


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()
lowercase_	   =     logging.get_logger(__name__)

lowercase_	   =     {
    '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',
}
lowercase_	   =     [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):





 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
    __lowerCamelCase		:							Any		 =						"""lm_head"""

  __lowerCamelCase		:							int		 =						getattr(lowerCAmelCase__      ,	lowerCAmelCase__						)

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

 logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						[]
 __lowerCamelCase		:							List[str]		 =						fairseq_model.state_dict()

 __lowerCamelCase		:							Tuple		 =						hf_model.unispeech.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCamelCase		:							Optional[int]		 =						False
  if "conv_layers" in name:
   load_conv_layer(
       lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__      ,	hf_model.config.feat_extract_norm == 'group'      ,	)
   __lowerCamelCase		:							Optional[Any]		 =						True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCamelCase		:							Optional[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]:
     __lowerCamelCase		:							Optional[int]		 =						True
     if "*" in mapped_key:
      __lowerCamelCase		:							List[str]		 =						name.split(lowerCAmelCase__						)[0].split('.'						)[-2]
      __lowerCamelCase		:							List[Any]		 =						mapped_key.replace('*'      ,	lowerCAmelCase__						)
     if "weight_g" in name:
      __lowerCamelCase		:							int		 =						"""weight_g"""
     elif "weight_v" in name:
      __lowerCamelCase		:							Union[str, Any]		 =						"""weight_v"""
     elif "bias" in name:
      __lowerCamelCase		:							Optional[int]		 =						"""bias"""
     elif "weight" in name:
      # TODO: don't match quantizer.weight_proj
      __lowerCamelCase		:							List[str]		 =						"""weight"""
     else:
      __lowerCamelCase		:							int		 =						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__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							List[Any]		 =						full_name.split('conv_layers.'						)[-1]
 __lowerCamelCase		:							str		 =						name.split('.'						)
 __lowerCamelCase		:							Dict		 =						int(items[0]						)
 __lowerCamelCase		:							Tuple		 =						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.'
   )
   __lowerCamelCase		:							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.'
   )
   __lowerCamelCase		:							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."
   )
   __lowerCamelCase		:							Union[str, 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.'
   )
   __lowerCamelCase		:							Dict		 =						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__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=None      ,	SCREAMING_SNAKE_CASE__=True						):





 if config_path is not None:
  __lowerCamelCase		:							str		 =						UniSpeechConfig.from_pretrained(lowerCAmelCase__						)
 else:
  __lowerCamelCase		:							Any		 =						UniSpeechConfig()

 if is_finetuned:
  if dict_path:
   __lowerCamelCase		:							str		 =						Dictionary.load_from_json(lowerCAmelCase__						)

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

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

  __lowerCamelCase		:							Optional[Any]		 =						UniSpeechForCTC(lowerCAmelCase__						)
 else:
  __lowerCamelCase		:							Dict		 =						UniSpeechForPreTraining(lowerCAmelCase__						)

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

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

 recursively_load_weights(lowerCAmelCase__      ,	lowerCAmelCase__      ,	lowerCAmelCase__						)

 hf_unispeech.save_pretrained(lowerCAmelCase__						)


if __name__ == "__main__":
       lowercase_	   =     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'
       )
       lowercase_	   =     parser.parse_args()
       convert_unispeech_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


lowercase_	   =     {
    'configuration_convnext': ['CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvNextConfig', 'ConvNextOnnxConfig']
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     ['ConvNextFeatureExtractor']
       lowercase_	   =     ['ConvNextImageProcessor']

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST',
           'ConvNextForImageClassification',
           'ConvNextModel',
           'ConvNextPreTrainedModel',
           'ConvNextBackbone',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowercase_	   =     [
           'TFConvNextForImageClassification',
           'TFConvNextModel',
           'TFConvNextPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_convnext import CONVNEXT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvNextConfig, ConvNextOnnxConfig

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_convnext import ConvNextFeatureExtractor
              from .image_processing_convnext import ConvNextImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_convnext import (
                  CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ConvNextBackbone,
                  ConvNextForImageClassification,
                  ConvNextModel,
                  ConvNextPreTrainedModel,
              )

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_convnext import TFConvNextForImageClassification, TFConvNextModel, TFConvNextPreTrainedModel


else:
       import sys

       lowercase_	   =     _LazyModule(__name__, globals()['__file__'], _import_structure)

