


import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch






def 						a    (		lowerCamelCase_       ):






				'''simple docstring'''
				lowercase__     				=    os.path.join(args.tf_model_dir	,     '''parameters.json'''       )
				lowercase__     				=    json.loads(open(__snake_case       ).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(__snake_case       ).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(__snake_case       )
												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(__snake_case       )
																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(__snake_case       )
																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(__snake_case       )
												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(__snake_case       )
																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(__snake_case       )
																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(__snake_case       )
																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(__snake_case       )
												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(__snake_case       )
																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(__snake_case       )
												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(__snake_case       )
																				lowercase__     				=    '''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
																				lowercase__     				=    torch.tensor(__snake_case       )
																				lowercase__     				=    '''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
																				lowercase__     				=    torch.tensor(__snake_case       )
																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(__snake_case       )
												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(__snake_case       )
																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(__snake_case       )
												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(__snake_case       )
																if key_name.startswith('''model/wte'''       ):
																				lowercase__     				=    '''lm_head.weight'''
																				lowercase__     				=    vnp.copy()  # same in embedded
																				lowercase__     				=    torch.tensor(__snake_case       )
												elif key_name.startswith('''model/wob'''       ):
																lowercase__     				=    '''final_logits_bias'''
																lowercase__     				=    vnp.copy()  # same in embedded
																lowercase__     				=    state.reshape((1, -1)       )
																lowercase__     				=    torch.tensor(__snake_case       )
												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(__snake_case       )
												elif key_name == "model/dense_1/bias":
																lowercase__     				=    '''model.last_project.bias'''
																lowercase__     				=    vnp.copy()  # same because it is one dimensional
																lowercase__     				=    torch.tensor(__snake_case       )
				torch.save(__snake_case	,     args.output       )


if __name__ == "__main__":
							A__			:    Optional[Any]         =				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__			:    str         =				parser.parse_args()
							convert_tf_gptsan_to_pt(args)












from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow

from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class    _UpperCAmelCase      ( A__			):

				"""simple docstring"""




				lowercase__       							= DistilBertTokenizer
				lowercase__       							= DistilBertTokenizerFast
				lowercase__       							= True




				@slow
				def        lowercase__     (  self		:					str						):

								'''simple docstring'''
								lowercase__     				=    DistilBertTokenizer.from_pretrained('''distilbert-base-uncased'''						)

								lowercase__     				=    tokenizer.encode('''sequence builders''',						add_special_tokens=lowerCamelCase						)
								lowercase__     				=    tokenizer.encode('''multi-sequence build''',						add_special_tokens=lowerCamelCase						)

								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase						)
								lowercase__     				=    tokenizer.build_inputs_with_special_tokens(lowerCamelCase,						lowerCamelCase						)

								assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
								assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
								    tokenizer.sep_token_id
								]




