

import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
      SpeechaTextaDecoder,
      SpeechaTextaForCausalLM,
  )






@require_torch
class 				A_					:


     def __init__(				self     :						Optional[int]     ,    UpperCAmelCase     :						Tuple     ,    UpperCAmelCase     :						Tuple=9_9     ,    UpperCAmelCase     :						List[Any]=1_3     ,    UpperCAmelCase     :						List[str]=1_6     ,    UpperCAmelCase     :						Optional[int]=7     ,    UpperCAmelCase     :						Tuple=True     ,    UpperCAmelCase     :						Tuple=True     ,    UpperCAmelCase     :						int=True     ,    UpperCAmelCase     :						Union[str, Any]=False     ,    UpperCAmelCase     :						List[str]=True     ,    UpperCAmelCase     :						Any=2     ,    UpperCAmelCase     :						Any=3_2     ,    UpperCAmelCase     :						Tuple=4     ,    UpperCAmelCase     :						Any=4     ,    UpperCAmelCase     :						List[Any]=3_0     ,    UpperCAmelCase     :						List[Any]=0     ,    UpperCAmelCase     :						List[Any]=1     ,    UpperCAmelCase     :						List[str]=2     ,    UpperCAmelCase     :						Union[str, Any]=None     ,    )				->      List[str]:
      __lowerCAmelCase:  int        = parent
      __lowerCAmelCase:  Dict        = batch_size
      __lowerCAmelCase:  Tuple        = decoder_seq_length
      # For common tests
      __lowerCAmelCase:  int        = self.decoder_seq_length
      __lowerCAmelCase:  Optional[Any]        = is_training
      __lowerCAmelCase:  Optional[Any]        = use_attention_mask
      __lowerCAmelCase:  Tuple        = use_labels

      __lowerCAmelCase:  Tuple        = vocab_size
      __lowerCAmelCase:  List[Any]        = d_model
      __lowerCAmelCase:  str        = d_model
      __lowerCAmelCase:  Tuple        = decoder_layers
      __lowerCAmelCase:  Optional[Any]        = decoder_layers
      __lowerCAmelCase:  int        = decoder_ffn_dim
      __lowerCAmelCase:  Union[str, Any]        = decoder_attention_heads
      __lowerCAmelCase:  List[Any]        = decoder_attention_heads
      __lowerCAmelCase:  str        = eos_token_id
      __lowerCAmelCase:  List[str]        = bos_token_id
      __lowerCAmelCase:  Union[str, Any]        = pad_token_id
      __lowerCAmelCase:  int        = decoder_start_token_id
      __lowerCAmelCase:  Optional[int]        = use_cache
      __lowerCAmelCase:  Union[str, Any]        = max_position_embeddings

      __lowerCAmelCase:  Optional[Any]        = None
      __lowerCAmelCase:  Dict        = decoder_seq_length
      __lowerCAmelCase:  Dict        = 2
      __lowerCAmelCase:  Dict        = 1


     def     UpperCAmelCase    (				self     :						Tuple   )				->      Any:
      __lowerCAmelCase:  Any        = ids_tensor([self.batch_size, self.decoder_seq_length]     ,    self.vocab_size   )

      __lowerCAmelCase:  Dict        = None
      if self.use_attention_mask:
       __lowerCAmelCase:  List[Any]        = ids_tensor([self.batch_size, self.decoder_seq_length]     ,    vocab_size=2   )

      __lowerCAmelCase:  Any        = None
      if self.use_labels:
       __lowerCAmelCase:  str        = ids_tensor([self.batch_size, self.decoder_seq_length]     ,    self.vocab_size   )

      __lowerCAmelCase:  Tuple        = SpeechaTextaConfig(
          vocab_size=self.vocab_size     ,    d_model=self.d_model     ,    decoder_layers=self.decoder_layers     ,    decoder_ffn_dim=self.decoder_ffn_dim     ,    decoder_attention_heads=self.decoder_attention_heads     ,    eos_token_id=self.eos_token_id     ,    bos_token_id=self.bos_token_id     ,    use_cache=self.use_cache     ,    pad_token_id=self.pad_token_id     ,    decoder_start_token_id=self.decoder_start_token_id     ,    max_position_embeddings=self.max_position_embeddings     ,    )

      return (
          config,
          input_ids,
          attention_mask,
          lm_labels,
      )


     def     UpperCAmelCase    (				self     :						str     ,    UpperCAmelCase     :						Any     ,    UpperCAmelCase     :						Dict     ,    UpperCAmelCase     :						Tuple     ,    UpperCAmelCase     :						str     ,    )				->      Dict:
      __lowerCAmelCase:  Optional[int]        = True
      __lowerCAmelCase:  Tuple        = SpeechaTextaDecoder(config=UpperCAmelCase   ).to(UpperCAmelCase   ).eval()
      __lowerCAmelCase:  Union[str, Any]        = input_ids[:2]

      input_ids[input_ids == 0] += 1
      # first forward pass
      __lowerCAmelCase:  List[str]        = model(UpperCAmelCase     ,    use_cache=UpperCAmelCase   )
      __lowerCAmelCase:  Tuple        = model(UpperCAmelCase   )
      __lowerCAmelCase:  Optional[Any]        = model(UpperCAmelCase     ,    use_cache=UpperCAmelCase   )

      self.parent.assertTrue(len(UpperCAmelCase   ) == len(UpperCAmelCase   )   )
      self.parent.assertTrue(len(UpperCAmelCase   ) == len(UpperCAmelCase   ) + 1   )

      __lowerCAmelCase:  Tuple        = outputs['past_key_values']

      # create hypothetical next token and extent to next_input_ids
      __lowerCAmelCase:  int        = ids_tensor((2, 1)     ,    config.vocab_size - 1   ) + 1

      # append to next input_ids and
      __lowerCAmelCase:  int        = torch.cat([input_ids, next_tokens]     ,    dim=-1   )

      print(UpperCAmelCase   )

      __lowerCAmelCase:  Optional[int]        = model(UpperCAmelCase   )['last_hidden_state']
      __lowerCAmelCase:  Dict        = model(UpperCAmelCase     ,    past_key_values=UpperCAmelCase   )['last_hidden_state']

      # select random slice
      __lowerCAmelCase:  Optional[int]        = ids_tensor((1,)     ,    output_from_past.shape[-1]   ).item()
      __lowerCAmelCase:  Union[str, Any]        = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
      __lowerCAmelCase:  Optional[Any]        = output_from_past[:, 0, random_slice_idx].detach()

      # test that outputs are equal for slice
      assert torch.allclose(UpperCAmelCase     ,    UpperCAmelCase     ,    atol=1E-3   )


     def     UpperCAmelCase    (				self     :						str   )				->      Optional[Any]:
      __lowerCAmelCase:  List[str]        = self.prepare_config_and_inputs()
      (
          (
          __lowerCAmelCase
      )      ,				(
          __lowerCAmelCase
      )      ,				(
          __lowerCAmelCase
      )      ,				(
          __lowerCAmelCase
      )      ,				
      ):  Tuple        = config_and_inputs

      __lowerCAmelCase:  Optional[Any]        = {
          'input_ids': input_ids,
          'attention_mask': attention_mask,
      }
      return config, inputs_dict







@require_torch
class 				A_					(						snake_case__				,      snake_case__				,      snake_case__				,      unittest.TestCase   ):
     _lowercase     :	Optional[Any]        =      (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
     _lowercase     :	Optional[Any]        =      (SpeechaTextaForCausalLM,) if is_torch_available() else ()
     _lowercase     :	Optional[Any]        =      {'text-generation': SpeechaTextaForCausalLM} if is_torch_available() else {}
     _lowercase     :	Any        =      True
     _lowercase     :	str        =      False


     def     UpperCAmelCase    (				self     :						Optional[int]     ,    )				->      Optional[Any]:
      __lowerCAmelCase:  Optional[Any]        = SpeechaTextaStandaloneDecoderModelTester(self     ,    is_training=UpperCAmelCase   )
      __lowerCAmelCase:  str        = ConfigTester(self     ,    config_class=UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Union[str, Any]   )				->      Any:
      pass


     def     UpperCAmelCase    (				self     :						int   )				->      Tuple:
      pass


     def     UpperCAmelCase    (				self     :						Any   )				->      Union[str, Any]:
      pass


     def     UpperCAmelCase    (				self     :						Optional[int]   )				->      Tuple:
      self.config_tester.run_common_tests()


     def     UpperCAmelCase    (				self     :						Any   )				->      Union[str, Any]:
      __lowerCAmelCase:  List[str]        = self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_decoder_model_past(*UpperCAmelCase   )


     def     UpperCAmelCase    (				self     :						Dict   )				->      str:
      return








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)





