





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
snake_case_           =      logging.get_logger(__name__)

snake_case_           =      {
    'post_extract_proj': 'feature_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.upsample.0': 'encoder.upsample.projection',
    'encoder.layer_norm': 'encoder.layer_norm',
    'w2v_model.layer_norm': 'layer_norm',
    'w2v_encoder.proj': 'lm_head',
    'mask_emb': 'masked_spec_embed',
}






def lowerCamelCase__   (							snake_case_     :  int	,				snake_case_     :  Optional[int]	,				snake_case_     :  List[str]	,				snake_case_     :  Optional[Any]	,				snake_case_     :  Dict	)				->    List[str]:
   for attribute in key.split('''.'''	):
      __snake_case								=       getattr(snake_case_	,				snake_case_	)

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

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






def lowerCamelCase__   (							snake_case_     :  Any	,				snake_case_     :  int	,				snake_case_     :  Optional[Any]	)				->    Tuple:
   __snake_case								=       []
   __snake_case								=       fairseq_model.state_dict()

   __snake_case								=       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      __snake_case								=       False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	,				hf_model.config.feat_extract_norm == '''group'''	,				)
         __snake_case								=       True
      else:
         for key, mapped_key in MAPPING.items():
            __snake_case								=       '''sew.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key

            if key in name or key.split('''w2v_model.'''	)[-1] == name.split('''.'''	)[0]:
               __snake_case								=       True
               if "*" in mapped_key:
                  __snake_case								=       name.split(snake_case_	)[0].split('''.'''	)[-2]
                  __snake_case								=       mapped_key.replace('''*'''	,				snake_case_	)
               if "weight_g" in name:
                  __snake_case								=       '''weight_g'''
               elif "weight_v" in name:
                  __snake_case								=       '''weight_v'''
               elif "weight" in name:
                  __snake_case								=       '''weight'''
               elif "bias" in name:
                  __snake_case								=       '''bias'''
               else:
                  __snake_case								=       None
               set_recursively(snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	,				snake_case_	)
            continue
      if not is_used:
         unused_weights.append(snake_case_	)

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






def lowerCamelCase__   (							snake_case_     :  int	,				snake_case_     :  Any	,				snake_case_     :  Any	,				snake_case_     :  Any	,				snake_case_     :  Optional[int]	)				->    str:
   __snake_case								=       full_name.split('''conv_layers.'''	)[-1]
   __snake_case								=       name.split('''.'''	)
   __snake_case								=       int(items[0]	)
   __snake_case								=       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."""
         )
         __snake_case								=       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."""
         )
         __snake_case								=       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."
         )
         __snake_case								=       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."""
         )
         __snake_case								=       value
         logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""	)
   else:
      unused_weights.append(snake_case_	)






def lowerCamelCase__   (							snake_case_     :  Tuple	,				snake_case_     :  str	)				->    Optional[int]:
   __snake_case								=       SEWConfig()
   if is_finetuned:
      __snake_case								=       model.wav_encoder.wav_model.cfg
   else:
      __snake_case								=       model.cfg

   __snake_case								=       fs_config.conv_bias
   __snake_case								=       eval(fs_config.conv_feature_layers	)
   __snake_case								=       [x[0] for x in conv_layers]
   __snake_case								=       [x[1] for x in conv_layers]
   __snake_case								=       [x[2] for x in conv_layers]
   __snake_case								=       '''gelu'''
   __snake_case								=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
   __snake_case								=       0.0
   __snake_case								=       fs_config.activation_fn.name
   __snake_case								=       fs_config.encoder_embed_dim
   __snake_case								=       0.02
   __snake_case								=       fs_config.encoder_ffn_embed_dim
   __snake_case								=       1e-5
   __snake_case								=       fs_config.encoder_layerdrop
   __snake_case								=       fs_config.encoder_attention_heads
   __snake_case								=       fs_config.conv_pos_groups
   __snake_case								=       fs_config.conv_pos
   __snake_case								=       len(snake_case_	)
   __snake_case								=       fs_config.encoder_layers
   __snake_case								=       fs_config.squeeze_factor

   # take care of any params that are overridden by the Wav2VecCtc model
   if is_finetuned:
      __snake_case								=       model.cfg
      __snake_case								=       fs_config.final_dropout
      __snake_case								=       fs_config.layerdrop
   __snake_case								=       fs_config.activation_dropout
   __snake_case								=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
   __snake_case								=       fs_config.attention_dropout
   __snake_case								=       fs_config.dropout_input
   __snake_case								=       fs_config.dropout
   __snake_case								=       fs_config.mask_channel_length
   __snake_case								=       fs_config.mask_channel_prob
   __snake_case								=       fs_config.mask_length
   __snake_case								=       fs_config.mask_prob

   __snake_case								=       '''Wav2Vec2FeatureExtractor'''
   __snake_case								=       '''Wav2Vec2CTCTokenizer'''

   return config






@torch.no_grad()
def lowerCamelCase__   (							snake_case_     :  Union[str, Any]	,				snake_case_     :  List[str]	,				snake_case_     :  Dict=None	,				snake_case_     :  Tuple=None	,				snake_case_     :  Optional[int]=True	)				->    str:

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

   if config_path is not None:
      __snake_case								=       SEWConfig.from_pretrained(snake_case_	)
   else:
      __snake_case								=       convert_config(model[0]	,				snake_case_	)
   __snake_case								=       model[0].eval()

   __snake_case								=       True if config.feat_extract_norm == '''layer''' else False
   __snake_case								=       WavaVecaFeatureExtractor(
       feature_size=1	,				sampling_rate=1_6000	,				padding_value=0	,				do_normalize=snake_case_	,				return_attention_mask=snake_case_	,				)

   if is_finetuned:
      if dict_path:
         __snake_case								=       Dictionary.load(snake_case_	)

         # important change bos & pad token id since CTC symbol is <pad> and
         # not <s> as in fairseq
         __snake_case								=       target_dict.pad_index
         __snake_case								=       target_dict.bos_index
         __snake_case								=       target_dict.pad_index
         __snake_case								=       target_dict.bos_index
         __snake_case								=       target_dict.eos_index
         __snake_case								=       len(target_dict.symbols	)
         __snake_case								=       os.path.join(snake_case_	,				'''vocab.json'''	)
         if not os.path.isdir(snake_case_	):
            logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case_	)	)
            return
         os.makedirs(snake_case_	,				exist_ok=snake_case_	)
         with open(snake_case_	,				'''w'''	,				encoding='''utf-8'''	) as vocab_handle:
            json.dump(target_dict.indices	,				snake_case_	)
         __snake_case								=       WavaVecaCTCTokenizer(
             snake_case_	,				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=snake_case_	,				)
         __snake_case								=       WavaVecaProcessor(feature_extractor=snake_case_	,				tokenizer=snake_case_	)
         processor.save_pretrained(snake_case_	)

      __snake_case								=       SEWForCTC(snake_case_	)
   else:
      __snake_case								=       SEWModel(snake_case_	)
      feature_extractor.save_pretrained(snake_case_	)

   recursively_load_weights(snake_case_	,				snake_case_	,				snake_case_	)

   hf_model.save_pretrained(snake_case_	)


if __name__ == "__main__":
    snake_case_           =      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(
        '--is_finetuned', action='store_true', help='Whether the model to convert is a fine-tuned model or not'
    )
    snake_case_           =      parser.parse_args()
    convert_sew_checkpoint(
        args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
    )








import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest

class 		SCREAMING_SNAKE_CASE__    (	_UpperCAmelCase  ):
      A_						:    Union[str, Any]							=   (DPMSolverSinglestepScheduler,)
      A_						:    Union[str, Any]							=   (('num_inference_steps', 25),)


      def        a			(self	:				Dict    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       {
             '''num_train_timesteps''': 1000,
             '''beta_start''': 0.0_0_0_1,
             '''beta_end''': 0.0_2,
             '''beta_schedule''': '''linear''',
             '''solver_order''': 2,
             '''prediction_type''': '''epsilon''',
             '''thresholding''': False,
             '''sample_max_value''': 1.0,
             '''algorithm_type''': '''dpmsolver++''',
             '''solver_type''': '''midpoint''',
             '''lambda_min_clipped''': -float('''inf'''				),
             '''variance_type''': None,
         }

         config.update(**a__				)
         return config


      def        a			(self	:				str    ,      a__	:				Any=0    ,      **a__	:				Tuple				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)
            # copy over dummy past residuals
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               new_scheduler.set_timesteps(a__				)
               # copy over dummy past residuals
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case	,     __snake_case								=       sample, sample
            for t in range(a__    ,      time_step + scheduler.config.solver_order + 1				):
               __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
               __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

               assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"


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





         """simple docstring"""



         pass


      def        a			(self	:				List[Any]    ,      a__	:				Dict=0    ,      **a__	:				List[str]				):





         """simple docstring"""



         __snake_case								=       dict(self.forward_default_kwargs				)
         __snake_case								=       kwargs.pop('''num_inference_steps'''    ,      a__				)
         __snake_case								=       self.dummy_sample
         __snake_case								=       0.1 * sample
         __snake_case								=       [residual + 0.2, residual + 0.1_5, residual + 0.1_0]

         for scheduler_class in self.scheduler_classes:
            __snake_case								=       self.get_scheduler_config()
            __snake_case								=       scheduler_class(**a__				)
            scheduler.set_timesteps(a__				)

            # copy over dummy past residuals (must be after setting timesteps)
            __snake_case								=       dummy_past_residuals[: scheduler.config.solver_order]

            with tempfile.TemporaryDirectory() as tmpdirname:
               scheduler.save_config(a__				)
               __snake_case								=       scheduler_class.from_pretrained(a__				)
               # copy over dummy past residuals
               new_scheduler.set_timesteps(a__				)

               # copy over dummy past residual (must be after setting timesteps)
               __snake_case								=       dummy_past_residuals[: new_scheduler.config.solver_order]

            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample
            __snake_case								=       new_scheduler.step(a__    ,      a__    ,      a__    ,      **a__				).prev_sample

            assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"


      def        a			(self	:				int    ,      a__	:				Tuple=None    ,      **a__	:				List[str]				):





         """simple docstring"""



         if scheduler is None:
            __snake_case								=       self.scheduler_classes[0]
            __snake_case								=       self.get_scheduler_config(**a__				)
            __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(**a__				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         return sample


      def        a			(self	:				str				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       50
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter
         scheduler.set_timesteps(a__				)

         # make sure that the first t is uneven
         for i, t in enumerate(scheduler.timesteps[3:]				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_5_7_4				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         for timesteps in [25, 50, 100, 999, 1000]:
            self.check_over_configs(num_train_timesteps=a__				)


      def        a			(self	:				List[str]				):





         """simple docstring"""



         __snake_case								=       DPMSolverSinglestepScheduler(**self.get_scheduler_config()				)
         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3

         __snake_case								=       DEISMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       UniPCMultistepScheduler.from_config(scheduler.config				)
         __snake_case								=       DPMSolverSinglestepScheduler.from_config(scheduler.config				)

         __snake_case								=       self.full_loop(scheduler=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(thresholding=a__				)
         for order in [1, 2, 3]:
            for solver_type in ["midpoint", "heun"]:
               for threshold in [0.5, 1.0, 2.0]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         thresholding=a__    ,      prediction_type=a__    ,      sample_max_value=a__    ,      algorithm_type='''dpmsolver++'''    ,      solver_order=a__    ,      solver_type=a__    ,      )


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





         """simple docstring"""



         for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=a__				)


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





         """simple docstring"""



         for algorithm_type in ["dpmsolver", "dpmsolver++"]:
            for solver_type in ["midpoint", "heun"]:
               for order in [1, 2, 3]:
                  for prediction_type in ["epsilon", "sample"]:
                     self.check_over_configs(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     __snake_case								=       self.full_loop(
                         solver_order=a__    ,      solver_type=a__    ,      prediction_type=a__    ,      algorithm_type=a__    ,      )
                     assert not torch.isnan(a__				).any(), "Samples have nan numbers"


      def        a			(self	:				List[str]				):





         """simple docstring"""



         self.check_over_configs(lower_order_final=a__				)
         self.check_over_configs(lower_order_final=a__				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         self.check_over_configs(lambda_min_clipped=-float('''inf'''				)				)
         self.check_over_configs(lambda_min_clipped=-5.1				)


      def        a			(self	:				Tuple				):





         """simple docstring"""



         self.check_over_configs(variance_type=a__				)
         self.check_over_configs(variance_type='''learned_range'''				)


      def        a			(self	:				int				):





         """simple docstring"""



         for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
            self.check_over_forward(num_inference_steps=a__    ,      time_step=0				)


      def        a			(self	:				Optional[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop()
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_7_9_1				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.full_loop(use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.2_2_4_8				) < 1E-3


      def        a			(self	:				Tuple				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.1_4_5_3				) < 1E-3


      def        a			(self	:				List[Any]				):





         """simple docstring"""



         __snake_case								=       self.full_loop(prediction_type='''v_prediction'''    ,      use_karras_sigmas=a__				)
         __snake_case								=       torch.mean(torch.abs(a__				)				)

         assert abs(result_mean.item() - 0.0_6_4_9				) < 1E-3


      def        a			(self	:				int				):





         """simple docstring"""



         __snake_case								=       self.scheduler_classes[0]
         __snake_case								=       self.get_scheduler_config(thresholding=a__    ,      dynamic_thresholding_ratio=0				)
         __snake_case								=       scheduler_class(**a__				)

         __snake_case								=       10
         __snake_case								=       self.dummy_model()
         __snake_case								=       self.dummy_sample_deter.half()
         scheduler.set_timesteps(a__				)

         for i, t in enumerate(scheduler.timesteps				):
            __snake_case								=       model(a__    ,      a__				)
            __snake_case								=       scheduler.step(a__    ,      a__    ,      a__				).prev_sample

         assert sample.dtype == torch.floataa

