





import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class 							snake_case_		(      __SCREAMING_SNAKE_CASE):
		lowerCamelCase     :torch.FloatTensor
		lowerCamelCase     :torch.FloatTensor


def        A__						(							SCREAMING_SNAKE_CASE_				,							SCREAMING_SNAKE_CASE_=0.9_9_9				,							SCREAMING_SNAKE_CASE_="cosine"				,							) ->  List[str]:
		if alpha_transform_type == "cosine":

				def alpha_bar_fn(SCREAMING_SNAKE_CASE_		):
						return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2		) ** 2

		elif alpha_transform_type == "exp":

				def alpha_bar_fn(SCREAMING_SNAKE_CASE_		):
						return math.exp(t * -1_2.0		)

		else:
				raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"		)

		lowerCamelCase :			Union[str, Any]    		=[]
		for i in range(a__		):
				lowerCamelCase :			int    		=i / num_diffusion_timesteps
				lowerCamelCase :			int    		=(i + 1) / num_diffusion_timesteps
				betas.append(min(1 - alpha_bar_fn(a__		) / alpha_bar_fn(a__		)				,							a__		)		)
		return torch.tensor(a__				,							dtype=torch.floataa		)



class 							snake_case_		(      __SCREAMING_SNAKE_CASE    ,       __SCREAMING_SNAKE_CASE):
		lowerCamelCase     :List[Any]							=  1







		@register_to_config
		def __init__(		self      ,  __lowercase = 1_0_0_0      ,  __lowercase = 0.0_0_0_1      ,  __lowercase = 0.0_2      ,  __lowercase = "linear"      ,  __lowercase = 0.0      ,  __lowercase = None      ,  __lowercase = True      ,  )					-> int:


				if trained_betas is not None:
						lowerCamelCase :			Tuple    		=torch.from_numpy(__UpperCamelCase )
				elif beta_schedule == "linear":
						lowerCamelCase :			Dict    		=torch.linspace(__UpperCamelCase      ,  __UpperCamelCase      ,  __UpperCamelCase      ,  dtype=torch.floataa )
				elif beta_schedule == "scaled_linear":
						# this schedule is very specific to the latent diffusion model.
						lowerCamelCase :			Tuple    		=(
						    torch.linspace(beta_start**0.5      ,  beta_end**0.5      ,  __UpperCamelCase      ,  dtype=torch.floataa ) ** 2
						)
				elif beta_schedule == "squaredcos_cap_v2":
						# Glide cosine schedule
						lowerCamelCase :			Union[str, Any]    		=betas_for_alpha_bar(__UpperCamelCase )
				elif beta_schedule == "sigmoid":
						# GeoDiff sigmoid schedule
						lowerCamelCase :			List[Any]    		=torch.linspace(-6      ,  6      ,  __UpperCamelCase )
						lowerCamelCase :			Dict    		=torch.sigmoid(__UpperCamelCase ) * (beta_end - beta_start) + beta_start
				else:
						raise NotImplementedError(F"{beta_schedule} does is not implemented for {self.__class__}" )

				lowerCamelCase :			List[Any]    		=1.0 - self.betas
				lowerCamelCase :			int    		=torch.cumprod(self.alphas      ,  dim=0 )
				lowerCamelCase :			Dict    		=torch.tensor(1.0 )

				lowerCamelCase :			Optional[int]    		=torch.tensor(1.0 )

				# standard deviation of the initial noise distribution
				lowerCamelCase :			str    		=1.0

				# setable values
				lowerCamelCase :			int    		=None
				lowerCamelCase :			Dict    		=torch.from_numpy(np.arange(0      ,  __UpperCamelCase )[::-1].copy() )

				lowerCamelCase :			Dict    		=eta







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase = None )					-> Optional[Any]:
				return sample







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase = 1_0      ,  __lowercase = 1_0      ,  __lowercase = None      ,  )					-> int:
				lowerCamelCase :			List[str]    		=min(self.config.num_train_timesteps      ,  __UpperCamelCase )
				lowerCamelCase :			List[str]    		=num_inference_steps

				lowerCamelCase :			int    		=[]

				lowerCamelCase :			Optional[Any]    		={}
				for j in range(0      ,  num_inference_steps - jump_length      ,  __UpperCamelCase ):
						lowerCamelCase :			List[Any]    		=jump_n_sample - 1

				lowerCamelCase :			int    		=num_inference_steps
				while t >= 1:
						lowerCamelCase :			List[Any]    		=t - 1
						timesteps.append(__UpperCamelCase )

						if jumps.get(__UpperCamelCase      ,  0 ) > 0:
								lowerCamelCase :			Optional[int]    		=jumps[t] - 1
								for _ in range(__UpperCamelCase ):
										lowerCamelCase :			Union[str, Any]    		=t + 1
										timesteps.append(__UpperCamelCase )

				lowerCamelCase :			Union[str, Any]    		=np.array(__UpperCamelCase ) * (self.config.num_train_timesteps // self.num_inference_steps)
				lowerCamelCase :			List[str]    		=torch.from_numpy(__UpperCamelCase ).to(__UpperCamelCase )







		def       __lowercase (		self      ,  __lowercase )					-> List[str]:
				lowerCamelCase :			Dict    		=t - self.config.num_train_timesteps // self.num_inference_steps

				lowerCamelCase :			List[Any]    		=self.alphas_cumprod[t]
				lowerCamelCase :			int    		=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCamelCase :			int    		=1 - alpha_prod_t
				lowerCamelCase :			Union[str, Any]    		=1 - alpha_prod_t_prev

				# For t > 0, compute predicted variance βt (see formula (6) and (7) from
				# https://arxiv.org/pdf/2006.11239.pdf) and sample from it to get
				# previous sample x_{t-1} ~ N(pred_prev_sample, variance) == add
				# variance to pred_sample
				# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
				# without eta.
				# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
				lowerCamelCase :			List[Any]    		=(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

				return variance







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  __lowercase = None      ,  __lowercase = True      ,  )					-> Optional[int]:
				lowerCamelCase :			Any    		=timestep
				lowerCamelCase :			Any    		=timestep - self.config.num_train_timesteps // self.num_inference_steps

				# 1. compute alphas, betas
				lowerCamelCase :			Optional[Any]    		=self.alphas_cumprod[t]
				lowerCamelCase :			List[Any]    		=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
				lowerCamelCase :			Dict    		=1 - alpha_prod_t

				# 2. compute predicted original sample from predicted noise also called
				# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
				lowerCamelCase :			List[str]    		=(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

				# 3. Clip "predicted x_0"
				if self.config.clip_sample:
						lowerCamelCase :			int    		=torch.clamp(__UpperCamelCase      ,  -1      ,  1 )

				# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
				# substitute formula (7) in the algorithm coming from DDPM paper
				# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
				# DDIM schedule gives the same results as DDPM with eta = 1.0
				# Noise is being reused in 7. and 8., but no impact on quality has
				# been observed.

				# 5. Add noise
				lowerCamelCase :			Tuple    		=model_output.device
				lowerCamelCase :			str    		=randn_tensor(model_output.shape      ,  generator=__UpperCamelCase      ,  device=__UpperCamelCase      ,  dtype=model_output.dtype )
				lowerCamelCase :			List[Any]    		=self.eta * self._get_variance(__UpperCamelCase ) ** 0.5

				lowerCamelCase :			str    		=0
				if t > 0 and self.eta > 0:
						lowerCamelCase :			List[Any]    		=std_dev_t * noise

				# 6. compute "direction pointing to x_t" of formula (12)
				# from https://arxiv.org/pdf/2010.02502.pdf
				lowerCamelCase :			Optional[int]    		=(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

				# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
				lowerCamelCase :			str    		=alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

				# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
				lowerCamelCase :			Tuple    		=(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

				# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
				lowerCamelCase :			Dict    		=mask * prev_known_part + (1.0 - mask) * prev_unknown_part

				if not return_dict:
						return (
						    pred_prev_sample,
						    pred_original_sample,
						)

				return RePaintSchedulerOutput(prev_sample=__UpperCamelCase      ,  pred_original_sample=__UpperCamelCase )







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase=None )					-> List[Any]:
				lowerCamelCase :			str    		=self.config.num_train_timesteps // self.num_inference_steps

				for i in range(__UpperCamelCase ):
						lowerCamelCase :			Union[str, Any]    		=self.betas[timestep + i]
						if sample.device.type == "mps":
								# randn does not work reproducibly on mps
								lowerCamelCase :			str    		=randn_tensor(sample.shape      ,  dtype=sample.dtype      ,  generator=__UpperCamelCase )
								lowerCamelCase :			List[Any]    		=noise.to(sample.device )
						else:
								lowerCamelCase :			Tuple    		=randn_tensor(sample.shape      ,  generator=__UpperCamelCase      ,  device=sample.device      ,  dtype=sample.dtype )

						# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
						lowerCamelCase :			List[str]    		=(1 - beta) ** 0.5 * sample + beta**0.5 * noise

				return sample







		def       __lowercase (		self      ,  __lowercase      ,  __lowercase      ,  __lowercase      ,  )					-> List[Any]:
				raise NotImplementedError('''Use `DDPMScheduler.add_noise()` to train for sampling with RePaint.''' )







		def __len__(		self )					-> Any:
				return self.config.num_train_timesteps



import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

_A							=       {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
_A							=       [
    '''lm_head''',
]




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Optional[int]						,   a__	:	Optional[Any]						,   a__	:	Tuple  )		->						str:
       for attribute in key.split("""."""  ):
              UpperCamelCase_											=							getattr(a__						,   a__  )

       if weight_type is not None:
              UpperCamelCase_											=							getattr(a__						,   a__  ).shape
       else:
              UpperCamelCase_											=							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_											=							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__					(    a__	:	List[str]						,   a__	:	int						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							[]
       UpperCamelCase_											=							fairseq_model.state_dict()

       if not is_headless:
              UpperCamelCase_											=							hf_model.dataavec_audio.feature_extractor
              UpperCamelCase_											=							hf_model.dataavec_audio.encoder.pos_conv_embed

       else:
              UpperCamelCase_											=							hf_model.feature_extractor
              UpperCamelCase_											=							hf_model.encoder.pos_conv_embed

       for name, value in fairseq_dict.items():
              UpperCamelCase_											=							False
              if "conv_layers" in name:
                     load_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              elif "pos_conv" in name:
                     load_pos_conv_layer(
                         a__						,   a__						,   a__						,   a__						,   )
                     UpperCamelCase_											=							True
              else:
                     for key, mapped_key in MAPPING.items():
                            if not is_headless:
                                   UpperCamelCase_											=							"""data2vec_audio.""" + 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(a__  )[0].split("""."""  )[-2]
                                          UpperCamelCase_											=							mapped_key.replace("""*"""						,   a__  )
                                   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(a__						,   a__						,   a__						,   a__						,   a__  )
                            continue
              if not is_used:
                     unused_weights.append(a__  )

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




def 					lowerCamelCase__					(    a__	:	Optional[Any]						,   a__	:	List[str]  )		->						int:
       UpperCamelCase_											=							path.split("""."""  )
       return reduce(a__						,   a__						,   a__  )




def 					lowerCamelCase__					(    a__	:	Union[str, Any]						,   a__	:	int						,   a__	:	Optional[int]						,   a__	:	int  )		->						Any:
       UpperCamelCase_											=							access_by_string(a__						,   a__  )
       UpperCamelCase_											=							hf_weight.data

       if fsq_value.shape != hf_value.shape:
              raise ValueError(f'''{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found.'''  )
       UpperCamelCase_											=							fsq_value
       logger.info(f'''{full_name} was correctly initialized from {hf_weight_path}.'''  )




def 					lowerCamelCase__					(    a__	:	Dict						,   a__	:	str						,   a__	:	str						,   a__	:	str  )		->						int:
       UpperCamelCase_											=							full_name.split("""conv_layers."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id == 0:
              UpperCamelCase_											=							"""conv"""
       elif type_id == 2:
              UpperCamelCase_											=							"""layer_norm"""
       else:
              unused_weights.append(a__  )
              return

       set_weights(a__						,   a__						,   a__						,   f'''conv_layers.{layer_id}.{layer_type}.{weight_type}'''  )




def 					lowerCamelCase__					(    a__	:	Tuple						,   a__	:	Any						,   a__	:	Tuple						,   a__	:	Dict  )		->						Any:
       UpperCamelCase_											=							full_name.split("""pos_conv."""  )[-1]
       UpperCamelCase_											=							name.split("""."""  )
       UpperCamelCase_											=							int(items[0]  )
       UpperCamelCase_											=							int(items[1]  )

       UpperCamelCase_											=							name.split("""."""  )[-1]
       if type_id != 0:
              unused_weights.append(a__  )
              return
       else:
              UpperCamelCase_											=							"""conv"""

       set_weights(a__						,   a__						,   a__						,   f'''layers.{layer_id}.{layer_type}.{weight_type}'''  )




@torch.no_grad()
def 					lowerCamelCase__					(    a__	:	List[Any]						,   a__	:	List[Any]						,   a__	:	Optional[int]=None						,   a__	:	List[str]=None						,   a__	:	Optional[int]=True  )		->						str:
       if config_path is not None:
              UpperCamelCase_											=							DataaVecAudioConfig.from_pretrained(a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioConfig()

       if not is_finetuned:
              # Modify final_proj layer name
              UpperCamelCase_											=							DataaVecAudioModel(a__  )
              UpperCamelCase_											=							os.path.dirname(a__  )

              UpperCamelCase_											=							torch.load(a__  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.weight"""  )
              UpperCamelCase_											=							state_dict["""model"""].pop("""final_proj.0.bias"""  )
              UpperCamelCase_											=							os.path.join(a__						,   """converted.pt"""  )
              torch.save(a__						,   a__  )
       else:
              UpperCamelCase_											=							DataaVecAudioForCTC(a__  )
              UpperCamelCase_											=							checkpoint_path

       def load_dataavec(a__	:	Optional[int]  ):
              UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_											=							fairseq.checkpoint_utils.load_model_ensemble_and_task([path]  )
              return model[0].eval()

       UpperCamelCase_											=							load_dataavec(a__  )

       recursively_load_weights(a__						,   a__						,   not is_finetuned  )

       UpperCamelCase_											=							WavaVecaProcessor.from_pretrained("""facebook/wav2vec2-large-lv60"""  )

       UpperCamelCase_											=							load_dataset("""patrickvonplaten/librispeech_asr_dummy"""						,   """clean"""						,   split="""validation"""  )
       UpperCamelCase_											=							[x["""array"""] for x in ds[:4]["""audio"""]]

       UpperCamelCase_											=							processor(a__						,   return_tensors="""pt"""						,   padding=a__  )

       UpperCamelCase_											=							inputs.input_values
       UpperCamelCase_											=							inputs.attention_mask
       #    input_values = inputs.input_values[:, :-1]
       #    attention_mask = inputs.attention_mask[:, :-1]

       hf_wavavec.eval()
       model.eval()
       if is_finetuned:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """encoder_out"""
              ].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""logits"""]

              UpperCamelCase_											=							torch.argmax(a__						,   dim=-1  )
              UpperCamelCase_											=							processor.batch_decode(a__  )

              print(f'''Expected Output: {ds[:4]["text"]}, Pred: {output_string}'''  )
       else:
              UpperCamelCase_											=							model(source=a__						,   padding_mask=(1 - attention_mask)						,   mask=a__						,   features_only=a__  )[
                  """layer_results"""
              ][-1][0].transpose(0						,   1  )
              UpperCamelCase_											=							hf_wavavec(a__						,   attention_mask=a__  )["""last_hidden_state"""]

       print(our_output.shape						,   their_output.shape  )
       UpperCamelCase_											=							torch.max(torch.abs(our_output - their_output  )  ).item()
       print(f'''max_absolute_diff = {max_absolute_diff}'''  )  # ~ 1e-7
       UpperCamelCase_											=							torch.allclose(a__						,   a__						,   atol=1e-3  )
       print("""Do both models output the same tensors?"""						,   """🔥""" if success else """💩"""  )
       if not success:
              raise Exception("""Something went wRoNg"""  )

       hf_wavavec.save_pretrained(a__  )

       if is_finetuned:
              processor.save_pretrained(a__  )
       else:
              processor.feature_extractor.save_pretrained(a__  )


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'''
      )
      _A							=       parser.parse_args()
      convert_wavaveca_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
      )



