





import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Dict="attention"						):





  """simple docstring"""



  a__     : Any  =params[f'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
  a__     : Optional[int]  =params[f'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
  a__     : str  =params[f'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
  a__     : List[Any]  =params[f'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
  return k, o, q, v
def 					_A	(		SCREAMING_SNAKE_CASE					:       List[Any]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       Tuple=False						):





  """simple docstring"""



  if split_mlp_wi:
    a__     : Dict  =params[f'''{prefix}/layers_{i}/mlp/wi_0/kernel''']
    a__     : Union[str, Any]  =params[f'''{prefix}/layers_{i}/mlp/wi_1/kernel''']
    a__     : List[str]  =(wi_a, wi_a)
  else:
    a__     : Union[str, Any]  =params[f'''{prefix}/layers_{i}/mlp/wi/kernel''']

  a__     : Union[str, Any]  =params[f'''{prefix}/layers_{i}/mlp/wo/kernel''']
  return wi, wo
def 					_A	(		SCREAMING_SNAKE_CASE					:       Optional[int]	,	SCREAMING_SNAKE_CASE					:       List[str]	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Optional[int]						):





  """simple docstring"""



  return params[f'''{prefix}/layers_{i}/{layer_name}/scale''']
def 					_A	(		SCREAMING_SNAKE_CASE					:       dict	,	*, SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       bool						):





  """simple docstring"""



  a__     : Dict  =traverse_util.flatten_dict(variables["target"]						)
  a__     : Tuple  ={"/".join(SCREAMING_SNAKE_CASE						): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  a__     : int  ="encoder/layers_0/mlp/wi_0/kernel" in old
  print("Split MLP:"	,	SCREAMING_SNAKE_CASE						)

  a__     : List[Any]  =collections.OrderedDict()

  # Shared embeddings.
  a__     : Tuple  =old["token_embedder/embedding"]

  # Encoder.
  for i in range(SCREAMING_SNAKE_CASE						):
    # Block i, layer 0 (Self Attention).
    a__     : int  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	"pre_attention_layer_norm"						)
    a__ ,							a__ ,							a__ ,							a__     : Dict  =tax_attention_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	"attention"						)
    a__     : List[str]  =layer_norm
    a__     : Optional[Any]  =k.T
    a__     : List[Any]  =o.T
    a__     : Union[str, Any]  =q.T
    a__     : str  =v.T

    # Block i, layer 1 (MLP).
    a__     : int  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	"pre_mlp_layer_norm"						)
    a__ ,							a__     : int  =tax_mlp_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"encoder"	,	SCREAMING_SNAKE_CASE						)
    a__     : Tuple  =layer_norm
    if split_mlp_wi:
      a__     : Optional[Any]  =wi[0].T
      a__     : Dict  =wi[1].T
    else:
      a__     : Dict  =wi.T
    a__     : Dict  =wo.T

  a__     : Any  =old[
      "encoder/relpos_bias/rel_embedding"
  ].T
  a__     : List[Any]  =old["encoder/encoder_norm/scale"]

  if not is_encoder_only:
    # Decoder.
    for i in range(SCREAMING_SNAKE_CASE						):
      # Block i, layer 0 (Self Attention).
      a__     : Union[str, Any]  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"pre_self_attention_layer_norm"						)
      a__ ,							a__ ,							a__ ,							a__     : Any  =tax_attention_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"self_attention"						)
      a__     : Union[str, Any]  =layer_norm
      a__     : List[Any]  =k.T
      a__     : Optional[int]  =o.T
      a__     : Any  =q.T
      a__     : Optional[int]  =v.T

      # Block i, layer 1 (Cross Attention).
      a__     : Optional[int]  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"pre_cross_attention_layer_norm"						)
      a__ ,							a__ ,							a__ ,							a__     : Optional[Any]  =tax_attention_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"encoder_decoder_attention"						)
      a__     : Optional[int]  =layer_norm
      a__     : Union[str, Any]  =k.T
      a__     : Optional[Any]  =o.T
      a__     : str  =q.T
      a__     : Any  =v.T

      # Block i, layer 2 (MLP).
      a__     : Tuple  =tax_layer_norm_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	"pre_mlp_layer_norm"						)
      a__ ,							a__     : Union[str, Any]  =tax_mlp_lookup(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	"decoder"	,	SCREAMING_SNAKE_CASE						)
      a__     : str  =layer_norm
      if split_mlp_wi:
        a__     : Tuple  =wi[0].T
        a__     : List[str]  =wi[1].T
      else:
        a__     : Tuple  =wi.T
      a__     : Union[str, Any]  =wo.T

    a__     : Union[str, Any]  =old["decoder/decoder_norm/scale"]
    a__     : str  =old[
        "decoder/relpos_bias/rel_embedding"
    ].T

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      a__     : List[str]  =old["decoder/logits_dense/kernel"].T

  return new
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       bool						):





  """simple docstring"""



  a__     : List[Any]  =collections.OrderedDict([(k, torch.from_numpy(v.copy()						)) for (k, v) in converted_params.items()]						)

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    a__     : Optional[Any]  =state_dict["shared.weight"]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      a__     : List[Any]  =state_dict["shared.weight"]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("Using shared word embeddings as lm_head."						)
      a__     : int  =state_dict["shared.weight"]

  return state_dict
def 					_A	(		SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       int	,	SCREAMING_SNAKE_CASE					:       Union[str, Any]	,	SCREAMING_SNAKE_CASE					:       Any						):





  """simple docstring"""



  a__     : Dict  =checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE						)
  a__     : str  =convert_tax_to_pytorch(SCREAMING_SNAKE_CASE	,	num_layers=config.num_layers	,	is_encoder_only=SCREAMING_SNAKE_CASE						)
  a__     : List[str]  =make_state_dict(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)
  model.load_state_dict(SCREAMING_SNAKE_CASE	,	strict=SCREAMING_SNAKE_CASE						)
def 					_A	(		SCREAMING_SNAKE_CASE					:       str	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       Tuple	,	SCREAMING_SNAKE_CASE					:       bool = False						):





  """simple docstring"""



  a__     : Optional[Any]  =TaConfig.from_json_file(SCREAMING_SNAKE_CASE						)
  print(f'''Building PyTorch model from configuration: {config}'''						)
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    a__     : Optional[Any]  =TaEncoderModel(SCREAMING_SNAKE_CASE						)
  else:
    a__     : Optional[Any]  =TaForConditionalGeneration(SCREAMING_SNAKE_CASE						)

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE	,	SCREAMING_SNAKE_CASE						)

  # Save pytorch-model
  print(f'''Save PyTorch model to {pytorch_dump_path}'''						)
  model.save_pretrained(SCREAMING_SNAKE_CASE						)

  # Verify that we can load the checkpoint.
  model.from_pretrained(SCREAMING_SNAKE_CASE						)
  print("Done"						)


if __name__ == "__main__":
     UpperCAmelCase     :     Any							=	argparse.ArgumentParser(description="""Converts a native T5X checkpoint into a PyTorch checkpoint.""")
     # Required parameters
     parser.add_argument(
         """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path to the T5X checkpoint."""
     )
     parser.add_argument(
         """--config_file""",
         default=None,
         type=str,
         required=True,
         help="""The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.""",
     )
     parser.add_argument(
         """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
     )
     parser.add_argument(
         """--is_encoder_only""", action="""store_true""", help="""Check if the model is encoder-decoder model""", default=False
     )
     UpperCAmelCase     :     List[Any]							=	parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )













from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : str            =      ["""vqvae"""]







      def __init__(   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				)       ->						Union[str, Any]:



        '''simple docstring'''




        super().__init__()
        self.register_modules(unet=lowerCAmelCase__		,				scheduler=lowerCAmelCase__		,				mel=lowerCAmelCase__		,				vqvae=lowerCAmelCase__							)







      def        _lowercase  (   self							)       ->						int:



        '''simple docstring'''




        return 5_0 if isinstance(self.scheduler		,				lowerCAmelCase__							) else 1_0_0_0







      @torch.no_grad()
      def __call__(   self		,				lowerCAmelCase__ = 1		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = 0		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__=True		,				)       ->						Union[
          Union[AudioPipelineOutput, ImagePipelineOutput],
          Tuple[List[Image.Image], Tuple[int, List[np.ndarray]]],
      ]:



        '''simple docstring'''




        a__     : List[Any]  =steps or self.get_default_steps()
        self.scheduler.set_timesteps(lowerCAmelCase__							)
        a__     : Tuple  =step_generator or generator
        # For backwards compatibility
        if type(self.unet.config.sample_size							) == int:
          a__     : List[str]  =(self.unet.config.sample_size, self.unet.config.sample_size)
        if noise is None:
          a__     : Optional[Any]  =randn_tensor(
              (
                  batch_size,
                  self.unet.config.in_channels,
                  self.unet.config.sample_size[0],
                  self.unet.config.sample_size[1],
              )		,				generator=lowerCAmelCase__		,				device=self.device		,				)
        a__     : List[str]  =noise
        a__     : Optional[Any]  =None

        if audio_file is not None or raw_audio is not None:
          self.mel.load_audio(lowerCAmelCase__		,				lowerCAmelCase__							)
          a__     : Tuple  =self.mel.audio_slice_to_image(lowerCAmelCase__							)
          a__     : List[Any]  =np.frombuffer(input_image.tobytes()		,				dtype="uint8"							).reshape(
              (input_image.height, input_image.width)							)
          a__     : Optional[Any]  =(input_image / 2_5_5) * 2 - 1
          a__     : Dict  =torch.tensor(input_image[np.newaxis, :, :]		,				dtype=torch.float							).to(self.device							)

          if self.vqvae is not None:
            a__     : str  =self.vqvae.encode(torch.unsqueeze(lowerCAmelCase__		,				0							)							).latent_dist.sample(
                generator=lowerCAmelCase__							)[0]
            a__     : Any  =self.vqvae.config.scaling_factor * input_images

          if start_step > 0:
            a__     : Optional[int]  =self.scheduler.add_noise(lowerCAmelCase__		,				lowerCAmelCase__		,				self.scheduler.timesteps[start_step - 1]							)

          a__     : Tuple  =(
              self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
          )
          a__     : Union[str, Any]  =int(mask_start_secs * pixels_per_second							)
          a__     : List[str]  =int(mask_end_secs * pixels_per_second							)
          a__     : Optional[Any]  =self.scheduler.add_noise(lowerCAmelCase__		,				lowerCAmelCase__		,				torch.tensor(self.scheduler.timesteps[start_step:]							)							)

        for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:]							)							):
          if isinstance(self.unet		,				lowerCAmelCase__							):
            a__     : List[str]  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]
          else:
            a__     : Optional[Any]  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]

          if isinstance(self.scheduler		,				lowerCAmelCase__							):
            a__     : int  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				eta=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]
          else:
            a__     : str  =self.scheduler.step(
                model_output=lowerCAmelCase__		,				timestep=lowerCAmelCase__		,				sample=lowerCAmelCase__		,				generator=lowerCAmelCase__		,				)["prev_sample"]

          if mask is not None:
            if mask_start > 0:
              a__     : List[Any]  =mask[:, step, :, :mask_start]
            if mask_end > 0:
              a__     : Union[str, Any]  =mask[:, step, :, -mask_end:]

        if self.vqvae is not None:
          # 0.18215 was scaling factor used in training to ensure unit variance
          a__     : Any  =1 / self.vqvae.config.scaling_factor * images
          a__     : str  =self.vqvae.decode(lowerCAmelCase__							)["sample"]

        a__     : str  =(images / 2 + 0.5).clamp(0		,				1							)
        a__     : int  =images.cpu().permute(0		,				2		,				3		,				1							).numpy()
        a__     : List[Any]  =(images * 2_5_5).round().astype("uint8"							)
        a__     : Dict  =list(
            (Image.fromarray(_[:, :, 0]							) for _ in images)
            if images.shape[3] == 1
            else (Image.fromarray(lowerCAmelCase__		,				mode="RGB"							).convert("L"							) for _ in images)							)

        a__     : str  =[self.mel.image_to_audio(lowerCAmelCase__							) for _ in images]
        if not return_dict:
          return images, (self.mel.get_sample_rate(), audios)

        return BaseOutput(**AudioPipelineOutput(np.array(lowerCAmelCase__							)[:, np.newaxis, :]							)		,				**ImagePipelineOutput(lowerCAmelCase__							)							)







      @torch.no_grad()
      def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__ = 5_0							)       ->						np.ndarray:



        '''simple docstring'''




        assert isinstance(self.scheduler		,				lowerCAmelCase__							)
        self.scheduler.set_timesteps(lowerCAmelCase__							)
        a__     : Union[str, Any]  =np.array(
            [np.frombuffer(image.tobytes()		,				dtype="uint8"							).reshape((1, image.height, image.width)							) for image in images]							)
        a__     : Tuple  =(sample / 2_5_5) * 2 - 1
        a__     : List[Any]  =torch.Tensor(lowerCAmelCase__							).to(self.device							)

        for t in self.progress_bar(torch.flip(self.scheduler.timesteps		,				(0,)							)							):
          a__     : str  =t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
          a__     : Dict  =self.scheduler.alphas_cumprod[t]
          a__     : Optional[Any]  =(
              self.scheduler.alphas_cumprod[prev_timestep]
              if prev_timestep >= 0
              else self.scheduler.final_alpha_cumprod
          )
          a__     : Optional[Any]  =1 - alpha_prod_t
          a__     : str  =self.unet(lowerCAmelCase__		,				lowerCAmelCase__							)["sample"]
          a__     : Optional[Any]  =(1 - alpha_prod_t_prev) ** 0.5 * model_output
          a__     : List[str]  =(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
          a__     : Optional[Any]  =sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

        return sample







      @staticmethod
      def        _lowercase  (   lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__							)       ->						torch.Tensor:



        '''simple docstring'''




        a__     : Any  =acos(torch.dot(torch.flatten(lowerCAmelCase__							)		,				torch.flatten(lowerCAmelCase__							)							) / torch.norm(lowerCAmelCase__							) / torch.norm(lowerCAmelCase__							)							)
        return sin((1 - alpha) * theta							) * xa / sin(lowerCAmelCase__							) + sin(alpha * theta							) * xa / sin(lowerCAmelCase__							)






