



from typing import Callable, List, Optional, Tuple, Union

import torch
from transformers import CLIPTextModel, CLIPTokenizer

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionScheduler
from ...utils import logging
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


__snake_case						:Union[str, Any]      		=     logging.get_logger(__name__)  # pylint: disable=invalid-name

class     _A       (	__UpperCAmelCase      ,__UpperCAmelCase    ):



       @register_to_config
       def __init__(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     bool     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = None):


           '''simple docstring'''

           super().__init__()

           __a						       =				learnable

           if self.learnable:
               assert hidden_size is not None, "learnable=True requires `hidden_size` to be set"
               assert length is not None, "learnable=True requires `length` to be set"

               __a						       =				torch.zeros(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           else:
               __a						       =				None

           __a						       =				torch.nn.Parameter(__SCREAMING_SNAKE_CASE)


class     _A       (	__UpperCAmelCase    ):
       UpperCamelCase__  :    VQModel
       UpperCamelCase__  :    CLIPTextModel
       UpperCamelCase__  :    CLIPTokenizer
       UpperCamelCase__  :    TransformeraDModel
       UpperCamelCase__  :    LearnedClassifierFreeSamplingEmbeddings
       UpperCamelCase__  :    VQDiffusionScheduler



       def __init__(		self							:     List[str]     ,		__SCREAMING_SNAKE_CASE							:     VQModel     ,		__SCREAMING_SNAKE_CASE							:     CLIPTextModel     ,		__SCREAMING_SNAKE_CASE							:     CLIPTokenizer     ,		__SCREAMING_SNAKE_CASE							:     TransformeraDModel     ,		__SCREAMING_SNAKE_CASE							:     VQDiffusionScheduler     ,		__SCREAMING_SNAKE_CASE							:     LearnedClassifierFreeSamplingEmbeddings     ,		):


           '''simple docstring'''

           super().__init__()

           self.register_modules(
               vqvae=__SCREAMING_SNAKE_CASE     ,		transformer=__SCREAMING_SNAKE_CASE     ,		text_encoder=__SCREAMING_SNAKE_CASE     ,		tokenizer=__SCREAMING_SNAKE_CASE     ,		scheduler=__SCREAMING_SNAKE_CASE     ,		learned_classifier_free_sampling_embeddings=__SCREAMING_SNAKE_CASE     ,		)



       def 		_lowerCamelCase			(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     Optional[int]     ,		__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				len(__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) else 1

           # get prompt text embeddings
           __a						       =				self.tokenizer(
               __SCREAMING_SNAKE_CASE     ,		padding='''max_length'''     ,		max_length=self.tokenizer.model_max_length     ,		return_tensors='''pt'''     ,		)
           __a						       =				text_inputs.input_ids

           if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
               __a						       =				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :])
               logger.warning(
                   '''The following part of your input was truncated because CLIP can only handle sequences up to'''
                   F' {self.tokenizer.model_max_length} tokens: {removed_text}')
               __a						       =				text_input_ids[:, : self.tokenizer.model_max_length]
           __a						       =				self.text_encoder(text_input_ids.to(self.device))[0]

           # NOTE: This additional step of normalizing the text embeddings is from VQ-Diffusion.
           # While CLIP does normalize the pooled output of the text transformer when combining
           # the image and text embeddings, CLIP does not directly normalize the last hidden state.
           #
           # CLIP normalizing the pooled output.
           # https://github.com/huggingface/transformers/blob/d92e22d1f28324f513f3080e5c47c071a3916721/src/transformers/models/clip/modeling_clip.py#L1052-L1053
           __a						       =				prompt_embeds / prompt_embeds.norm(dim=-1     ,		keepdim=__SCREAMING_SNAKE_CASE)

           # duplicate text embeddings for each generation per prompt
           __a						       =				prompt_embeds.repeat_interleave(__SCREAMING_SNAKE_CASE     ,		dim=0)

           if do_classifier_free_guidance:
               if self.learned_classifier_free_sampling_embeddings.learnable:
                   __a						       =				self.learned_classifier_free_sampling_embeddings.embeddings
                   __a						       =				negative_prompt_embeds.unsqueeze(0).repeat(__SCREAMING_SNAKE_CASE     ,		1     ,		1)
               else:
                   __a						       =				[''''''] * batch_size

                   __a						       =				text_input_ids.shape[-1]
                   __a						       =				self.tokenizer(
                       __SCREAMING_SNAKE_CASE     ,		padding='''max_length'''     ,		max_length=__SCREAMING_SNAKE_CASE     ,		truncation=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt'''     ,		)
                   __a						       =				self.text_encoder(uncond_input.input_ids.to(self.device))[0]
                   # See comment for normalizing text embeddings
                   __a						       =				negative_prompt_embeds / negative_prompt_embeds.norm(dim=-1     ,		keepdim=__SCREAMING_SNAKE_CASE)

               # duplicate unconditional embeddings for each generation per prompt, using mps friendly method
               __a						       =				negative_prompt_embeds.shape[1]
               __a						       =				negative_prompt_embeds.repeat(1     ,		__SCREAMING_SNAKE_CASE     ,		1)
               __a						       =				negative_prompt_embeds.view(batch_size * num_images_per_prompt     ,		__SCREAMING_SNAKE_CASE     ,		-1)

               # For classifier free guidance, we need to do two forward passes.
               # Here we concatenate the unconditional and text embeddings into a single batch
               # to avoid doing two forward passes
               __a						       =				torch.cat([negative_prompt_embeds, prompt_embeds])

           return prompt_embeds



       @torch.no_grad()
       def __call__(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     Union[str, List[str]]     ,		__SCREAMING_SNAKE_CASE							:     int = 100     ,		__SCREAMING_SNAKE_CASE							:     float = 5.0     ,		__SCREAMING_SNAKE_CASE							:     float = 1.0     ,		__SCREAMING_SNAKE_CASE							:     int = 1     ,		__SCREAMING_SNAKE_CASE							:     Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[torch.FloatTensor] = None     ,		__SCREAMING_SNAKE_CASE							:     Optional[str] = "pil"     ,		__SCREAMING_SNAKE_CASE							:     bool = True     ,		__SCREAMING_SNAKE_CASE							:     Optional[Callable[[int, int, torch.FloatTensor], None]] = None     ,		__SCREAMING_SNAKE_CASE							:     int = 1     ,		):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				1
           elif isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				len(__SCREAMING_SNAKE_CASE)
           else:
               raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE)}')

           __a						       =				batch_size * num_images_per_prompt

           __a						       =				guidance_scale > 1.0

           __a						       =				self._encode_prompt(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           if (callback_steps is None) or (
               callback_steps is not None and (not isinstance(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE) or callback_steps <= 0)
           ):
               raise ValueError(
                   F'`callback_steps` has to be a positive integer but is {callback_steps} of type'
                   F' {type(__SCREAMING_SNAKE_CASE)}.')

           # get the initial completely masked latents unless the user supplied it

           __a						       =				(batch_size, self.transformer.num_latent_pixels)
           if latents is None:
               __a						       =				self.transformer.num_vector_embeds - 1
               __a						       =				torch.full(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE).to(self.device)
           else:
               if latents.shape != latents_shape:
                   raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {latents_shape}')
               if (latents < 0).any() or (latents >= self.transformer.num_vector_embeds).any():
                   raise ValueError(
                       '''Unexpected latents value(s). All latents be valid embedding indices i.e. in the range 0,'''
                       F' {self.transformer.num_vector_embeds - 1} (inclusive).')
               __a						       =				latents.to(self.device)

           # set timesteps
           self.scheduler.set_timesteps(__SCREAMING_SNAKE_CASE     ,		device=self.device)

           __a						       =				self.scheduler.timesteps.to(self.device)

           __a						       =				latents

           for i, t in enumerate(self.progress_bar(__SCREAMING_SNAKE_CASE)):
               # expand the sample if we are doing classifier free guidance
               __a						       =				torch.cat([sample] * 2) if do_classifier_free_guidance else sample

               # predict the un-noised image
               # model_output == `log_p_x_0`
               __a						       =				self.transformer(__SCREAMING_SNAKE_CASE     ,		encoder_hidden_states=__SCREAMING_SNAKE_CASE     ,		timestep=__SCREAMING_SNAKE_CASE).sample

               if do_classifier_free_guidance:
                   __a       ,   __a						       =				model_output.chunk(2)
                   __a						       =				model_output_uncond + guidance_scale * (model_output_text - model_output_uncond)
                   model_output -= torch.logsumexp(__SCREAMING_SNAKE_CASE     ,		dim=1     ,		keepdim=__SCREAMING_SNAKE_CASE)

               __a						       =				self.truncate(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

               # remove `log(0)`'s (`-inf`s)
               __a						       =				model_output.clamp(-70)

               # compute the previous noisy sample x_t -> x_t-1
               __a						       =				self.scheduler.step(__SCREAMING_SNAKE_CASE     ,		timestep=__SCREAMING_SNAKE_CASE     ,		sample=__SCREAMING_SNAKE_CASE     ,		generator=__SCREAMING_SNAKE_CASE).prev_sample

               # call the callback, if provided
               if callback is not None and i % callback_steps == 0:
                   callback(__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				self.vqvae.config.vq_embed_dim
           __a						       =				(batch_size, self.transformer.height, self.transformer.width, embedding_channels)
           __a						       =				self.vqvae.quantize.get_codebook_entry(__SCREAMING_SNAKE_CASE     ,		shape=__SCREAMING_SNAKE_CASE)
           __a						       =				self.vqvae.decode(__SCREAMING_SNAKE_CASE     ,		force_not_quantize=__SCREAMING_SNAKE_CASE).sample

           __a						       =				(image / 2 + 0.5).clamp(0     ,		1)
           __a						       =				image.cpu().permute(0     ,		2     ,		3     ,		1).numpy()

           if output_type == "pil":
               __a						       =				self.numpy_to_pil(__SCREAMING_SNAKE_CASE)

           if not return_dict:
               return (image,)

           return ImagePipelineOutput(images=__SCREAMING_SNAKE_CASE)





       def 		_lowerCamelCase			(		self							:     List[Any]     ,		__SCREAMING_SNAKE_CASE							:     torch.FloatTensor     ,		__SCREAMING_SNAKE_CASE							:     float):


           '''simple docstring'''

           __a       ,   __a						       =				torch.sort(__SCREAMING_SNAKE_CASE     ,		1     ,		descending=__SCREAMING_SNAKE_CASE)
           __a						       =				torch.exp(__SCREAMING_SNAKE_CASE)
           __a						       =				sorted_p_x_0.cumsum(dim=1) < truncation_rate

           # Ensure that at least the largest probability is not zeroed out
           __a						       =				torch.full_like(keep_mask[:, 0:1, :]     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				torch.cat((all_true, keep_mask)     ,		dim=1)
           __a						       =				keep_mask[:, :-1, :]

           __a						       =				keep_mask.gather(1     ,		indices.argsort(1))

           __a						       =				log_p_x_0.clone()

           __a						       =				-torch.inf  # -inf = log(0)

           return rv












import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case__					:							Dict													=     logging.get_logger(__name__)

snake_case__					:							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',
    'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
    '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',
    'label_embs_concat': 'label_embeddings_concat',
    'mask_emb': 'masked_spec_embed',
    'spk_proj': 'speaker_proj',
}
snake_case__					:							Optional[int]													=     [
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'label_embeddings_concat',
    'speaker_proj',
    'layer_norm_for_extract',
]



def _a				(	lowerCamelCase:   List[Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   Union[str, Any]					,				lowerCamelCase:   Any					,				lowerCamelCase:   int )	->						List[str]:







      '''simple docstring'''






      for attribute in key.split('''.''' ):
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase )

      if weight_type is not None:
            __A          =				getattr(lowerCamelCase					,				lowerCamelCase ).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				(	lowerCamelCase:   List[str]					,				lowerCamelCase:   Optional[int] )	->						Tuple:







      '''simple docstring'''






      __A          =				[]
      __A          =				fairseq_model.state_dict()

      __A          =				hf_model.unispeech_sat.feature_extractor

      for name, value in fairseq_dict.items():
            __A          =				False
            if "conv_layers" in name:
                  load_conv_layer(
                      lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				hf_model.config.feat_extract_norm == '''group'''					,				)
                  __A          =				True
            else:
                  for key, mapped_key in MAPPING.items():
                        __A          =				'''unispeech_sat.''' + 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]:
                              if "layer_norm_for_extract" in name and (".".join(name.split('''.''' )[:-1] ) != key):
                                    # special case since naming is very similar
                                    continue
                              __A          =				True
                              if "*" in mapped_key:
                                    __A          =				name.split(lowerCamelCase )[0].split('''.''' )[-2]
                                    __A          =				mapped_key.replace('''*'''					,				lowerCamelCase )
                              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(lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase					,				lowerCamelCase )
                        continue
            if not is_used:
                  unused_weights.append(lowerCamelCase )

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



def _a				(	lowerCamelCase:   int					,				lowerCamelCase:   Any					,				lowerCamelCase:   int					,				lowerCamelCase:   int					,				lowerCamelCase:   List[str] )	->						Union[str, Any]:







      '''simple docstring'''






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

      if type_id == 0:
            if "bias" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
                  __A          =				value
                  logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
                  __A          =				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:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
                  __A          =				value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
            elif "weight" in name:
                  if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                        raise ValueError(
                            F"""{full_name} has size {value.shape}, but"""
                            F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
                  __A          =				value
                  logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
      else:
            unused_weights.append(lowerCamelCase )



@torch.no_grad()
def _a				(	lowerCamelCase:   Tuple					,				lowerCamelCase:   int					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[Any]=None					,				lowerCamelCase:   Optional[int]=True )	->						List[Any]:







      '''simple docstring'''






      if config_path is not None:
            __A          =				UniSpeechSatConfig.from_pretrained(lowerCamelCase )
      else:
            __A          =				UniSpeechSatConfig()

      __A          =				''''''

      if is_finetuned:
            __A          =				UniSpeechSatForCTC(lowerCamelCase )
      else:
            __A          =				UniSpeechSatForPreTraining(lowerCamelCase )

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

      recursively_load_weights(lowerCamelCase					,				lowerCamelCase )

      hf_wavavec.save_pretrained(lowerCamelCase )


if __name__ == "__main__":
       snake_case__					:							Tuple													=     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'
       )
       snake_case__					:							Any													=     parser.parse_args()
       convert_unispeech_sat_checkpoint(
           args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
       )





