SCREAMING_SNAKE_CASE_     ) + count_parameters(SCREAMING_SNAKE_CASE_     )

       assert torch.allclose(SCREAMING_SNAKE_CASE_     ,  SCREAMING_SNAKE_CASE_     ,  atol=1e-3     )

       hf_model.save_pretrained(SCREAMING_SNAKE_CASE_     )


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 flava checkpoint')
    parser.add_argument('--codebook_path', default=None, type=str, help='Path to flava codebook checkpoint')
    parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
    lowerCAmelCase__     						=			parser.parse_args()

    convert_flava_checkpoint(args.checkpoint_path, args.codebook_path, args.pytorch_dump_folder_path, args.config_path)

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
lowerCAmelCase__     						=			[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]






def 				__lowerCamelCase		(						lowerCAmelCase__     ):
       lowerCAmelCase__					     = {}
       with open(lowerCAmelCase__     ,  'r'     ) as file:
              for line_number, line in enumerate(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = line.strip()
                     if line:
                            lowerCAmelCase__					     = line.split()
                            lowerCAmelCase__					     = line_number
                            lowerCAmelCase__					     = words[0]
                            lowerCAmelCase__					     = value
       return result






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       for attribute in key.split('.'     ):
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )

       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     ).shape
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = hf_pointer
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = shape_pointer.shape

              # let's reduce dimension
              lowerCAmelCase__					     = value[0]
       else:
              lowerCAmelCase__					     = 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":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_g":
              lowerCAmelCase__					     = value
       elif weight_type == "weight_v":
              lowerCAmelCase__					     = value
       elif weight_type == "bias":
              lowerCAmelCase__					     = value
       elif weight_type == "param":
              for attribute in hf_param_name.split('.'     ):
                     lowerCAmelCase__					     = getattr(lowerCAmelCase__     ,  lowerCAmelCase__     )
              lowerCAmelCase__					     = value
       else:
              lowerCAmelCase__					     = value

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = None
       for param_key in PARAM_MAPPING.keys():
              if full_name.endswith(lowerCAmelCase__     ):
                     lowerCAmelCase__					     = PARAM_MAPPING[full_name.split('.'     )[-1]]
                     lowerCAmelCase__					     = 'param'

       if weight_type is not None and weight_type != "param":
              lowerCAmelCase__					     = '.'.join([key, weight_type]     )
       elif weight_type is not None and weight_type == "param":
              lowerCAmelCase__					     = '.'.join([key, hf_param_name]     )
       else:
              lowerCAmelCase__					     = key

       lowerCAmelCase__					     = value if 'lm_head' in full_key else value[0]


lowerCAmelCase__     						=			{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ):
       lowerCAmelCase__					     = False
       for key, mapped_key in MAPPING.items():
              lowerCAmelCase__					     = 'wav2vec2.' + 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]:
                     lowerCAmelCase__					     = True
                     if "*" in mapped_key:
                            lowerCAmelCase__					     = name.split(lowerCAmelCase__     )[0].split('.'     )[-2]
                            lowerCAmelCase__					     = mapped_key.replace('*'     ,  lowerCAmelCase__     )
                     if "weight_g" in name:
                            lowerCAmelCase__					     = 'weight_g'
                     elif "weight_v" in name:
                            lowerCAmelCase__					     = 'weight_v'
                     elif "bias" in name:
                            lowerCAmelCase__					     = 'bias'
                     elif "weight" in name:
                            # TODO: don't match quantizer.weight_proj
                            lowerCAmelCase__					     = 'weight'
                     else:
                            lowerCAmelCase__					     = None
                     if hf_dict is not None:
                            rename_dict(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     else:
                            set_recursively(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
                     return is_used
       return is_used






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = []
       lowerCAmelCase__					     = fairseq_model.state_dict()

       lowerCAmelCase__					     = hf_model.wavaveca.feature_extractor

       for name, value in fairseq_dict.items():
              lowerCAmelCase__					     = False
              if "conv_layers" in name:
                     load_conv_layer(
                         lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  hf_model.config.feat_extract_norm == 'group'     ,  )
                     lowerCAmelCase__					     = True
              else:
                     lowerCAmelCase__					     = load_wavaveca_layer(lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     )
              if not is_used:
                     unused_weights.append(lowerCAmelCase__     )

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






def 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__     ):
       lowerCAmelCase__					     = full_name.split('conv_layers.'     )[-1]
       lowerCAmelCase__					     = name.split('.'     )
       lowerCAmelCase__					     = int(items[0]     )
       lowerCAmelCase__					     = 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."""     )
                     lowerCAmelCase__					     = 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."""     )
                     lowerCAmelCase__					     = 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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."""     )
                     lowerCAmelCase__					     = 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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."""     )
                     lowerCAmelCase__					     = 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 				__lowerCamelCase		(						lowerCAmelCase__     ,  lowerCAmelCase__     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=None     ,  lowerCAmelCase__=True     ,  lowerCAmelCase__=False     ):
       if config_path is not None:
              lowerCAmelCase__					     = WavaVecaConfig.from_pretrained(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaConfig()

       if is_seq_class:
              lowerCAmelCase__					     = read_txt_into_dict(lowerCAmelCase__     )
              lowerCAmelCase__					     = idalabel
              lowerCAmelCase__					     = WavaVecaForSequenceClassification(lowerCAmelCase__     )
              lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                  feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
              feature_extractor.save_pretrained(lowerCAmelCase__     )

       elif is_finetuned:
              if dict_path:
                     lowerCAmelCase__					     = Dictionary.load(lowerCAmelCase__     )

                     # important change bos & pad token id since CTC symbol is <pad> and
                     # not <s> as in fairseq
                     lowerCAmelCase__					     = target_dict.pad_index
                     lowerCAmelCase__					     = target_dict.bos_index
                     lowerCAmelCase__					     = target_dict.eos_index
                     lowerCAmelCase__					     = len(target_dict.symbols     )
                     lowerCAmelCase__					     = 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__     )
                     lowerCAmelCase__					     = target_dict.indices

                     # fairseq has the <pad> and <s> switched
                     lowerCAmelCase__					     = 0
                     lowerCAmelCase__					     = 1
                     with open(lowerCAmelCase__     ,  'w'     ,  encoding='utf-8'     ) as vocab_handle:
                            json.dump(lowerCAmelCase__     ,  lowerCAmelCase__     )
                     lowerCAmelCase__					     = 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__     ,  )
                     lowerCAmelCase__					     = True if config.feat_extract_norm == 'layer' else False
                     lowerCAmelCase__					     = WavaVecaFeatureExtractor(
                         feature_size=1     ,  sampling_rate=1_6_0_0_0     ,  padding_value=0     ,  do_normalize=lowerCAmelCase__     ,  return_attention_mask=lowerCAmelCase__     ,  )
                     lowerCAmelCase__					     = WavaVecaProcessor(feature_extractor=lowerCAmelCase__     ,  tokenizer=lowerCAmelCase__     )
                     processor.save_pretrained(lowerCAmelCase__     )

              lowerCAmelCase__					     = WavaVecaForCTC(lowerCAmelCase__     )
       else:
              lowerCAmelCase__					     = WavaVecaForPreTraining(lowerCAmelCase__     )

       if is_finetuned or is_seq_class:
              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task(
                  [checkpoint_path]     ,  arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
       else:
              lowerCAmelCase__					     = argparse.Namespace(task='audio_pretraining'     )
              lowerCAmelCase__					     = fairseq.tasks.setup_task(lowerCAmelCase__     )

              lowerCAmelCase__     , lowerCAmelCase__     , lowerCAmelCase__					     = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]     ,  task=lowerCAmelCase__     )

       lowerCAmelCase__					     = model[0].eval()

       recursively_load_weights(lowerCAmelCase__     ,  lowerCAmelCase__     ,  not is_finetuned     )

       hf_wavavec.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'
    )
    parser.add_argument(
        '--is_seq_class',
        action='store_true',
        help='Whether the model to convert is a fine-tuned sequence classification model or not',
    )
    lowerCAmelCase__     						=			parser.parse_args()

    lowerCAmelCase__     						=			not args.not_finetuned and not args.is_seq_class
    convert_wavaveca_checkpoint(
        args.checkpoint_path,
        args.pytorch_dump_folder_path,
        args.config_path,
        args.dict_path,
        is_finetuned,
        args.is_seq_class,
    )
