






"""simple docstring"""






import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
UpperCAmelCase_     :				List[str]         =				logging.get_logger(__name__)

UpperCAmelCase_     :				List[Any]         =				{
    """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""",
    """encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
    """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""",
    """label_embs_concat""": """label_embeddings_concat""",
    """mask_emb""": """masked_spec_embed""",
    """spk_proj""": """speaker_proj""",
}
UpperCAmelCase_     :				Any         =				[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
    """label_embeddings_concat""",
    """speaker_proj""",
    """layer_norm_for_extract""",
]



def       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> str:
    """simple docstring"""






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

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

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



def       _A    (__a					,       __a							)				-> List[str]:
    """simple docstring"""






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

    SCREAMING_SNAKE_CASE_   :						str							=	hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        SCREAMING_SNAKE_CASE_   :						Any							=	False
        if "conv_layers" in name:
            load_conv_layer(
                __a					,       __a					,       __a					,       __a					,       hf_model.config.feat_extract_norm == '''group'''					,       )
            SCREAMING_SNAKE_CASE_   :						List[Any]							=	True
        else:
            for key, mapped_key in MAPPING.items():
                SCREAMING_SNAKE_CASE_   :						List[str]							=	'''unispeech_sat.''' + 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]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''							)[:-1]							) != key):
                        # special case since naming is very similar
                        continue
                    SCREAMING_SNAKE_CASE_   :						int							=	True
                    if "*" in mapped_key:
                        SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	name.split(__a							)[0].split('''.'''							)[-2]
                        SCREAMING_SNAKE_CASE_   :						Optional[int]							=	mapped_key.replace('''*'''					,       __a							)
                    if "weight_g" in name:
                        SCREAMING_SNAKE_CASE_   :						Any							=	'''weight_g'''
                    elif "weight_v" in name:
                        SCREAMING_SNAKE_CASE_   :						Any							=	'''weight_v'''
                    elif "bias" in name:
                        SCREAMING_SNAKE_CASE_   :						List[str]							=	'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        SCREAMING_SNAKE_CASE_   :						str							=	'''weight'''
                    else:
                        SCREAMING_SNAKE_CASE_   :						List[Any]							=	None
                    set_recursively(__a					,       __a					,       __a					,       __a					,       __a							)
                continue
        if not is_used:
            unused_weights.append(__a							)

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



def       _A    (__a					,       __a					,       __a					,       __a					,       __a							)				-> int:
    """simple docstring"""






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



@torch.no_grad()
def       _A    (__a					,       __a					,       __a=None					,       __a=None					,       __a=True							)				-> Dict:
    """simple docstring"""






    if config_path is not None:
        SCREAMING_SNAKE_CASE_   :						int							=	UniSpeechSatConfig.from_pretrained(__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	UniSpeechSatConfig()

    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	''''''

    if is_finetuned:
        SCREAMING_SNAKE_CASE_   :						List[str]							=	UniSpeechSatForCTC(__a							)
    else:
        SCREAMING_SNAKE_CASE_   :						int							=	UniSpeechSatForPreTraining(__a							)

    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_   :						Optional[int]							=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]					,       arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
    SCREAMING_SNAKE_CASE_   :						Tuple							=	model[0].eval()

    recursively_load_weights(__a					,       __a							)

    hf_wavavec.save_pretrained(__a							)


if __name__ == "__main__":
  UpperCAmelCase_     :				Any         =				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"""
  )
  UpperCAmelCase_     :				List[Any]         =				parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )










"""simple docstring"""






import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device


enable_full_determinism()




class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      @property
      def _SCREAMING_SNAKE_CASE				(		self  :						Tuple):




          '''simple docstring'''


          torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	UNetaDModel(
              block_out_channels=(32, 64)		,			layers_per_block=2		,			sample_size=32		,			in_channels=3		,			out_channels=3		,			down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''')		,			up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''')		,			)
          return model



      def _SCREAMING_SNAKE_CASE				(		self  :						Optional[Any]):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						int							=	self.dummy_uncond_unet
          SCREAMING_SNAKE_CASE_   :						int							=	ScoreSdeVeScheduler()

          SCREAMING_SNAKE_CASE_   :						List[str]							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Tuple							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Dict							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Optional[Any]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	sde_ve(num_inference_steps=2		,			output_type='''numpy'''		,			generator=lowercase_		,			return_dict=lowercase_)[
              0
          ]

          SCREAMING_SNAKE_CASE_   :						Dict							=	image[0, -3:, -3:, -1]
          SCREAMING_SNAKE_CASE_   :						List[Any]							=	image_from_tuple[0, -3:, -3:, -1]

          assert image.shape == (1, 32, 32, 3)
          SCREAMING_SNAKE_CASE_   :						str							=	np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
          assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2




@slow
@require_torch
class 							lowerCAmelCase__       (    unittest.TestCase				):






      '''simple docstring'''

      def _SCREAMING_SNAKE_CASE				(		self  :						Dict):




          '''simple docstring'''


          SCREAMING_SNAKE_CASE_   :						Union[str, Any]							=	'''google/ncsnpp-church-256'''
          SCREAMING_SNAKE_CASE_   :						str							=	UNetaDModel.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVeScheduler.from_pretrained(lowercase_)

          SCREAMING_SNAKE_CASE_   :						Any							=	ScoreSdeVePipeline(unet=lowercase_		,			scheduler=lowercase_)
          sde_ve.to(lowercase_)
          sde_ve.set_progress_bar_config(disable=lowercase_)

          SCREAMING_SNAKE_CASE_   :						Optional[int]							=	torch.manual_seed(0)
          SCREAMING_SNAKE_CASE_   :						List[str]							=	sde_ve(num_inference_steps=10		,			output_type='''numpy'''		,			generator=lowercase_).images

          SCREAMING_SNAKE_CASE_   :						Any							=	image[0, -3:, -3:, -1]

          assert image.shape == (1, 256, 256, 3)

          SCREAMING_SNAKE_CASE_   :						int							=	np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0])

          assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2


