

"""simple docstring"""



import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch





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


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_    :		Tuple							     =  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')
 SCREAMING_SNAKE_CASE_    :		Union[str, Any]							     =  parser.parse_args()
 convert_tf_gptsan_to_pt(args)







'''simple docstring'''




from urllib.parse import quote

import pytest

from datasets.utils.hub import hf_hub_url




@pytest.mark.parametrize("""repo_id"""      ,      ["""canonical_dataset_name""", """org-name/dataset-name"""] )
@pytest.mark.parametrize("""path"""      ,      ["""filename.csv""", """filename with blanks.csv"""] )
@pytest.mark.parametrize("""revision"""      ,      [None, """v2"""] )
def 							lowerCamelCase	(      UpperCAmelCase__       :					int      ,      UpperCAmelCase__       :					Optional[int]      ,      UpperCAmelCase__       :					Optional[int] )			->      Optional[int]:
							lowercase_					:			Union[str, Any]										=       hf_hub_url(repo_id=UpperCAmelCase__      ,      path=UpperCAmelCase__      ,      revision=UpperCAmelCase__ )
							assert url == F'''https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(UpperCAmelCase__ )}'''





