

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch




def   _A  (  UpperCamelCase_					:	Dict)     ->						Optional[int]:
   '''simple docstring'''


   __lowercase								=						os.path.join(args.tf_model_dir,      "parameters.json")
   __lowercase								=						json.loads(open(UpperCamelCase_).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"):
      __lowercase								=						args.output + ".pt"
   __lowercase								=						OrderedDict()
   with tf.device("/CPU:0"):
      __lowercase								=						tf.train.load_checkpoint(args.tf_model_dir)
      __lowercase								=						reader.get_variable_to_shape_map()
      for key_name in shapes.keys():
         __lowercase								=						reader.get_tensor(UpperCamelCase_).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"):
               __lowercase								=						int(key_name[9])
            elif key_name.startswith("pasts/out"):
               __lowercase								=						8
            __lowercase								=						"model.sqout.%d.weight" % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
            __lowercase								=						vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
            __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name.startswith("model/moe"):
            __lowercase								=						int(key_name[9:].split("/")[0])
            if key_name.endswith("/switch_gating/kernel"):
               __lowercase								=						"model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
               __lowercase								=						vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/softmlp/kernel"):
               __lowercase								=						"model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
               __lowercase								=						vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/wo/kernel") or key_name.endswith("/wi/kernel"):
               __lowercase								=						key_name[-9:-7]
               for i in range(16):
                  __lowercase								=						"model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
                  __lowercase								=						(
                      vnp[i].transpose([1, 0]).copy()
                  )  # In Mesh-Tensorflow, it is one array, so it is divided
                  __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name.startswith("model/mlp"):
            __lowercase								=						int(key_name[9:].split("/")[0])
            if key_name.endswith("/p1/kernel"):
               __lowercase								=						"model.blocks.%d.feed_forward.mlp.wi.weight" % player
               __lowercase								=						vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/p1/bias"):
               __lowercase								=						"model.blocks.%d.feed_forward.mlp.wi.bias" % player
               __lowercase								=						vnp.copy()  # same because it is one dimensional
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/p2/kernel"):
               __lowercase								=						"model.blocks.%d.feed_forward.mlp.wo.weight" % player
               __lowercase								=						vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/p2/bias"):
               __lowercase								=						"model.blocks.%d.feed_forward.mlp.wo.bias" % player
               __lowercase								=						vnp.copy()  # same because it is one dimensional
               __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name.startswith("model/ln"):
            __lowercase								=						int(key_name[8:].split("/")[0])
            if key_name.endswith("/b"):
               __lowercase								=						"model.blocks.%d.feed_forward.norm.bias" % player
               __lowercase								=						vnp.copy()  # same because it is one dimensional
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/g"):
               __lowercase								=						"model.blocks.%d.feed_forward.norm.weight" % player
               __lowercase								=						vnp.copy()  # same because it is one dimensional
               __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name.startswith("model/att"):
            __lowercase								=						int(key_name[9:].split("/")[0])
            if key_name.endswith("/qkv/kernel"):
               __lowercase								=						vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
               __lowercase								=						state[:, 0, :, :]
               __lowercase								=						state[:, 1, :, :]
               __lowercase								=						state[:, 2, :, :]
               __lowercase								=						(
                   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
               __lowercase								=						(
                   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
               __lowercase								=						(
                   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
               __lowercase								=						"model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
               __lowercase								=						torch.tensor(UpperCamelCase_)
               __lowercase								=						"model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
               __lowercase								=						torch.tensor(UpperCamelCase_)
               __lowercase								=						"model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/o/kernel"):
               __lowercase								=						"model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
               __lowercase								=						(
                   vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]).transpose([1, 0]).copy()
               )  # Mesh-Tensorflow is a diagonal matrix
               __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name.startswith("model/an"):
            __lowercase								=						int(key_name[8:].split("/")[0])
            if key_name.endswith("/b"):
               __lowercase								=						"model.blocks.%d.self_attn.norm.bias" % player
               __lowercase								=						vnp.copy()  # same because it is one dimensional
               __lowercase								=						torch.tensor(UpperCamelCase_)
            elif key_name.endswith("/g"):
               __lowercase								=						"model.blocks.%d.self_attn.norm.weight" % player
               __lowercase								=						vnp.copy()  # same because it is one dimensional
               __lowercase								=						torch.tensor(UpperCamelCase_)
         elif (
             key_name.startswith("model/wte")
             or key_name.startswith("model/wpe")
             or key_name.startswith("model/ete")
         ):
            __lowercase								=						{"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
                key_name[-3:]
            ]
            __lowercase								=						"model.%s.weight" % nlayer
            __lowercase								=						vnp.copy()  # same in embedded
            __lowercase								=						torch.tensor(UpperCamelCase_)
            if key_name.startswith("model/wte"):
               __lowercase								=						"lm_head.weight"
               __lowercase								=						vnp.copy()  # same in embedded
               __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name.startswith("model/wob"):
            __lowercase								=						"final_logits_bias"
            __lowercase								=						vnp.copy()  # same in embedded
            __lowercase								=						state.reshape((1, -1))
            __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name == "model/dense/kernel":
            __lowercase								=						"model.last_project.weight"
            __lowercase								=						vnp.transpose([1, 0]).copy()  # Mesh-Tensorflow is a diagonal matrix
            __lowercase								=						torch.tensor(UpperCamelCase_)
         elif key_name == "model/dense_1/bias":
            __lowercase								=						"model.last_project.bias"
            __lowercase								=						vnp.copy()  # same because it is one dimensional
            __lowercase								=						torch.tensor(UpperCamelCase_)
   torch.save(UpperCamelCase_,      args.output)


if __name__ == "__main__":
   _a							=							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')
   _a							=							parser.parse_args()
   convert_tf_gptsan_to_pt(args)







"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_a							=							logging.get_logger(__name__)

_a							=							{
    'MIT/ast-finetuned-audioset-10-10-0.4593': (
        'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
    ),
}




class  _lowerCAmelCase  (     lowercase       ):







 """simple docstring"""







 __UpperCAmelCase     : Any       		=     "audio-spectrogram-transformer"


 def __init__(      self							:      Optional[Any], UpperCAmelCase__							:      Dict=7_6_8, UpperCAmelCase__							:      Optional[Any]=1_2, UpperCAmelCase__							:      Optional[int]=1_2, UpperCAmelCase__							:      List[str]=3_0_7_2, UpperCAmelCase__							:      List[str]="gelu", UpperCAmelCase__							:      Dict=0.0, UpperCAmelCase__							:      str=0.0, UpperCAmelCase__							:      Any=0.02, UpperCAmelCase__							:      Any=1E-12, UpperCAmelCase__							:      Dict=1_6, UpperCAmelCase__							:      Optional[int]=True, UpperCAmelCase__							:      Union[str, Any]=1_0, UpperCAmelCase__							:      List[Any]=1_0, UpperCAmelCase__							:      Optional[int]=1_0_2_4, UpperCAmelCase__							:      List[str]=1_2_8, **UpperCAmelCase__							:      List[Any], ):
    super().__init__(**UpperCAmelCase__     )

    __lowercase								=						hidden_size
    __lowercase								=						num_hidden_layers
    __lowercase								=						num_attention_heads
    __lowercase								=						intermediate_size
    __lowercase								=						hidden_act
    __lowercase								=						hidden_dropout_prob
    __lowercase								=						attention_probs_dropout_prob
    __lowercase								=						initializer_range
    __lowercase								=						layer_norm_eps
    __lowercase								=						patch_size
    __lowercase								=						qkv_bias
    __lowercase								=						frequency_stride
    __lowercase								=						time_stride
    __lowercase								=						max_length
    __lowercase								=						num_mel_bins




