




"""simple docstring"""




from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowerCAmelCase							:    Optional[int]  							=							8



def 					a__				(       snake_case__ ,				snake_case__=BITS       )		->      Dict:
			lowerCamelCase       =      x.device

			lowerCamelCase       =      (x * 2_55).int().clamp(0 ,				2_55       )

			lowerCamelCase       =      2 ** torch.arange(bits - 1 ,				-1 ,				-1 ,				device=snake_case__       )
			lowerCamelCase       =      rearrange(snake_case__ ,				"""d -> d 1 1"""       )
			lowerCamelCase       =      rearrange(snake_case__ ,				"""b c h w -> b c 1 h w"""       )

			lowerCamelCase       =      ((x & mask) != 0).float()
			lowerCamelCase       =      rearrange(snake_case__ ,				"""b c d h w -> b (c d) h w"""       )
			lowerCamelCase       =      bits * 2 - 1
			return bits



def 					a__				(       snake_case__ ,				snake_case__=BITS       )		->      Optional[Any]:
			lowerCamelCase       =      x.device

			lowerCamelCase       =      (x > 0).int()
			lowerCamelCase       =      2 ** torch.arange(bits - 1 ,				-1 ,				-1 ,				device=snake_case__ ,				dtype=torch.intaa       )

			lowerCamelCase       =      rearrange(snake_case__ ,				"""d -> d 1 1"""       )
			lowerCamelCase       =      rearrange(snake_case__ ,				"""b (c d) h w -> b c d h w""" ,				d=8       )
			lowerCamelCase       =      reduce(x * mask ,				"""b c d h w -> b c h w""" ,				"""sum"""       )
			return (dec / 2_55).clamp(0.0 ,				1.0       )



def 					a__				(       self ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ = 0.0 ,				snake_case__ = True ,				snake_case__=None ,				snake_case__ = True ,				)		->      Union[DDIMSchedulerOutput, Tuple]:
			if self.num_inference_steps is None:
						raise ValueError(
						    """Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"""       )

			# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
			# Ideally, read DDIM paper in-detail understanding

			# Notation (<variable name> -> <name in paper>
			# - pred_noise_t -> e_theta(x_t, t)
			# - pred_original_sample -> f_theta(x_t, t) or x_0
			# - std_dev_t -> sigma_t
			# - eta -> η
			# - pred_sample_direction -> "direction pointing to x_t"
			# - pred_prev_sample -> "x_t-1"

			# 1. get previous step value (=t-1)
			lowerCamelCase       =      timestep - self.config.num_train_timesteps // self.num_inference_steps

			# 2. compute alphas, betas
			lowerCamelCase       =      self.alphas_cumprod[timestep]
			lowerCamelCase       =      self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

			lowerCamelCase       =      1 - alpha_prod_t

			# 3. compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			lowerCamelCase       =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

			# 4. Clip "predicted x_0"
			lowerCamelCase       =      self.bit_scale
			if self.config.clip_sample:
						lowerCamelCase       =      torch.clamp(snake_case__ ,				-scale ,				snake_case__       )

			# 5. compute variance: "sigma_t(η)" -> see formula (16)
			# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
			lowerCamelCase       =      self._get_variance(snake_case__ ,				snake_case__       )
			lowerCamelCase       =      eta * variance ** 0.5

			if use_clipped_model_output:
						# the model_output is always re-derived from the clipped x_0 in Glide
						lowerCamelCase       =      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

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

			# 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
			lowerCamelCase       =      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

			if eta > 0:
						# randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
						lowerCamelCase       =      model_output.device if torch.is_tensor(snake_case__       ) else """cpu"""
						lowerCamelCase       =      torch.randn(model_output.shape ,				dtype=model_output.dtype ,				generator=snake_case__       ).to(snake_case__       )
						lowerCamelCase       =      self._get_variance(snake_case__ ,				snake_case__       ) ** 0.5 * eta * noise

						lowerCamelCase       =      prev_sample + variance

			if not return_dict:
						return (prev_sample,)

			return DDIMSchedulerOutput(prev_sample=snake_case__ ,				pred_original_sample=snake_case__       )



def 					a__				(       self ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__="epsilon" ,				snake_case__=None ,				snake_case__ = True ,				)		->      Union[DDPMSchedulerOutput, Tuple]:
			lowerCamelCase       =      timestep

			if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
						lowerCamelCase   ,     lowerCamelCase       =      torch.split(snake_case__ ,				sample.shape[1] ,				dim=1       )
			else:
						lowerCamelCase       =      None

			# 1. compute alphas, betas
			lowerCamelCase       =      self.alphas_cumprod[t]
			lowerCamelCase       =      self.alphas_cumprod[t - 1] if t > 0 else self.one
			lowerCamelCase       =      1 - alpha_prod_t
			lowerCamelCase       =      1 - alpha_prod_t_prev

			# 2. compute predicted original sample from predicted noise also called
			# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
			if prediction_type == "epsilon":
						lowerCamelCase       =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
			elif prediction_type == "sample":
						lowerCamelCase       =      model_output
			else:
						raise ValueError(F'Unsupported prediction_type {prediction_type}.'       )

			# 3. Clip "predicted x_0"
			lowerCamelCase       =      self.bit_scale
			if self.config.clip_sample:
						lowerCamelCase       =      torch.clamp(snake_case__ ,				-scale ,				snake_case__       )

			# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
			# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
			lowerCamelCase       =      (alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
			lowerCamelCase       =      self.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

			# 5. Compute predicted previous sample µ_t
			# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
			lowerCamelCase       =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

			# 6. Add noise
			lowerCamelCase       =      0
			if t > 0:
						lowerCamelCase       =      torch.randn(
						    model_output.size() ,				dtype=model_output.dtype ,				layout=model_output.layout ,				generator=snake_case__       ).to(model_output.device       )
						lowerCamelCase       =      (self._get_variance(snake_case__ ,				predicted_variance=snake_case__       ) ** 0.5) * noise

			lowerCamelCase       =      pred_prev_sample + variance

			if not return_dict:
						return (pred_prev_sample,)

			return DDPMSchedulerOutput(prev_sample=snake_case__ ,				pred_original_sample=snake_case__       )






class 					__magic_name__							( UpperCAmelCase__       ):






				'''simple docstring'''





				def __init__( self	,				_a	,				_a	,				_a = 1.0	,				):





							"""simple docstring"""
							super().__init__()
							lowerCamelCase       =      bit_scale
							lowerCamelCase       =      (
							    ddim_bit_scheduler_step if isinstance(_a	,				_a				) else ddpm_bit_scheduler_step
							)

							self.register_modules(unet=_a	,				scheduler=_a				)





				@torch.no_grad()
				def __call__( self	,				_a = 256	,				_a = 256	,				_a = 50	,				_a = None	,				_a = 1	,				_a = "pil"	,				_a = True	,				**_a	,				):





							"""simple docstring"""
							lowerCamelCase       =      torch.randn(
							    (batch_size, self.unet.config.in_channels, height, width)	,				generator=_a	,				)
							lowerCamelCase       =      decimal_to_bits(_a				) * self.bit_scale
							lowerCamelCase       =      latents.to(self.device				)

							self.scheduler.set_timesteps(_a				)

							for t in self.progress_bar(self.scheduler.timesteps				):
										# predict the noise residual
										lowerCamelCase       =      self.unet(_a	,				_a				).sample

										# compute the previous noisy sample x_t -> x_t-1
										lowerCamelCase       =      self.scheduler.step(_a	,				_a	,				_a				).prev_sample

							lowerCamelCase       =      bits_to_decimal(_a				)

							if output_type == "pil":
										lowerCamelCase       =      self.numpy_to_pil(_a				)

							if not return_dict:
										return (image,)

							return ImagePipelineOutput(images=_a				)









"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from torch import nn

from transformers import (
    SpeechaTextaConfig,
    SpeechaTextaForCausalLM,
    SpeechaTextaTokenizer,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowerCAmelCase							:    Optional[int]  							=							logging.get_logger(__name__)

lowerCAmelCase							:    Optional[Any]  							=							{
    """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""": """lm_head""",
    """mask_emb""": """masked_spec_embed""",
}
lowerCAmelCase							:    Optional[Any]  							=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      List[Any]:
			for attribute in key.split("""."""       ):
						lowerCamelCase       =      getattr(snake_case__ ,				snake_case__       )

			if weight_type is not None:
						lowerCamelCase       =      getattr(snake_case__ ,				snake_case__       ).shape
			else:
						lowerCamelCase       =      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":
						lowerCamelCase       =      value
			elif weight_type == "weight_g":
						lowerCamelCase       =      value
			elif weight_type == "weight_v":
						lowerCamelCase       =      value
			elif weight_type == "bias":
						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__				(       snake_case__ ,				snake_case__       )		->      Tuple:
			lowerCamelCase       =      []
			lowerCamelCase       =      fairseq_model.state_dict()

			lowerCamelCase       =      hf_model.feature_extractor

			# if encoder has different dim to decoder -> use proj_weight
			lowerCamelCase       =      None

			for name, value in fairseq_dict.items():
						lowerCamelCase       =      False
						if "conv_layers" in name:
									load_conv_layer(
									    snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				hf_model.config.feat_extract_norm == """group""" ,				)
									lowerCamelCase       =      True
						elif name.split("""."""       )[0] == "proj":
									lowerCamelCase       =      fairseq_model.proj
									lowerCamelCase       =      True
						else:
									for key, mapped_key in MAPPING.items():
												if key in name or key.split("""w2v_model."""       )[-1] == name.split("""."""       )[0]:
															lowerCamelCase       =      True
															if "*" in mapped_key:
																		lowerCamelCase       =      name.split(snake_case__       )[0].split("""."""       )[-2]
																		lowerCamelCase       =      mapped_key.replace("""*""" ,				snake_case__       )
															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:
																		lowerCamelCase       =      """weight"""
															else:
																		lowerCamelCase       =      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}'       )

			return proj_weight



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__       )		->      Optional[int]:
			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:
									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.'
									)
									lowerCamelCase       =      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.'
									)
									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:
									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."
									)
									lowerCamelCase       =      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.'
									)
									lowerCamelCase       =      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 					a__				(       snake_case__       )		->      Tuple:
			lowerCamelCase   ,     lowerCamelCase       =      emb.weight.shape
			lowerCamelCase       =      nn.Linear(snake_case__ ,				snake_case__ ,				bias=snake_case__       )
			lowerCamelCase       =      emb.weight.data
			return lin_layer



def 					a__				(       snake_case__       )		->      Optional[int]:
			with open(snake_case__ ,				"""r""" ,				encoding="""utf-8"""       ) as f:
						lowerCamelCase       =      f.readlines()
						lowerCamelCase       =      [line.split(""" """       )[0] for line in lines]

			lowerCamelCase       =      len(snake_case__       )

			lowerCamelCase       =      {
			    """<s>""": 0,
			    """<pad>""": 1,
			    """</s>""": 2,
			    """<unk>""": 3,
			}

			vocab_dict.update(dict(zip(snake_case__ ,				range(4 ,				num_words + 4       )       )       )       )
			return vocab_dict



@torch.no_grad()
def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				)		->      Optional[int]:
			lowerCamelCase       =      WavaVecaConfig.from_pretrained(snake_case__       )
			lowerCamelCase       =      SpeechaTextaConfig.from_pretrained(
			    snake_case__ ,				vocab_size=snake_case__ ,				decoder_layers=snake_case__ ,				do_stable_layer_norm=snake_case__       )

			lowerCamelCase       =      WavaVecaFeatureExtractor(
			    feature_size=1 ,				sampling_rate=1_60_00 ,				padding_value=0 ,				do_normalize=snake_case__ ,				return_attention_mask=snake_case__ ,				)

			lowerCamelCase   ,     lowerCamelCase   ,     lowerCamelCase       =      fairseq.checkpoint_utils.load_model_ensemble_and_task(
			    [checkpoint_path] ,				arg_overrides={"""data""": """/""".join(dict_path.split("""/"""       )[:-1]       )}       )
			lowerCamelCase       =      model[0].eval()

			# set weights for wav2vec2 encoder
			lowerCamelCase       =      WavaVecaModel(snake_case__       )
			lowerCamelCase       =      recursively_load_weights_wavaveca(model.encoder ,				snake_case__       )

			lowerCamelCase       =      SpeechaTextaForCausalLM(snake_case__       )
			lowerCamelCase   ,     lowerCamelCase       =      hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() ,				strict=snake_case__       )

			# set output linear layer
			unexpected_keys.remove("""embed_out"""       )
			lowerCamelCase       =      nn.Parameter(model.decoder.embed_out.detach()       )

			# layer norm is init to identity matrix so leaving it is fine
			logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}'       )
			logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}'       )

			lowerCamelCase       =      SpeechEncoderDecoderModel(encoder=snake_case__ ,				decoder=snake_case__       )
			lowerCamelCase       =      False

			# add projection layer
			lowerCamelCase       =      nn.Parameter(projection_layer.weight       )
			lowerCamelCase       =      nn.Parameter(projection_layer.bias       )

			lowerCamelCase       =      create_vocab_dict(snake_case__       )

			with open(os.path.join(snake_case__ ,				"""vocab.json"""       ) ,				"""w"""       ) as fp:
						json.dump(snake_case__ ,				snake_case__       )

			lowerCamelCase       =      SpeechaTextaTokenizer(os.path.join(snake_case__ ,				"""vocab.json"""       )       )
			tokenizer.save_pretrained(snake_case__       )

			lowerCamelCase       =      hf_wavavec.config.to_dict()
			lowerCamelCase       =      tokenizer.pad_token_id
			lowerCamelCase       =      tokenizer.bos_token_id
			lowerCamelCase       =      tokenizer.eos_token_id
			lowerCamelCase       =      """speech_to_text_2"""
			lowerCamelCase       =      """wav2vec2"""

			lowerCamelCase       =      SpeechEncoderDecoderConfig.from_dict(snake_case__       )

			hf_wavavec.save_pretrained(snake_case__       )
			feature_extractor.save_pretrained(snake_case__       )


if __name__ == "__main__":
					lowerCAmelCase							:    Any  							=							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(
					    """--encoder_config_path""",
					    default="""facebook/wav2vec2-large-lv60""",
					    type=str,
					    help="""Path to hf encoder wav2vec2 checkpoint config""",
					)
					parser.add_argument(
					    """--decoder_config_path""",
					    default="""facebook/s2t-small-mustc-en-fr-st""",
					    type=str,
					    help="""Path to hf decoder s2t checkpoint config""",
					)
					parser.add_argument("""--vocab_size""", default=1_0224, type=int, help="""Vocab size of decoder""")
					parser.add_argument("""--num_decoder_layers""", default=7, type=int, help="""Number of decoder layers""")

					lowerCAmelCase							:    str  							=							parser.parse_args()
					convert_wavaveca_checkpoint(
					    args.checkpoint_path,
					    args.pytorch_dump_folder_path,
					    args.dict_path,
					    encoder_config_path=args.encoder_config_path,
					    decoder_config_path=args.decoder_config_path,
					    vocab_size=args.vocab_size,
					    num_decoder_layers=args.num_decoder_layers,
					)



