



"""simple docstring"""
import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class 					a	(  UpperCAmelCase__	,      UpperCAmelCase__       ):



  @register_to_config
  def __init__(						self :					Dict       ,		*,
      lowerCAmelCase :					int = 4       ,		lowerCAmelCase :					int = 768       ,		lowerCAmelCase :					int       ,		lowerCAmelCase :					Optional[int]       ,		)     ->					Any:


     '''simple docstring'''




     super().__init__()

     SCREAMING_SNAKE_CASE_: str     			=nn.Parameter(torch.zeros(lowerCAmelCase		)		)

     # parameters for additional clip time embeddings
     SCREAMING_SNAKE_CASE_: List[str]     			=nn.Linear(lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=nn.Linear(lowerCAmelCase       ,		lowerCAmelCase		)

     # parameters for encoder hidden states
     SCREAMING_SNAKE_CASE_: Tuple     			=clip_extra_context_tokens
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=nn.Linear(
         lowerCAmelCase       ,		self.clip_extra_context_tokens * cross_attention_dim		)
     SCREAMING_SNAKE_CASE_: List[str]     			=nn.Linear(lowerCAmelCase       ,		lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Any     			=nn.LayerNorm(lowerCAmelCase		)





  def 	lowerCamelCase__       (						self :					Union[str, Any]       ,		*, lowerCAmelCase :					Union[str, Any]       ,		lowerCAmelCase :					List[str]       ,		lowerCAmelCase :					Dict       ,		lowerCAmelCase :					Dict		)     ->					Optional[Any]:


     '''simple docstring'''




     if do_classifier_free_guidance:
        # Add the classifier free guidance embeddings to the image embeddings
        SCREAMING_SNAKE_CASE_: Optional[int]     			=image_embeddings.shape[0]
        SCREAMING_SNAKE_CASE_: Any     			=self.learned_classifier_free_guidance_embeddings.unsqueeze(0		)
        SCREAMING_SNAKE_CASE_: Optional[Any]     			=classifier_free_guidance_embeddings.expand(
            lowerCAmelCase       ,		-1		)
        SCREAMING_SNAKE_CASE_: List[Any]     			=torch.cat([classifier_free_guidance_embeddings, image_embeddings]       ,		dim=0		)

     # The image embeddings batch size and the text embeddings batch size are equal
     assert image_embeddings.shape[0] == prompt_embeds.shape[0]

     SCREAMING_SNAKE_CASE_: Optional[Any]     			=prompt_embeds.shape[0]

     # "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
     # adding CLIP embeddings to the existing timestep embedding, ...
     SCREAMING_SNAKE_CASE_: Dict     			=self.embedding_proj(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.clip_image_embeddings_project_to_time_embeddings(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=time_projected_image_embeddings + time_projected_prompt_embeds

     # ... and by projecting CLIP embeddings into four
     # extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
     SCREAMING_SNAKE_CASE_: Optional[Any]     			=self.clip_extra_context_tokens_proj(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[Any]     			=clip_extra_context_tokens.reshape(lowerCAmelCase       ,		-1       ,		self.clip_extra_context_tokens		)
     SCREAMING_SNAKE_CASE_: str     			=clip_extra_context_tokens.permute(0       ,		2       ,		1		)

     SCREAMING_SNAKE_CASE_: Union[str, Any]     			=self.encoder_hidden_states_proj(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: int     			=self.text_encoder_hidden_states_norm(lowerCAmelCase		)
     SCREAMING_SNAKE_CASE_: List[str]     			=torch.cat([clip_extra_context_tokens, text_encoder_hidden_states]       ,		dim=1		)

     return text_encoder_hidden_states, additive_clip_time_embeddings








"""simple docstring"""
import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_UpperCAmelCase     =			logging.get_logger("""transformers.models.speecht5""")





def      __magic_name__				(							lowercase							,			lowercase							,			lowercase ):
   hf_model.apply_weight_norm()

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""input_conv.weight_g"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""input_conv.bias"""]

   for i in range(len(config.upsample_rates ) ):
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_g''']
      SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''upsamples.{i}.1.weight_v''']
      SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint[f'''upsamples.{i}.1.bias''']

   for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
      for j in range(len(config.resblock_dilation_sizes ) ):
         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: Union[str, Any]     			=checkpoint[f'''blocks.{i}.convs1.{j}.1.bias''']

         SCREAMING_SNAKE_CASE_: Dict     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_g''']
         SCREAMING_SNAKE_CASE_: Any     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.weight_v''']
         SCREAMING_SNAKE_CASE_: List[Any]     			=checkpoint[f'''blocks.{i}.convs2.{j}.1.bias''']

   SCREAMING_SNAKE_CASE_: Tuple     			=checkpoint["""output_conv.1.weight_g"""]
   SCREAMING_SNAKE_CASE_: List[str]     			=checkpoint["""output_conv.1.weight_v"""]
   SCREAMING_SNAKE_CASE_: Optional[int]     			=checkpoint["""output_conv.1.bias"""]

   hf_model.remove_weight_norm()





@torch.no_grad()
def      __magic_name__				(							lowercase							,			lowercase							,			lowercase							,			lowercase=None							,			lowercase=None							,			):
   if config_path is not None:
      SCREAMING_SNAKE_CASE_: List[Any]     			=SpeechTaHifiGanConfig.from_pretrained(lowercase )
   else:
      SCREAMING_SNAKE_CASE_: Optional[Any]     			=SpeechTaHifiGanConfig()

   SCREAMING_SNAKE_CASE_: Union[str, Any]     			=SpeechTaHifiGan(lowercase )

   SCREAMING_SNAKE_CASE_: Any     			=torch.load(lowercase )
   load_weights(orig_checkpoint["""model"""]["""generator"""]							,			lowercase							,			lowercase )

   SCREAMING_SNAKE_CASE_: List[Any]     			=np.load(lowercase )
   SCREAMING_SNAKE_CASE_: Any     			=stats[0].reshape(-1 )
   SCREAMING_SNAKE_CASE_: str     			=stats[1].reshape(-1 )
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()
   SCREAMING_SNAKE_CASE_: Dict     			=torch.from_numpy(lowercase ).float()

   model.save_pretrained(lowercase )

   if repo_id:
      print("""Pushing to the hub...""" )
      model.push_to_hub(lowercase )


if __name__ == "__main__":
   _UpperCAmelCase     =			argparse.ArgumentParser()
   parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
   parser.add_argument("""--stats_path""", required=True, default=None, type=str, help="""Path to stats.npy file""")
   parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
   parser.add_argument(
       """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
   )
   parser.add_argument(
       """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
   )

   _UpperCAmelCase     =			parser.parse_args()
   convert_hifigan_checkpoint(
       args.checkpoint_path,
       args.stats_path,
       args.pytorch_dump_folder_path,
       args.config_path,
       args.push_to_hub,
   )



