'''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__							(			_A:					Optional[Any] )    ->	Any:

		'''simple docstring'''


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


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





'''simple docstring'''




from __future__ import annotations

from typing import Dict

from ...configuration_utils import PretrainedConfig


__lowercase          =  {
    '''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
    '''susnato/ernie-m-large_pytorch''': '''https://huggingface.co/susnato/ernie-m-large_pytorch/blob/main/config.json''',
}






class 				a__(					lowerCAmelCase__	):


			'''simple docstring'''






			UpperCAmelCase_			:     List[str]      =       '''ernie_m'''
			UpperCAmelCase_			:     Dict[str, str]      =       {"dropout": "classifier_dropout", "num_classes": "num_labels"}



			def __init__(   self       ,    __lowerCAmelCase = 250002       ,    __lowerCAmelCase = 768       ,    __lowerCAmelCase = 12       ,    __lowerCAmelCase = 12       ,    __lowerCAmelCase = 3072       ,    __lowerCAmelCase = "gelu"       ,    __lowerCAmelCase = 0.1       ,    __lowerCAmelCase = 0.1       ,    __lowerCAmelCase = 514       ,    __lowerCAmelCase = 0.02       ,    __lowerCAmelCase = 1       ,    __lowerCAmelCase = 1E-0_5       ,    __lowerCAmelCase=None       ,    __lowerCAmelCase=False       ,    __lowerCAmelCase=0.0       ,    **__lowerCAmelCase       ,    ):




					"""simple docstring"""


					super().__init__(pad_token_id=__lowerCAmelCase       ,    **__lowerCAmelCase)
					lowerCAmelCase   					=  vocab_size
					lowerCAmelCase   					=  hidden_size
					lowerCAmelCase   					=  num_hidden_layers
					lowerCAmelCase   					=  num_attention_heads
					lowerCAmelCase   					=  intermediate_size
					lowerCAmelCase   					=  hidden_act
					lowerCAmelCase   					=  hidden_dropout_prob
					lowerCAmelCase   					=  attention_probs_dropout_prob
					lowerCAmelCase   					=  max_position_embeddings
					lowerCAmelCase   					=  initializer_range
					lowerCAmelCase   					=  layer_norm_eps
					lowerCAmelCase   					=  classifier_dropout
					lowerCAmelCase   					=  is_decoder
					lowerCAmelCase   					=  act_dropout




