






'''simple docstring'''
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()
__lowerCAmelCase							=logging.get_logger(__name__)

__lowerCAmelCase							={
    "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",
}
__lowerCAmelCase							=[
    "lm_head",
]

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Tuple:






					"""simple docstring"""







					for attribute in key.split('.' ):
										a_									=   getattr(_UpperCAmelCase					,  _UpperCAmelCase )

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

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

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[Any]:






					"""simple docstring"""







					a_									=   []
					a_									=   fairseq_model.state_dict()

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

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

					for name, value in fairseq_dict.items():
										a_									=   False
										if "conv_layers" in name:
															load_conv_layer(
															    _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  )
															a_									=   True
										elif "pos_conv" in name:
															load_pos_conv_layer(
															    _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  )
															a_									=   True
										else:
															for key, mapped_key in MAPPING.items():
																				if not is_headless:
																									a_									=   '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]:
																									a_									=   True
																									if "*" in mapped_key:
																														a_									=   name.split(_UpperCAmelCase )[0].split('.' )[-2]
																														a_									=   mapped_key.replace('*'					,  _UpperCAmelCase )
																									if "weight_g" in name:
																														a_									=   'weight_g'
																									elif "weight_v" in name:
																														a_									=   'weight_v'
																									elif "bias" in name:
																														a_									=   'bias'
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														a_									=   'weight'
																									else:
																														a_									=   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 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				Any:






					"""simple docstring"""







					a_									=   path.split('.' )
					return reduce(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







					a_									=   access_by_string(_UpperCAmelCase					,  _UpperCAmelCase )
					a_									=   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.''' )
					a_									=   fsq_value
					logger.info(F'''{full_name} was correctly initialized from {hf_weight_path}.''' )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Dict:






					"""simple docstring"""







					a_									=   full_name.split('conv_layers.' )[-1]
					a_									=   name.split('.' )
					a_									=   int(items[0] )
					a_									=   int(items[1] )

					a_									=   name.split('.' )[-1]
					if type_id == 0:
										a_									=   'conv'
					elif type_id == 2:
										a_									=   'layer_norm'
					else:
										unused_weights.append(_UpperCAmelCase )
										return

					set_weights(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  F'''conv_layers.{layer_id}.{layer_type}.{weight_type}''' )

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Optional[Any]:






					"""simple docstring"""







					a_									=   full_name.split('pos_conv.' )[-1]
					a_									=   name.split('.' )
					a_									=   int(items[0] )
					a_									=   int(items[1] )

					a_									=   name.split('.' )[-1]
					if type_id != 0:
										unused_weights.append(_UpperCAmelCase )
										return
					else:
										a_									=   'conv'

					set_weights(_UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  F'''layers.{layer_id}.{layer_type}.{weight_type}''' )

@torch.no_grad()
def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase=None					,  _UpperCAmelCase=None					,  _UpperCAmelCase=True )				->				Optional[int]:






					"""simple docstring"""







					if config_path is not None:
										a_									=   DataaVecAudioConfig.from_pretrained(_UpperCAmelCase )
					else:
										a_									=   DataaVecAudioConfig()

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

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

					def load_dataavec(_UpperCAmelCase ):
										a_							,					a_							,					a_									=   fairseq.checkpoint_utils.load_model_ensemble_and_task([path] )
										return model[0].eval()

					a_									=   load_dataavec(_UpperCAmelCase )

					recursively_load_weights(_UpperCAmelCase					,  _UpperCAmelCase					,  not is_finetuned )

					a_									=   WavaVecaProcessor.from_pretrained('facebook/wav2vec2-large-lv60' )

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

					a_									=   processor(_UpperCAmelCase					,  return_tensors='pt'					,  padding=_UpperCAmelCase )

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

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

										a_									=   torch.argmax(_UpperCAmelCase					,  dim=-1 )
										a_									=   processor.batch_decode(_UpperCAmelCase )

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

					print(our_output.shape					,  their_output.shape )
					a_									=   torch.max(torch.abs(our_output - their_output ) ).item()
					print(F'''max_absolute_diff = {max_absolute_diff}''' )  # ~ 1e-7
					a_									=   torch.allclose(_UpperCAmelCase					,  _UpperCAmelCase					,  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(_UpperCAmelCase )

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


if __name__ == "__main__":
						__lowerCAmelCase							=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"
						)
						__lowerCAmelCase							=parser.parse_args()
						convert_wavaveca_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)









'''simple docstring'''
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class    _snake_case   (				snake_case					):

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__   , )      ->	List[str]:
										super().__init__()

										self.register_modules(vae=UpperCAmelCase__   , text_encoder=UpperCAmelCase__   , tokenizer=UpperCAmelCase__   , unet=UpperCAmelCase__   , scheduler=UpperCAmelCase__     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	int:
										with torch.no_grad():
															a_									=   self.vae.encode(tfms.ToTensor()(UpperCAmelCase__     ).unsqueeze(0     ).to(self.device     ) * 2 - 1     )
															a_									=   0.1_8_2_1_5 * latent.latent_dist.sample()
										return latent




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	List[str]:
										a_									=   (1 / 0.1_8_2_1_5) * latent
										with torch.no_grad():
															a_									=   self.vae.decode(UpperCAmelCase__     ).sample
										a_									=   (img / 2 + 0.5).clamp(0   , 1     )
										a_									=   img.detach().cpu().permute(0   , 2   , 3   , 1     ).numpy()
										a_									=   (img * 255).round().astype('uint8'     )
										return Image.fromarray(img[0]     )




					def  __SCREAMING_SNAKE_CASE (     self   , UpperCAmelCase__     )      ->	Any:
										a_									=   self.tokenizer(
										    UpperCAmelCase__   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(text_input.input_ids.to(self.device     )     )[0]

										a_									=   self.tokenizer(
										    ''   , padding='max_length'   , max_length=self.tokenizer.model_max_length   , truncation=UpperCAmelCase__   , return_tensors='pt'   , )

										a_									=   self.text_encoder(uncond_input.input_ids.to(self.device     )     )[0]

										return torch.cat([uncond_embedding, text_embedding]     )




					def __call__(     self   , UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__ = 0.3   , UpperCAmelCase__ = 0.6   , UpperCAmelCase__ = 0.5   , UpperCAmelCase__ = 42   , UpperCAmelCase__ = 50   , UpperCAmelCase__ = 7.5   , )      ->	Image.Image:
										a_									=   steps - int(kmin * steps     )
										a_									=   steps - int(kmax * steps     )

										a_									=   self.prep_text(UpperCAmelCase__     )

										self.scheduler.set_timesteps(UpperCAmelCase__     )

										a_							,					a_									=   img.size
										a_									=   self.encode(UpperCAmelCase__     )

										torch.manual_seed(UpperCAmelCase__     )
										a_									=   torch.randn(
										    (1, self.unet.config.in_channels, height // 8, width // 8)   , ).to(self.device     )

										a_									=   self.scheduler.add_noise(
										    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=self.scheduler.timesteps[tmax]   , )

										a_									=   torch.cat([latents] * 2     )

										a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , self.scheduler.timesteps[tmax]     )

										with torch.no_grad():
															a_									=   self.unet(
															    UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , encoder_hidden_states=UpperCAmelCase__   , ).sample

										a_							,					a_									=   pred.chunk(2     )
										a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

										a_									=   self.scheduler.step(UpperCAmelCase__   , self.scheduler.timesteps[tmax]   , UpperCAmelCase__     ).prev_sample

										for i, t in enumerate(tqdm(self.scheduler.timesteps     )     ):
															if i > tmax:
																				if i < tmin:  # layout generation phase
																									a_									=   self.scheduler.add_noise(
																									    UpperCAmelCase__   , UpperCAmelCase__   , timesteps=UpperCAmelCase__   , )

																									a_									=   (mix_factor * latents) + (
																									    1 - mix_factor
																									) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
																									a_									=   torch.cat([input] * 2     )

																				else:  # content generation phase
																									a_									=   torch.cat([latents] * 2     )

																				a_									=   self.scheduler.scale_model_input(UpperCAmelCase__   , UpperCAmelCase__     )

																				with torch.no_grad():
																									a_									=   self.unet(
																									    UpperCAmelCase__   , UpperCAmelCase__   , encoder_hidden_states=UpperCAmelCase__   , ).sample

																				a_							,					a_									=   pred.chunk(2     )
																				a_									=   pred_uncond + guidance_scale * (pred_text - pred_uncond)

																				a_									=   self.scheduler.step(UpperCAmelCase__   , UpperCAmelCase__   , UpperCAmelCase__     ).prev_sample

										return self.decode(UpperCAmelCase__     )

