

import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast






@require_vision
class 				A_					(						unittest.TestCase   ):


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Tuple:
      __lowerCAmelCase:  int        = tempfile.mkdtemp()

      __lowerCAmelCase:  List[Any]        = CLIPImageProcessor()
      __lowerCAmelCase:  Any        = BertTokenizer.from_pretrained(
          'hf-internal-testing/tiny-random-BertModel'     ,    model_input_names=['input_ids', 'attention_mask']   )

      __lowerCAmelCase:  List[Any]        = GitProcessor(UpperCAmelCase     ,    UpperCAmelCase   )

      processor.save_pretrained(self.tmpdirname   )


     def     UpperCAmelCase    (				self     :						Optional[Any]     ,    **UpperCAmelCase     :						List[Any]   )				->      List[str]:
      return AutoProcessor.from_pretrained(self.tmpdirname     ,    **UpperCAmelCase   ).tokenizer


     def     UpperCAmelCase    (				self     :						List[str]     ,    **UpperCAmelCase     :						Union[str, Any]   )				->      Optional[int]:
      return AutoProcessor.from_pretrained(self.tmpdirname     ,    **UpperCAmelCase   ).image_processor


     def     UpperCAmelCase    (				self     :						str   )				->      Union[str, Any]:
      shutil.rmtree(self.tmpdirname   )


     def     UpperCAmelCase    (				self     :						Any   )				->      str:
      __lowerCAmelCase:  Optional[int]        = [np.random.randint(2_5_5     ,    size=(3, 3_0, 4_0_0)     ,    dtype=np.uinta   )]

      __lowerCAmelCase:  List[str]        = [Image.fromarray(np.moveaxis(UpperCAmelCase     ,    0     ,    -1   )   ) for x in image_inputs]

      return image_inputs


     def     UpperCAmelCase    (				self     :						Optional[int]   )				->      List[Any]:
      __lowerCAmelCase:  str        = GitProcessor(tokenizer=self.get_tokenizer()     ,    image_processor=self.get_image_processor()   )
      processor.save_pretrained(self.tmpdirname   )

      __lowerCAmelCase:  Any        = self.get_tokenizer(bos_token='(BOS)'     ,    eos_token='(EOS)'   )
      __lowerCAmelCase:  Optional[Any]        = self.get_image_processor(do_normalize=UpperCAmelCase     ,    padding_value=1.0   )

      __lowerCAmelCase:  List[Any]        = GitProcessor.from_pretrained(
          self.tmpdirname     ,    bos_token='(BOS)'     ,    eos_token='(EOS)'     ,    do_normalize=UpperCAmelCase     ,    padding_value=1.0   )

      self.assertEqual(processor.tokenizer.get_vocab()     ,    tokenizer_add_kwargs.get_vocab()   )
      self.assertIsInstance(processor.tokenizer     ,    UpperCAmelCase   )

      self.assertEqual(processor.image_processor.to_json_string()     ,    image_processor_add_kwargs.to_json_string()   )
      self.assertIsInstance(processor.image_processor     ,    UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Any   )				->      List[Any]:
      __lowerCAmelCase:  List[str]        = self.get_image_processor()
      __lowerCAmelCase:  Any        = self.get_tokenizer()

      __lowerCAmelCase:  List[str]        = GitProcessor(tokenizer=UpperCAmelCase     ,    image_processor=UpperCAmelCase   )

      __lowerCAmelCase:  List[str]        = self.prepare_image_inputs()

      __lowerCAmelCase:  Optional[int]        = image_processor(UpperCAmelCase     ,    return_tensors='np'   )
      __lowerCAmelCase:  Any        = processor(images=UpperCAmelCase     ,    return_tensors='np'   )

      for key in input_feat_extract.keys():
       self.assertAlmostEqual(input_feat_extract[key].sum()     ,    input_processor[key].sum()     ,    delta=1E-2   )


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      Optional[int]:
      __lowerCAmelCase:  Optional[int]        = self.get_image_processor()
      __lowerCAmelCase:  Any        = self.get_tokenizer()

      __lowerCAmelCase:  Dict        = GitProcessor(tokenizer=UpperCAmelCase     ,    image_processor=UpperCAmelCase   )

      __lowerCAmelCase:  int        = 'lower newer'

      __lowerCAmelCase:  Union[str, Any]        = processor(text=UpperCAmelCase   )

      __lowerCAmelCase:  List[Any]        = tokenizer(UpperCAmelCase     ,    return_token_type_ids=UpperCAmelCase   )

      for key in encoded_tok.keys():
       self.assertListEqual(encoded_tok[key]     ,    encoded_processor[key]   )


     def     UpperCAmelCase    (				self     :						Optional[Any]   )				->      Optional[Any]:
      __lowerCAmelCase:  List[Any]        = self.get_image_processor()
      __lowerCAmelCase:  Optional[int]        = self.get_tokenizer()

      __lowerCAmelCase:  Optional[int]        = GitProcessor(tokenizer=UpperCAmelCase     ,    image_processor=UpperCAmelCase   )

      __lowerCAmelCase:  Union[str, Any]        = 'lower newer'
      __lowerCAmelCase:  List[Any]        = self.prepare_image_inputs()

      __lowerCAmelCase:  Tuple        = processor(text=UpperCAmelCase     ,    images=UpperCAmelCase   )

      self.assertListEqual(list(inputs.keys()   )     ,    ['input_ids', 'attention_mask', 'pixel_values']   )

      # test if it raises when no input is passed
      with pytest.raises(UpperCAmelCase   ):
       processor()


     def     UpperCAmelCase    (				self     :						Any   )				->      Optional[int]:
      __lowerCAmelCase:  Any        = self.get_image_processor()
      __lowerCAmelCase:  List[str]        = self.get_tokenizer()

      __lowerCAmelCase:  str        = GitProcessor(tokenizer=UpperCAmelCase     ,    image_processor=UpperCAmelCase   )

      __lowerCAmelCase:  int        = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      __lowerCAmelCase:  Optional[int]        = processor.batch_decode(UpperCAmelCase   )
      __lowerCAmelCase:  Optional[int]        = tokenizer.batch_decode(UpperCAmelCase   )

      self.assertListEqual(UpperCAmelCase     ,    UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Tuple:
      __lowerCAmelCase:  Optional[int]        = self.get_image_processor()
      __lowerCAmelCase:  List[str]        = self.get_tokenizer()

      __lowerCAmelCase:  Any        = GitProcessor(tokenizer=UpperCAmelCase     ,    image_processor=UpperCAmelCase   )

      __lowerCAmelCase:  Dict        = 'lower newer'
      __lowerCAmelCase:  int        = self.prepare_image_inputs()

      __lowerCAmelCase:  List[str]        = processor(text=UpperCAmelCase     ,    images=UpperCAmelCase   )

      # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
      self.assertListEqual(list(inputs.keys()   )     ,    ['input_ids', 'attention_mask', 'pixel_values']   )








import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

_a									=  {
    '''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''',
    '''mask_emb''': '''masked_spec_embed''',
}







def _a					(			SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     List[str]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]						)       ->       str:

 """simple docstring"""






 for attribute in key.split('.'						):
  __lowerCAmelCase:  Optional[int]        = getattr(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

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

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







def _a					(			SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Any						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  Union[str, Any]        = []
 __lowerCAmelCase:  Optional[int]        = fairseq_model.state_dict()

 __lowerCAmelCase:  int        = hf_model.feature_extractor

 for name, value in fairseq_dict.items():
  __lowerCAmelCase:  List[str]        = False
  if "conv_layers" in name:
   load_conv_layer(
       SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   hf_model.config.feat_extract_norm == 'group'       ,   )
   __lowerCAmelCase:  List[str]        = True
  else:
   for key, mapped_key in MAPPING.items():
    __lowerCAmelCase:  List[Any]        = mapped_key

    if key in name:
     __lowerCAmelCase:  List[Any]        = True
     if "*" in mapped_key:
      __lowerCAmelCase:  Any        = name.split(SCREAMING_SNAKE_CASE						)[0].split('.'						)[-2]
      __lowerCAmelCase:  Dict        = mapped_key.replace('*'       ,   SCREAMING_SNAKE_CASE						)
     if "weight_g" in name:
      __lowerCAmelCase:  str        = 'weight_g'
     elif "weight_v" in name:
      __lowerCAmelCase:  Tuple        = 'weight_v'
     elif "weight" in name:
      __lowerCAmelCase:  Tuple        = 'weight'
     elif "bias" in name:
      __lowerCAmelCase:  Optional[Any]        = 'bias'
     else:
      __lowerCAmelCase:  Optional[Any]        = None
     set_recursively(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)
    continue
  if not is_used:
   unused_weights.append(SCREAMING_SNAKE_CASE						)

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







def _a					(			SCREAMING_SNAKE_CASE						:     Union[str, Any]       ,   SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]       ,   SCREAMING_SNAKE_CASE						:     Tuple       ,   SCREAMING_SNAKE_CASE						:     List[str]						)       ->       Optional[int]:

 """simple docstring"""






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







def _a					(			SCREAMING_SNAKE_CASE						:     str						)       ->       str:

 """simple docstring"""






 __lowerCAmelCase:  str        = HubertConfig()
 __lowerCAmelCase:  Any        = model.config

 __lowerCAmelCase:  str        = fs_config.activation_dropout
 __lowerCAmelCase:  Optional[Any]        = False
 __lowerCAmelCase:  Tuple        = fs_config.attention_dropout
 __lowerCAmelCase:  Tuple        = False
 __lowerCAmelCase:  Tuple        = eval(fs_config.extractor_conv_feature_layers						)
 __lowerCAmelCase:  Optional[int]        = [x[0] for x in conv_layers]
 __lowerCAmelCase:  Dict        = [x[1] for x in conv_layers]
 __lowerCAmelCase:  List[str]        = [x[2] for x in conv_layers]
 __lowerCAmelCase:  Dict        = 'gelu'
 __lowerCAmelCase:  List[str]        = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
 __lowerCAmelCase:  int        = False
 __lowerCAmelCase:  Dict        = 0.0
 __lowerCAmelCase:  str        = 0.0
 __lowerCAmelCase:  List[Any]        = fs_config.activation_fn
 __lowerCAmelCase:  str        = fs_config.dropout
 __lowerCAmelCase:  List[str]        = fs_config.encoder_embed_dim
 __lowerCAmelCase:  str        = 0.0_2
 __lowerCAmelCase:  List[Any]        = fs_config.encoder_ffn_embed_dim
 __lowerCAmelCase:  List[str]        = 1E-5
 __lowerCAmelCase:  Optional[Any]        = 0.0
 __lowerCAmelCase:  int        = fs_config.encoder_attention_heads
 __lowerCAmelCase:  Any        = fs_config.conv_pos_groups
 __lowerCAmelCase:  str        = fs_config.conv_pos
 __lowerCAmelCase:  Optional[int]        = len(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Dict        = fs_config.encoder_layers

 return config







@torch.no_grad()
def _a					(			SCREAMING_SNAKE_CASE						:     Optional[int]       ,   SCREAMING_SNAKE_CASE						:     List[Any]=None						)       ->       Optional[int]:

 """simple docstring"""






 __lowerCAmelCase:  List[str]        = distilhubert().model.model

 if config_path is not None:
  __lowerCAmelCase:  Dict        = HubertConfig.from_pretrained(SCREAMING_SNAKE_CASE						)
 else:
  __lowerCAmelCase:  Union[str, Any]        = convert_config(SCREAMING_SNAKE_CASE						)
 __lowerCAmelCase:  Any        = model.eval()

 __lowerCAmelCase:  Tuple        = WavaVecaFeatureExtractor(
     feature_size=1       ,   sampling_rate=1_60_00       ,   padding_value=0       ,   do_normalize=SCREAMING_SNAKE_CASE       ,   return_attention_mask=SCREAMING_SNAKE_CASE       ,   )
 __lowerCAmelCase:  Tuple        = HubertModel(SCREAMING_SNAKE_CASE						)

 recursively_load_weights(SCREAMING_SNAKE_CASE       ,   SCREAMING_SNAKE_CASE						)

 feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE						)
 hf_model.save_pretrained(SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
  _a									=  argparse.ArgumentParser()
  parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
  _a									=  parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





