





'''simple docstring'''

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

import numpy as np
import torch

from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor


if is_onnx_available():
    from ..onnx_utils import OnnxRuntimeModel

from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder


a   	=    logging.get_logger(__name__)  # pylint: disable=invalid-name

a   	=    256







class __a			( _snake_case      ):
   __UpperCamelCase :			Any						   =						['melgan']





   def __init__(       self					:      str					,lowerCamelCase					:      SpectrogramNotesEncoder					,lowerCamelCase					:      SpectrogramContEncoder					,lowerCamelCase					:      TaFilmDecoder					,lowerCamelCase					:      DDPMScheduler					,lowerCamelCase					:      OnnxRuntimeModel if is_onnx_available() else Any					,):

      '''simple docstring'''

      super().__init__()

      # From MELGAN
      __SCREAMING_SNAKE_CASE   				=							math.log(1E-5     )  # Matches MelGAN training.
      __SCREAMING_SNAKE_CASE   				=							4.0  # Largest value for most examples
      __SCREAMING_SNAKE_CASE   				=							128

      self.register_modules(
          notes_encoder=lowerCamelCase					,continuous_encoder=lowerCamelCase					,decoder=lowerCamelCase					,scheduler=lowerCamelCase					,melgan=lowerCamelCase					,)





   def   UpperCAmelCase__  (       self					:      Union[str, Any]					,lowerCamelCase					:      Optional[Any]					,lowerCamelCase					:      List[str]=(-1.0, 1.0)					,lowerCamelCase					:      int=False     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							output_range
      if clip:
         __SCREAMING_SNAKE_CASE   				=							torch.clip(lowerCamelCase					,self.min_value					,self.max_value     )
      # Scale to [0, 1].
      __SCREAMING_SNAKE_CASE   				=							(features - self.min_value) / (self.max_value - self.min_value)
      # Scale to [min_out, max_out].
      return zero_one * (max_out - min_out) + min_out





   def   UpperCAmelCase__  (       self					:      List[str]					,lowerCamelCase					:      List[str]					,lowerCamelCase					:      List[str]=(-1.0, 1.0)					,lowerCamelCase					:      int=False     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							input_range
      __SCREAMING_SNAKE_CASE   				=							torch.clip(lowerCamelCase					,lowerCamelCase					,lowerCamelCase     ) if clip else outputs
      # Scale to [0, 1].
      __SCREAMING_SNAKE_CASE   				=							(outputs - min_out) / (max_out - min_out)
      # Scale to [self.min_value, self.max_value].
      return zero_one * (self.max_value - self.min_value) + self.min_value





   def   UpperCAmelCase__  (       self					:      Dict					,lowerCamelCase					:      Union[str, Any]					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      int     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							input_tokens > 0
      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.notes_encoder(
          encoder_input_tokens=lowerCamelCase					,encoder_inputs_mask=lowerCamelCase     )

      __SCREAMING_SNAKE_CASE				,  __SCREAMING_SNAKE_CASE   				=							self.continuous_encoder(
          encoder_inputs=lowerCamelCase					,encoder_inputs_mask=lowerCamelCase     )

      return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]





   def   UpperCAmelCase__  (       self					:      Tuple					,lowerCamelCase					:      List[Any]					,lowerCamelCase					:      Any					,lowerCamelCase					:      List[Any]     ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE   				=							noise_time
      if not torch.is_tensor(lowerCamelCase     ):
         __SCREAMING_SNAKE_CASE   				=							torch.tensor([timesteps]					,dtype=torch.long					,device=input_tokens.device     )
      elif torch.is_tensor(lowerCamelCase     ) and len(timesteps.shape     ) == 0:
         __SCREAMING_SNAKE_CASE   				=							timesteps[None].to(input_tokens.device     )

      # broadcast to batch dimension in a way that's compatible with ONNX/Core ML
      __SCREAMING_SNAKE_CASE   				=							timesteps * torch.ones(input_tokens.shape[0]					,dtype=timesteps.dtype					,device=timesteps.device     )

      __SCREAMING_SNAKE_CASE   				=							self.decoder(
          encodings_and_masks=lowerCamelCase					,decoder_input_tokens=lowerCamelCase					,decoder_noise_time=lowerCamelCase     )
      return logits





   @torch.no_grad()
   def __call__(       self					:      Dict					,lowerCamelCase					:      List[List[int]]					,lowerCamelCase					:      Optional[torch.Generator] = None					,lowerCamelCase					:      int = 100					,lowerCamelCase					:      bool = True					,lowerCamelCase					:      str = "numpy"					,lowerCamelCase					:      Optional[Callable[[int, int, torch.FloatTensor], None]] = None					,lowerCamelCase					:      int = 1					,):

      '''simple docstring'''

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

      __SCREAMING_SNAKE_CASE   				=							np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims]					,dtype=np.floataa     )
      __SCREAMING_SNAKE_CASE   				=							np.zeros([1, 0, self.n_dims]					,np.floataa     )
      __SCREAMING_SNAKE_CASE   				=							torch.ones((1, TARGET_FEATURE_LENGTH)					,dtype=lowerCamelCase					,device=self.device     )

      for i, encoder_input_tokens in enumerate(lowerCamelCase     ):
         if i == 0:
            __SCREAMING_SNAKE_CASE   				=							torch.from_numpy(pred_mel[:1].copy()     ).to(
                device=self.device					,dtype=self.decoder.dtype     )
            # The first chunk has no previous context.
            __SCREAMING_SNAKE_CASE   				=							torch.zeros((1, TARGET_FEATURE_LENGTH)					,dtype=lowerCamelCase					,device=self.device     )
         else:
            # The full song pipeline does not feed in a context feature, so the mask
            # will be all 0s after the feature converter. Because we know we're
            # feeding in a full context chunk from the previous prediction, set it
            # to all 1s.
            __SCREAMING_SNAKE_CASE   				=							ones

         __SCREAMING_SNAKE_CASE   				=							self.scale_features(
             lowerCamelCase					,output_range=[-1.0, 1.0]					,clip=lowerCamelCase     )

         __SCREAMING_SNAKE_CASE   				=							self.encode(
             input_tokens=torch.IntTensor([encoder_input_tokens]     ).to(device=self.device     )					,continuous_inputs=lowerCamelCase					,continuous_mask=lowerCamelCase					,)

         # Sample encoder_continuous_inputs shaped gaussian noise to begin loop
         __SCREAMING_SNAKE_CASE   				=							randn_tensor(
             shape=encoder_continuous_inputs.shape					,generator=lowerCamelCase					,device=self.device					,dtype=self.decoder.dtype					,)

         # set step values
         self.scheduler.set_timesteps(lowerCamelCase     )

         # Denoising diffusion loop
         for j, t in enumerate(self.progress_bar(self.scheduler.timesteps     )     ):
            __SCREAMING_SNAKE_CASE   				=							self.decode(
                encodings_and_masks=lowerCamelCase					,input_tokens=lowerCamelCase					,noise_time=t / self.scheduler.config.num_train_timesteps					,)

            # Compute previous output: x_t -> x_t-1
            __SCREAMING_SNAKE_CASE   				=							self.scheduler.step(lowerCamelCase					,lowerCamelCase					,lowerCamelCase					,generator=lowerCamelCase     ).prev_sample

         __SCREAMING_SNAKE_CASE   				=							self.scale_to_features(lowerCamelCase					,input_range=[-1.0, 1.0]     )
         __SCREAMING_SNAKE_CASE   				=							mel[:1]
         __SCREAMING_SNAKE_CASE   				=							mel.cpu().float().numpy()

         __SCREAMING_SNAKE_CASE   				=							np.concatenate([full_pred_mel, pred_mel[:1]]					,axis=1     )

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

         logger.info("""Generated segment"""					,lowerCamelCase     )

      if output_type == "numpy" and not is_onnx_available():
         raise ValueError(
             """Cannot return output in 'np' format if ONNX is not available. Make sure to have ONNX installed or set 'output_type' to 'mel'."""     )
      elif output_type == "numpy" and self.melgan is None:
         raise ValueError(
             """Cannot return output in 'np' format if melgan component is not defined. Make sure to define `self.melgan` or set 'output_type' to 'mel'."""     )

      if output_type == "numpy":
         __SCREAMING_SNAKE_CASE   				=							self.melgan(input_features=full_pred_mel.astype(np.floataa     )     )
      else:
         __SCREAMING_SNAKE_CASE   				=							full_pred_mel

      if not return_dict:
         return (output,)

      return AudioPipelineOutput(audios=lowerCamelCase     )












'''simple docstring'''

import argparse
import re

from flax.traverse_util import flatten_dict, unflatten_dict
from tax import checkpoints

from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
from transformers.utils import logging


logging.set_verbosity_info()


# should not include what is already done by the `from_pt` argument
a   	=    {
    "/attention/": "/0/SelfAttention/",
    "/self_attention/": "/0/SelfAttention/",
    "/encoder_decoder_attention/": "/1/EncDecAttention/",
    "value": "v",
    "query": "q",
    "key": "k",
    "out": "o",
    "pre_self_attention_layer_norm": "0/layer_norm",
    "pre_cross_attention_layer_norm": "1/layer_norm",
    "pre_attention_layer_norm": "0/layer_norm",  # previously 1, but seems wrong
    "token_embedder": "shared",
    "encoder_norm": "final_layer_norm",
    "decoder_norm": "final_layer_norm",
    "relpos_bias/rel_embedding": "block/0/layer/0/SelfAttention/relative_attention_bias/weight",
    "router/router_weights/w/": "router/classifier/",
    "roer/roer_weights/w/": "router/classifier/",
    "logits_dense": "lm_head",
}

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							list(s_dict.keys()   )
   for key in keys:
      __SCREAMING_SNAKE_CASE   				=							R""".*/layers_(\d+)"""
      __SCREAMING_SNAKE_CASE   				=							key
      if re.match(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							re.sub(R"""layers_(\d+)"""				,						R"""block/\1/layer"""				,						__UpperCAmelCase   )

      __SCREAMING_SNAKE_CASE   				=							R"""(encoder|decoder)\/"""

      if re.match(__UpperCAmelCase				,						__UpperCAmelCase   ):
         __SCREAMING_SNAKE_CASE   				=							re.match(__UpperCAmelCase				,						__UpperCAmelCase   ).groups()
         if groups[0] == "encoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/1/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/pre_mlp_layer_norm/"""				,						R"""/1/layer_norm/"""				,						__UpperCAmelCase   )

         elif groups[0] == "decoder":
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/mlp/"""				,						R"""/2/mlp/"""				,						__UpperCAmelCase   )
            __SCREAMING_SNAKE_CASE   				=							re.sub(R"""/pre_mlp_layer_norm/"""				,						R"""/2/layer_norm/"""				,						__UpperCAmelCase   )

        # 2. Convert other classic mappings
      for old_key, temp_key in MOE_LAYER_NAME_MAPPING.items():
         if old_key in new_key:
            __SCREAMING_SNAKE_CASE   				=							new_key.replace(__UpperCAmelCase				,						__UpperCAmelCase   )

      print(f"""{key} -> {new_key}"""   )
      __SCREAMING_SNAKE_CASE   				=							s_dict.pop(__UpperCAmelCase   )

   if "encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      __SCREAMING_SNAKE_CASE   				=							s_dict[
          """encoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
      ].T
   if "decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight" in s_dict:
      __SCREAMING_SNAKE_CASE   				=							s_dict[
          """decoder/block/0/layer/0/SelfAttention/relative_attention_bias/weight"""
      ].T

   # 3. Take extra care of the EXPERTS layer
   for key in list(s_dict.keys()   ):
      if "expert" in key:
         __SCREAMING_SNAKE_CASE   				=							s_dict[key].shape[0]
         __SCREAMING_SNAKE_CASE   				=							s_dict[key]
         for idx in range(__UpperCAmelCase   ):
            __SCREAMING_SNAKE_CASE   				=							expert_weihts[idx]
            print(f"""{key} -> {key.replace('expert/'				,						'nested fstring'   )}"""   )

         s_dict.pop(__UpperCAmelCase   )

   return s_dict


a   	=    {
    "NUM_ENCODER_LAYERS": "num_layers",
    "NUM_DECODER_LAYERS": "num_decoder_layers",
    "NUM_HEADS": "num_heads",
    "HEAD_DIM": "d_kv",
    "EMBED_DIM": "d_model",
    "MLP_DIM": "d_ff",
    "NUM_SELECTED_EXPERTS": "num_selected_experts",
    "NUM_ENCODER_SPARSE_LAYERS": "num_sparse_encoder_layers",
    "NUM_DECODER_SPARSE_LAYERS": "num_sparse_decoder_layers",
    "dense.MlpBlock.activations": "feed_forward_proj",
}

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       str:

   '''simple docstring'''







   import regex as re

   with open(__UpperCAmelCase				,						"""r"""   ) as f:
      __SCREAMING_SNAKE_CASE   				=							f.read()

   __SCREAMING_SNAKE_CASE   				=							re.findall(R"""(.*) = ([0-9.]*)"""				,						__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							{}
   for param, value in regex_match:
      if param in GIN_TO_CONFIG_MAPPING and value != "":
         __SCREAMING_SNAKE_CASE   				=							float(__UpperCAmelCase   ) if """.""" in value else int(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							re.findall(R"""(.*activations) = \(\'(.*)\',\)"""				,						__UpperCAmelCase   )[0]
   __SCREAMING_SNAKE_CASE   				=							str(activation[1]   )

   __SCREAMING_SNAKE_CASE   				=							num_experts
   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig(**__UpperCAmelCase   )
   return config

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase=None				,						__UpperCAmelCase="./"				,						__UpperCAmelCase=8   )    ->       str:

   '''simple docstring'''







   print(f"""Loading flax weights from : {flax_checkpoint_path}"""   )
   __SCREAMING_SNAKE_CASE   				=							checkpoints.load_tax_checkpoint(__UpperCAmelCase   )

   if gin_file is not None:
      __SCREAMING_SNAKE_CASE   				=							convert_gin_to_config(__UpperCAmelCase				,						__UpperCAmelCase   )
   else:
      __SCREAMING_SNAKE_CASE   				=							SwitchTransformersConfig.from_pretrained(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							SwitchTransformersForConditionalGeneration(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							flax_params["""target"""]
   __SCREAMING_SNAKE_CASE   				=							flatten_dict(__UpperCAmelCase				,						sep="""/"""   )
   __SCREAMING_SNAKE_CASE   				=							rename_keys(__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							unflatten_dict(__UpperCAmelCase				,						sep="""/"""   )

   # Load the flax params in the PT model
   load_flax_weights_in_pytorch_model(__UpperCAmelCase				,						__UpperCAmelCase   )

   print(f"""Save PyTorch model to {pytorch_dump_path}"""   )
   pt_model.save_pretrained(__UpperCAmelCase   )


if __name__ == "__main__":
    a   	=    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        "--switch_t5x_checkpoint_path",
        default=None,
        type=str,
        required=True,
        help=(
            "The config json file corresponding to the pre-trained SwitchTransformers model. \nThis specifies the"
            " model architecture. If not provided, a `gin_file` has to be provided."
        ),
    )
    parser.add_argument(
        "--gin_file",
        default=None,
        type=str,
        required=False,
        help="Path to the gin config file. If not provided, a `config_file` has to be passed   ",
    )
    parser.add_argument(
        "--config_name", default=None, type=str, required=False, help="Config name of SwitchTransformers model."
    )
    parser.add_argument(
        "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output pytorch model."
    )
    parser.add_argument("--num_experts", default=8, type=int, required=False, help="Number of experts")
    a   	=    parser.parse_args()
    convert_flax_checkpoint_to_pytorch(
        args.switch_tax_checkpoint_path,
        args.config_name,
        args.gin_file,
        args.pytorch_dump_folder_path,
        args.num_experts,
    )





