



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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',
    'adapter_layer': 'encoder.layers.*.adapter_layer',
    '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',
    'pooling_layer.linear': 'projector',
    'pooling_layer.projection': 'classifier',
}
a_			=					[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]





def 		a__			(		_UpperCamelCase			: List[str]							):
   __lowerCamelCase													=    {}
   with open(__lowerCAmelCase ,'''r'''							) as file:
      for line_number, line in enumerate(__lowerCAmelCase							):
         __lowerCamelCase													=    line.strip()
         if line:
            __lowerCamelCase													=    line.split()
            __lowerCamelCase													=    line_number
            __lowerCamelCase													=    words[0]
            __lowerCamelCase													=    value
   return result





def 		a__			(		_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: int ,_UpperCamelCase			: List[str] ,_UpperCamelCase			: str ,_UpperCamelCase			: List[Any]							):
   for attribute in key.split('''.'''							):
      __lowerCamelCase													=    getattr(__lowerCAmelCase ,__lowerCAmelCase							)

   __lowerCamelCase													=    None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__lowerCAmelCase							):
         __lowerCamelCase													=    PARAM_MAPPING[full_name.split('''.'''							)[-1]]
         __lowerCamelCase													=    '''param'''

   if weight_type is not None and weight_type != "param":
      __lowerCamelCase													=    getattr(__lowerCAmelCase ,__lowerCAmelCase							).shape
   elif weight_type is not None and weight_type == "param":
      __lowerCamelCase													=    hf_pointer
      for attribute in hf_param_name.split('''.'''							):
         __lowerCamelCase													=    getattr(__lowerCAmelCase ,__lowerCAmelCase							)
      __lowerCamelCase													=    shape_pointer.shape

      # let's reduce dimension
      __lowerCamelCase													=    value[0]
   else:
      __lowerCamelCase													=    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":
      __lowerCamelCase													=    value
   elif weight_type == "weight_g":
      __lowerCamelCase													=    value
   elif weight_type == "weight_v":
      __lowerCamelCase													=    value
   elif weight_type == "bias":
      __lowerCamelCase													=    value
   elif weight_type == "param":
      for attribute in hf_param_name.split('''.'''							):
         __lowerCamelCase													=    getattr(__lowerCAmelCase ,__lowerCAmelCase							)
      __lowerCamelCase													=    value
   else:
      __lowerCamelCase													=    value

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





def 		a__			(		_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: int ,_UpperCamelCase			: int ,_UpperCamelCase			: int							):
   __lowerCamelCase													=    None
   for param_key in PARAM_MAPPING.keys():
      if full_name.endswith(__lowerCAmelCase							):
         __lowerCamelCase													=    PARAM_MAPPING[full_name.split('''.'''							)[-1]]
         __lowerCamelCase													=    '''param'''

   if weight_type is not None and weight_type != "param":
      __lowerCamelCase													=    '''.'''.join([key, weight_type]							)
   elif weight_type is not None and weight_type == "param":
      __lowerCamelCase													=    '''.'''.join([key, hf_param_name]							)
   else:
      __lowerCamelCase													=    key

   __lowerCamelCase													=    value if '''lm_head''' in full_key else value[0]


a_			=					{
    'W_a': 'linear_1.weight',
    'W_b': 'linear_2.weight',
    'b_a': 'linear_1.bias',
    'b_b': 'linear_2.bias',
    'ln_W': 'norm.weight',
    'ln_b': 'norm.bias',
}





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Any ,_UpperCamelCase			: str=None ,_UpperCamelCase			: Optional[int]=None							):
   __lowerCamelCase													=    False
   for key, mapped_key in MAPPING.items():
      __lowerCamelCase													=    '''wav2vec2.''' + 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													=    True
         if "*" in mapped_key:
            __lowerCamelCase													=    name.split(__lowerCAmelCase							)[0].split('''.'''							)[-2]
            __lowerCamelCase													=    mapped_key.replace('''*''' ,__lowerCAmelCase							)
         if "weight_g" in name:
            __lowerCamelCase													=    '''weight_g'''
         elif "weight_v" in name:
            __lowerCamelCase													=    '''weight_v'''
         elif "bias" in name:
            __lowerCamelCase													=    '''bias'''
         elif "weight" in name:
            # TODO: don't match quantizer.weight_proj
            __lowerCamelCase													=    '''weight'''
         else:
            __lowerCamelCase													=    None
         if hf_dict is not None:
            rename_dict(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase							)
         else:
            set_recursively(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase							)
         return is_used
   return is_used





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: List[Any] ,_UpperCamelCase			: int							):
   __lowerCamelCase													=    []
   __lowerCamelCase													=    fairseq_model.state_dict()

   __lowerCamelCase													=    hf_model.wavaveca.feature_extractor

   for name, value in fairseq_dict.items():
      __lowerCamelCase													=    False
      if "conv_layers" in name:
         load_conv_layer(
             __lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase ,hf_model.config.feat_extract_norm == '''group''' ,)
         __lowerCamelCase													=    True
      else:
         __lowerCamelCase													=    load_wavaveca_layer(__lowerCAmelCase ,__lowerCAmelCase ,__lowerCAmelCase							)
      if not is_used:
         unused_weights.append(__lowerCAmelCase							)

   logger.warning(F"""Unused weights: {unused_weights}"""							)





def 		a__			(		_UpperCamelCase			: Any ,_UpperCamelCase			: List[Any] ,_UpperCamelCase			: Optional[int] ,_UpperCamelCase			: Tuple ,_UpperCamelCase			: Union[str, Any]							):
   __lowerCamelCase													=    full_name.split('''conv_layers.'''							)[-1]
   __lowerCamelCase													=    name.split('''.'''							)
   __lowerCamelCase													=    int(items[0]							)
   __lowerCamelCase													=    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."""							)
         __lowerCamelCase													=    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."""							)
         __lowerCamelCase													=    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."""							)
         __lowerCamelCase													=    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."""							)
         __lowerCamelCase													=    value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
   else:
      unused_weights.append(__lowerCAmelCase							)





@torch.no_grad()
def 		a__			(		_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: Dict ,_UpperCamelCase			: Optional[int]=None ,_UpperCamelCase			: Any=None ,_UpperCamelCase			: Any=True ,_UpperCamelCase			: str=False							):
   if config_path is not None:
      __lowerCamelCase													=    WavaVecaConfig.from_pretrained(__lowerCAmelCase							)
   else:
      __lowerCamelCase													=    WavaVecaConfig()

   if is_seq_class:
      __lowerCamelCase													=    read_txt_into_dict(__lowerCAmelCase							)
      __lowerCamelCase													=    idalabel
      __lowerCamelCase													=    WavaVecaForSequenceClassification(__lowerCAmelCase							)
      __lowerCamelCase													=    WavaVecaFeatureExtractor(
          feature_size=1 ,sampling_rate=1_60_00 ,padding_value=0 ,do_normalize=__lowerCAmelCase ,return_attention_mask=__lowerCAmelCase ,)
      feature_extractor.save_pretrained(__lowerCAmelCase							)

   elif is_finetuned:
      if dict_path:
         __lowerCamelCase													=    Dictionary.load(__lowerCAmelCase							)

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

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

      __lowerCamelCase													=    WavaVecaForCTC(__lowerCAmelCase							)
   else:
      __lowerCamelCase													=    WavaVecaForPreTraining(__lowerCAmelCase							)

   if is_finetuned or is_seq_class:
      __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path] ,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
   else:
      __lowerCamelCase													=    argparse.Namespace(task='''audio_pretraining'''							)
      __lowerCamelCase													=    fairseq.tasks.setup_task(__lowerCAmelCase							)

      __lowerCamelCase							,__lowerCamelCase							,__lowerCamelCase													=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,task=__lowerCAmelCase							)

   __lowerCamelCase													=    model[0].eval()

   recursively_load_weights(__lowerCAmelCase ,__lowerCAmelCase ,not is_finetuned							)

   hf_wavavec.save_pretrained(__lowerCAmelCase							)


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("""--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"""
  )
  parser.add_argument(
      """--is_seq_class""",
      action="""store_true""",
      help="""Whether the model to convert is a fine-tuned sequence classification model or not""",
  )
  a_			=					parser.parse_args()

  a_			=					not args.not_finetuned and not args.is_seq_class
  convert_wavaveca_checkpoint(
      args.checkpoint_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.dict_path,
      is_finetuned,
      args.is_seq_class,
  )






import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


a_			=					False



class  __lowerCAmelCase     (	unittest.TestCase   ):
 pass



@nightly
@require_torch_gpu
class  __lowerCAmelCase     (	unittest.TestCase   ):




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
    torch.cuda.empty_cache()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)

    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    with tempfile.TemporaryDirectory() as tmpdirname:
       pipe.save_pretrained(__UpperCAmelCase				)
       __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained(__UpperCAmelCase	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    generator.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt='''first prompt'''	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=2	,				output_type='''numpy'''	,				).images

    assert np.abs(image - new_image				).sum() < 1E-5, "Models don't have the same forward pass"




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    VersatileDiffusionPipeline.from_pretrained('''shi-labs/versatile-diffusion'''	,				torch_dtype=torch.floataa				)
    pipe.to(__UpperCAmelCase				)
    pipe.set_progress_bar_config(disable=__UpperCAmelCase				)

    __lowerCamelCase													=    '''cyberpunk 2077'''
    __lowerCamelCase													=    load_image(
        '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg'''				)
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.dual_guided(
        prompt=__UpperCAmelCase	,				image=__UpperCAmelCase	,				text_to_image_strength=0.75	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''	,				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001]				)

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

    __lowerCamelCase													=    '''A painting of a squirrel eating a burger '''
    __lowerCamelCase													=    torch.manual_seed(0				)
    __lowerCamelCase													=    pipe.text_to_image(
        prompt=__UpperCAmelCase	,				generator=__UpperCAmelCase	,				guidance_scale=7.5	,				num_inference_steps=50	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778]				)

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

    __lowerCamelCase													=    pipe.image_variation(__UpperCAmelCase	,				generator=__UpperCAmelCase	,				output_type='''numpy'''				).images

    __lowerCamelCase													=    image[0, 253:256, 253:256, -1]

    assert image.shape == (1, 512, 512, 3)
    __lowerCamelCase													=    np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456]				)

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






