



'''simple docstring'''





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()
_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''',
    '''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''',
}
_lowerCAmelCase										=     [
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    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__			:  str											= """lm_head"""

        lowerCAmelCase__			:  Any											= getattr(UpperCamelCase							,   UpperCamelCase      )

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

    logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  Union[str, Any]											= []
    lowerCAmelCase__			:  Dict											= fairseq_model.state_dict()

    lowerCAmelCase__			:  Optional[Any]											= hf_model.unispeech.feature_extractor

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

    logger.warning(f"""Unused weights: {unused_weights}"""      )
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      ):



    """simple docstring"""





    lowerCAmelCase__			:  List[str]											= full_name.split("""conv_layers."""      )[-1]
    lowerCAmelCase__			:  Optional[int]											= name.split("""."""      )
    lowerCAmelCase__			:  int											= int(items[0]      )
    lowerCAmelCase__			:  Optional[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."""
            )
            lowerCAmelCase__			:  str											= 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__			:  Any											= 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__			:  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__			:  List[str]											= value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""      )
    else:
        unused_weights.append(UpperCamelCase      )
@torch.no_grad()
def        _SCREAMING_SNAKE_CASE  (	UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase=None							,   UpperCamelCase=None							,   UpperCamelCase=True      ):



    """simple docstring"""





    if config_path is not None:
        lowerCAmelCase__			:  List[str]											= UniSpeechConfig.from_pretrained(UpperCamelCase      )
    else:
        lowerCAmelCase__			:  str											= UniSpeechConfig()

    if is_finetuned:
        if dict_path:
            lowerCAmelCase__			:  Optional[int]											= Dictionary.load_from_json(UpperCamelCase      )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            lowerCAmelCase__			:  str											= target_dict.pad_index
            lowerCAmelCase__			:  int											= target_dict.bos_index
            lowerCAmelCase__			:  Optional[Any]											= target_dict.eos_index
            lowerCAmelCase__			:  Union[str, Any]											= len(target_dict.symbols      )
            lowerCAmelCase__			:  int											= os.path.join(UpperCamelCase							,   """vocab.json"""      )
            if not os.path.isdir(UpperCamelCase      ):
                logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(UpperCamelCase      )      )
                return
            os.makedirs(UpperCamelCase							,   exist_ok=UpperCamelCase      )
            lowerCAmelCase__			:  str											= target_dict.indices

            # fairseq has the <pad> and <s> switched
            lowerCAmelCase__			:  Tuple											= 42
            lowerCAmelCase__			:  int											= 43
            with open(UpperCamelCase							,   """w"""							,   encoding="""utf-8"""      ) as vocab_handle:
                json.dump(UpperCamelCase							,   UpperCamelCase      )
            lowerCAmelCase__			:  List[str]											= WavaVecaPhonemeCTCTokenizer(
                UpperCamelCase							,   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=UpperCamelCase							,   )
            lowerCAmelCase__			:  Optional[Any]											= True if config.feat_extract_norm == """layer""" else False
            lowerCAmelCase__			:  Dict											= WavaVecaFeatureExtractor(
                feature_size=1							,   sampling_rate=16000							,   padding_value=0							,   do_normalize=UpperCamelCase							,   return_attention_mask=UpperCamelCase							,   )
            lowerCAmelCase__			:  int											= WavaVecaProcessor(feature_extractor=UpperCamelCase							,   tokenizer=UpperCamelCase      )
            processor.save_pretrained(UpperCamelCase      )

        lowerCAmelCase__			:  Tuple											= UniSpeechForCTC(UpperCamelCase      )
    else:
        lowerCAmelCase__			:  List[str]											= UniSpeechForPreTraining(UpperCamelCase      )

    if is_finetuned:
        lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  int											= fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]							,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""      )[:-1]      ), """w2v_path""": checkpoint_path}      )
    else:
        lowerCAmelCase__				,       lowerCAmelCase__				,       lowerCAmelCase__			:  Optional[Any]											= fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]      )

    lowerCAmelCase__			:  Tuple											= model[0].eval()

    recursively_load_weights(UpperCamelCase							,   UpperCamelCase							,   UpperCamelCase      )

    hf_unispeech.save_pretrained(UpperCamelCase      )


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'''
 )
 _lowerCAmelCase										=     parser.parse_args()
 convert_unispeech_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
 )







'''simple docstring'''





import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()






class 	lowerCAmelCase_(    SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_						,			unittest.TestCase					):
  '''simple docstring'''




  __lowercase							:				Optional[Any]           =				VQModel
  __lowercase							:				Tuple           =				'''sample'''




  @property
  def 							UpperCAmelCase_     (   self  ,__UpperCAmelCase=(32, 32)				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= 4
      lowerCAmelCase__			:  List[str]											= 3

      lowerCAmelCase__			:  Dict											= floats_tensor((batch_size, num_channels) + sizes				).to(__UpperCAmelCase				)

      return {"sample": image}




  @property
  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      return (3, 32, 32)




  @property
  def 							UpperCAmelCase_     (   self				)   ->		Optional[Any]:
      return (3, 32, 32)




  def 							UpperCAmelCase_     (   self				)   ->		List[Any]:
      lowerCAmelCase__			:  Dict											= {
          """block_out_channels""": [32, 64],
          """in_channels""": 3,
          """out_channels""": 3,
          """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
          """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
          """latent_channels""": 3,
      }
      lowerCAmelCase__			:  Any											= self.dummy_input
      return init_dict, inputs_dict




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




  def 							UpperCAmelCase_     (   self				)   ->		Optional[int]:
      pass




  def 							UpperCAmelCase_     (   self				)   ->		Tuple:
      lowerCAmelCase__				,       lowerCAmelCase__			:  Union[str, Any]											= VQModel.from_pretrained("""fusing/vqgan-dummy"""  ,output_loading_info=__UpperCAmelCase				)
      self.assertIsNotNone(__UpperCAmelCase				)
      self.assertEqual(len(loading_info["""missing_keys"""]				)  ,0				)

      model.to(__UpperCAmelCase				)
      lowerCAmelCase__			:  List[Any]											= model(**self.dummy_input				)

      assert image is not None, "Make sure output is not None"






  def 							UpperCAmelCase_     (   self				)   ->		List[str]:
      lowerCAmelCase__			:  Tuple											= VQModel.from_pretrained("""fusing/vqgan-dummy"""				)
      model.to(__UpperCAmelCase				).eval()

      torch.manual_seed(0				)
      if torch.cuda.is_available():
          torch.cuda.manual_seed_all(0				)

      lowerCAmelCase__			:  List[str]											= torch.randn(1  ,model.config.in_channels  ,model.config.sample_size  ,model.config.sample_size				)
      lowerCAmelCase__			:  Optional[Any]											= image.to(__UpperCAmelCase				)
      with torch.no_grad():
          lowerCAmelCase__			:  Optional[Any]											= model(__UpperCAmelCase				).sample

      lowerCAmelCase__			:  Tuple											= output[0, -1, -3:, -3:].flatten().cpu()
      # fmt: off
      lowerCAmelCase__			:  Tuple											= torch.tensor([-0.0_1_5_3, -0.4_0_4_4, -0.1_8_8_0, -0.5_1_6_1, -0.2_4_1_8, -0.4_0_7_2, -0.1_6_1_2, -0.0_6_3_3, -0.0_1_4_3]				)
      # fmt: on
      self.assertTrue(torch.allclose(__UpperCAmelCase  ,__UpperCAmelCase  ,atol=1E-3				)				)


