





"""simple docstring"""





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

lowercase__				:List[Any]				   =       {
    'post_extract_proj': 'feature_projection.projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.linear_k': 'encoder.layers.*.self_attn.linear_k',
    'self_attn.linear_v': 'encoder.layers.*.self_attn.linear_v',
    'self_attn.linear_q': 'encoder.layers.*.self_attn.linear_q',
    'self_attn.pos_bias_u': 'encoder.layers.*.self_attn.pos_bias_u',
    'self_attn.pos_bias_v': 'encoder.layers.*.self_attn.pos_bias_v',
    'self_attn.linear_out': 'encoder.layers.*.self_attn.linear_out',
    'self_attn.linear_pos': 'encoder.layers.*.self_attn.linear_pos',
    'self_attn.rotary_emb': 'encoder.embed_positions',
    'self_attn_layer_norm': 'encoder.layers.*.self_attn_layer_norm',
    'conv_module.pointwise_conv1': 'encoder.layers.*.conv_module.pointwise_conv1',
    'conv_module.pointwise_conv2': 'encoder.layers.*.conv_module.pointwise_conv2',
    'conv_module.depthwise_conv': 'encoder.layers.*.conv_module.depthwise_conv',
    'conv_module.batch_norm': 'encoder.layers.*.conv_module.batch_norm',
    'conv_module.layer_norm': 'encoder.layers.*.conv_module.layer_norm',
    'ffn1.w_1': 'encoder.layers.*.ffn1.intermediate_dense',
    'ffn1.w_2': 'encoder.layers.*.ffn1.output_dense',
    'ffn1.layer_norm': 'encoder.layers.*.ffn1_layer_norm',
    'ffn2.w_1': 'encoder.layers.*.ffn2.intermediate_dense',
    'ffn2.w_2': 'encoder.layers.*.ffn2.output_dense',
    'ffn2.layer_norm': 'encoder.layers.*.ffn2_layer_norm',
    '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': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}
lowercase__				:Tuple				   =       [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->str:





  """simple docstring"""


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

  if weight_type is not None:
    __UpperCAmelCase    :						Any       =						getattr(UpperCAmelCase_  ,			UpperCAmelCase_  ).shape
  else:
    __UpperCAmelCase    :						Optional[int]       =						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":
    __UpperCAmelCase    :						List[str]       =						value
  elif weight_type == "weight_g":
    __UpperCAmelCase    :						List[str]       =						value
  elif weight_type == "weight_v":
    __UpperCAmelCase    :						str       =						value
  elif weight_type == "bias":
    __UpperCAmelCase    :						Optional[int]       =						value
  elif weight_type == "running_mean":
    __UpperCAmelCase    :						List[Any]       =						value
  elif weight_type == "running_var":
    __UpperCAmelCase    :						Tuple       =						value
  elif weight_type == "num_batches_tracked":
    __UpperCAmelCase    :						int       =						value
  elif weight_type == "inv_freq":
    __UpperCAmelCase    :						List[str]       =						value
  else:
    __UpperCAmelCase    :						Tuple       =						value

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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->Optional[Any]:





  """simple docstring"""


  __UpperCAmelCase    :						List[Any]       =						[]
  __UpperCAmelCase    :						str       =						fairseq_model.state_dict()

  __UpperCAmelCase    :						Union[str, Any]       =						hf_model.wavaveca_conformer.feature_extractor

  for name, value in fairseq_dict.items():
    __UpperCAmelCase    :						Union[str, Any]       =						False
    if "conv_layers" in name:
      load_conv_layer(
          UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			hf_model.config.feat_extract_norm == '''group'''  ,			)
      __UpperCAmelCase    :						List[str]       =						True
    else:
      for key, mapped_key in MAPPING.items():
        __UpperCAmelCase    :						Optional[Any]       =						'''wav2vec2_conformer.''' + 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]:
          __UpperCAmelCase    :						Optional[int]       =						True
          if "*" in mapped_key:
            __UpperCAmelCase    :						Optional[Any]       =						name.split(UpperCAmelCase_  )[0].split('''.'''  )[-2]
            __UpperCAmelCase    :						Any       =						mapped_key.replace('''*'''  ,			UpperCAmelCase_  )
          if "pos_bias_u" in name:
            __UpperCAmelCase    :						Optional[int]       =						None
          elif "pos_bias_v" in name:
            __UpperCAmelCase    :						Tuple       =						None
          elif "weight_g" in name:
            __UpperCAmelCase    :						Tuple       =						'''weight_g'''
          elif "weight_v" in name:
            __UpperCAmelCase    :						Union[str, Any]       =						'''weight_v'''
          elif "bias" in name:
            __UpperCAmelCase    :						Tuple       =						'''bias'''
          elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __UpperCAmelCase    :						List[str]       =						'''weight'''
          elif "running_mean" in name:
            __UpperCAmelCase    :						Union[str, Any]       =						'''running_mean'''
          elif "inv_freq" in name:
            __UpperCAmelCase    :						List[str]       =						'''inv_freq'''
          elif "running_var" in name:
            __UpperCAmelCase    :						Dict       =						'''running_var'''
          elif "num_batches_tracked" in name:
            __UpperCAmelCase    :						Optional[Any]       =						'''num_batches_tracked'''
          else:
            __UpperCAmelCase    :						Optional[int]       =						None
          set_recursively(UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )
        continue
    if not is_used:
      unused_weights.append(UpperCAmelCase_  )

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


def       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_  )						->List[str]:





  """simple docstring"""


  __UpperCAmelCase    :						Dict       =						full_name.split('''conv_layers.'''  )[-1]
  __UpperCAmelCase    :						Dict       =						name.split('''.'''  )
  __UpperCAmelCase    :						List[str]       =						int(items[0]  )
  __UpperCAmelCase    :						Tuple       =						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.'''  )
      __UpperCAmelCase    :						Optional[Any]       =						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.'''  )
      __UpperCAmelCase    :						Optional[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:
      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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''  )
      __UpperCAmelCase    :						Dict       =						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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''  )
      __UpperCAmelCase    :						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       lowerCamelCase_       (   UpperCAmelCase_  ,			UpperCAmelCase_  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=None  ,			UpperCAmelCase_=True  )						->List[str]:





  """simple docstring"""


  if config_path is not None:
    __UpperCAmelCase    :						Dict       =						WavaVecaConformerConfig.from_pretrained(UpperCAmelCase_  ,			hidden_act='''swish'''  )
  else:
    __UpperCAmelCase    :						List[Any]       =						WavaVecaConformerConfig()

  if "rope" in checkpoint_path:
    __UpperCAmelCase    :						Union[str, Any]       =						'''rotary'''

  if is_finetuned:
    if dict_path:
      __UpperCAmelCase    :						Tuple       =						Dictionary.load(UpperCAmelCase_  )

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

      # fairseq has the <pad> and <s> switched
      __UpperCAmelCase    :						Dict       =						0
      __UpperCAmelCase    :						str       =						1
      with open(UpperCAmelCase_  ,			'''w'''  ,			encoding='''utf-8'''  ) as vocab_handle:
        json.dump(UpperCAmelCase_  ,			UpperCAmelCase_  )
      __UpperCAmelCase    :						Tuple       =						WavaVecaCTCTokenizer(
          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_  ,			)
      __UpperCAmelCase    :						Optional[int]       =						True if config.feat_extract_norm == '''layer''' else False
      __UpperCAmelCase    :						str       =						WavaVecaFeatureExtractor(
          feature_size=1  ,			sampling_rate=1_60_00  ,			padding_value=0  ,			do_normalize=UpperCAmelCase_  ,			return_attention_mask=UpperCAmelCase_  ,			)
      __UpperCAmelCase    :						Dict       =						WavaVecaProcessor(feature_extractor=UpperCAmelCase_  ,			tokenizer=UpperCAmelCase_  )
      processor.save_pretrained(UpperCAmelCase_  )

    __UpperCAmelCase    :						Union[str, Any]       =						WavaVecaConformerForCTC(UpperCAmelCase_  )
  else:
    __UpperCAmelCase    :						Optional[Any]       =						WavaVecaConformerForPreTraining(UpperCAmelCase_  )

  if is_finetuned:
    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						Tuple       =						fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]  ,			arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''  )[:-1]  )}  )
  else:
    __UpperCAmelCase    :						List[Any]       =						argparse.Namespace(task='''audio_pretraining'''  )
    __UpperCAmelCase    :						int       =						fairseq.tasks.setup_task(UpperCAmelCase_  )

    __UpperCAmelCase       ,     __UpperCAmelCase       ,     __UpperCAmelCase    :						List[str]       =						fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]  ,			task=UpperCAmelCase_  )

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

  recursively_load_weights(UpperCAmelCase_  ,			UpperCAmelCase_  ,			not is_finetuned  )

  hf_wavavec.save_pretrained(UpperCAmelCase_  )


if __name__ == "__main__":
  lowercase__				:Tuple				   =       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__				:Dict				   =       parser.parse_args()
  convert_wavaveca_conformer_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )






"""simple docstring"""





import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
  import jax
  import jax.numpy as jnp
  from flax.jax_utils import replicate
  from flax.training.common_utils import shard




@slow
@require_flax
class 							snake_case							(			unittest.TestCase ):

      '''simple docstring'''



      def       A_					(							self       :      str ):


        '''simple docstring'''




        super().tearDown()
        gc.collect()

      def       A_					(							self       :      List[Any] ):


        '''simple docstring'''




        __UpperCAmelCase       ,     __UpperCAmelCase    :						List[Any]       =						FlaxStableDiffusionPipeline.from_pretrained(
            '''stabilityai/stable-diffusion-2'''						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)

        __UpperCAmelCase    :						Dict       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						Any       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						List[Any]       =						replicate(__lowercase )
        __UpperCAmelCase    :						Optional[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						str       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						Dict       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						Tuple       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Any       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Union[str, Any]       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						str       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						List[Any]       =						jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2







      def       A_					(							self       :      Union[str, Any] ):


        '''simple docstring'''




        __UpperCAmelCase    :						Optional[int]       =						'''stabilityai/stable-diffusion-2'''
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Any       =						FlaxDPMSolverMultistepScheduler.from_pretrained(__lowercase						,				subfolder='''scheduler''' )
        __UpperCAmelCase       ,     __UpperCAmelCase    :						Optional[int]       =						FlaxStableDiffusionPipeline.from_pretrained(
            __lowercase						,				scheduler=__lowercase						,				revision='''bf16'''						,				dtype=jnp.bfloataa						,				)
        __UpperCAmelCase    :						Dict       =						scheduler_params

        __UpperCAmelCase    :						int       =						'''A painting of a squirrel eating a burger'''
        __UpperCAmelCase    :						List[Any]       =						jax.device_count()
        __UpperCAmelCase    :						Optional[int]       =						num_samples * [prompt]
        __UpperCAmelCase    :						Tuple       =						sd_pipe.prepare_inputs(__lowercase )

        __UpperCAmelCase    :						Optional[int]       =						replicate(__lowercase )
        __UpperCAmelCase    :						List[Any]       =						shard(__lowercase )

        __UpperCAmelCase    :						Union[str, Any]       =						jax.random.PRNGKey(0 )
        __UpperCAmelCase    :						List[Any]       =						jax.random.split(__lowercase						,				jax.device_count() )

        __UpperCAmelCase    :						List[Any]       =						sd_pipe(__lowercase						,				__lowercase						,				__lowercase						,				num_inference_steps=25						,				jit=__lowercase )[0]
        assert images.shape == (jax.device_count(), 1, 768, 768, 3)

        __UpperCAmelCase    :						Tuple       =						images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
        __UpperCAmelCase    :						Any       =						images[0, 253:256, 253:256, -1]

        __UpperCAmelCase    :						List[str]       =						jnp.asarray(jax.device_get(image_slice.flatten() ) )
        __UpperCAmelCase    :						Any       =						jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
        print(f'''output_slice: {output_slice}''' )
        assert jnp.abs(output_slice - expected_slice ).max() < 1e-2