






'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch
def     __lowercase  (_lowercase     )   ->     str:


     """simple docstring"""
     __lowerCamelCase						:       Any				   =				os.path.join(args.tf_model_dir,						"""parameters.json"""     )
     __lowerCamelCase						:       Tuple				   =				json.loads(open(_lowercase     ).read()     )
     if not params:
          raise ValueError(
              f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."     )
     if not args.output.endswith(""".pt"""     ):
          __lowerCamelCase						:       List[Any]				   =				args.output + """.pt"""
     __lowerCamelCase						:       str				   =				OrderedDict()
     with tf.device("""/CPU:0"""     ):
          __lowerCamelCase						:       int				   =				tf.train.load_checkpoint(args.tf_model_dir     )
          __lowerCamelCase						:       Union[str, Any]				   =				reader.get_variable_to_shape_map()
          for key_name in shapes.keys():
               __lowerCamelCase						:       Tuple				   =				reader.get_tensor(_lowercase     ).astype(np.floataa     )
               if key_name.endswith("""/adam_m"""     ) or key_name.endswith("""/adam_v"""     ):
                    continue
               if key_name.startswith("""pasts/"""     ):
                    if key_name.startswith("""pasts/mlp"""     ):
                         __lowerCamelCase						:       Any				   =				int(key_name[9]     )
                    elif key_name.startswith("""pasts/out"""     ):
                         __lowerCamelCase						:       Dict				   =				8
                    __lowerCamelCase						:       Tuple				   =				"""model.sqout.%d.weight""" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                    __lowerCamelCase						:       Optional[int]				   =				vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    __lowerCamelCase						:       List[str]				   =				torch.tensor(_lowercase     )
               elif key_name.startswith("""model/moe"""     ):
                    __lowerCamelCase						:       Optional[Any]				   =				int(key_name[9:].split("""/"""     )[0]     )
                    if key_name.endswith("""/switch_gating/kernel"""     ):
                         __lowerCamelCase						:       Any				   =				"""model.blocks.%d.feed_forward.mlp.router.classifier.weight""" % player
                         __lowerCamelCase						:       Dict				   =				vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       int				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/softmlp/kernel"""     ):
                         __lowerCamelCase						:       Any				   =				"""model.blocks.%d.feed_forward.soft_bypass_mlp.weight""" % player
                         __lowerCamelCase						:       int				   =				vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       List[str]				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/wo/kernel"""     ) or key_name.endswith("""/wi/kernel"""     ):
                         __lowerCamelCase						:       Any				   =				key_name[-9:-7]
                         for i in range(16     ):
                              __lowerCamelCase						:       str				   =				"""model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight""" % (player, i, nlayer)
                              __lowerCamelCase						:       int				   =				(
                                  vnp[i].transpose([1, 0]     ).copy()
                              )  # In Mesh-Tensorflow, it is one array, so it is divided
                              __lowerCamelCase						:       Union[str, Any]				   =				torch.tensor(_lowercase     )
               elif key_name.startswith("""model/mlp"""     ):
                    __lowerCamelCase						:       Dict				   =				int(key_name[9:].split("""/"""     )[0]     )
                    if key_name.endswith("""/p1/kernel"""     ):
                         __lowerCamelCase						:       Union[str, Any]				   =				"""model.blocks.%d.feed_forward.mlp.wi.weight""" % player
                         __lowerCamelCase						:       Optional[Any]				   =				vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       List[Any]				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/p1/bias"""     ):
                         __lowerCamelCase						:       Dict				   =				"""model.blocks.%d.feed_forward.mlp.wi.bias""" % player
                         __lowerCamelCase						:       int				   =				vnp.copy()  # same because it is one dimensional
                         __lowerCamelCase						:       Dict				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/p2/kernel"""     ):
                         __lowerCamelCase						:       Any				   =				"""model.blocks.%d.feed_forward.mlp.wo.weight""" % player
                         __lowerCamelCase						:       Union[str, Any]				   =				vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       List[Any]				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/p2/bias"""     ):
                         __lowerCamelCase						:       Tuple				   =				"""model.blocks.%d.feed_forward.mlp.wo.bias""" % player
                         __lowerCamelCase						:       str				   =				vnp.copy()  # same because it is one dimensional
                         __lowerCamelCase						:       Tuple				   =				torch.tensor(_lowercase     )
               elif key_name.startswith("""model/ln"""     ):
                    __lowerCamelCase						:       Optional[int]				   =				int(key_name[8:].split("""/"""     )[0]     )
                    if key_name.endswith("""/b"""     ):
                         __lowerCamelCase						:       Optional[Any]				   =				"""model.blocks.%d.feed_forward.norm.bias""" % player
                         __lowerCamelCase						:       Optional[int]				   =				vnp.copy()  # same because it is one dimensional
                         __lowerCamelCase						:       List[str]				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/g"""     ):
                         __lowerCamelCase						:       int				   =				"""model.blocks.%d.feed_forward.norm.weight""" % player
                         __lowerCamelCase						:       Union[str, Any]				   =				vnp.copy()  # same because it is one dimensional
                         __lowerCamelCase						:       Optional[int]				   =				torch.tensor(_lowercase     )
               elif key_name.startswith("""model/att"""     ):
                    __lowerCamelCase						:       List[Any]				   =				int(key_name[9:].split("""/"""     )[0]     )
                    if key_name.endswith("""/qkv/kernel"""     ):
                         __lowerCamelCase						:       Tuple				   =				vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                         __lowerCamelCase						:       int				   =				state[:, 0, :, :]
                         __lowerCamelCase						:       Union[str, Any]				   =				state[:, 1, :, :]
                         __lowerCamelCase						:       Tuple				   =				state[:, 2, :, :]
                         __lowerCamelCase						:       int				   =				(
                             state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]     )
                             .transpose([1, 0]     )
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       Tuple				   =				(
                             state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]     )
                             .transpose([1, 0]     )
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       Optional[Any]				   =				(
                             state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]     )
                             .transpose([1, 0]     )
                             .copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       str				   =				"""model.blocks.%d.self_attn.self_attn.q_proj.weight""" % player
                         __lowerCamelCase						:       List[str]				   =				torch.tensor(_lowercase     )
                         __lowerCamelCase						:       Union[str, Any]				   =				"""model.blocks.%d.self_attn.self_attn.k_proj.weight""" % player
                         __lowerCamelCase						:       Tuple				   =				torch.tensor(_lowercase     )
                         __lowerCamelCase						:       Tuple				   =				"""model.blocks.%d.self_attn.self_attn.v_proj.weight""" % player
                         __lowerCamelCase						:       int				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/o/kernel"""     ):
                         __lowerCamelCase						:       Tuple				   =				"""model.blocks.%d.self_attn.self_attn.out_proj.weight""" % player
                         __lowerCamelCase						:       Union[str, Any]				   =				(
                             vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]     ).transpose([1, 0]     ).copy()
                         )  # Mesh-Tensorflow is a diagonal matrix
                         __lowerCamelCase						:       str				   =				torch.tensor(_lowercase     )
               elif key_name.startswith("""model/an"""     ):
                    __lowerCamelCase						:       Any				   =				int(key_name[8:].split("""/"""     )[0]     )
                    if key_name.endswith("""/b"""     ):
                         __lowerCamelCase						:       Optional[int]				   =				"""model.blocks.%d.self_attn.norm.bias""" % player
                         __lowerCamelCase						:       List[Any]				   =				vnp.copy()  # same because it is one dimensional
                         __lowerCamelCase						:       Union[str, Any]				   =				torch.tensor(_lowercase     )
                    elif key_name.endswith("""/g"""     ):
                         __lowerCamelCase						:       Optional[int]				   =				"""model.blocks.%d.self_attn.norm.weight""" % player
                         __lowerCamelCase						:       str				   =				vnp.copy()  # same because it is one dimensional
                         __lowerCamelCase						:       Tuple				   =				torch.tensor(_lowercase     )
               elif (
                   key_name.startswith("""model/wte"""     )
                   or key_name.startswith("""model/wpe"""     )
                   or key_name.startswith("""model/ete"""     )
               ):
                    __lowerCamelCase						:       Optional[Any]				   =				{"""wte""": """embed_tokens""", """wpe""": """position_embeddings""", """ete""": """extra_position_embeddings"""}[
                        key_name[-3:]
                    ]
                    __lowerCamelCase						:       Optional[int]				   =				"""model.%s.weight""" % nlayer
                    __lowerCamelCase						:       List[Any]				   =				vnp.copy()  # same in embedded
                    __lowerCamelCase						:       int				   =				torch.tensor(_lowercase     )
                    if key_name.startswith("""model/wte"""     ):
                         __lowerCamelCase						:       str				   =				"""lm_head.weight"""
                         __lowerCamelCase						:       int				   =				vnp.copy()  # same in embedded
                         __lowerCamelCase						:       List[Any]				   =				torch.tensor(_lowercase     )
               elif key_name.startswith("""model/wob"""     ):
                    __lowerCamelCase						:       Optional[Any]				   =				"""final_logits_bias"""
                    __lowerCamelCase						:       List[Any]				   =				vnp.copy()  # same in embedded
                    __lowerCamelCase						:       List[Any]				   =				state.reshape((1, -1)     )
                    __lowerCamelCase						:       Any				   =				torch.tensor(_lowercase     )
               elif key_name == "model/dense/kernel":
                    __lowerCamelCase						:       Union[str, Any]				   =				"""model.last_project.weight"""
                    __lowerCamelCase						:       Union[str, Any]				   =				vnp.transpose([1, 0]     ).copy()  # Mesh-Tensorflow is a diagonal matrix
                    __lowerCamelCase						:       Optional[Any]				   =				torch.tensor(_lowercase     )
               elif key_name == "model/dense_1/bias":
                    __lowerCamelCase						:       Dict				   =				"""model.last_project.bias"""
                    __lowerCamelCase						:       Any				   =				vnp.copy()  # same because it is one dimensional
                    __lowerCamelCase						:       int				   =				torch.tensor(_lowercase     )
     torch.save(_lowercase,						args.output     )


if __name__ == "__main__":
   UpperCAmelCase__						:str          =			argparse.ArgumentParser(
       description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
   )
   parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
   parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
   UpperCAmelCase__						:Union[str, Any]          =			parser.parse_args()
   convert_tf_gptsan_to_pt(args)











'''simple docstring'''

from typing import List, Optional, Tuple, Union

import torch

from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline


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







class 	SCREAMING_SNAKE_CASE	(      lowerCAmelCase_  ):




       def __init__(					self      : int	,    A__      : List[Any]	,    A__      : Tuple					):


            """simple docstring"""







            super().__init__()
            self.register_modules(unet=A__	,    scheduler=A__					)




       @torch.no_grad()
       def __call__(					self      : List[str]	,    A__      : int = 1	,    A__      : int = 100	,    A__      : Optional[Union[torch.Generator, List[torch.Generator]]] = None	,    A__      : Optional[float] = None	,    A__      : bool = True	,    ):


            """simple docstring"""







            if audio_length_in_s is None:
                 __lowerCamelCase						:       Union[str, Any]				   =				self.unet.config.sample_size / self.unet.config.sample_rate

            __lowerCamelCase						:       int				   =				audio_length_in_s * self.unet.config.sample_rate

            __lowerCamelCase						:       List[Any]				   =				2 ** len(self.unet.up_blocks					)
            if sample_size < 3 * down_scale_factor:
                 raise ValueError(
                     f"{audio_length_in_s} is too small. Make sure it's bigger or equal to"
                     f" {3 * down_scale_factor / self.unet.config.sample_rate}."					)

            __lowerCamelCase						:       Any				   =				int(A__					)
            if sample_size % down_scale_factor != 0:
                 __lowerCamelCase						:       Optional[Any]				   =				(
                     (audio_length_in_s * self.unet.config.sample_rate) // down_scale_factor + 1
                 ) * down_scale_factor
                 logger.info(
                     f"{audio_length_in_s} is increased to {sample_size / self.unet.config.sample_rate} so that it can be handled"
                     f" by the model. It will be cut to {original_sample_size / self.unet.config.sample_rate} after the denoising"
                     """ process."""					)
            __lowerCamelCase						:       Dict				   =				int(A__					)

            __lowerCamelCase						:       Optional[int]				   =				next(iter(self.unet.parameters()					)					).dtype
            __lowerCamelCase						:       Optional[Any]				   =				(batch_size, self.unet.config.in_channels, sample_size)
            if isinstance(A__	,    A__					) and len(A__					) != batch_size:
                 raise ValueError(
                     f"You have passed a list of generators of length {len(A__					)}, but requested an effective batch"
                     f" size of {batch_size}. Make sure the batch size matches the length of the generators."					)

            __lowerCamelCase						:       int				   =				randn_tensor(A__	,    generator=A__	,    device=self.device	,    dtype=A__					)

            # set step values
            self.scheduler.set_timesteps(A__	,    device=audio.device					)
            __lowerCamelCase						:       List[Any]				   =				self.scheduler.timesteps.to(A__					)

            for t in self.progress_bar(self.scheduler.timesteps					):
                 # 1. predict noise model_output
                 __lowerCamelCase						:       List[str]				   =				self.unet(A__	,    A__					).sample

                 # 2. compute previous image: x_t -> t_t-1
                 __lowerCamelCase						:       Dict				   =				self.scheduler.step(A__	,    A__	,    A__					).prev_sample

            __lowerCamelCase						:       Optional[int]				   =				audio.clamp(-1	,    1					).float().cpu().numpy()

            __lowerCamelCase						:       int				   =				audio[:, :, :original_sample_size]

            if not return_dict:
                 return (audio,)

            return AudioPipelineOutput(audios=A__					)



