'''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()
lowercase__  :      Optional[Any]        =					logging.get_logger(__name__)

lowercase__  :      str        =					{
    """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""",
}
lowercase__  :      List[str]        =					[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def  __lowerCamelCase       (      _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  Dict							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  Any					):


      '''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
                        UpperCAmelCase_            =  "lm_head"

            UpperCAmelCase_            =  getattr(_UpperCamelCase							,    _UpperCamelCase					)

      if weight_type is not None:
            UpperCAmelCase_            =  getattr(_UpperCamelCase							,    _UpperCamelCase					).shape
      else:
            UpperCAmelCase_            =  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":
            UpperCAmelCase_            =  value
      elif weight_type == "weight_g":
            UpperCAmelCase_            =  value
      elif weight_type == "weight_v":
            UpperCAmelCase_            =  value
      elif weight_type == "bias":
            UpperCAmelCase_            =  value
      else:
            UpperCAmelCase_            =  value

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



def  __lowerCamelCase       (      _UpperCamelCase  :  str							,    _UpperCamelCase  :  Optional[int]							,    _UpperCamelCase  :  Any					):


      '''simple docstring'''


      UpperCAmelCase_            =  []
      UpperCAmelCase_            =  fairseq_model.state_dict()

      UpperCAmelCase_            =  hf_model.unispeech.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCAmelCase_            =  False
            if "conv_layers" in name:
                  load_conv_layer(
                      _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase							,    hf_model.config.feat_extract_norm == '''group'''							,    )
                  UpperCAmelCase_            =  True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCAmelCase_            =  "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]:
                              UpperCAmelCase_            =  True
                              if "*" in mapped_key:
                                    UpperCAmelCase_            =  name.split(_UpperCamelCase					)[0].split('''.'''					)[-2]
                                    UpperCAmelCase_            =  mapped_key.replace('''*'''							,    _UpperCamelCase					)
                              if "weight_g" in name:
                                    UpperCAmelCase_            =  "weight_g"
                              elif "weight_v" in name:
                                    UpperCAmelCase_            =  "weight_v"
                              elif "bias" in name:
                                    UpperCAmelCase_            =  "bias"
                              elif "weight" in name:
                                    # TODO: don't match quantizer.weight_proj
                                    UpperCAmelCase_            =  "weight"
                              else:
                                    UpperCAmelCase_            =  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  __lowerCamelCase       (      _UpperCamelCase  :  int							,    _UpperCamelCase  :  Any							,    _UpperCamelCase  :  int							,    _UpperCamelCase  :  str							,    _UpperCamelCase  :  str					):


      '''simple docstring'''


      UpperCAmelCase_            =  full_name.split('''conv_layers.'''					)[-1]
      UpperCAmelCase_            =  name.split('''.'''					)
      UpperCAmelCase_            =  int(items[0]					)
      UpperCAmelCase_            =  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."""
                  )
                  UpperCAmelCase_            =  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."""
                  )
                  UpperCAmelCase_            =  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."
                  )
                  UpperCAmelCase_            =  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."""
                  )
                  UpperCAmelCase_            =  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  :  Any							,    _UpperCamelCase  :  List[str]							,    _UpperCamelCase  :  int=None							,    _UpperCamelCase  :  Union[str, Any]=None							,    _UpperCamelCase  :  str=True					):


      '''simple docstring'''


      if config_path is not None:
            UpperCAmelCase_            =  UniSpeechConfig.from_pretrained(_UpperCamelCase					)
      else:
            UpperCAmelCase_            =  UniSpeechConfig()

      if is_finetuned:
            if dict_path:
                  UpperCAmelCase_            =  Dictionary.load_from_json(_UpperCamelCase					)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCAmelCase_            =  target_dict.pad_index
                  UpperCAmelCase_            =  target_dict.bos_index
                  UpperCAmelCase_            =  target_dict.eos_index
                  UpperCAmelCase_            =  len(target_dict.symbols					)
                  UpperCAmelCase_            =  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_            =  target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCAmelCase_            =  42
                  UpperCAmelCase_            =  43
                  with open(_UpperCamelCase							,    '''w'''							,    encoding='''utf-8'''					) as vocab_handle:
                        json.dump(_UpperCamelCase							,    _UpperCamelCase					)
                  UpperCAmelCase_            =  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							,    )
                  UpperCAmelCase_            =  True if config.feat_extract_norm == "layer" else False
                  UpperCAmelCase_            =  WavaVecaFeatureExtractor(
                      feature_size=1							,    sampling_rate=1_6000							,    padding_value=0							,    do_normalize=_UpperCamelCase							,    return_attention_mask=_UpperCamelCase							,    )
                  UpperCAmelCase_            =  WavaVecaProcessor(feature_extractor=_UpperCamelCase							,    tokenizer=_UpperCamelCase					)
                  processor.save_pretrained(_UpperCamelCase					)

            UpperCAmelCase_            =  UniSpeechForCTC(_UpperCamelCase					)
      else:
            UpperCAmelCase_            =  UniSpeechForPreTraining(_UpperCamelCase					)

      if is_finetuned:
            UpperCAmelCase_            =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					), '''w2v_path''': checkpoint_path}					)
      else:
            UpperCAmelCase_            =  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

      UpperCAmelCase_            =  model[0].eval()

      recursively_load_weights(_UpperCamelCase							,    _UpperCamelCase							,    _UpperCamelCase					)

      hf_unispeech.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
      lowercase__  :      Dict        =					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__  :      Optional[int]        =					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 gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()







@slow
@require_torch_gpu
class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()






  def  lowerCAmelCase__ (      self			:       str  )				->List[str]:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_euler'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe([prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=9.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]  )

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






  def  lowerCAmelCase__ (      self			:       List[str]  )				->int:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_euler'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe([prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=9.0  ,							num_inference_steps=20  ,							output_type='''np'''  )

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]  )

        assert np.abs(image_slice.flatten() - expected_slice  ).max() < 5e-1






  def  lowerCAmelCase__ (      self			:       List[Any]  )				->Optional[int]:
        UpperCAmelCase_            =  StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''  )
        UpperCAmelCase_            =  sd_pipe.to(UpperCAmelCase__  )
        sd_pipe.set_progress_bar_config(disable=UpperCAmelCase__  )

        sd_pipe.set_scheduler('''sample_dpmpp_2m'''  )

        UpperCAmelCase_            =  '''A painting of a squirrel eating a burger'''
        UpperCAmelCase_            =  torch.manual_seed(0  )
        UpperCAmelCase_            =  sd_pipe(
            [prompt]  ,							generator=UpperCAmelCase__  ,							guidance_scale=7.5  ,							num_inference_steps=15  ,							output_type='''np'''  ,							use_karras_sigmas=UpperCAmelCase__  ,							)

        UpperCAmelCase_            =  output.images

        UpperCAmelCase_            =  image[0, -3:, -3:, -1]

        assert image.shape == (1, 512, 512, 3)
        UpperCAmelCase_            =  np.array(
            [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]  )

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





